Projeto_Cancer_4C.ipynb

{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"id":"LPq8HrN2sJ8d"},"source":["<table align=\"left\">\n","  <td>\n","    <a href=\"https://is.gd/M5qGmU\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n","  </td>\n","</table>"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"t_IH8B04w_61"},"source":["# Applied Machine Learning Techniques to Breast Cancer Recurrence Classification\n","\n","## Abstract\n","\n","\n","\n","Diego R. Páez Ardila - 2120653\n","\n","\n","**Updated version: 20/04/2023**\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"5Tj2oXIQ2umu"},"source":["# Methodology\n","\n","1. **General Pre-Processing (GPP)**: The dataset is studied to identify missing data, analyze the correlation between the attributes and the target class, observe if there is an imbalance in the target class and identify if it is necessary to apply regularization to the data.\n","\n","    After the analysis of the data, the following actions were taken:\n","    \n","    1. **Remove \"Sex\" attribute**: The number of men record (4) is not significant to be considered in the analysis. If only men records are removed the attribute will have only one value (Women).\n","\n","    2. **Standardize all continuous variables**: The continuous variables are standardized to have a mean of 0 and a standard deviation of 1.\n","\n","    3. **Categorical attributes with int values**: The categorical attributes with int values stay as they are.\n","\n","    4. **Correlation between attributes**: Winter Hypoxia Score and Ragnum Hypoxia Score are highly correlated with Buffa Hypoxia Score are (0.84 and 0.72). The attribute Buffa Hypoxia Score is removed from the dataset.\n","\n","    5. **Target Class**: The target class is unbalanced and has 3 classes related to the recurrence of breast cancer. Where class 3 is related to the absence of recurrence and classes 1 and 2 are related to the time when the recurrence occurred. In this stage no action is taken to balance the target class.\n","\n","2. **Experiments**: Four experiments (E-1 to E-4) were defined to evaluate the performance of different machine learning models in breast cancer recurrence classification.\n","\n","    1. **E-1**: Training of Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), k nearest neighbors (KNN) and XGBoots (XGB) models using the original dataset with the target class grouped in three classes ( 0 = Early recurrence, 1 = Mid/Late recurrence and 3 = No recurrence). The target class is unbalanced and has 4 classes related to the recurrence of breast cancer. Where class 3 is related to the absence of recurrence and classes 0, 1 and 2 are related to the time when the recurrence occurred. Class 0 is related to an early recurrence, class 1 is related to an intermediate recurrence and class 2 is related to a late recurrence. The analysis of the target class shows that the classes 0, 1 and 2 can be grouped in two classes using k-means clustering. The classes 0, 1 and 2 are grouped in class 0 early recurrence and class 1 mid/late recurrence.\n","\n","    2. **E-2 (E-1 + GPP)**: Training of Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), k nearest neighbors (KNN) and XGBoots (XGB) models using the dataset with GPP.\n","\n","    3. **E-3 (E-2 + Feature Selection)**: A feature selection techniques (mRMR) is applied to the dataset with GPP to select the most significant attributes for the target class. Then, the training of Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), k nearest neighbors (KNN) and XGBoots (XGB) models is performed.\n","\n","    4. **E-4 (E-3 + Oversample)**: The technique SMOTE is applied to the dataset with GPP + Feature Selection to balance the target class. Then, the training of Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), k nearest neighbors (KNN) and XGBoots (XGB) models is performed.\n","\n","3. **Test E-1 to E-4**: All the models trained in the experiments are tested using the test dataset.\n","\n","4. **Models Evaluation**: Precision, Recall, F1-score, Cohen Kappa Score, AUROC, Accuracy. \n","\n","<a href=\"https://ibb.co/3YCjfWk\"><img src=\"https://i.ibb.co/qNdzF57/DS-Cancer-v2.jpg\" alt=\"DS-Cancer-v2\" width=400 border=\"0\"></a>"]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n","  %reload_ext autoreload\n"]}],"source":["%matplotlib inline\n","%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":37,"metadata":{"id":"5WJ4vV_INFiR"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import seaborn as sns\n","import matplotlib as mpl\n","import matplotlib.pyplot as plt\n","import warnings\n","warnings.filterwarnings(\"ignore\")\n","#mpl.rc('axes', labelsize=14)\n","#mpl.rc('xtick', labelsize=12)\n","#mpl.rc('ytick', labelsize=12)\n","\n","# to make this notebook's output stable across runs\n","np.random.seed(42)\n","from collections import Counter\n","\n","\n","from utilities.preprocessing import gpp_preprocess, mrmr_preprocess, kmeans_preprocess\n","from utilities.models import run_experiment\n","from utilities.tuning import load_params, tune_experiment, save_tuned_params\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"rosed5Yt49pW"},"source":["# 1 - General Pre-Processing (GPP)\n","\n","The dataset is studied to identify missing data, analyze the correlation between the attributes and the target class, observe if there is an imbalance in the target class and identify if it is necessary to apply regularization to the data."]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":645},"executionInfo":{"elapsed":2903,"status":"ok","timestamp":1657314024447,"user":{"displayName":"Diego P","userId":"14448528258399483719"},"user_tz":180},"id":"pam9mI_U7Bpo","outputId":"1e769cd0-2399-42ec-b0e0-d50001229490"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Cancer Type Detailed</th>\n","      <th>Subtype</th>\n","      <th>Diagnosis Age</th>\n","      <th>Sex</th>\n","      <th>Ethnicity Category</th>\n","      <th>Race Category</th>\n","      <th>Neoplasm Cancer Code</th>\n","      <th>Aneuploidy Score</th>\n","      <th>Buffa Hypoxia Score</th>\n","      <th>Ragnum Hypoxia Score</th>\n","      <th>Winter Hypoxia Score</th>\n","      <th>Fraction Genome Altered</th>\n","      <th>In PanCan Pathway Analysis</th>\n","      <th>Mutation Count</th>\n","      <th>Post Initial Therapy</th>\n","      <th>Prior Diagnosis</th>\n","      <th>Radiation Therapy</th>\n","      <th>Tissue Prospective Indicator</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>55</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>11</td>\n","      <td>19</td>\n","      <td>-21</td>\n","      <td>0</td>\n","      <td>-28</td>\n","      <td>0.7787</td>\n","      <td>1</td>\n","      <td>24</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>50</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>22</td>\n","      <td>5</td>\n","      <td>18</td>\n","      <td>20</td>\n","      <td>0.7164</td>\n","      <td>1</td>\n","      <td>458</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>54</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>16</td>\n","      <td>27</td>\n","      <td>14</td>\n","      <td>22</td>\n","      <td>0.4163</td>\n","      <td>1</td>\n","      <td>62</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>66</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>-27</td>\n","      <td>-8</td>\n","      <td>-20</td>\n","      <td>0.0862</td>\n","      <td>1</td>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>36</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>17</td>\n","      <td>12</td>\n","      <td>4</td>\n","      <td>0.6407</td>\n","      <td>1</td>\n","      <td>128</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Cancer Type Detailed  Subtype  Diagnosis Age  Sex  Ethnicity Category  \\\n","0                     2        2             55    0                   1   \n","1                     1        1             50    0                   1   \n","2                     0        0             54    0                   1   \n","3                     1        2             66    0                   0   \n","4                     1        0             36    0                   1   \n","\n","   Race Category  Neoplasm Cancer Code  Aneuploidy Score  Buffa Hypoxia Score  \\\n","0              2                    11                19                  -21   \n","1              1                     5                22                    5   \n","2              0                     4                16                   27   \n","3              2                     1                 3                  -27   \n","4              1                     9                 9                   17   \n","\n","   Ragnum Hypoxia Score  Winter Hypoxia Score  Fraction Genome Altered  \\\n","0                     0                   -28                   0.7787   \n","1                    18                    20                   0.7164   \n","2                    14                    22                   0.4163   \n","3                    -8                   -20                   0.0862   \n","4                    12                     4                   0.6407   \n","\n","   In PanCan Pathway Analysis  Mutation Count  Post Initial Therapy  \\\n","0                           1              24                     0   \n","1                           1             458                     0   \n","2                           1              62                     1   \n","3                           1              41                     0   \n","4                           1             128                     1   \n","\n","   Prior Diagnosis  Radiation Therapy  Tissue Prospective Indicator  Class  \n","0                0                  0                             0      1  \n","1                0                  1                             0      3  \n","2                0                  1                             0      1  \n","3                0                  1                             0      3  \n","4                0                  1                             0      0  "]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["E_data = pd.read_excel('dadoscancer_4classes.xlsx')\n","E_data.head()"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1657314024447,"user":{"displayName":"Diego