modules/model_selection_evaluation.ipynb

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    "<a href=\"https://colab.research.google.com/github/ekrell/ams_ai_shortcourse_2024/blob/main/modules/model_selection_evaluation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "cell_type": "markdown",
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   "source": [
    "## AMS AI Short Course: Model Evaluation, and Selection in Machine Learning\n",
    "##### Notebook by Hamid Kamangir (University of California, Davis)\n",
    "\n",
    "\n",
    "The correct use of model evaluation, model selection is vital in machine learning research domain. <br> \n",
    "We will review different techniques that can be used for each of them and discuss the main advantages and disadvantages of each. \n",
    "This notebook will explain how to evaluate the models and select the optimal model.\n",
    "\n",
    "    - Introduction\n",
    "        I. Choose a propare validation strategy\n",
    "        II. Evaluation Metrics\n",
    "        III. Calibration\n",
    "    - Implementation\n",
    "        I. Regression \n",
    "        II. Classification\n",
    "        III. Imbalance Learning\n",
    "        IV. Hyper-parameter Tunning and Model Selection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction \n",
    "\n",
    "### How to evaluate machine learning models and select the best one?\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "    <img src=\"./thinking.png\" width=\"500\"/>\n",
    "</div>\n",
    "\n",
    "##### What is model evaluation?\n",
    "Model evaluation is a process of assessing the model’s performance on a chosen evaluation setup. \n",
    "<br>\n",
    "It is done by calculating quantitative performance metrics like F1 score or RMSE or assessing the results qualitatively by the subject matter experts. \n",
    "<br> \n",
    "The machine learning evaluation metrics you choose should reflect the business metrics you want to optimize with the machine learning solution.\n",
    "\n",
    "##### What is model selection?\n",
    "Model selection is the process of choosing the best ml model for a given task. \n",
    "<br>\n",
    "It is done by comparing various model candidates on chosen evaluation metrics calculated on a designed evaluation schema. \n",
    "<br>\n",
    "Choosing the correct evaluation schema, whether a simple train test split or a complex cross-validation strategy, is the crucial first step of building any machine learning solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Choose a proper validation strategy:\n",
    "Can’t stress this enough, without a reliable way to validate your model performance, no amount of hyperparameter tuning and state-of-the-art models will help you.\n",
    "<img src=\"./evaluation.png\" width=\"1200\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Random Split\n",
    "Random Splits are used to randomly sample a percentage of data into training, testing, and preferably validation sets. <br>\n",
    "\n",
    "<b>Pros</b>: <br>\n",
    "    - There is a good chance that the original population is well represented in all the three sets. <br>\n",
    "<b>Cons</b>:  <br>\n",
    "    - The model would not generalizaed enough for unseen test dataset based on different conditions (for weather forecasting, this strategy will jumble up the seasonal pattern). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time-Based Split\n",
    "Splitting the data based on specific time range of temporal observation. <br>\n",
    "<b>Pros</b>: <br>\n",
    "    - The model is more generalizaed to unseen dataset by learning the temporal signal. <br>\n",
    "<b>Cons</b>:  <br>\n",
    "    - Because of sewer condition the model might not be generalalized to the future. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validation\n",
    "The cross-validation technique works by randomly shuffling the dataset and then splitting it into k groups. \n",
    "<br> \n",
