RT and PAUT  analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "217f740d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#inroducing the packages we will be using in this analysis\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e96b2964",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the data for PAUT\n",
    "data_paut = {\n",
    "    'WELDED PLATE ID': ['PLATE 1155', 'PLATE 1155', 'PLATE 14820', 'PLATE 14820'],\n",
    "    'TYPES OF DISCONTINUITIES': ['Porosity', 'Excessive Penetration', 'Crack', 'Porosity'],\n",
    "    'LOCATION FROM 0 (mm)': [92.5, 168, 3.5, 136],\n",
    "    'LENGTH OF DEFECT (mm)': [18.5, 19.0, 28.5, 25.0]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "79b86a46",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_paut = pd.DataFrame(data_paut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6c1cb2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WELDED PLATE ID</th>\n",
       "      <th>TYPES OF DISCONTINUITIES</th>\n",
       "      <th>LOCATION FROM 0 (mm)</th>\n",
       "      <th>LENGTH OF DEFECT (mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>92.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Excessive Penetration</td>\n",
       "      <td>168.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Crack</td>\n",
       "      <td>3.5</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>136.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WELDED PLATE ID TYPES OF DISCONTINUITIES  LOCATION FROM 0 (mm)  \\\n",
       "0      PLATE 1155                 Porosity                  92.5   \n",
       "1      PLATE 1155    Excessive Penetration                 168.0   \n",
       "2     PLATE 14820                    Crack                   3.5   \n",
       "3     PLATE 14820                 Porosity                 136.0   \n",
       "\n",
       "   LENGTH OF DEFECT (mm)  \n",
       "0                   18.5  \n",
       "1                   19.0  \n",
       "2                   28.5  \n",
       "3                   25.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_paut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b3ee643",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the data for RT\n",
    "data_rt = {\n",
    "    'WELDING PLATE': ['PLATE 1155', 'PLATE 1155', 'PLATE 14820', 'PLATE 14820'],\n",
    "    'TYPES OF DISCONTINUITIES': ['Porosity', 'Excessive Penetration', 'Crack', 'Porosity'],\n",
    "    'LOCATION FROM 0 (mm)': [95.5, 187, 19.9, 150],\n",
    "    'LENGTH OF DEFECT (mm)': [3.24, 17.8, 25.7, 21.0]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae5e34f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paired t-test statistic: 1.8171083877095258\n",
      "P-value: 0.16680151712224353\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_rel\n",
    "\n",
    "# Merge the data on 'WELDED PLATE ID' and 'TYPES OF DISCONTINUITIES' to align the defects for paired comparison\n",
    "merged_data = pd.merge(\n",
    "    df_paut,\n",
    "    df_rt,\n",
    "    on=['WELDED PLATE ID', 'TYPES OF DISCONTINUITIES'],\n",
    "    suffixes=('_PAUT', '_RT')\n",
    ")\n",
    "\n",
    "# Perform paired t-test\n",
    "stat, p_value = ttest_rel(merged_data['LENGTH OF DEFECT (mm)_PAUT'], merged_data['LENGTH OF DEFECT (mm)_RT'])\n",
    "\n",
    "print('Paired t-test statistic:', stat)\n",
    "print('P-value:', p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "662d108e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create DataFrame for DIR\n",
    "df_rt = pd.DataFrame(data_rt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff813ff4",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WELDING PLATE</th>\n",
       "      <th>TYPES OF DISCONTINUITIES</th>\n",
       "      <th>LOCATION FROM 0 (mm)</th>\n",
       "      <th>LENGTH OF DEFECT (mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>95.5</td>\n",
       "      <td>3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Excessive Penetration</td>\n",
       "      <td>187.0</td>\n",
       "      <td>17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Crack</td>\n",
       "      <td>19.9</td>\n",
       "      <td>25.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>150.0</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WELDING PLATE TYPES OF DISCONTINUITIES  LOCATION FROM 0 (mm)  \\\n",
       "0    PLATE 1155                 Porosity                  95.5   \n",
       "1    PLATE 1155    Excessive Penetration                 187.0   \n",
       "2   PLATE 14820                    Crack                  19.9   \n",
       "3   PLATE 14820                 Porosity                 150.0   \n",
       "\n",
       "   LENGTH OF DEFECT (mm)  \n",
       "0                   3.24  \n",
       "1                  17.80  \n",
