agent.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\a847933\\Anaconda3\\envs\\lang\\Lib\\site-packages\\pydantic\\_internal\\_generate_schema.py:547: UserWarning: <built-in function any> is not a Python type (it may be an instance of an object), Pydantic will allow any object with no validation since we cannot even enforce that the input is an instance of the given type. To get rid of this error wrap the type with `pydantic.SkipValidation`.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langgraph.graph import StateGraph, START, END\n",
    "from nodes.domain_identification import domain_identification\n",
    "from nodes.dataset_type_detection import dataset_type_detection\n",
    "from nodes.machine_learning_task_classification import task_classification\n",
    "from nodes.data_preprocessing import data_preprocessing, fan_out_data_preprocessing\n",
    "from nodes.pipeline_generation import pipeline_generation\n",
    "from nodes.data_statistics_analysis import data_statistics_analysis\n",
    "from nodes.algorithm_recommendation import regression_algorithm_selection, \\\n",
    "                                            classification_algorithm_selection, \\\n",
    "                                            anomaly_detection_algorithm_selection, \\\n",
    "                                            algorithm_selection_branch\n",
    "\n",
    "from states.state import OverallState\n",
    "from IPython.display import Image\n",
    "\n",
    "DataSet = StateGraph(OverallState)\n",
    "DataSet.add_node(\"Domain_Identification\", domain_identification)\n",
    "DataSet.add_node(\"Dataset_Type_Detection\", dataset_type_detection)\n",
    "DataSet.add_node(\"Machine_Learning_\\nTask_Classification\", task_classification)\n",
    "DataSet.add_node(\"Data_Statistics_Analysis\", data_statistics_analysis)\n",
    "DataSet.add_node(\"Regression\", regression_algorithm_selection)\n",
    "DataSet.add_node(\"Classification\", classification_algorithm_selection)\n",
    "DataSet.add_node(\"Anomaly_Detection\", anomaly_detection_algorithm_selection)\n",
    "DataSet.add_node(\"Data_Preprocessing\", data_preprocessing)\n",
    "DataSet.add_node(\"Pipeline_Generation\", pipeline_generation)\n",
    "\n",
    "DataSet.add_edge(START, \"Domain_Identification\")\n",
    "DataSet.add_edge(\"Domain_Identification\", \"Dataset_Type_Detection\")\n",
    "DataSet.add_edge(\"Dataset_Type_Detection\",\"Data_Statistics_Analysis\")\n",
    "DataSet.add_edge(\"Data_Statistics_Analysis\",\"Machine_Learning_\\nTask_Classification\")\n",
    "\n",
    "DataSet.add_conditional_edges(\"Machine_Learning_\\nTask_Classification\", algorithm_selection_branch, [\"Regression\", \"Classification\",\"Anomaly_Detection\", END])\n",
    "DataSet.add_conditional_edges(\"Classification\", fan_out_data_preprocessing, [\"Data_Preprocessing\"])\n",
    "DataSet.add_conditional_edges(\"Regression\", fan_out_data_preprocessing, [\"Data_Preprocessing\"])\n",
    "DataSet.add_conditional_edges(\"Anomaly_Detection\", fan_out_data_preprocessing, [\"Data_Preprocessing\"])\n",
    "DataSet.add_edge(\"Data_Preprocessing\", \"Pipeline_Generation\")\n",
    "DataSet.add_edge(\"Pipeline_Generation\", END)\n",
    "\n",
    "app = DataSet.compile()\n",
    "Image(app.get_graph().draw_mermaid_png())\n",
    "\n",
    "final_state = app.invoke({\"description\": \"\"\"The dataset contains information about the Boiler values.\"\"\",\"dataframe\": df})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Set the random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "n_samples = 100\n",
    "\n",
    "feature1 = np.random.rand(n_samples) * 10\n",
    "feature2 = np.random.randint(1, 100, n_samples)\n",
    "feature3 = np.random.normal(50, 10, n_samples)\n",
    "\n",
    "target = np.random.choice([0, 1], size=n_samples, p=[0.5, 0.5])\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'Feature1': feature1,\n",
    "    'Feature2': feature2,\n",
    "    'Feature3': feature3,\n",
    "    'Target': target\n",
    "})\n",
    "\n",
    "csv_file = 'generated_data.csv'\n",
    "df.to_csv(csv_file, index=False)\n",
    "\n",
    "sta = app.invoke({\"description\": \"\"\"The dataset contains information about the Boiler values.\"\"\",\"dataframe\": df})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dataframe':     Feature1  Feature2   Feature3  Target\n",
       " 0   3.745401        63  45.835436       0\n",
       " 1   9.507143        96  58.575033       1\n",
       " 2   7.319939        52  57.181283       0\n",
       " 3   5.986585        96  40.485282       1\n",
       " 4   1.560186         4  44.638399       0\n",
       " ..       ...       ...        ...     ...\n",
       " 95  4.937956        73  51.409074       0\n",
       " 96  5.227328        59  53.544196       0\n",
       " 97  4.275410        70  31.196874       1\n",
       " 98  0.254191        80  59.663920       1\n",
       " 99  1.078914        93  44.890555       1\n",
       " \n",
       " [100 rows x 4 columns],\n",
       " 'description': 'The dataset contains information about the Boiler values.',\n",
       " 'domain': {'type': 'industry',\n",