    "Thereafter, on iterating over each group, the group needs to be considered as a test set while all other groups are clubbed together into the training set. <br>\n",
    "The model is tested on the test group and the process continues for k groups.<br>\n",
    "\n",
    "Thus, by the end of the process, one has k different results on k different test groups. The best model can then be selected easily by choosing the one with the highest score.\n",
    "\n",
    "#### concept of window sets\n",
    "Where the model is trained till a particular date and tested on the future dates iteratively such that the training window keeps ioncresing shifting by specific time scale. \n",
    "\n",
    "\n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://www.researchgate.net/profile/Rayan-H-Assaad/publication/355889701/figure/fig1/AS:1086138706071552@1635967055039/Classical-k-fold-cross-validation-vs-time-series-split-cross-validation.ppm\" alt=\"Alt text for the image\"/>\n",
    "        <figcaption>Standard K-Fold Cross Validation vs Timeseries. Figure Credit: <a href=\"https://www.researchgate.net/figure/Classical-k-fold-cross-validation-vs-time-series-split-cross-validation_fig1_355889701\">Link</a></figcaption>\n",
    "    </p>\n",
    "</figure>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Evaluation Metrics for Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we explore various evaluation metrics used in machine learning for both classification and regression tasks. <br>\n",
    "Understanding these metrics is crucial for interpreting the performance of a machine learning model and for making informed decisions on model selection and optimization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Regression Metrics \n",
    "##### Mean Absolute Error (MAE)\n",
    "This metric measures the average of the absolute differences between the predicted and actual values. It gives equal weight to all errors and is less sensitive to outliers. MAE is useful when the goal is to minimize the overall error in the model while avoiding large errors.\n",
    "\n",
    "\n",
    "$$MAE = \\frac{1}{n} \\sum_{i=1}^{n} |y_i - \\hat{y}_i|$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_error(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate the mean absolute error between the true and predicted values.\n",
    "\n",
    "    Parameters:\n",
    "    y_true (list or array): Actual true values.\n",
    "    y_pred (list or array): Predicted values.\n",
    "\n",
    "    Returns:\n",
    "    float: The mean absolute error.\n",
    "    \"\"\"\n",
    "    # Ensure that the inputs are of the same length\n",
    "    if len(y_true) != len(y_pred):\n",
    "        raise ValueError(\"Both true and predicted values should have the same length.\")\n",
    "\n",
    "    # Calculate the absolute errors\n",
    "    error = sum(abs(y_t - y_p) for y_t, y_p in zip(y_true, y_pred))\n",
    "\n",
    "    # Return the mean absolute error\n",
    "    return error / len(y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Root Mean Square Error: \n",
    "RMSE is useful when the goal is to minimize the overall error in the model.\n",
    "\n",
    "\n",
    "$$\\text{RMSE} = \\sqrt{\\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2}$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def root_mean_squared_error(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate the Root Mean Squared Error between the true and predicted values.\n",
    "\n",
    "    Parameters:\n",
    "    y_true (list or array): Actual true values.\n",
    "    y_pred (list or array): Predicted values.\n",
    "\n",
    "    Returns:\n",
    "    float: The Root Mean Squared Error.\n",
    "    \"\"\"\n",
    "    # Ensure that the inputs are of the same length\n",
    "    if len(y_true) != len(y_pred):\n",
    "        raise ValueError(\"Both true and predicted values should have the same length.\")\n",
    "\n",
    "    # Calculate the squared differences and mean\n",