P","userId":"14448528258399483719"},"user_tz":180},"id":"u0Tav6qAbTzp","outputId":"3f59065a-73e5-4bb2-9eab-9f4a2cdd2002"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 344 entries, 0 to 343\n","Data columns (total 19 columns):\n"," #   Column                        Non-Null Count  Dtype  \n","---  ------                        --------------  -----  \n"," 0   Cancer Type Detailed          344 non-null    int64  \n"," 1   Subtype                       344 non-null    int64  \n"," 2   Diagnosis Age                 344 non-null    int64  \n"," 3   Sex                           344 non-null    int64  \n"," 4   Ethnicity Category            344 non-null    int64  \n"," 5   Race Category                 344 non-null    int64  \n"," 6   Neoplasm Cancer Code          344 non-null    int64  \n"," 7   Aneuploidy Score              344 non-null    int64  \n"," 8   Buffa Hypoxia Score           344 non-null    int64  \n"," 9   Ragnum Hypoxia Score          344 non-null    int64  \n"," 10  Winter Hypoxia Score          344 non-null    int64  \n"," 11  Fraction Genome Altered       344 non-null    float64\n"," 12  In PanCan Pathway Analysis    344 non-null    int64  \n"," 13  Mutation Count                344 non-null    int64  \n"," 14  Post Initial Therapy          344 non-null    int64  \n"," 15  Prior Diagnosis               344 non-null    int64  \n"," 16  Radiation Therapy             344 non-null    int64  \n"," 17  Tissue Prospective Indicator  344 non-null    int64  \n"," 18  Class                         344 non-null    int64  \n","dtypes: float64(1), int64(18)\n","memory usage: 51.2 KB\n"]}],"source":["E_data.info()"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1657314024448,"user":{"displayName":"Diego P","userId":"14448528258399483719"},"user_tz":180},"id":"mGZTEFQ6bZQ-","outputId":"b241733b-d550-4b85-896e-a4bd56f5962c"},"outputs":[{"data":{"text/plain":["Cancer Type Detailed              5\n","Subtype                           5\n","Diagnosis Age                    61\n","Sex                               2\n","Ethnicity Category                2\n","Race Category                     3\n","Neoplasm Cancer Code             12\n","Aneuploidy Score                 33\n","Buffa Hypoxia Score              44\n","Ragnum Hypoxia Score             27\n","Winter Hypoxia Score             47\n","Fraction Genome Altered         335\n","In PanCan Pathway Analysis        2\n","Mutation Count                  120\n","Post Initial Therapy              2\n","Prior Diagnosis                   2\n","Radiation Therapy                 2\n","Tissue Prospective Indicator      2\n","Class                             4\n","dtype: int64"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["E_data.nunique()"]},{"cell_type":"code","execution_count":41,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1657314024448,"user":{"displayName":"Diego P","userId":"14448528258399483719"},"user_tz":180},"id":"cxibEXAG8Zfb","outputId":"9e2ffe38-353f-45b8-a036-283236b1634a"},"outputs":[{"data":{"text/plain":["Cancer Type Detailed            0\n","Subtype                         0\n","Diagnosis Age                   0\n","Sex                             0\n","Ethnicity Category              0\n","Race Category                   0\n","Neoplasm Cancer Code            0\n","Aneuploidy Score                0\n","Buffa Hypoxia Score             0\n","Ragnum Hypoxia Score            0\n","Winter Hypoxia Score            0\n","Fraction Genome Altered         0\n","In PanCan Pathway Analysis      0\n","Mutation Count                  0\n","Post Initial Therapy            0\n","Prior Diagnosis                 0\n","Radiation Therapy               0\n","Tissue Prospective Indicator    0\n","Class                           0\n","dtype: int64"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["E_data.isnull().sum()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"Z8nst8FTKbKS"},"source":["## Histograms Matrix "]},{"cell_type":"code","execution_count":42,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":828},"executionInfo":{"elapsed":2962,"status":"ok","timestamp":1657314027403,"user":{"displayName":"Diego P","userId":"14448528258399483719"},"user_tz":180},"id":"oxG7WadOMzuu","outputId":"acce1939-7bab-420d-e47e-647000104c08"},"outputs":[],"source":["#E_data.hist(figsize=(14,14))\n","#plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"53fmxRs6D1Lu"},"source":["## Graphs Analysis \n","\n","Out of 18 attributes of the dataset, 6 continuous variables and 12 categorical variables were identified.\n","\n","<img src=\"images/dataset.png\" alt=\"Metodolog-a-3\"  width=\"600\" border=\"0\"> "]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Sex Attribute\n","\n","In the histogram of the dataset it was observed that the **sex attribute** has records for both women and men. Although there is a possibility that breast cancer occurs in men, the number of records present in the dataset is not significant, therefore, we proceed to identify how many male records exist in the dataset and eliminate them. By eliminating the records associated with men, the sex variable only presents one category, therefore, it is discarded. "]},{"cell_type":"code","execution_count":43,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1657314033538,"user":{"displayName":"Diego P","userId":"14448528258399483719"},"user_tz":180},"id":"f6sC9yB0D1kj","outputId":"a61696fc-c840-4964-d184-5e38e29eb450"},"outputs":[{"name":"stdout","output_type":"stream","text":["Counter({0: 340, 1: 4})\n","Women : 340\n","Men : 4\n","[ 72 100 129 232]\n"]}],"source":["counter = Counter(E_data[\"Sex\"])\n","print(counter)\n","print('%s : %d' % ('Women', counter[0]))\n","print('%s : %d' % ('Men', counter[1]))\n","\n","#Smote\n","idx_0 = np.where(E_data.Sex == 0)\n","idx_Men = np.where(E_data.Sex == 1)\n","\n","print(idx_Men[0])"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"d9WJSWcy64fg"},"source":["## Correlation Matrix\n","\n","The correlation between the attributes is analyzed to identify if there are attributes that are highly correlated. If there are highly correlated attributes, one of them is removed. The correlation matrix is shown below. "]},{"cell_type":"code","execution_count":44,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":2450,"status":"ok","timestamp":1657314035977,"user":{"displayName":"Diego P","userId":"14448528258399483719"},"user_tz":180},"id":"Y188szix2c6f","outputId":"dbcec0ca-0930-4793-ae22-a778e0809c30"},"outputs":[],"source":["#f, ax = plt.subplots(1, figsize=(15,15))\n","#sns.heatmap(E_data.corr(), annot=True, ax=ax,fmt=\"0.2f\");"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"cVRQ4JPAZdqd"},"source":["## Target Class Analysis\n","\n","It was identified that the distribution of data in the target class is unbalanced."]},{"cell_type":"code","execution_count":45,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"executionInfo":{"elapsed":32,"status":"ok","timestamp":1657314035977,"user":{"displayName":"Diego P","userId":"14448528258399483719"},"user_tz":180},"id":"Vdm4-M6mN_ge","outputId":"0b4bd84d-7e61-4050-aae8-324c67e4cab7"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAc8AAAHACAYAAADELuP+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAmIklEQVR4nO3deXDUdZ7/8VcHTMuRw0DOJZwihwRExJjCRY4MEBhcioy7MswYBWF0EiwSj2xquWJphZFRWRmE2SkBp4Ysro6BEXdQ5Eg8AnJMBvBIkUw0UCSBhUnaBOlc/fvDon/2hOsTk3z7eD6qvlX09/vpb95tpuZZ3+5Ot83lcrkEAABuWJDVAwAA4GuIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhrpbPYA3aG1t1ZkzZxQSEiKbzWb1OAAAC7hcLn3zzTeKi4tTUNC1ry2Jp6QzZ84oPj7e6jEAAF7g1KlT6tev3zXXEE9JISEhkr77DxYaGmrxNAAAKzgcDsXHx7ubcC3EU3I/VRsaGko8ASDA3cjLd7xhCAAAQ8QTAABDxBMAAEPEEwAAQ8QTAABDxBMAAEPEEwAAQ8QTAABDxBMAAEOWxjMvL0/jx49XSEiIoqKiNGfOHJWWlnqsuXTpktLT09WnTx/17t1bqampqqmp8VhTWVmpWbNmqWfPnoqKitLTTz+t5ubmrnwoAIAAYmk8CwsLlZ6ergMHDmj37t1qamrStGnT1NDQ4F6TmZmpd955R2+++aYKCwt15swZzZ071328paVFs2bNUmNjoz755BO9/vrr2rJli1asWGHFQwIABACby+VyWT3EZefOnVNUVJQKCws1ceJE1dXVKTIyUvn5+frJT34iSfryyy81YsQIFRcX65577tGf//xn/fjHP9aZM2cUHR0tSdq4caOys7N17tw5BQcHX/fnOhwOhYWFqa6ujs+2BYAAZdICr3rNs66uTpIUEREhSTpy5IiampqUnJzsXjN8+HD1799fxcXFkqTi4mIlJCS4wylJ06dPl8Ph0GeffXbFn+N0OuVwODw2AABulNfEs7W1VUuXLtWECRM0atQoSVJ1dbWCg4MVHh7usTY6OlrV1dXuNd8P5+Xjl49dSV5ensLCwtwb3+UJADDhNfFMT0/XiRMntG3btk7/WTk5Oaqrq3Nvp06d6vSfCQDwH17xfZ4ZGRnauXOnioqKPL69OyYmRo2NjaqtrfW4+qypqVFMTIx7zaeffupxvsvvxr285h/Z7XbZ7fYOfhQA0DUKJ95n9Qg+576iwg49n6VXni6XSxkZGSooKNDevXs1aNAgj+Pjxo3TTTfdpD179rj3lZaWqrKyUklJSZKkpKQkHT9+XGfPnnWv2b17t0JDQzVy5MiueSAAgIBi6ZVnenq68vPztWPHDoWEhLhfowwLC1OPHj0UFhamhQsXKisrSxEREQoNDdWSJUuUlJSke+65R5I0bdo0jRw5Uj//+c/1wgsvqLq6WsuWLVN6ejpXlwCATmFpPDds2CBJmjRpksf+zZs36+GHH5YkvfzyywoKClJqaqqcTqemT5+uV1991b22W7du2rlzpx5//HElJSWpV69eSktL07PPPttVDwMAEGC86u88rcLfeQLwJbzmae5GXvP02b/zBADAFxBPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMWRrPoqIizZ49W3FxcbLZbNq+fbvHcZvNdsVtzZo17jUDBw5sc3z16tVd/EgAAIHE0ng2NDRozJgxWr9+/RWPV1VVeWybNm2SzWZTamqqx7pnn33WY92SJUu6YnwAQIDqbuUPT0lJUUpKylWPx8TEeNzesWOHJk+erMGDB3vsDwkJabMWAIDO4jOvedbU1Ojdd9/VwoUL2xxbvXq1+vTpo7Fjx2rNmjVqbm6+5rmcTqccDofHBgDAjbL0ytPE66+/rpCQEM2dO9dj/xNPPKE777xTERER+uSTT5STk6Oqqiq99NJLVz1XXl6ecnNzO3tkAICfsrlcLpfVQ0jfvTmooKBAc+bMueLx4cOH60c/+pHWrVt3zfNs2rRJv/jFL1RfXy+73X7FNU6nU06n033b4XAoPj5edXV1Cg0NbfdjAICuUDjxPqtH8Dn3FRVed43D4VBYWNgNtcAnrjw//PBDlZaW6o033rju2sTERDU3N+urr77SsGHDrrjGbrdfNawAAFyPT7zm+dprr2ncuHEaM2bMddeWlJQoKChIUVFRXTAZACAQWXrlWV9fr7KyMvftiooKlZSUKCIiQv3795f03WX0m2++qRdffLHN/YuLi3Xw4EFNnjxZISEhKi4uVmZmpn72s5/plltu6bLHAQAILJbG8/Dhw5o8ebL7dlZWliQpLS1NW7ZskSRt27ZNLpdL8+bNa3N/u92ubdu2adWqVXI6nRo0aJAyMzPd5wEAoDN4zRuGrGTyIjEAWI03DJnr6DcM+cRrngAAeBPiCQCAIeIJAIAh4gkAgCHiCQCAIeIJAIAh4gkAgCHiCQCAIeIJAIAh4gkAgCHiCQCAIeIJAIAh4gkAgCHiCQCAIeIJAIAh4gkAgCHiCQCAIeIJAIAh4gkAgCHiCQCAIeIJAIAh4gkAgCHiCQCAIeIJAIAh4gkAgCHiCQCAIeIJAIAh4gkAgCHiCQCAIeIJAIAh4gkAgCHiCQCAIeIJAIAh4gkAgCHiCQCAIUvjWVRUpNmzZysuLk42m03bt2/3OP7www/LZrN5bDNmzPBYc+HCBc2fP1+hoaEKDw/XwoULVV9f34WPAgAQaCyNZ0NDg8aMGaP169dfdc2MGTNUVVXl3v77v//b4/j8+fP12Wefaffu3dq5c6eKioq0ePHizh4dABDAulv5w1NSUpSSknLNNXa7XTExMVc89sUXX2jXrl06dOiQ7rrrLknSunXrNHPmTP36179WXFxch88MAIDXv+a5f/9+RUVFadiwYXr88cd1/vx597Hi4mKFh4e7wylJycnJCgoK0sGDB696TqfTKYfD4bEBAHCjvDqeM2bM0O9//3vt2bNHv/rVr1RYWKiUlBS1tLRIkqqrqxUVFeVxn+7duysiIkLV1dVXPW9eXp7CwsLcW3x8fKc+DgCAf7H0advrefDBB93/TkhI0OjRozVkyBDt379fU6dObfd5c3JylJWV5b7tcDgIKADghnn1lec/Gjx4sPr27auysjJJUkxMjM6ePeuxprm5WRcuXLjq66TSd6+jhoaGemwAANwon4rn6dOndf78ecXGxkqSkpKSVFtbqyNHjrjX7N27V62trUpMTLRqTACAn7P0adv6+nr3VaQkVVRUqKSkRBEREYqIiFBubq5SU1MVExOj8vJyPfPMM7r11ls1ffp0SdKIESM0Y8YMLVq0SBs3blRTU5MyMjL04IMP8k5bAECnsfTK8/Dhwxo7dqzGjh0rScrKytLYsWO1YsUKdevWTceOHdP999+v2267TQsXLtS4ceP04Ycfym63u8+xdetWDR8+XFOnTtXMmTN177336r/+67+sekgAgABgc7lcLquHsJrD4VBYWJjq6up4/ROA1yuceJ/VI/ic+4oKr7vGpAU+9ZonAADegHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGDI0ngWFRVp9uzZiouLk81m0/bt293HmpqalJ2drYSEBPXq1UtxcXF66KGHdObMGY9zDBw4UDabzWNbvXp1Fz8SAEAgsTSeDQ0NGjNmjNavX9/m2MWLF3X06FEtX75cR48e1dtvv63S0lLdf//9bdY+++yzqqqqcm9LlizpivEBAAGqu5U/PCUlRSkpKVc8FhYWpt27d3vs+81vfqO7775blZWV6t+/v3t/SEiIYmJiOnVWAAAu86nXPOvq6mSz2RQeHu6xf/Xq1erTp4/Gjh2rNWvWqLm5+ZrncTqdcjgcHhsAADfK0itPE5cuXVJ2drbmzZun0NBQ9/4nnnhCd955pyIiIvTJJ58oJydHVVVVeumll656rry8POXm5nbF2AAAP2RzuVwuq4eQJJvNpoKCAs2ZM6fNsaamJqWmpur06dPav3+/Rzz/0aZNm/SLX/xC9fX1stvtV1zjdDrldDrdtx0Oh+Lj41VXV3fNcwOANyiceJ/VI/ic+4oKr7vG4XAoLCzshlrg9VeeTU1N+td//Vd9/fXX2rt373UfUGJiopqbm/XVV19p2LBhV1xjt9uvGlYAAK7Hq+N5OZwnT57Uvn371KdPn+vep6SkREFBQYqKiuqCCQEAgcjSeNbX16usrMx9u6KiQiUlJYqIiFBsbKx+8pOf6OjRo9q5c6daWlpUXV0tSYqIiFBwcLCKi4t18OBBTZ48WSEhISouLlZmZqZ+9rOf6ZZbbrHqYQEA/Jyl8Tx8+LAmT57svp2VlSVJSktL06pVq/SnP/1JknTHHXd43G/fvn2aNGmS7Ha7tm3bplWrVsnpdGrQoEHKzMx0nwcAgM5gaTwnTZqka71f6XrvZbrzzjt14MCBjh4LAIBr8qm/8wQAwBsQTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADLUrnlOmTFFtbW2b/Q6HQ1OmTPmhMwEA4NXaFc/9+/ersbGxzf5Lly7pww8//MFDAQDgzYy+DPvYsWPuf3/++eeqrq52325padGuXbv0T//0Tx03HQAAXsgonnfccYdsNptsNtsVn57t0aOH1q1b12HDAQDgjYziWVFRIZfLpcGDB+vTTz9VZGSk+1hwcLCioqLUrVu3Dh8SAABvYhTPAQMGSJJaW1s7ZRgAAHyBUTy/7+TJk9q3b5/Onj3bJqYrVqz4wYMBAOCt2hXP3/3ud3r88cfVt29fxcTEyGazuY/ZbDbiCQDwa+2K53PPPafnn39e2dnZHT0PAABer11/5/n3v/9dDzzwQEfPAgCAT2hXPB944AG9//77HT0LAAA+oV1P2956661avny5Dhw4oISEBN10000ex5944okOGQ4AAG9kc7lcLtM7DRo06OontNn0t7/97QcN1dUcDofCwsJUV1en0NBQq8cBgGsqnHif1SP4nPuKCq+7xqQF7bryrKioaM/dAADwC3wlGQAAhtp15blgwYJrHt+0aVO7hgEAwBe0K55///vfPW43NTXpxIkTqq2t5fs8AQB+r13xLCgoaLOvtbVVjz/+uIYMGfKDhwIAwJt12GueQUFBysrK0ssvv9xRpwQAwCt16BuGysvL1dzc3JGnBADA67TradusrCyP2y6XS1VVVXr33XeVlpbWIYMBAOCt2nXl+Ze//MVjO3bsmCTpxRdf1Nq1a2/4PEVFRZo9e7bi4uJks9m0fft2j+Mul0srVqxQbGysevTooeTkZJ08edJjzYULFzR//nyFhoYqPDxcCxcuVH19fXseFgAAN6RdV5779u3rkB/e0NCgMWPGaMGCBZo7d26b4y+88IJeeeUVvf766xo0aJCWL1+u6dOn6/PPP9fNN98sSZo/f76qqqq0e/duNTU16ZFHHtHixYuVn5/fITMCAPCP2vXxfJedO3dOpaWlkqRhw4YpMjKy/YPYbCooKNCcOXMkfXfVGRcXpyeffFJPPfWUJKmurk7R0dHasmWLHnzwQX3xxRcaOXKkDh06pLvuukuStGvXLs2cOVOnT59WXFzcDf1sPp4PgC/h4/nMdfTH87XraduGhgYtWLBAsbGxmjhxoiZOnKi4uDgtXLhQFy9ebM8p26ioqFB1dbWSk5Pd+8LCwpSYmKji4mJJUnFxscLDw93hlKTk5GQFBQXp4MGDVz230+mUw+Hw2AAAuFHtimdWVpYKCwv1zjvvqLa2VrW1tdqxY4cKCwv15JNPdshg1dXVkqTo6GiP/dHR0e5j1dXVioqK8jjevXt3RUREuNdcSV5ensLCwtxbfHx8h8wMAAgM7YrnH//4R7322mtKSUlRaGioQkNDNXPmTP3ud7/TW2+91dEzdricnBzV1dW5t1OnTlk9EgDAh7QrnhcvXmxzRShJUVFRHfa0bUxMjCSppqbGY39NTY37WExMjM6ePetxvLm5WRcuXHCvuRK73e6O/uUNAIAb1a54JiUlaeXKlbp06ZJ737fffqvc3FwlJSV1yGCDBg1STEyM9uzZ497ncDh08OBB989ISkpSbW2tjhw54l6zd+9etba2KjExsUPmAADgH7XrT1XWrl2rGTNmqF+/fhozZowk6a9//avsdrvef//9Gz5PfX29ysrK3LcrKipUUlKiiIgI9e/fX0uXLtVzzz2noUOHuv9UJS4uzv2O3BEjRmjGjBlatGiRNm7cqKamJmVkZOjBBx+84XfaAgBgql3xTEhI0MmTJ7V161Z9+eWXkqR58+Zp/vz56tGjxw2f5/Dhw5o8ebL79uVPLkpLS9OWLVv0zDPPqKGhQYsXL1Ztba3uvfde7dq1y/03npK0detWZWRkaOrUqQoKClJqaqpeeeWV9jwsAABuSLv+zjMvL0/R0dFtvtdz06ZNOnfunLKzsztswK7A33kC8CX8nac5r/g7z9/+9rcaPnx4m/233367Nm7c2J5TAgDgM9oVz+rqasXGxrbZHxkZqaqqqh88FAAA3qxd8YyPj9fHH3/cZv/HH3/MG3UAAH6vXW8YWrRokZYuXaqmpiZNmTJFkrRnzx4988wzHfYJQwAAeKt2xfPpp5/W+fPn9ctf/lKNjY2SpJtvvlnZ2dnKycnp0AEBAPA27YqnzWbTr371Ky1fvlxffPGFevTooaFDh8put3f0fAAAeJ12xfOy3r17a/z48R01CwAAPqFdbxgCACCQEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMeX08Bw4cKJvN1mZLT0+XJE2aNKnNsccee8ziqQEA/qy71QNcz6FDh9TS0uK+feLECf3oRz/SAw884N63aNEiPfvss+7bPXv27NIZAQCBxevjGRkZ6XF79erVGjJkiO677z73vp49eyomJqarRwMABCivf9r2+xobG/WHP/xBCxYskM1mc+/funWr+vbtq1GjRiknJ0cXL160cEoAgL/z+ivP79u+fbtqa2v18MMPu/f99Kc/1YABAxQXF6djx44pOztbpaWlevvtt696HqfTKafT6b7tcDg6c2wAgJ/xqXi+9tprSklJUVxcnHvf4sWL3f9OSEhQbGyspk6dqvLycg0ZMuSK58nLy1Nubm6nzwsA8E8+87Tt119/rQ8++ECPPvroNdclJiZKksrKyq66JicnR3V1de7t1KlTHTorAMC/+cyV5+bNmxUVFaVZs2Zdc11JSYkkKTY29qpr7Ha77HZ7R44HAAggPhHP1tZWbd68WWlpaere/f+PXF5ervz8fM2cOVN9+vTRsWPHlJmZqYkTJ2r06NEWTgwA8Gc+Ec8PPvhAlZWVWrBggcf+4OBgffDBB1q7dq0aGhoUHx+v1NRULVu2zKJJAQCBwCfiOW3aNLlcrjb74+PjVVhYaMFEAIBA5jNvGAIAwFsQTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADBFPAAAMEU8AAAwRTwAADHW3egBfMu7p31s9gk86suYhq0cAgA7FlScAAIa48oRPqXw2weoRfFL/FcetHgHwK1x5AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYIh4AgBgiHgCAGCIeAIAYMir47lq1SrZbDaPbfjw4e7jly5dUnp6uvr06aPevXsrNTVVNTU1Fk4MAAgEXh1PSbr99ttVVVXl3j766CP3sczMTL3zzjt68803VVhYqDNnzmju3LkWTgsACATdrR7gerp3766YmJg2++vq6vTaa68pPz9fU6ZMkSRt3rxZI0aM0IEDB3TPPfd09agAgADh9VeeJ0+eVFxcnAYPHqz58+ersrJSknTkyBE1NTUpOTnZvXb48OHq37+/iouLrRoXABAAvPrKMzExUVu2bNGwYcNUVVWl3Nxc/fM//7NOnDih6upqBQcHKzw83OM+0dHRqq6uvuZ5nU6nnE6n+7bD4eiM8QEAfsqr45mSkuL+9+jRo5WYmKgBAwbof/7nf9SjR492nzcvL0+5ubkdMSIAIAB5/dO23xceHq7bbrtNZWVliomJUWNjo2praz3W1NTUXPE10u/LyclRXV2dezt16lQnTg0A8Dc+Fc/6+nqVl5crNjZW48aN00033aQ9e/a4j5eWlqqyslJJSUnXPI/dbldoaKjHBgDAjfLqp22feuopzZ49WwMGDNCZM2e0cuVKdevWTfPmzVNYWJgWLlyorKwsRUREKDQ0VEuWLFFSUhLvtAUAdCqvjufp06c1b948nT9/XpGRkbr33nt14MABRUZGSpJefvllBQUFKTU1VU6nU9OnT9err75q8dQAAH/n1fHctm3bNY/ffPPNWr9+vdavX99FEwEA4GOveQIA4A2IJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhrw6nnl5eRo/frxCQkIUFRWlOXPmqLS01GPNpEmTZLPZPLbHHnvMookBAIHAq+NZWFio9PR0HThwQLt371ZTU5OmTZumhoYGj3WLFi1SVVWVe3vhhRcsmhgAEAi6Wz3Atezatcvj9pYtWxQVFaUjR45o4sSJ7v09e/ZUTExMV48HAAhQXn3l+Y/q6uokSRERER77t27dqr59+2rUqFHKycnRxYsXrRgPABAgvPrK8/taW1u1dOlSTZgwQaNGjXLv/+lPf6oBAwYoLi5Ox44dU3Z2tkpLS/X2229f9VxOp1NOp9N92+FwdOrsAAD/4jPxTE9P14kTJ/TRRx957F+8eLH73wkJCYqNjdXUqVNVXl6uIUOGXPFceXl5ys3N7dR5AQD+yyeets3IyNDOnTu1b98+9evX75prExMTJUllZWVXXZOTk6O6ujr3durUqQ6dFwDg37z6ytPlcmnJkiUqKCjQ/v37NWjQoOvep6SkRJIUGxt71TV2u112u72jxgQABBivjmd6erry8/O1Y8cOhYSEqLq6WpIUFhamHj16qLy8XPn5+Zo5c6b69OmjY8eOKTMzUxMnTtTo0aMtnh4A4K+8Op4bNmyQ9N0HIXzf5s2b9fDDDys4OFgffPCB1q5dq4aGBsXHxys1NVXLli2zYFoAQKDw6ni6XK5rHo+Pj1dhYWEXTQNAkiasm2D1CD7p4yUfWz0COpBPvGEIAABvQjwBADBEPAEAMEQ8AQAwRDwBADBEPAEAMEQ8AQAwRDwBADBEPAEAMEQ8AQAwRDwBADBEPAEAMEQ8AQAwRDwBADBEPAEAMEQ8AQAwRDwBADBEPAEAMEQ8AQAwRDwBADBEPAEAMEQ8AQAwRDwBADBEPAEAMEQ8AQAwRDwBADBEPAEAMEQ8AQAwRDwBADBEPAEAMEQ8AQAwRDwBADBEPAEAMEQ8AQAwRDwBADDkN/Fcv369Bg4cqJtvvlmJiYn69NNPrR4JAOCn/CKeb7zxhrKysrRy5UodPXpUY8aM0fTp03X27FmrRwMA+CG/iOdLL72kRYsW6ZFHHtHIkSO1ceNG9ezZU5s2bbJ6NACAH/L5eDY2NurIkSNKTk527wsKClJycrKKi4stnAwA4K+6Wz3AD/V///d/amlpUXR0tMf+6Ohoffnll1e8j9PplNPpdN+uq6uTJDkcjmv+rBbntz9w2sB0vf+uJr651NJh5wokHfk7aP62ucPOFUg68nfQ0MzvwNSN/Pe/vMblcl13rc/Hsz3y8vKUm5vbZn98fLwF0/i/sHWPWT0C8sKsniDghWXzO7BU2I3/9//mm28Udp31Ph/Pvn37qlu3bqqpqfHYX1NTo5iYmCveJycnR1lZWe7bra2tunDhgvr06SObzdap83YGh8Oh+Ph4nTp1SqGhoVaPE5D4HViP34H1fP134HK59M033yguLu66a30+nsHBwRo3bpz27NmjOXPmSPouhnv27FFGRsYV72O322W32z32hYeHd/KknS80NNQn/wfrT/gdWI/fgfV8+XdwvSvOy3w+npKUlZWltLQ03XXXXbr77ru1du1aNTQ06JFHHrF6NACAH/KLeP7bv/2bzp07pxUrVqi6ulp33HGHdu3a1eZNRAAAdAS/iKckZWRkXPVpWn9nt9u1cuXKNk9Fo+vwO7AevwPrBdLvwOa6kffkAgAAN5//kAQAALoa8QQAwBDxBADAEPEEAMAQ8fQDfJepdYqKijR79mzFxcXJZrNp+/btVo8UUPLy8jR+/HiFhIQoKipKc+bMUWlpqdVjBZQNGzZo9OjR7g9GSEpK0p///Gerx+p0xNPH8V2m1mpoaNCYMWO0fv16q0cJSIWFhUpPT9eBAwe0e/duNTU1adq0aWpoaLB6tIDRr18/rV69WkeOHNHhw4c1ZcoU/cu//Is+++wzq0frVPypio9LTEzU+PHj9Zvf/EbSdx9NGB8fryVLlujf//3fLZ4usNhsNhUUFLg/JhJd79y5c4qKilJhYaEmTpxo9TgBKyIiQmvWrNHChQutHqXTcOXpw/guU8DT5a8XjIiIsHiSwNTS0qJt27apoaFBSUlJVo/TqfzmE4YCUXu+yxTwV62trVq6dKkmTJigUaNGWT1OQDl+/LiSkpJ06dIl9e7dWwUFBRo5cqTVY3Uq4gnAL6Snp+vEiRP66KOPrB4l4AwbNkwlJSWqq6vTW2+9pbS0NBUWFvp1QImnD2vPd5kC/igjI0M7d+5UUVGR+vXrZ/U4ASc4OFi33nqrJGncuHE6dOiQ/vM//1O//e1vLZ6s8/Capw/7/neZXnb5u0z9/fUGQPruy4szMjJUUFCgvXv3atCgQVaPBH33/0NOp9PqMToVV54+ju8ytVZ9fb3KysrctysqKlRSUqKIiAj179/fwskCQ3p6uvLz87Vjxw6FhISourpa0ndfaNyjRw+LpwsMOTk5SklJUf/+/fXNN98oPz9f+/fv13vvvWf1aJ3LBZ+3bt06V//+/V3BwcGuu+++23XgwAGrRwoY+/btc0lqs6WlpVk9WkC40n97Sa7NmzdbPVrAWLBggWvAgAGu4OBgV2RkpGvq1Kmu999/3+qxOh1/5wkAgCFe8wQAwBDxBADAEPEEAMAQ8QQAwBDxBADAEPEEAMAQ8QQAwBDxBAKIzWbT9u3brR4D8HnEE/Aj1dXVWrJkiQYPHiy73a74+HjNnj3b4/OPAfxwfLYt4Ce++uorTZgwQeHh4VqzZo0SEhLU1NSk9957T+np6XzHK9CBuPIE/MQvf/lL2Ww2ffrpp0pNTdVtt92m22+/XVlZWTpw4MAV75Odna3bbrtNPXv21ODBg7V8+XI1NTW5j//1r3/V5MmTFRISotDQUI0bN06HDx+WJH399deaPXu2brnlFvXq1Uu33367/vd//7dLHitgNa48AT9w4cIF7dq1S88//7x69erV5nh4ePgV7xcSEqItW7YoLi5Ox48f16JFixQSEqJnnnlGkjR//nyNHTtWGzZsULdu3VRSUqKbbrpJ0nffaNLY2KiioiL16tVLn3/+uXr37t1pjxHwJsQT8ANlZWVyuVwaPny40f2WLVvm/vfAgQP11FNPadu2be54VlZW6umnn3afd+jQoe71lZWVSk1NVUJCgiRp8ODBP/RhAD6Dp20BP9DeL0d64403NGHCBMXExKh3795atmyZKisr3cezsrL06KOPKjk5WatXr1Z5ebn72BNPPKHnnntOEyZM0MqVK3Xs2LEf/DgAX0E8AT8wdOhQ2Ww2ozcFFRcXa/78+Zo5c6Z27typv/zlL/qP//gPNTY2utesWrVKn332mWbNmqW9e/dq5MiRKigokCQ9+uij+tvf/qaf//znOn78uO666y6tW7euwx8b4I34Pk/AT6SkpOj48eMqLS1t87pnbW2twsPDZbPZVFBQoDlz5ujFF1/Uq6++6nE1+eijj+qtt95SbW3tFX/GvHnz1NDQoD/96U9tjuXk5Ojdd9/lChQBgStPwE+sX79eLS0tuvvuu/XHP/5RJ0+e1BdffKFXXnlFSUlJbdYPHTpUlZWV2rZtm8rLy/XKK6+4ryol6dtvv1VGRob279+vr7/+Wh9//LEOHTqkESNGSJKWLl2q9957TxUVFTp69Kj27dvnPgb4O94wBPiJwYMH6+jRo3r++ef15JNPqqqqSpGRkRo3bpw2bNjQZv3999+vzMxMZWRkyOl0atasWVq+fLlWrVolSerWrZvOnz+vhx56SDU1Nerbt6/mzp2r3NxcSVJLS4vS09N1+vRphYaGasaMGXr55Ze78iEDluFpWwAADPG0LQAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIaIJwAAhognAACGiCcAAIb+H7bCvmVtkcoAAAAAAElFTkSuQmCC","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(5,5))\n","sns.countplot(x=E_data.Class);"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26,"status":"ok","timestamp":1657314035977,"user":{"displayName":"Diego P","userId":"14448528258399483719"},"user_tz":180},"id":"fy9I-XXEJeQf","outputId":"39df21a3-b609-4177-ee5f-e372d5476438"},"outputs":[{"name":"stdout","output_type":"stream","text":["Early Recurrence : 62\n","Middle Recurrence : 61\n","Late Recurrence : 22\n","Non Recurrence : 199\n"]}],"source":["# Generate and plot a synthetic imbalanced classification dataset\n","counter = Counter(E_data[\"Class\"])\n","print('%s : %d' % ('Early Recurrence', counter[0]))\n","print('%s : %d' % ('Middle Recurrence', counter[1]))\n","print('%s : %d' % ('Late Recurrence', counter[2]))\n","print('%s : %d' % ('Non Recurrence', counter[3]))"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## GPP Conclusions and Actions\n","\n","Considering the previus analysis, the following actions were taken:\n","\n","1. **Remove \"Sex\" attribute**: The number of men record (4) is not significant to be considered in the analysis. If only men records are removed the attribute will have only one value (Women).