       "  'cause': \"The mention of 'Boiler values' suggests a focus on industrial equipment or processes, indicating a domain related to industry.\"},\n",
       " 'task': {'type': 'classification',\n",
       "  'target': 'Target',\n",
       "  'cause': 'The target column is of type int32 with only 2 unique values, indicating a classification problem with discrete classes.'},\n",
       " 'methods': {'algorithms': ['Logistic Regression',\n",
       "   'RandomForestClassifier',\n",
       "   'GradientBoostingClassifier'],\n",
       "  'cause': 'The dataset has a normal target distribution and no missing values, indicating that linear models like Logistic Regression could be effective if the decision boundary is linear. However, given the high feature correlation, it is also reasonable to consider non-linear models like RandomForestClassifier and GradientBoostingClassifier, which can capture complex relationships between features. Additionally, the dataset is not high-dimensional, and with 100 samples and 3 features, these algorithms can efficiently handle the data without overfitting.'},\n",
       " 'structure': {'type': 'Tabular',\n",
       "  'column': '',\n",
       "  'cause': 'No time-related terms in column names, no time-related data types, and the index does not contain datetime-like values.'},\n",
       " 'statistics':                                        Feature1 Feature2   Feature3 Target\n",
       " Maximum Gap Between Records                 1.0      1.0        1.0    1.0\n",
       " Average Gap Between Records                 1.0      1.0        1.0    1.0\n",
       " Mean                                   4.701807     52.3  49.054074    0.5\n",
       " Median                                 4.641425     56.0   50.04169    0.5\n",
       " IQR                                    5.370024     47.0  14.099652    1.0\n",
       " Max - Min                              9.813648     97.0  51.528021    1.0\n",
       " Missing Time Before First Valid Value       0.0      0.0        0.0    0.0\n",
       " Missing Time After Last Valid Value         0.0      0.0        0.0    0.0\n",
       " Data Type of Column                     float64    int32    float64  int32\n",
       " Percentage of Unique Values               100.0     63.0      100.0    2.0\n",
       " percenatge of Missing Value                 0.0      0.0        0.0    0.0\n",
       " Total Number of Data Points                 100      100        100    100,\n",
       " 'configs': [{'column': 'Feature1',\n",
       "   'methods': ['Standardization', 'Log Transformation'],\n",
       "   'algorithm': 'Logistic Regression',\n",
       "   'reason': 'Feature1 is a numerical column with a mean and median close in value, indicating a relatively normal distribution. However, the IQR and max-min range suggest potential skewness or outliers. Standardization is suitable for Logistic Regression as it is sensitive to feature scaling, while Log Transformation can help normalize the distribution if skewness is present.'},\n",
       "  {'column': 'Feature1',\n",
       "   'methods': ['Skip Processing'],\n",
       "   'algorithm': 'RandomForestClassifier',\n",
       "   'reason': 'The column has no missing values, a high percentage of unique values, and is already in a suitable format for the RandomForestClassifier, which is robust to scale and does not require additional preprocessing.'},\n",
       "  {'column': 'Feature1',\n",
       "   'methods': ['Skip Processing'],\n",
       "   'algorithm': 'GradientBoostingClassifier',\n",
       "   'reason': 'The column has no missing values, a high percentage of unique values, and is already in a suitable format for the Gradient Boosting Classifier, which is robust to the scale of the data.'},\n",
       "  {'column': 'Feature2',\n",
       "   'methods': ['Standardization', 'Skip Processing'],\n",
       "   'algorithm': 'Logistic Regression',\n",
       "   'reason': 'Feature2 is a numerical column with no missing values and a moderate percentage of unique values. Standardization is suitable for Logistic Regression as it is sensitive to the scale of the features. Since there are no missing values and the data appears clean, further processing is unnecessary.'},\n",
       "  {'column': 'Feature2',\n",
       "   'methods': ['Skip Processing'],\n",
       "   'algorithm': 'RandomForestClassifier',\n",
       "   'reason': 'The column has no missing values, a reasonable number of unique values, and is of integer type, making it suitable for direct use in the RandomForestClassifier without additional processing.'},\n",
       "  {'column': 'Feature2',\n",
       "   'methods': ['Skip Processing'],\n",
       "   'algorithm': 'GradientBoostingClassifier',\n",
       "   'reason': 'The column has no missing values, a reasonable number of unique values, and is of integer type, making it suitable for direct use in the Gradient Boosting Classifier without additional processing.'},\n",
       "  {'column': 'Feature3',\n",
       "   'methods': ['Standardization', 'Skip Processing'],\n",
       "   'algorithm': 'Logistic Regression',\n",
       "   'reason': 'Feature3 is a numerical column with no missing values and a normal distribution, making it suitable for standardization to ensure the model performs optimally. Since the data is already clean, no additional processing is necessary.'},\n",