    "    squared_diffs = [(y_t - y_p) ** 2 for y_t, y_p in zip(y_true, y_pred)]\n",
    "    mean_squared_diff = sum(squared_diffs) / len(y_true)\n",
    "\n",
    "    # Return the square root of the mean squared difference\n",
    "    return np.sqrt(mean_squared_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### R^2\n",
    "\n",
    "This metric measures the proportion of variance in the target variable explained by the model. It ranges from 0 to 1, with higher values indicating better performance. R2 is useful when the goal is to explain the variability in the target variable using the predictors.\n",
    "\n",
    "$$R^2 = 1 - \\frac{\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2}{\\sum_{i=1}^{n} (y_i - \\bar{y})^2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def r_squared(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate the R-squared, a statistical measure of how close the data are to the fitted regression line.\n",
    "\n",
    "    Parameters:\n",
    "    y_true (list or array): Actual true values.\n",
    "    y_pred (list or array): Predicted values.\n",
    "\n",
    "    Returns:\n",
    "    float: The R-squared value.\n",
    "    \"\"\"\n",
    "    # Ensure that the inputs are of the same length\n",
    "    if len(y_true) != len(y_pred):\n",
    "        raise ValueError(\"Both true and predicted values should have the same length.\")\n",
    "\n",
    "    # Calculate the mean of the true values\n",
    "    mean_y_true = sum(y_true) / len(y_true)\n",
    "\n",
    "    # Calculate the total sum of squares (TSS) and the residual sum of squares (RSS)\n",
    "    total_sum_of_squares = sum((y_t - mean_y_true) ** 2 for y_t in y_true)\n",
    "    residual_sum_of_squares = sum((y_t - y_p) ** 2 for y_t, y_p in zip(y_true, y_pred))\n",
    "\n",
    "    # Calculate R-squared\n",
    "    r_squared_value = 1 - (residual_sum_of_squares / total_sum_of_squares)\n",
    "\n",
    "    return r_squared_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Classification \n",
    "\n",
    "Confusion Matrix\n",
    "\n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://miro.medium.com/v2/resize:fit:1334/format:webp/1*3yGLac6F4mTENnj5dBNvNQ.jpeg\" alt=\"Alt text for the image\" width=\"500px\"/>\n",
    "        <figcaption>Confusion Matrix. Figure Credit: <a href=\"https://medium.com/analytics-vidhya/what-is-a-confusion-matrix-d1c0f8feda5\">Link</a></figcaption>\n",
    "    </p>\n",
    "</figure>\n",
    "\n",
    "- A good model is one which has high TP and TN rates, while low FP and FN rates.\n",
    "- If you have an imbalanced dataset to work with, it’s always better to use confusion matrix as your evaluation criteria for your machine learning model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Accuracy\n",
    "$$Accuracy = \\frac{TP + TN}{TP + TN + FP + FN}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Precision\n",
    "$$Precision = \\frac{TP}{TP + FP}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Recall (Sensitivity)\n",
    "$$Recall = \\frac{TP}{TP + FN}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### F1-Score\n",
    "$$F1\\ Score = 2 \\times \\frac{Precision \\times Recall}{Precision + Recall}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_binary_classification(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Evaluate binary classification performance by calculating the confusion matrix,\n",
    "    accuracy, precision, recall, and F1 score.\n",
    "\n",
    "    Parameters:\n",
    "    y_true (list or array): Actual true labels.\n",
    "    y_pred (list or array): Predicted labels.\n",
    "\n",
    "    Returns:\n",
    "    dict: A dictionary containing the confusion matrix, accuracy, precision, recall, and F1 score.\n",
    "    \"\"\"\n",
    "    # Calculate the elements of the confusion matrix\n",
    "    TP = sum((y_true == 1) & (y_pred == 1))\n",
    "    TN = sum((y_true == 0) & (y_pred == 0))\n",
    "    FP = sum((y_true == 0) & (y_pred == 1))\n",