\n","\n","2. **Standardize all continuous variables**: The continuous variables are standardized to have a mean of 0 and a standard deviation of 1.\n","\n","3. **Categorical attributes with int values**: The categorical attributes with int values stay as they are.\n","\n","4. **Correlation between attributes**: Winter Hypoxia Score and Ragnum Hypoxia Score are highly correlated with Buffa Hypoxia Score are (0.84 and 0.72). The attribute Buffa Hypoxia Score is removed from the dataset.\n","\n","5. **Target Class**: The target class is unbalanced and has 3 classes related to the recurrence of breast cancer. Where class 3 is related to the absence of recurrence and classes 1 and 2 are related to the time when the recurrence occurred. In this stage no action is taken to balance the target class. The classes 1 and 2 are grouped in class 1 mid/late recurrence."]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-1 Dataset"]},{"cell_type":"code","execution_count":47,"metadata":{},"outputs":[{"data":{"text/plain":["Class\n","2    199\n","1     83\n","0     62\n","dtype: int64"]},"execution_count":47,"metadata":{},"output_type":"execute_result"}],"source":["# Merge class 1 and 2 into class 1, and replace 3 with 2\n","E_data['Class'] = E_data['Class'].replace([2, 3], [1,2])\n","E_data.value_counts('Class')"]},{"cell_type":"code","execution_count":48,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Cancer Type Detailed</th>\n","      <th>Subtype</th>\n","      <th>Diagnosis Age</th>\n","      <th>Sex</th>\n","      <th>Ethnicity Category</th>\n","      <th>Race Category</th>\n","      <th>Neoplasm Cancer Code</th>\n","      <th>Aneuploidy Score</th>\n","      <th>Buffa Hypoxia Score</th>\n","      <th>Ragnum Hypoxia Score</th>\n","      <th>Winter Hypoxia Score</th>\n","      <th>Fraction Genome Altered</th>\n","      <th>In PanCan Pathway Analysis</th>\n","      <th>Mutation Count</th>\n","      <th>Post Initial Therapy</th>\n","      <th>Prior Diagnosis</th>\n","      <th>Radiation Therapy</th>\n","      <th>Tissue Prospective Indicator</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>55</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>11</td>\n","      <td>19</td>\n","      <td>-21</td>\n","      <td>0</td>\n","      <td>-28</td>\n","      <td>0.7787</td>\n","      <td>1</td>\n","      <td>24</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>50</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>22</td>\n","      <td>5</td>\n","      <td>18</td>\n","      <td>20</td>\n","      <td>0.7164</td>\n","      <td>1</td>\n","      <td>458</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>54</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>16</td>\n","      <td>27</td>\n","      <td>14</td>\n","      <td>22</td>\n","      <td>0.4163</td>\n","      <td>1</td>\n","      <td>62</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>66</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>-27</td>\n","      <td>-8</td>\n","      <td>-20</td>\n","      <td>0.0862</td>\n","      <td>1</td>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>36</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>17</td>\n","      <td>12</td>\n","      <td>4</td>\n","      <td>0.6407</td>\n","      <td>1</td>\n","      <td>128</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Cancer Type Detailed  Subtype  Diagnosis Age  Sex  Ethnicity Category  \\\n","0                     2        2             55    0                   1   \n","1                     1        1             50    0                   1   \n","2                     0        0             54    0                   1   \n","3                     1        2             66    0                   0   \n","4                     1        0             36    0                   1   \n","\n","   Race Category  Neoplasm Cancer Code  Aneuploidy Score  Buffa Hypoxia Score  \\\n","0              2                    11                19                  -21   \n","1              1                     5                22                    5   \n","2              0                     4                16                   27   \n","3              2                     1                 3                  -27   \n","4              1                     9                 9                   17   \n","\n","   Ragnum Hypoxia Score  Winter Hypoxia Score  Fraction Genome Altered  \\\n","0                     0                   -28                   0.7787   \n","1                    18                    20                   0.7164   \n","2                    14                    22                   0.4163   \n","3                    -8                   -20                   0.0862   \n","4                    12                     4                   0.6407   \n","\n","   In PanCan Pathway Analysis  Mutation Count  Post Initial Therapy  \\\n","0                           1              24                     0   \n","1                           1             458                     0   \n","2                           1              62                     1   \n","3                           1              41                     0   \n","4                           1             128                     1   \n","\n","   Prior Diagnosis  Radiation Therapy  Tissue Prospective Indicator  Class  \n","0                0                  0                             0      1  \n","1                0                  1                             0      2  \n","2                0                  1                             0      1  \n","3                0                  1                             0      2  \n","4                0                  1                             0      0  "]},"execution_count":48,"metadata":{},"output_type":"execute_result"}],"source":["E1_data = E_data.copy()\n","E1_data.head()"]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["figura = plt.figure(figsize=(5,3))\n","sns.countplot(x=E1_data.Class);"]},{"cell_type":"code","execution_count":50,"metadata":{},"outputs":[{"data":{"text/plain":["2    199\n","1     83\n","0     62\n","Name: Class, dtype: int64"]},"execution_count":50,"metadata":{},"output_type":"execute_result"}],"source":["E1_data[\"Class\"].value_counts()"]},{"cell_type":"code","execution_count":51,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Early Recurrence : 62\n","Mid/late Recurrence : 83\n","Non Recurrence : 199\n"]}],"source":["counter = Counter(E1_data[\"Class\"])\n","print('%s : %d' % ('Early Recurrence', counter[0]))\n","print('%s : %d' % ('Mid/late Recurrence', counter[1]))\n","print('%s : %d' % ('Non Recurrence', counter[2]))"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-2 Dataset"]},{"cell_type":"code","execution_count":52,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Cancer Type Detailed</th>\n","      <th>Subtype</th>\n","      <th>Diagnosis Age</th>\n","      <th>Ethnicity Category</th>\n","      <th>Race Category</th>\n","      <th>Neoplasm Cancer Code</th>\n","      <th>Aneuploidy Score</th>\n","      <th>Ragnum Hypoxia Score</th>\n","      <th>Winter Hypoxia Score</th>\n","      <th>Fraction Genome Altered</th>\n","      <th>In PanCan Pathway Analysis</th>\n","      <th>Mutation Count</th>\n","      <th>Post Initial Therapy</th>\n","      <th>Prior Diagnosis</th>\n","      <th>Radiation Therapy</th>\n","      <th>Tissue Prospective Indicator</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>-0.099707</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>11</td>\n","      <td>0.917510</td>\n","      <td>-0.232988</td>\n","      <td>-0.786458</td>\n","      <td>2.306375</td>\n","      <td>1</td>\n","      <td>-0.208468</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>-0.468189</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>1.318307</td>\n","      <td>1.269528</td>\n","      <td>1.531165</td>\n","      <td>2.011235</td>\n","      <td>1</td>\n","      <td>1.568424</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>-0.173403</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>0.516713</td>\n","      <td>0.935635</td>\n","      <td>1.627732</td>\n","      <td>0.589544</td>\n","      <td>1</td>\n","      <td>-0.052888</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0.710954</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>-1.220072</td>\n","      <td>-0.900773</td>\n","      <td>-0.400188</td>\n","      <td>-0.974270</td>\n","      <td>1</td>\n","      <td>-0.138866</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>-1.499940</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>9</td>\n","      <td>-0.418479</td>\n","      <td>0.768689</td>\n","      <td>0.758624</td>\n","      <td>1.652615</td>\n","      <td>1</td>\n","      <td>0.217331</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Cancer Type Detailed  Subtype  Diagnosis Age  Ethnicity Category  \\\n","0                     2        2      -0.099707                   1   \n","1                     1        1      -0.468189                   1   \n","2                     0        0      -0.173403                   1   \n","3                     1        2       0.710954                   0   \n","4                     1        0      -1.499940                   1   \n","\n","   Race Category  Neoplasm Cancer Code  Aneuploidy Score  \\\n","0              2                    11          0.917510   \n","1              1                     5          1.318307   \n","2              0                     4          0.516713   \n","3              2                     1         -1.220072   \n","4              1                     9         -0.418479   \n","\n","   Ragnum Hypoxia Score  Winter Hypoxia Score  Fraction Genome Altered  \\\n","0             -0.232988             -0.786458                 2.306375   \n","1              1.269528              1.531165                 2.011235   \n","2              0.935635              