       "2                  25.70  \n",
       "3                  21.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a4d91cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns in DIR DataFrame to match PAUT DataFrame\n",
    "df_rt.rename(columns={'WELDING PLATE': 'WELDED PLATE ID'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b01849e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a source column to differentiate between PAUT and DIR data\n",
    "df_paut['SOURCE'] = 'PAUT'\n",
    "df_rt['SOURCE'] = 'RT'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66bcc69a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine both DataFrames\n",
    "df_combined = pd.concat([df_paut, df_rt], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "73c0115e",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WELDED PLATE ID</th>\n",
       "      <th>TYPES OF DISCONTINUITIES</th>\n",
       "      <th>LOCATION FROM 0 (mm)</th>\n",
       "      <th>LENGTH OF DEFECT (mm)</th>\n",
       "      <th>SOURCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>92.5</td>\n",
       "      <td>18.50</td>\n",
       "      <td>PAUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Excessive Penetration</td>\n",
       "      <td>168.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>PAUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Crack</td>\n",
       "      <td>3.5</td>\n",
       "      <td>28.50</td>\n",
       "      <td>PAUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>136.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>PAUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>95.5</td>\n",
       "      <td>3.24</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Excessive Penetration</td>\n",
       "      <td>187.0</td>\n",
       "      <td>17.80</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Crack</td>\n",
       "      <td>19.9</td>\n",
       "      <td>25.70</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>150.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WELDED PLATE ID TYPES OF DISCONTINUITIES  LOCATION FROM 0 (mm)  \\\n",
       "0      PLATE 1155                 Porosity                  92.5   \n",
       "1      PLATE 1155    Excessive Penetration                 168.0   \n",
       "2     PLATE 14820                    Crack                   3.5   \n",
       "3     PLATE 14820                 Porosity                 136.0   \n",
       "4      PLATE 1155                 Porosity                  95.5   \n",
       "5      PLATE 1155    Excessive Penetration                 187.0   \n",
       "6     PLATE 14820                    Crack                  19.9   \n",
       "7     PLATE 14820                 Porosity                 150.0   \n",
       "\n",
       "   LENGTH OF DEFECT (mm) SOURCE  \n",
       "0                  18.50   PAUT  \n",
       "1                  19.00   PAUT  \n",
       "2                  28.50   PAUT  \n",
       "3                  25.00   PAUT  \n",
       "4                   3.24     RT  \n",
       "5                  17.80     RT  \n",
       "6                  25.70     RT  \n",
       "7                  21.00     RT  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad10582a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SOURCE                                    PAUT     RT\n",
      "WELDED PLATE ID TYPES OF DISCONTINUITIES             \n",
      "PLATE 1155      Excessive Penetration     19.0  17.80\n",
      "                Porosity                  18.5   3.24\n",
      "PLATE 14820     Crack                     28.5  25.70\n",
      "                Porosity                  25.0  21.00\n"
     ]
    }
   ],
   "source": [
    "# Group by WELDED PLATE ID and TYPES OF DISCONTINUITIES\n",
    "comparison = df_combined.groupby(['WELDED PLATE ID', 'TYPES OF DISCONTINUITIES', 'SOURCE'])['LENGTH OF DEFECT (mm)'].mean().unstack()\n",
    "print(comparison)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "72197092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SOURCE           PAUT     RT\n",
      "WELDED PLATE ID             \n",
      "PLATE 1155       37.5  21.04\n",
      "PLATE 14820      53.5  46.70\n"
     ]
    }
   ],
   "source": [
    "# Compare total lengths of defects per plate\n",
    "total_length_comparison = df_combined.groupby(['WELDED PLATE ID', 'SOURCE'])['LENGTH OF DEFECT (mm)'].sum().unstack()\n",
    "print(total_length_comparison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "55c7b1dd",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for total length of defects by plate and source\n",
    "total_length_comparison.plot(kind='bar')\n",
    "plt.title('Total Length of Defects by Welded Plate ID and Source')\n",
    "plt.xlabel('Welded Plate ID')\n",