    "    FN = sum((y_true == 1) & (y_pred == 0))\n",
    "\n",
    "    # Calculate metrics\n",
    "    accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
    "    precision = TP / (TP + FP) if (TP + FP) != 0 else 0\n",
    "    recall = TP / (TP + FN) if (TP + FN) != 0 else 0\n",
    "    f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) != 0 else 0\n",
    "\n",
    "    # Compile results into a dictionary\n",
    "    results = {\n",
    "        'confusion_matrix': {'TP': TP, 'TN': TN, 'FP': FP, 'FN': FN},\n",
    "        'accuracy': accuracy,\n",
    "        'precision': precision,\n",
    "        'recall': recall,\n",
    "        'f1_score': f1_score\n",
    "    }\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weather / Meteorological / Atmospheric Forecasting Skill Metrics \n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://learncuriously.files.wordpress.com/2018/10/confusion-matrix-cell-labels1.png?w=1000\" alt=\"Alt text for the image\" width=\"500px\"/>\n",
    "        <figcaption>Confusion Matrix (environmental version). Figure Credit: <a href=\"https://learncuriously.wordpress.com/2018/10/21/confused-by-the-confusion-matrix/\">Link</a></figcaption>\n",
    "    </p>\n",
    "</figure>\n",
    "\n",
    "\n",
    "Hit (H): True Positives\n",
    "Miss (M): False Negatives\n",
    "False Alarm (FA): False Positives\n",
    "Correct Rejection (CR): True Negatives\n",
    "\n",
    "\n",
    "##### POD\n",
    "$$POD = \\frac{Hit}{Hit + Miss}$$\n",
    "\n",
    "##### False Alarm Rate [0, 1]  (F)\n",
    "$$F = \\frac{FA}{FA + CR}$$\n",
    "\n",
    "##### False Alarm Ratio [0, 1] (FAR)\n",
    "$$FAR = \\frac{FA}{Hit + FA}$$\n",
    "\n",
    "##### Critical Success Index (CSI) \n",
    "\n",
    "$$CSI = \\frac{Hit}{Hit + FA + Miss}$$\n",
    "\n",
    "##### Perice Skill Score (PSS) \n",
    "$$PSS = \\frac{(Hit \\times CR) - (FA \\times Miss)}{(FA + CR) \\times (Hit + Miss)}$$\n",
    "\n",
    "##### Heidke Skill Score (HSS)\n",
    "$$HSS = \\frac{2 \\times ((Hit \\times CR) - (FA \\times Miss))}{((Hit + Miss) \\times (Miss + CR)) + ((Hit + FA) \\times (FA + CR))}$$\n",
    "\n",
    "#### Clayton Skill Score (CSS)\n",
    "$$CSS = \\frac{(Hit \\times CR) - (FA \\times Miss)}{(Hit + FA) \\times (Miss + CR)}$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluation_skill_metrics(y_true, y_pred):\n",
    "        \n",
    "    # Calculate the elements of the confusion matrix\n",
    "    Hit = sum((y_true == 1) & (y_pred == 1))\n",
    "    CR = sum((y_true == 0) & (y_pred == 0))\n",
    "    FA = sum((y_true == 0) & (y_pred == 1))\n",
    "    MISS = sum((y_true == 1) & (y_pred == 0))\n",
    "\n",
    "\n",
    "    POD = Hit/(Hit+MISS)\n",
    "    F   = FA/(FA+CR)\n",
    "    FAR  = FA/(Hit+FA)\n",
    "    CSI = Hit/(Hit+FA+MISS)\n",
    "    PSS = ((Hit*CR)-(FA*MISS))/((FA+CR)*(Hit+MISS))\n",
    "    HSS = (2*((Hit*CR)-(FA*MISS)))/(((Hit+MISS)*(MISS+CR))+((Hit+FA)*(FA+CR)))\n",
    "    CSS = ((Hit*CR)-(FA*MISS))/((Hit+FA)*(MISS+CR))\n",
    "\n",
    "    # Compile results into a dictionary\n",
    "    results = {\n",
    "        'confusion_matrix': {'Hit': Hit, 'Miss': MISS, 'FA': FA, 'CR': CR},\n",
    "        'POD': POD,\n",
    "        'F': F,\n",
    "        'FAR': FAR,\n",
    "        'CSI': CSI, \n",
    "        'PSS': PSS,\n",
    "        'HSS': HSS,\n",
    "        'CSS': CSS, \n",
    "    }\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. Calibration\n",
    "Probabilities can be more informative than labels: <br>\n",
    "    - Tomorrow is rainy or Tomorrow is 80% rainy! <br>\n",
    "\n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://miro.medium.com/v2/resize:fit:1400/format:webp/1*ql48EYNBKGjt5s2WrN_4rg.png\" alt=\"Alt text for the image\" width=\"800px\" />\n",
    "        <figcaption> Probability Forecasting. Figure Credit: <a href=\"https://www.researchgate.net/figure/Classical-k-fold-cross-validation-vs-time-series-split-cross-validation_fig1_355889701\">Link</a></figcaption>\n",