1.627732                 0.589544   \n","3             -0.900773             -0.400188                -0.974270   \n","4              0.768689              0.758624                 1.652615   \n","\n","   In PanCan Pathway Analysis  Mutation Count  Post Initial Therapy  \\\n","0                           1       -0.208468                     0   \n","1                           1        1.568424                     0   \n","2                           1       -0.052888                     1   \n","3                           1       -0.138866                     0   \n","4                           1        0.217331                     1   \n","\n","   Prior Diagnosis  Radiation Therapy  Tissue Prospective Indicator  Class  \n","0                0                  0                             0      1  \n","1                0                  1                             0      2  \n","2                0                  1                             0      1  \n","3                0                  1                             0      2  \n","4                0                  1                             0      0  "]},"execution_count":52,"metadata":{},"output_type":"execute_result"}],"source":["drop_cols = ['Sex', 'Buffa Hypoxia Score']\n","standardize_cols = [\"Diagnosis Age\",\"Aneuploidy Score\",\"Ragnum Hypoxia Score\",\n","                    \"Winter Hypoxia Score\",\"Fraction Genome Altered\", \"Mutation Count\"]\n","E2_data = gpp_preprocess(E1_data, drop_cols, standardize_cols)\n","E2_data.head()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-3 Dataset\n","This dataset is used in the experiment E-3. It is the dataset with GPP + Feature Selection with mRMR."]},{"cell_type":"code","execution_count":53,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:01<00:00,  5.53it/s]\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Post Initial Therapy</th>\n","      <th>Cancer Type Detailed</th>\n","      <th>Tissue Prospective Indicator</th>\n","      <th>Neoplasm Cancer Code</th>\n","      <th>Aneuploidy Score</th>\n","      <th>Radiation Therapy</th>\n","      <th>Winter Hypoxia Score</th>\n","      <th>Prior Diagnosis</th>\n","      <th>Mutation Count</th>\n","      <th>Diagnosis Age</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>11</td>\n","      <td>0.917510</td>\n","      <td>0</td>\n","      <td>-0.786458</td>\n","      <td>0</td>\n","      <td>-0.208468</td>\n","      <td>-0.099707</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>1.318307</td>\n","      <td>1</td>\n","      <td>1.531165</td>\n","      <td>0</td>\n","      <td>1.568424</td>\n","      <td>-0.468189</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>0.516713</td>\n","      <td>1</td>\n","      <td>1.627732</td>\n","      <td>0</td>\n","      <td>-0.052888</td>\n","      <td>-0.173403</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>-1.220072</td>\n","      <td>1</td>\n","      <td>-0.400188</td>\n","      <td>0</td>\n","      <td>-0.138866</td>\n","      <td>0.710954</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>9</td>\n","      <td>-0.418479</td>\n","      <td>1</td>\n","      <td>0.758624</td>\n","      <td>0</td>\n","      <td>0.217331</td>\n","      <td>-1.499940</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Post Initial Therapy  Cancer Type Detailed  Tissue Prospective Indicator  \\\n","0                     0                     2                             0   \n","1                     0                     1                             0   \n","2                     1                     0                             0   \n","3                     0                     1                             0   \n","4                     1                     1                             0   \n","\n","   Neoplasm Cancer Code  Aneuploidy Score  Radiation Therapy  \\\n","0                    11          0.917510                  0   \n","1                     5          1.318307                  1   \n","2                     4          0.516713                  1   \n","3                     1         -1.220072                  1   \n","4                     9         -0.418479                  1   \n","\n","   Winter Hypoxia Score  Prior Diagnosis  Mutation Count  Diagnosis Age  Class  \n","0             -0.786458                0       -0.208468      -0.099707      1  \n","1              1.531165                0        1.568424      -0.468189      2  \n","2              1.627732                0       -0.052888      -0.173403      1  \n","3             -0.400188                0       -0.138866       0.710954      2  \n","4              0.758624                0        0.217331      -1.499940      0  "]},"execution_count":53,"metadata":{},"output_type":"execute_result"}],"source":["E3_data = mrmr_preprocess(E2_data, \"Class\")\n","E3_data.head()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-4 Dataset\n","\n","This dataset is used in the experiment E-4. It is the dataset with GPP + Feature Selection with mRMR + Oversample with SMOTE. We are going to do a copy of E-3 dataset to use it in E-4 and then apply the SMOTE technique to balance the target class when the experiment is executed."]},{"cell_type":"code","execution_count":54,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Post Initial Therapy</th>\n","      <th>Cancer Type Detailed</th>\n","      <th>Tissue Prospective Indicator</th>\n","      <th>Neoplasm Cancer Code</th>\n","      <th>Aneuploidy Score</th>\n","      <th>Radiation Therapy</th>\n","      <th>Winter Hypoxia Score</th>\n","      <th>Prior Diagnosis</th>\n","      <th>Mutation Count</th>\n","      <th>Diagnosis Age</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>11</td>\n","      <td>0.917510</td>\n","      <td>0</td>\n","      <td>-0.786458</td>\n","      <td>0</td>\n","      <td>-0.208468</td>\n","      <td>-0.099707</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>1.318307</td>\n","      <td>1</td>\n","      <td>1.531165</td>\n","      <td>0</td>\n","      <td>1.568424</td>\n","      <td>-0.468189</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>0.516713</td>\n","      <td>1</td>\n","      <td>1.627732</td>\n","      <td>0</td>\n","      <td>-0.052888</td>\n","      <td>-0.173403</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>-1.220072</td>\n","      <td>1</td>\n","      <td>-0.400188</td>\n","      <td>0</td>\n","      <td>-0.138866</td>\n","      <td>0.710954</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>9</td>\n","      <td>-0.418479</td>\n","      <td>1</td>\n","      <td>0.758624</td>\n","      <td>0</td>\n","      <td>0.217331</td>\n","      <td>-1.499940</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Post Initial Therapy  Cancer Type Detailed  Tissue Prospective Indicator  \\\n","0                     0                     2                             0   \n","1                     0                     1                             0   \n","2                     1                     0                             0   \n","3                     0                     1                             0   \n","4                     1                     1                             0   \n","\n","   Neoplasm Cancer Code  Aneuploidy Score  Radiation Therapy  \\\n","0                    11          0.917510                  0   \n","1                     5          1.318307                  1   \n","2                     4          0.516713                  1   \n","3                     1         -1.220072                  1   \n","4                     9         -0.418479                  1   \n","\n","   Winter Hypoxia Score  Prior Diagnosis  Mutation Count  Diagnosis Age  Class  \n","0             -0.786458                0       -0.208468      -0.099707      1  \n","1              1.531165                0        1.568424      -0.468189      2  \n","2              1.627732                0       -0.052888      -0.173403      1  \n","3             -0.400188                0       -0.138866       0.710954      2  \n","4              0.758624                0        0.217331      -1.499940      0  "]},"execution_count":54,"metadata":{},"output_type":"execute_result"}],"source":["E4_data = E3_data.copy()\n","E4_data.head()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"I_E09BTrW4i4"},"source":["\n","\n","---\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"l_AdgCpiYUWG"},"source":["# 2 - Run Experiments\n","\n"]},{"cell_type":"code","execution_count":55,"metadata":{},"outputs":[],"source":["# Function to print the results in latex format\n","table_labels = [\"mean_Accuracy Score\",\"std_Accuracy Score\",\"mean_Precision Score\",\"std_Precision Score\", \"mean_Recall Score\", \"std_Recall Score\"]\n","def print_latex_rows(data:pd.DataFrame, table_labels: list):\n","    for index, row in data.iterrows():\n","        print(\"%s & %.3f $\\pm$ %.3f & %.3f $\\pm$ %.3f & %.3f $\\pm$ %.3f \\\\\\\\\" % (index, row[table_labels[0]], row[table_labels[1]], row[table_labels[2]], row[table_labels[3]], row[table_labels[4]], row[table_labels[5]]))"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"FkM7DVxhL3vF"},"source":["## E-1 (Experiment 1)\n","\n","**E-1**: Training of Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), k nearest neighbors (KNN) models using the original dataset.\n","\n","Calculation of performance metrics using the original dataset, to have an initial reference point. "]},{"cell_type":"code","execution_count":56,"metadata":{},"outputs":[],"source":["default_params = load_params(\"tune_params/default_params_4C.json\")"]},{"cell_type":"code","execution_count":57,"metadata":{},"outputs":[],"source":["#E1_Results = run_experiment(E1_data, \"Class\",\"E1\", default_params)\n","#E1_Results"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-2 (Experiment 2)\n","\n","**E-2**: Training of Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), k nearest neighbors (KNN) models using the dataset with GPP."]