       "  {'column': 'Feature3',\n",
       "   'methods': ['Skip Processing'],\n",
       "   'algorithm': 'RandomForestClassifier',\n",
       "   'reason': 'The column has no missing values, a low variance, and is already in a suitable format for the RandomForestClassifier, which is robust to scale and does not require additional preprocessing.'},\n",
       "  {'column': 'Feature3',\n",
       "   'methods': ['Skip Processing', 'Standardization'],\n",
       "   'algorithm': 'GradientBoostingClassifier',\n",
       "   'reason': 'The data in Feature3 is clean with no missing values and a uniform distribution, making it suitable for direct use in the GradientBoostingClassifier. Standardization is not necessary for tree-based models like Gradient Boosting, which are scale-invariant.'},\n",
       "  {'column': 'Target',\n",
       "   'methods': ['Standardization', 'Log Transformation'],\n",
       "   'algorithm': 'Logistic Regression',\n",
       "   'reason': \"The 'Target' column is numerical with a low percentage of unique values and no missing values, making it suitable for standardization to ensure the model performs optimally. Additionally, since the mean and median are equal, indicating a potential skew, a log transformation can help normalize the distribution, which is beneficial for logistic regression.\"},\n",
       "  {'column': 'Target',\n",
       "   'methods': ['Skip Processing'],\n",
       "   'algorithm': 'RandomForestClassifier',\n",
       "   'reason': \"The 'Target' column has no missing values, a low percentage of unique values, and is suitable for classification tasks. Given that RandomForestClassifier is a tree-based model, it is robust to the scale of the data and does not require additional preprocessing.\"},\n",
       "  {'column': 'Target',\n",
       "   'methods': ['Skip Processing'],\n",
       "   'algorithm': 'GradientBoostingClassifier',\n",
       "   'reason': \"The 'Target' column has no missing values, a low percentage of unique values, and is suitable for classification tasks. Since it is an integer type with a low variance, it does not require further processing for the Gradient Boosting Classifier, which can handle such data effectively.\"}],\n",
       " 'pipeline': {'LogisticRegression': Pipeline(steps=[('data_preprocessing',\n",
       "                   ColumnTransformer(remainder='passthrough',\n",
       "                                     transformers=[('StandardScaler',\n",
       "                                                    StandardScaler(),\n",
       "                                                    ['Feature1', 'Feature2',\n",
       "                                                     'Feature3', 'Target']),\n",
       "                                                   ('PowerTransformer',\n",
       "                                                    PowerTransformer(),\n",
       "                                                    ['Feature1', 'Target'])])),\n",
       "                  ('Imputing', 'passthrough'),\n",
       "                  ('model', LogisticRegression(max_iter=1000, random_state=42))]),\n",
       "  'RandomForestClassifier': Pipeline(steps=[('data_preprocessing', 'passthrough'),\n",
       "                  ('Imputing', 'passthrough'),\n",
       "                  ('model', RandomForestClassifier(random_state=42))]),\n",
       "  'GradientBoostingClassifier': Pipeline(steps=[('data_preprocessing',\n",
       "                   ColumnTransformer(remainder='passthrough',\n",
       "                                     transformers=[('StandardScaler',\n",
       "                                                    StandardScaler(),\n",
       "                                                    ['Feature3'])])),\n",
       "                  ('Imputing', 'passthrough'),\n",
       "                  ('model', GradientBoostingClassifier(random_state=42))])}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;data_preprocessing&#x27;, &#x27;passthrough&#x27;),\n",
       "                (&#x27;Imputing&#x27;, &#x27;passthrough&#x27;),\n",
       "                (&#x27;model&#x27;, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;data_preprocessing&#x27;, &#x27;passthrough&#x27;),\n",
       "                (&#x27;Imputing&#x27;, &#x27;passthrough&#x27;),\n",
       "                (&#x27;model&#x27;, RandomForestClassifier(random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>passthrough</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>passthrough</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('data_preprocessing', 'passthrough'),\n",
       "                ('Imputing', 'passthrough'),\n",
       "                ('model', RandomForestClassifier(random_state=42))])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta[\"pipeline\"][\"RandomForestClassifier\"].fit(df.drop(\"Target\", axis=1), df[\"Target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta[\"pipeline\"][\"RandomForestClassifier\"].predict(df.drop(\"Target\", axis=1))"
   ]
  }
 ],
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