    "    </p>\n",
    "</figure>\n",
    "\n",
    "    \n",
    "Calibration basically tells you how much you can trust the model.\n",
    "\n",
    "\n",
    "\n",
    "#### AUC-ROC Curve: \n",
    "ROC curve is a plot of true positive rate (recall) [it would be FAR in environmental case] against false positive rate (TN / (TN+FP)) [POD]. AUC-ROC stands for Area Under the Receiver Operating Characteristics and the higher the area, the better is the model performance.\n",
    "\n",
    "https://miro.medium.com/v2/resize:fit:1400/format:webp/0*vlaAZ0jKCoihMaI9.png\n",
    "\n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://miro.medium.com/v2/resize:fit:1400/format:webp/0*vlaAZ0jKCoihMaI9.png\" alt=\"Alt text for the image\" width=\"500px\"/>\n",
    "        <figcaption> AUC-ROC. Figure Credit: <a href=\"https://learncuriously.wordpress.com/2018/10/21/confused-by-the-confusion-matrix/\">Link</a></figcaption>\n",
    "    </p>\n",
    "</figure>\n",
    "\n",
    "#### Reliability Diagram\n",
    "\n",
    "\n",
    "\n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://confluence.ecmwf.int/download/attachments/340763675/Sample%20ROC%20Reliability%20Wk1.png?version=1&modificationDate=1688631863383&api=v2\" alt=\"Alt text for the image\" width=\"1000px\" />\n",
    "        <figcaption>Reliability Diagram. Figure Credit: <a href=\"https://twitter.com/jfd118/status/1540295108138094594/photo/1\">Link</a></figcaption>\n",
    "    </p>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_pod_far(y_true, y_pred):\n",
    "    # Initialize variables\n",
    "    pods = []\n",
    "    fars = []\n",
    "    thresholds = np.arange(0, 1, 0.001)\n",
    "\n",
    "    # Convert to numpy arrays if not already\n",
    "    y_true = np.array(y_true)\n",
    "    y_pred = np.array(y_pred)\n",
    "\n",
    "    for i in thresholds:\n",
    "        pred_classes = y_pred >= i\n",
    "        # Calculate the elements of the confusion matrix\n",
    "        Hit = np.sum((y_true == 1) & (pred_classes == 1))\n",
    "        CR = np.sum((y_true == 0) & (pred_classes == 0))\n",
    "        FA = np.sum((y_true == 0) & (pred_classes == 1))\n",
    "        MISS = np.sum((y_true == 1) & (pred_classes == 0))\n",
    "\n",
    "        POD = Hit / (Hit + MISS) if (Hit + MISS) != 0 else 0\n",
    "        F = FA / (FA + CR) if (FA + CR) != 0 else 0\n",
    "        pods.append(POD)\n",
    "        fars.append(F)\n",
    "\n",
    "    return pods, fars\n",
    "\n",
    "def roc_curve(y_true, y_pred):\n",
    "\n",
    "    PODs, FARs = calc_pod_far(y_true, y_pred)\n",
    "\n",
    "    roc_data = {\"POD\": PODs, \"FAR\": FARs}\n",
    "    roc_df_logit = pd.DataFrame(roc_data).sort_values(by = [\"POD\", \"FAR\"])\n",
    "\n",
    "    ROC_AUC = np.trapz(roc_df_logit[\"POD\"], roc_df_logit[\"FAR\"])\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(10,6))\n",
    "\n",
    "    ax.plot(roc_df_logit[\"FAR\"], roc_df_logit[\"POD\"], linewidth=3,)\n",
    "    ax.plot([0,1], [0,1],  linestyle='--', color='k')\n",
    "\n",
    "    ax.grid()\n",
    "    ax.legend(loc='lower right')\n",
    "    ax.set_xlim([-0.005, 1.0])\n",
    "    ax.set_ylim([0.0, 1.005])\n",
    "    ax.set_xlabel('Probability of false detection (FAR)',  fontsize=14)\n",
    "    ax.set_ylabel('Probability of detection (POD)', fontsize=14)\n",
    "    ax.set_title(f'AUC: {ROC_AUC:0.3f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reliability_diagram(y_true, y_pred):\n",
    "    # labels and probabilities\n",
    "    observed_labels =y_true\n",
    "    forecast_probabilities = y_pred\n",
    "    # check the format and input size\n",
    "    assert np.all(np.logical_or(\n",
    "        observed_labels == 0, observed_labels == 1\n",
    "    ))\n",
    "\n",
    "    assert np.all(np.logical_and(\n",
    "        forecast_probabilities >= 0, forecast_probabilities <= 1\n",
    "    ))\n",
    "    num_bins = 10\n",
    "    assert num_bins > 1\n",
    "\n",