},{"cell_type":"code","execution_count":58,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Running experiment: 1,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["2,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["3,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["4,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["5,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["6,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["7,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["8,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["9,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["10,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":[" Done! "]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Models</th>\n","      <th>mean_Accuracy (kfold)</th>\n","      <th>std_Accuracy (kfold)</th>\n","      <th>mean_Accuracy Score</th>\n","      <th>std_Accuracy Score</th>\n","      <th>mean_Precision Score</th>\n","      <th>std_Precision Score</th>\n","      <th>mean_Recall Score</th>\n","      <th>std_Recall Score</th>\n","      <th>mean_F1 Score</th>\n","      <th>std_F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>LR</td>\n","      <td>0.824</td>\n","      <td>0.010</td>\n","      <td>0.806</td>\n","      <td>0.052</td>\n","      <td>0.920</td>\n","      <td>0.039</td>\n","      <td>0.987</td>\n","      <td>0.013</td>\n","      <td>0.952</td>\n","      <td>0.022</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>NB</td>\n","      <td>0.779</td>\n","      <td>0.008</td>\n","      <td>0.781</td>\n","      <td>0.036</td>\n","      <td>0.914</td>\n","      <td>0.030</td>\n","      <td>0.928</td>\n","      <td>0.029</td>\n","      <td>0.920</td>\n","      <td>0.020</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>SVM</td>\n","      <td>0.803</td>\n","      <td>0.014</td>\n","      <td>0.813</td>\n","      <td>0.032</td>\n","      <td>0.884</td>\n","      <td>0.028</td>\n","      <td>0.974</td>\n","      <td>0.027</td>\n","      <td>0.927</td>\n","      <td>0.023</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>KNN</td>\n","      <td>0.679</td>\n","      <td>0.009</td>\n","      <td>0.654</td>\n","      <td>0.022</td>\n","      <td>0.741</td>\n","      <td>0.052</td>\n","      <td>0.898</td>\n","      <td>0.035</td>\n","      <td>0.810</td>\n","      <td>0.023</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>XGB</td>\n","      <td>0.786</td>\n","      <td>0.010</td>\n","      <td>0.760</td>\n","      <td>0.035</td>\n","      <td>0.886</td>\n","      <td>0.030</td>\n","      <td>0.954</td>\n","      <td>0.032</td>\n","      <td>0.918</td>\n","      <td>0.020</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Models  mean_Accuracy (kfold)  std_Accuracy (kfold)  mean_Accuracy Score  \\\n","0     LR                  0.824                 0.010                0.806   \n","1     NB                  0.779                 0.008                0.781   \n","2    SVM                  0.803                 0.014                0.813   \n","3    KNN                  0.679                 0.009                0.654   \n","4    XGB                  0.786                 0.010                0.760   \n","\n","   std_Accuracy Score  mean_Precision Score  std_Precision Score  \\\n","0               0.052                 0.920                0.039   \n","1               0.036                 0.914                0.030   \n","2               0.032                 0.884                0.028   \n","3               0.022                 0.741                0.052   \n","4               0.035                 0.886                0.030   \n","\n","   mean_Recall Score  std_Recall Score  mean_F1 Score  std_F1 Score  \n","0              0.987             0.013          0.952         0.022  \n","1              0.928             0.029          0.920         0.020  \n","2              0.974             0.027          0.927         0.023  \n","3              0.898             0.035          0.810         0.023  \n","4              0.954             0.032          0.918         0.020  "]},"execution_count":58,"metadata":{},"output_type":"execute_result"}],"source":["E2_Results = run_experiment(E2_data, \"Class\",\"E2\", default_params, num_exp=10)\n","E2_Results"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-3 (Experiment 3)\n","\n","**E-3**: A feature selection techniques (mRMR) is applied to the dataset with GPP to select the most significant attributes for the target class. Then, the training of Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), k nearest neighbors (KNN) models is performed."]},{"cell_type":"code","execution_count":60,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Running experiment: 1,2,3,4,5,6,7,8,9,10, Done! "]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Models</th>\n","      <th>mean_Accuracy (kfold)</th>\n","      <th>std_Accuracy (kfold)</th>\n","      <th>mean_Accuracy Score</th>\n","      <th>std_Accuracy Score</th>\n","      <th>mean_Precision Score</th>\n","      <th>std_Precision Score</th>\n","      <th>mean_Recall Score</th>\n","      <th>std_Recall Score</th>\n","      <th>mean_F1 Score</th>\n","      <th>std_F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>LR</td>\n","      <td>0.821</td>\n","      <td>0.006</td>\n","      <td>0.807</td>\n","      <td>0.026</td>\n","      <td>0.911</td>\n","      <td>0.028</td>\n","      <td>0.995</td>\n","      <td>0.011</td>\n","      <td>0.951</td>\n","      <td>0.017</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>NB</td>\n","      <td>0.814</td>\n","      <td>0.002</td>\n","      <td>0.791</td>\n","      <td>0.043</td>\n","      <td>0.909</td>\n","      <td>0.046</td>\n","      <td>0.948</td>\n","      <td>0.028</td>\n","      <td>0.927</td>\n","      <td>0.029</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>SVM</td>\n","      <td>0.797</td>\n","      <td>0.006</td>\n","      <td>0.819</td>\n","      <td>0.038</td>\n","      <td>0.895</td>\n","      <td>0.044</td>\n","      <td>0.988</td>\n","      <td>0.013</td>\n","      <td>0.939</td>\n","      <td>0.025</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>KNN</td>\n","      <td>0.700</td>\n","      <td>0.011</td>\n","      <td>0.687</td>\n","      <td>0.048</td>\n","      <td>0.767</td>\n","      <td>0.051</td>\n","      <td>0.904</td>\n","      <td>0.042</td>\n","      <td>0.828</td>\n","      <td>0.027</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>XGB</td>\n","      <td>0.759</td>\n","      <td>0.008</td>\n","      <td>0.759</td>\n","      <td>0.028</td>\n","      <td>0.920</td>\n","      <td>0.038</td>\n","      <td>0.956</td>\n","      <td>0.034</td>\n","      <td>0.937</td>\n","      <td>0.019</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Models  mean_Accuracy (kfold)  std_Accuracy (kfold)  mean_Accuracy Score  \\\n","0     LR                  0.821                 0.006                0.807   \n","1     NB                  0.814                 0.002                0.791   \n","2    SVM                  0.797                 0.006                0.819   \n","3    KNN                  0.700                 0.011                0.687   \n","4    XGB                  0.759                 0.008                0.759   \n","\n","   std_Accuracy Score  mean_Precision Score  std_Precision Score  \\\n","0               0.026                 0.911                0.028   \n","1               0.043                 0.909                0.046   \n","2               0.038                 0.895                0.044   \n","3               0.048                 0.767                0.051   \n","4               0.028                 0.920                0.038   \n","\n","   mean_Recall Score  std_Recall Score  mean_F1 Score  std_F1 Score  \n","0              0.995             0.011          0.951         0.017  \n","1              0.948             0.028          0.927         0.029  \n","2              0.988             0.013          0.939         0.025  \n","3              0.904             0.042          0.828         0.027  \n","4              0.956             0.034          0.937         0.019  "]},"execution_count":60,"metadata":{},"output_type":"execute_result"}],"source":["E3_Results = run_experiment(E3_data, \"Class\",\"E3\", default_params, num_exp=10)\n","E3_Results"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-4 (Experiment 4)\n","\n","**E-4**: The technique SMOTE is applied to the dataset with GPP + Feature Selection to balance the target class. Then, the training of Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), k nearest neighbors (KNN) models is performed."]},{"cell_type":"code","execution_count":63,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Running experiment: 1,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["2,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["3,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["4,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["5,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["6,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["7,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["8,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["9,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":["10,"]},{"name":"stderr","output_type":"stream","text":["/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n","/home/diripar8/anaconda3/envs/uldev/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"name":"stdout","output_type":"stream","text":[" Done! "]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Models</th>\n","      <th>mean_Accuracy (kfold)</th>\n","      <th>std_Accuracy (kfold)</th>\n","      <th>mean_Accuracy Score</th>\n","      <th>std_Accuracy Score</th>\n","      <th>mean_Precision Score</th>\n","      <th>std_Precision Score</th>\n","      <th>mean_Recall Score</th>\n","      <th>std_Recall Score</th>\n","      <th>mean_F1 Score</th>\n","      <th>std_F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>LR</td>\n","      <td>0.703</td>\n","      <td>0.005</td>\n","      <td>0.714</td>\n","      <td>0.033</td>\n","      <td>0.775</td>\n","      <td>0.047</td>\n","      <td>0.960</td>\n","      <td>0.030</td>\n","      <td>0.857</td>\n","      <td>0.029</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>NB</td>\n","      <td>0.663</td>\n","      <td>0.002</td>\n","      <td>0.671</td>\n","      <td>0.042</td>\n","      <td>0.750</td>\n","      <td>0.059</td>\n","      <td>0.958</td>\n","      <td>0.019</td>\n","      <td>0.840</td>\n","      <td>0.036</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>SVM</td>\n","      <td>0.745</td>\n","      <td>0.004</td>\n","      <td>0.748</td>\n","      <td>0.045</td>\n","      <td>0.771</td>\n","      <td>0.071</td>\n","      <td>0.970</td>\n","      <td>0.027</td>\n","      <td>0.858</td>\n","      <td>0.052</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>KNN</td>\n","      <td>0.800</td>\n","      <td>0.005</td>\n","      <td>0.793</td>\n","      <td>0.033</td>\n","      <td>0.846</td>\n","      <td>0.068</td>\n","      <td>0.734</td>\n","      <td>0.056</td>\n","      <td>0.784</td>\n","      <td>0.045</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>XGB</td>\n","      <td>0.837</td>\n","      <td>0.006</td>\n","      <td>0.825</td>\n","      <td>0.029</td>\n","      <td>0.833</td>\n","      <td>0.074</td>\n","      <td>0.876</td>\n","      <td>0.027</td>\n","      <td>0.852</td>\n","      <td>0.044</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Models  mean_Accuracy (kfold)  std_Accuracy (kfold)  mean_Accuracy Score  \\\n","0     LR                  0.703                 0.005                0.714   \n","1     NB                  0.663                 0.002                0.671   \n","2    SVM                  0.745                 0.004                0.748   \n","3    KNN                  0.800                 0.005                0.793   \n","4    XGB                  0.837                 0.006                0.825   \n","\n","   std_Accuracy Score  mean_Precision Score  std_Precision Score  \\\n","0               0.033                 0.775                0.047   \n","1               0.042                 0.750                0.059   \n","2               0.045                 0.771                0.071   \n","3               0.033                 0.846                0.068   \n","4               0.029                 0.833                0.074   \n","\n","   mean_Recall Score  std_Recall Score  mean_F1 Score  std_F1 Score  \n","0              0.960             0.030          0.857         0.029  \n","1              0.958             0.019          0.840         0.036  \n","2              0.970             0.027          0.858         0.052  \n","3              0.734             0.056          0.784         0.045  \n","4              0.876             0.027          0.852         0.044  "]},"execution_count":63,"metadata":{},"output_type":"execute_result"}],"source":["E4_Results = run_experiment(E4_data, \"Class\",\"E4\", default_params, num_exp=10)\n","E4_Results"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["----"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"Eol1oU58XQDf"},"source":["# 3 - Tuning with GridSearchCV\n","\n","The models are tuned using GridSearchCV to find the best parameters for each model. The parameters are defined in [tuning_parameters.yml](tune_params/tuning_parameters.yml) file.\n","\n","The results of the tuning are saved in `json` files in the folder `tune_params/`."]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[],"source":["tune_params = load_params(\"tune_params/tuning_parameters.json\")"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Tuning E-1"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[],"source":["#E1_tune_bests = tune_experiment(E1_data,\"Class\",\"E1\",tune_params,default_params)\n","#file_path = \"tune_params/best_4C/E1_best_params.json\"\n","#save_tuned_params(E1_tune_bests, file_path)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Tuning E-2"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Tuning hyperparameters for LR...\n","Fitting 10 folds for each of 168 candidates, totalling 1680 fits\n","Tuning hyperparameters for NB...\n","Fitting 10 folds for each of 1000 candidates, totalling 10000 fits\n","Tuning hyperparameters for SVM...\n","Fitting 10 folds for each of 40 candidates, totalling 400 fits\n","Tuning hyperparameters for KNN...\n","Fitting 10 folds for each of 1920 candidates, totalling 19200 fits\n","Tuning hyperparameters for XGB...\n","Fitting 10 folds for each of 12600 candidates, totalling 126000 fits\n","Tuned parameters saved to ...\n"]}],"source":["E2_tune_bests = tune_experiment(E2_data,\"Class\",\"E2\",tune_params,default_params)\n","file_path = \"tune_params/best_4C/E2_best_params.json\"\n","save_tuned_params(E2_tune_bests, file_path)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Tuning E-3"]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Tuning hyperparameters for LR...\n","Fitting 10 folds for each of 168 candidates, totalling 1680 fits\n","Tuning hyperparameters for NB...\n","Fitting 10 folds for each of 1000 candidates, totalling 10000 fits\n","Tuning hyperparameters for SVM...\n","Fitting 10 folds for each of 40 candidates, totalling 400 fits\n","Tuning hyperparameters for KNN...\n","Fitting 10 folds for each of 1920 candidates, totalling 19200 fits\n","Tuning hyperparameters for XGB...\n","Fitting 10 folds for each of 12600 candidates, totalling 126000 fits\n","Tuned parameters saved to ...\n"]}],"source":["E3_tune_bests = tune_experiment(E3_data,\"Class\",\"E3\",tune_params,default_params)\n","file_path = \"tune_params/best_4C/E3_best_params.json\"\n","save_tuned_params(E3_tune_bests, file_path)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Tuning E-4"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Tuning hyperparameters for LR...\n","Fitting 10 folds for each of 168 candidates, totalling 1680 fits\n","Tuning hyperparameters for NB...\n","Fitting 10 folds for each of 1000 candidates, totalling 10000 fits\n","Tuning hyperparameters for SVM...\n","Fitting 10 folds for each of 40 candidates, totalling 400 fits\n","Tuning hyperparameters for KNN...\n","Fitting 10 folds for each of 1920 candidates, totalling 19200 fits\n","Tuning hyperparameters for XGB...\n","Fitting 10 folds for each of 12600 candidates, totalling 126000 fits\n","Tuned parameters saved to ...\n"]}],"source":["E4_tune_bests = tune_experiment(E4_data,\"Class\",\"E4\",tune_params,default_params)\n","file_path = \"tune_params/best_4C/E4_best_params.json\"\n","save_tuned_params(E4_tune_bests, file_path)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["---"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"ZBdhKd2bXZnH"},"source":["# 4 - Run Experiments With Tuning Parameters"]},{"cell_type":"code","execution_count":70,"metadata":{},"outputs":[],"source":["#E1_best_params = load_params(\"tune_params/best_4C/E1_best_params.json\")\n","E2_best_params = load_params(\"tune_params/best_4C/E2_best_params.json\")\n","E3_best_params = load_params(\"tune_params/best_4C/E3_best_params.json\")\n","E4_best_params = load_params(\"tune_params/best_4C/E4_best_params.json\")"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-1 (Experiment 1)"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[],"source":["#E1_Results = run_experiment(E1_data, \"Class\",\"E1\", E1_best_params)\n","#E1_Results"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-2 (Experiment 2)"]},{"cell_type":"code","execution_count":71,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Running experiment: 1,2,3,4,5,6,7,8,9,10, Done! "]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Models</th>\n","      <th>mean_Accuracy (kfold)</th>\n","      <th>std_Accuracy (kfold)</th>\n","      <th>mean_Accuracy Score</th>\n","      <th>std_Accuracy Score</th>\n","      <th>mean_Precision Score</th>\n","      <th>std_Precision Score</th>\n","      <th>mean_Recall Score</th>\n","      <th>std_Recall Score</th>\n","      <th>mean_F1 Score</th>\n","      <th>std_F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>LR</td>\n","      <td>0.821</td>\n","      <td>0.006</td>\n","      <td>0.819</td>\n","      <td>0.053</td>\n","      <td>0.905</td>\n","      <td>0.037</td>\n","      <td>1.000</td>\n","      <td>0.000</td>\n","      <td>0.950</td>\n","      <td>0.021</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>NB</td>\n","      <td>0.814</td>\n","      <td>0.008</td>\n","      <td>0.806</td>\n","      <td>0.033</td>\n","      <td>0.901</td>\n","      <td>0.032</td>\n","      <td>0.981</td>\n","      <td>0.012</td>\n","      <td>0.939</td>\n","      <td>0.020</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>SVM</td>\n","      <td>0.819</td>\n","      <td>0.010</td>\n","      <td>0.800</td>\n","      <td>0.030</td>\n","      <td>0.895</td>\n","      <td>0.032</td>\n","      <td>0.993</td>\n","      <td>0.014</td>\n","      <td>0.941</td>\n","      <td>0.017</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>KNN</td>\n","      <td>0.729</td>\n","      <td>0.009</td>\n","      <td>0.706</td>\n","      <td>0.063</td>\n","      <td>0.732</td>\n","      <td>0.059</td>\n","      <td>0.968</td>\n","      <td>0.042</td>\n","      <td>0.832</td>\n","      <td>0.048</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>XGB</td>\n","      <td>0.822</td>\n","      <td>0.007</td>\n","      <td>0.810</td>\n","      <td>0.022</td>\n","      <td>0.895</td>\n","      <td>0.045</td>\n","      <td>0.988</td>\n","      <td>0.013</td>\n","      <td>0.938</td>\n","      <td>0.026</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Models  mean_Accuracy (kfold)  std_Accuracy (kfold)  mean_Accuracy Score  \\\n","0     LR                  0.821                 0.006                0.819   \n","1     NB                  0.814                 0.008                0.806   \n","2    SVM                  0.819                 0.010                0.800   \n","3    KNN                  0.729                 0.009                0.706   \n","4    XGB                  0.822                 0.007                0.810   \n","\n","   std_Accuracy Score  mean_Precision Score  std_Precision Score  \\\n","0               0.053                 0.905                0.037   \n","1               0.033                 0.901                0.032   \n","2               0.030                 0.895                0.032   \n","3               0.063                 0.732                0.059   \n","4               0.022                 0.895                0.045   \n","\n","   mean_Recall Score  std_Recall Score  mean_F1 Score  std_F1 Score  \n","0              1.000             0.000          0.950         