    "plt.ylabel('Total Length of Defects (mm)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b9a6c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for defect locations by source\n",
    "for plate_id in df_combined['WELDED PLATE ID'].unique():\n",
    "    df_plate = df_combined[df_combined['WELDED PLATE ID'] == plate_id]\n",
    "    plt.figure()\n",
    "    for source in df_plate['SOURCE'].unique():\n",
    "        df_source = df_plate[df_plate['SOURCE'] == source]\n",
    "        plt.scatter(df_source['LOCATION FROM 0 (mm)'], df_source['LENGTH OF DEFECT (mm)'], label=source)\n",
    "    plt.title(f'Defect Locations and Lengths for {plate_id}')\n",
    "    plt.xlabel('Location from 0 (mm)')\n",
    "    plt.ylabel('Length of Defect (mm)')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd5723b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paired t-test statistic: 1.8171083877095258\n",
      "P-value: 0.16680151712224353\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_rel\n",
    "\n",
    "# Assuming you have matching samples for paired t-test\n",
    "lengths_paut = df_paut['LENGTH OF DEFECT (mm)']\n",
    "lengths_rt = df_rt['LENGTH OF DEFECT (mm)']\n",
    "\n",
    "# Perform paired t-test\n",
    "stat, p_value = ttest_rel(lengths_paut, lengths_rt)\n",
    "\n",
    "print('Paired t-test statistic:', stat)\n",
    "print('P-value:', p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2f892272",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5ee6dc4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define features and target\n",
    "X = df_combined.drop(columns=['WELDED PLATE ID'])\n",
    "y = df_combined['SOURCE']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "16553a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the model\n",
    "model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1042299a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the data for PAUT\n",
    "data_paut = {\n",
    "    'WELDED PLATE ID': ['PLATE 1155', 'PLATE 1155', 'PLATE 14820', 'PLATE 14820'],\n",
    "    'TYPES OF DISCONTINUITIES': ['Porosity', 'Excessive Penetration', 'Crack', 'Porosity'],\n",
    "    'LOCATION FROM 0 (mm)': [92.5, 168, 3.5, 136],\n",
    "    'LENGTH OF DEFECT (mm)': [18.5, 19.0, 28.5, 25.0]\n",
    "}\n",
    "\n",
    "# Define the data for RT\n",
    "data_rt = {\n",
    "    'WELDING PLATE': ['PLATE 1155', 'PLATE 1155', 'PLATE 14820', 'PLATE 14820'],\n",
    "    'TYPES OF DISCONTINUITIES': ['Porosity', 'Excessive Penetration', 'Crack', 'Porosity'],\n",
    "    'LOCATION FROM 0 (mm)': [95.5, 187, 19.9, 150],\n",
    "    'LENGTH OF DEFECT (mm)': [3.24, 17.8, 25.7, 21.0]\n",
    "}\n",
    "\n",
    "# Create DataFrames\n",
    "df_paut = pd.DataFrame(data_paut)\n",
    "df_rt = pd.DataFrame(data_rt)\n",
    "df_rt.rename(columns={'WELDING PLATE': 'WELDED PLATE ID'}, inplace=True)\n",
    "\n",
    "# Add a source column\n",
    "df_paut['SOURCE'] = 'PAUT'\n",
    "df_rt['SOURCE'] = 'RT'\n",
    "\n",
    "# Combine DataFrames\n",
    "df_combined = pd.concat([df_paut, df_rt], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d54b9672",
   "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>WELDED PLATE ID</th>\n",
       "      <th>TYPES OF DISCONTINUITIES</th>\n",
       "      <th>LOCATION FROM 0 (mm)</th>\n",
       "      <th>LENGTH OF DEFECT (mm)</th>\n",
       "      <th>SOURCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>92.5</td>\n",
       "      <td>18.50</td>\n",
       "      <td>PAUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Excessive Penetration</td>\n",
       "      <td>168.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>PAUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Crack</td>\n",
       "      <td>3.5</td>\n",
       "      <td>28.50</td>\n",
       "      <td>PAUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>136.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>PAUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>95.5</td>\n",
       "      <td>3.24</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PLATE 1155</td>\n",
       "      <td>Excessive Penetration</td>\n",
       "      <td>187.0</td>\n",
       "      <td>17.80</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Crack</td>\n",
       "      <td>19.9</td>\n",
       "      <td>25.70</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PLATE 14820</td>\n",
       "      <td>Porosity</td>\n",
       "      <td>150.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WELDED PLATE ID TYPES OF DISCONTINUITIES  LOCATION FROM 0 (mm)  \\\n",
       "0      PLATE 1155                 Porosity                  92.5   \n",
       "1      PLATE 1155    Excessive Penetration                 168.0   \n",
       "2     PLATE 14820                    Crack                   3.5   \n",