0.021  \n","1              0.981             0.012          0.939         0.020  \n","2              0.993             0.014          0.941         0.017  \n","3              0.968             0.042          0.832         0.048  \n","4              0.988             0.013          0.938         0.026  "]},"execution_count":71,"metadata":{},"output_type":"execute_result"}],"source":["E2_Results = run_experiment(E2_data, \"Class\",\"E2\", E2_best_params, num_exp=10)\n","E2_Results"]},{"cell_type":"code","execution_count":72,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0 & 0.819 $\\pm$ 0.053 & 0.905 $\\pm$ 0.037 & 1.000 $\\pm$ 0.000 \\\\\n","1 & 0.806 $\\pm$ 0.033 & 0.901 $\\pm$ 0.032 & 0.981 $\\pm$ 0.012 \\\\\n","2 & 0.800 $\\pm$ 0.030 & 0.895 $\\pm$ 0.032 & 0.993 $\\pm$ 0.014 \\\\\n","3 & 0.706 $\\pm$ 0.063 & 0.732 $\\pm$ 0.059 & 0.968 $\\pm$ 0.042 \\\\\n","4 & 0.810 $\\pm$ 0.022 & 0.895 $\\pm$ 0.045 & 0.988 $\\pm$ 0.013 \\\\\n"]}],"source":["print_latex_rows(E2_Results, table_labels)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-3 (Experiment 3)"]},{"cell_type":"code","execution_count":73,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Running experiment: 1,2,3,4,5,6,7,8,9,10, Done! "]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Models</th>\n","      <th>mean_Accuracy (kfold)</th>\n","      <th>std_Accuracy (kfold)</th>\n","      <th>mean_Accuracy Score</th>\n","      <th>std_Accuracy Score</th>\n","      <th>mean_Precision Score</th>\n","      <th>std_Precision Score</th>\n","      <th>mean_Recall Score</th>\n","      <th>std_Recall Score</th>\n","      <th>mean_F1 Score</th>\n","      <th>std_F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>LR</td>\n","      <td>0.821</td>\n","      <td>0.006</td>\n","      <td>0.825</td>\n","      <td>0.033</td>\n","      <td>0.926</td>\n","      <td>0.034</td>\n","      <td>0.995</td>\n","      <td>0.010</td>\n","      <td>0.959</td>\n","      <td>0.019</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>NB</td>\n","      <td>0.810</td>\n","      <td>0.007</td>\n","      <td>0.803</td>\n","      <td>0.036</td>\n","      <td>0.910</td>\n","      <td>0.025</td>\n","      <td>0.981</td>\n","      <td>0.017</td>\n","      <td>0.944</td>\n","      <td>0.018</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>SVM</td>\n","      <td>0.800</td>\n","      <td>0.007</td>\n","      <td>0.816</td>\n","      <td>0.034</td>\n","      <td>0.911</td>\n","      <td>0.036</td>\n","      <td>0.995</td>\n","      <td>0.010</td>\n","      <td>0.951</td>\n","      <td>0.020</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>KNN</td>\n","      <td>0.747</td>\n","      <td>0.008</td>\n","      <td>0.710</td>\n","      <td>0.051</td>\n","      <td>0.792</td>\n","      <td>0.066</td>\n","      <td>0.942</td>\n","      <td>0.031</td>\n","      <td>0.858</td>\n","      <td>0.038</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>XGB</td>\n","      <td>0.796</td>\n","      <td>0.008</td>\n","      <td>0.797</td>\n","      <td>0.051</td>\n","      <td>0.898</td>\n","      <td>0.050</td>\n","      <td>0.997</td>\n","      <td>0.010</td>\n","      <td>0.944</td>\n","      <td>0.030</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Models  mean_Accuracy (kfold)  std_Accuracy (kfold)  mean_Accuracy Score  \\\n","0     LR                  0.821                 0.006                0.825   \n","1     NB                  0.810                 0.007                0.803   \n","2    SVM                  0.800                 0.007                0.816   \n","3    KNN                  0.747                 0.008                0.710   \n","4    XGB                  0.796                 0.008                0.797   \n","\n","   std_Accuracy Score  mean_Precision Score  std_Precision Score  \\\n","0               0.033                 0.926                0.034   \n","1               0.036                 0.910                0.025   \n","2               0.034                 0.911                0.036   \n","3               0.051                 0.792                0.066   \n","4               0.051                 0.898                0.050   \n","\n","   mean_Recall Score  std_Recall Score  mean_F1 Score  std_F1 Score  \n","0              0.995             0.010          0.959         0.019  \n","1              0.981             0.017          0.944         0.018  \n","2              0.995             0.010          0.951         0.020  \n","3              0.942             0.031          0.858         0.038  \n","4              0.997             0.010          0.944         0.030  "]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["E3_Results = run_experiment(E3_data, \"Class\",\"E3\", E3_best_params, num_exp=10)\n","E3_Results"]},{"cell_type":"code","execution_count":74,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0 & 0.825 $\\pm$ 0.033 & 0.926 $\\pm$ 0.034 & 0.995 $\\pm$ 0.010 \\\\\n","1 & 0.803 $\\pm$ 0.036 & 0.910 $\\pm$ 0.025 & 0.981 $\\pm$ 0.017 \\\\\n","2 & 0.816 $\\pm$ 0.034 & 0.911 $\\pm$ 0.036 & 0.995 $\\pm$ 0.010 \\\\\n","3 & 0.710 $\\pm$ 0.051 & 0.792 $\\pm$ 0.066 & 0.942 $\\pm$ 0.031 \\\\\n","4 & 0.797 $\\pm$ 0.051 & 0.898 $\\pm$ 0.050 & 0.997 $\\pm$ 0.010 \\\\\n"]}],"source":["print_latex_rows(E3_Results, table_labels)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## E-4 (Experiment 4)"]},{"cell_type":"code","execution_count":75,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Running experiment: 1,2,3,4,5,6,7,8,9,10, Done! "]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Models</th>\n","      <th>mean_Accuracy (kfold)</th>\n","      <th>std_Accuracy (kfold)</th>\n","      <th>mean_Accuracy Score</th>\n","      <th>std_Accuracy Score</th>\n","      <th>mean_Precision Score</th>\n","      <th>std_Precision Score</th>\n","      <th>mean_Recall Score</th>\n","      <th>std_Recall Score</th>\n","      <th>mean_F1 Score</th>\n","      <th>std_F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>LR</td>\n","      <td>0.699</td>\n","      <td>0.004</td>\n","      <td>0.713</td>\n","      <td>0.035</td>\n","      <td>0.770</td>\n","      <td>0.038</td>\n","      <td>0.940</td>\n","      <td>0.042</td>\n","      <td>0.845</td>\n","      <td>0.029</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>NB</td>\n","      <td>0.682</td>\n","      <td>0.003</td>\n","      <td>0.680</td>\n","      <td>0.040</td>\n","      <td>0.752</td>\n","      <td>0.061</td>\n","      <td>0.945</td>\n","      <td>0.022</td>\n","      <td>0.836</td>\n","      <td>0.037</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>SVM</td>\n","      <td>0.865</td>\n","      <td>0.005</td>\n","      <td>0.865</td>\n","      <td>0.035</td>\n","      <td>0.870</td>\n","      <td>0.056</td>\n","      <td>0.864</td>\n","      <td>0.045</td>\n","      <td>0.865</td>\n","      <td>0.034</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>KNN</td>\n","      <td>0.853</td>\n","      <td>0.007</td>\n","      <td>0.864</td>\n","      <td>0.039</td>\n","      <td>0.899</td>\n","      <td>0.054</td>\n","      <td>0.811</td>\n","      <td>0.060</td>\n","      <td>0.852</td>\n","      <td>0.051</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>XGB</td>\n","      <td>0.850</td>\n","      <td>0.008</td>\n","      <td>0.833</td>\n","      <td>0.031</td>\n","      <td>0.880</td>\n","      <td>0.047</td>\n","      <td>0.896</td>\n","      <td>0.054</td>\n","      <td>0.886</td>\n","      <td>0.035</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Models  mean_Accuracy (kfold)  std_Accuracy (kfold)  mean_Accuracy Score  \\\n","0     LR                  0.699                 0.004                0.713   \n","1     NB                  0.682                 0.003                0.680   \n","2    SVM                  0.865                 0.005                0.865   \n","3    KNN                  0.853                 0.007                0.864   \n","4    XGB                  0.850                 0.008                0.833   \n","\n","   std_Accuracy Score  mean_Precision Score  std_Precision Score  \\\n","0               0.035                 0.770                0.038   \n","1               0.040                 0.752                0.061   \n","2               0.035                 0.870                0.056   \n","3               0.039                 0.899                0.054   \n","4               0.031                 0.880                0.047   \n","\n","   mean_Recall Score  std_Recall Score  mean_F1 Score  std_F1 Score  \n","0              0.940             0.042          0.845         0.029  \n","1              0.945             0.022          0.836         0.037  \n","2              0.864             0.045          0.865         0.034  \n","3              0.811             0.060          0.852         0.051  \n","4              0.896             0.054          0.886         0.035  "]},"execution_count":75,"metadata":{},"output_type":"execute_result"}],"source":["E4_Results = run_experiment(E4_data, \"Class\",\"E4\", E4_best_params, num_exp=10)\n","E4_Results"]},{"cell_type":"code","execution_count":76,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0 & 0.713 $\\pm$ 0.035 & 0.770 $\\pm$ 0.038 & 0.940 $\\pm$ 0.042 \\\\\n","1 & 0.680 $\\pm$ 0.040 & 0.752 $\\pm$ 0.061 & 0.945 $\\pm$ 0.022 \\\\\n","2 & 0.865 $\\pm$ 0.035 & 0.870 $\\pm$ 0.056 & 0.864 $\\pm$ 0.045 \\\\\n","3 & 0.864 $\\pm$ 0.039 & 0.899 $\\pm$ 0.054 & 0.811 $\\pm$ 0.060 \\\\\n","4 & 0.833 $\\pm$ 0.031 & 0.880 $\\pm$ 0.047 & 0.896 $\\pm$ 0.054 \\\\\n"]}],"source":["print_latex_rows(E4_Results, table_labels)"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2606,"status":"ok","timestamp":1657329888176,"user":{"displayName":"Diego P","userId":"14448528258399483719"},"user_tz":180},"id":"nORF94X3zs2B","outputId":"d5bacd1c-a95b-4960-a2f3-65b4998b156b"},"outputs":[],"source":["#%%shell\n","#jupyter nbconvert --to html /content/Projeto_DS.ipynb"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyPtwu2gmZT87dNDu0uIlulz","collapsed_sections":[],"mount_file_id":"1iuzJjCZj4WjEZiDU6MEKmTEQ05jG0AvP","name":"Projeto_DS.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","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":"3.10.9"}},"nbformat":4,"nbformat_minor":0}