       "3     PLATE 14820                 Porosity                 136.0   \n",
       "4      PLATE 1155                 Porosity                  95.5   \n",
       "5      PLATE 1155    Excessive Penetration                 187.0   \n",
       "6     PLATE 14820                    Crack                  19.9   \n",
       "7     PLATE 14820                 Porosity                 150.0   \n",
       "\n",
       "   LENGTH OF DEFECT (mm) SOURCE  \n",
       "0                  18.50   PAUT  \n",
       "1                  19.00   PAUT  \n",
       "2                  28.50   PAUT  \n",
       "3                  25.00   PAUT  \n",
       "4                   3.24     RT  \n",
       "5                  17.80     RT  \n",
       "6                  25.70     RT  \n",
       "7                  21.00     RT  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d7c9d85d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  WELDED PLATE ID  LOCATION FROM 0 (mm)  LENGTH OF DEFECT (mm)  SOURCE  \\\n",
      "0      PLATE 1155                  92.5                  18.50       0   \n",
      "1      PLATE 1155                 168.0                  19.00       0   \n",
      "2     PLATE 14820                   3.5                  28.50       0   \n",
      "3     PLATE 14820                 136.0                  25.00       0   \n",
      "4      PLATE 1155                  95.5                   3.24       1   \n",
      "\n",
      "   TYPES OF DISCONTINUITIES_Crack  \\\n",
      "0                           False   \n",
      "1                           False   \n",
      "2                            True   \n",
      "3                           False   \n",
      "4                           False   \n",
      "\n",
      "   TYPES OF DISCONTINUITIES_Excessive Penetration  \\\n",
      "0                                           False   \n",
      "1                                            True   \n",
      "2                                           False   \n",
      "3                                           False   \n",
      "4                                           False   \n",
      "\n",
      "   TYPES OF DISCONTINUITIES_Porosity  \n",
      "0                               True  \n",
      "1                              False  \n",
      "2                              False  \n",
      "3                               True  \n",
      "4                               True  \n"
     ]
    }
   ],
   "source": [
    "# One-hot encode the 'TYPES OF DISCONTINUITIES' column\n",
    "df_combined = pd.get_dummies(df_combined, columns=['TYPES OF DISCONTINUITIES'])\n",
    "\n",
    "# Label encode the 'SOURCE' column\n",
    "df_combined['SOURCE'] = df_combined['SOURCE'].map({'PAUT': 0, 'RT': 1})\n",
    "\n",
    "print(df_combined.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4e9ae67c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define features and target\n",
    "X = df_combined.drop(columns=['WELDED PLATE ID'])\n",
    "y = df_combined['SOURCE']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8a088ee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the model\n",
    "model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b1d54a1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.00\n",
      "Confusion Matrix:\n",
      "[[1 0]\n",
      " [0 1]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         1\n",
      "           1       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           1.00         2\n",
      "   macro avg       1.00      1.00      1.00         2\n",
      "weighted avg       1.00      1.00      1.00         2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Make predictions\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.2f}')\n",
    "\n",
    "# Confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print('Confusion Matrix:')\n",
    "print(conf_matrix)\n",
    "\n",
    "# Classification report\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "print('Classification Report:')\n",
    "print(class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fa4029c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for accuracy\n",
    "plt.bar(['Accuracy'], [accuracy])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a0781d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for predictions\n",
    "plt.scatter(X_test['LOCATION FROM 0 (mm)'], X_test['LENGTH OF DEFECT (mm)'], c=y_pred, cmap='viridis')\n",
    "plt.title('Predicted Defect Locations and Lengths')\n",
    "plt.xlabel('Location from 0 (mm)')\n",
    "plt.ylabel('Length of Defect (mm)')\n",
    "plt.colorbar(label='Predicted Source')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "81415e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77f087d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}