    "    # inputs_to_bins = _get_histogram(\n",
    "    #     input_values=forecast_probabilities, num_bins=num_bins, min_value=0.,\n",
    "    #     max_value=1.)\n",
    "    \n",
    "    bin_cutoffs = np.linspace(0., 1., num=num_bins + 1)\n",
    "\n",
    "    inputs_to_bins = np.digitize(\n",
    "        forecast_probabilities, bin_cutoffs, right=False\n",
    "    ) - 1\n",
    "\n",
    "    inputs_to_bins[inputs_to_bins < 0] = 0\n",
    "    inputs_to_bins[inputs_to_bins > num_bins - 1] = num_bins - 1\n",
    "\n",
    "    mean_forecast_probs = np.full(num_bins, np.nan)\n",
    "    mean_event_frequencies = np.full(num_bins, np.nan)\n",
    "    num_examples_by_bin = np.full(num_bins, -1, dtype=int)\n",
    "\n",
    "    for k in range(num_bins):\n",
    "        these_example_indices = np.where(inputs_to_bins == k)[0]\n",
    "        num_examples_by_bin[k] = len(these_example_indices)\n",
    "\n",
    "        mean_forecast_probs[k] = np.mean(\n",
    "            forecast_probabilities[these_example_indices])\n",
    "\n",
    "        mean_event_frequencies[k] = np.mean(\n",
    "            observed_labels[these_example_indices].astype(float)\n",
    "        )\n",
    "\n",
    "    fig, axes_object = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(6, 6),\n",
    "                        gridspec_kw={\"height_ratios\": [4, 1]})\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.subplots_adjust(hspace=0.03)\n",
    "\n",
    "    perfect_x_coords = np.array([0, 1], dtype=float)\n",
    "    perfect_y_coords = perfect_x_coords + 0.\n",
    "    axes_object[0].plot(\n",
    "        perfect_x_coords, perfect_y_coords, color='gray',\n",
    "        linestyle='dashed', linewidth=3)\n",
    "\n",
    "    real_indices = np.where(np.invert(np.logical_or(\n",
    "        np.isnan(mean_forecast_probs), np.isnan(mean_event_frequencies)\n",
    "    )))[0]\n",
    "\n",
    "    axes_object[0].plot(\n",
    "        mean_forecast_probs[real_indices], mean_event_frequencies[real_indices],\n",
    "        color='b',\n",
    "        linestyle='solid', linewidth=1)\n",
    "\n",
    "    accuracies = mean_event_frequencies\n",
    "    confidences = mean_forecast_probs\n",
    "    counts = num_examples_by_bin\n",
    "    bin_cutoffs = np.linspace(0, 1, num=10 + 1)\n",
    "    bins = bin_cutoffs\n",
    "\n",
    "    bin_size = 1.0 / len(counts)\n",
    "    positions = bins[:-1] + bin_size/2.0\n",
    "\n",
    "\n",
    "    colors = np.zeros((10, 4))\n",
    "    colors[:, 0] = 240 / 255.\n",
    "    colors[:, 1] = 60 / 255.\n",
    "    colors[:, 2] = 60 / 255.\n",
    "    colors[:, 3] = 0.2\n",
    "\n",
    "    gap_plt = axes_object[0].bar(positions, np.abs(accuracies - confidences), \n",
    "                        bottom=np.minimum(accuracies, confidences), width=0.1,\n",
    "                        edgecolor=colors, color=colors, linewidth=1, label=\"Gap\")\n",
    "\n",
    "    acc_plt = axes_object[0].bar(positions, 0, bottom=accuracies, width=0.1,\n",
    "                        edgecolor=\"black\", color=\"black\", alpha=1.0, linewidth=3,\n",
    "                        label=\"Accuracy\")\n",
    "\n",
    "    # axes_object[0].set_xlabel('Forecast probability')\n",
    "    axes_object[0].set_ylabel('Expected frequency')\n",
    "    axes_object[0].set_xlim(0., 1.)\n",
    "    axes_object[0].set_ylim(0., 1.)\n",
    "    axes_object[0].legend(handles=[gap_plt, acc_plt])\n",
    "\n",
    "    orig_counts = counts\n",
    "    bin_data = -orig_counts\n",
    "    axes_object[1].bar(positions, bin_data, width=bin_size * 0.9)\n",
    "\n",
    "    axes_object[1].set_xlim(0, 1)\n",
    "    # axes_object[1].set_yscale('log')\n",
    "    axes_object[1].set_xlabel('Forecast probability')\n",
    "    axes_object[1].set_ylabel('Count')\n",
    "\n",
    "    # Also negate the ticks for the upside-down histogram.\n",
    "    new_ticks = np.abs(axes_object[1].get_yticks()).astype(np.int)\n",
    "    axes_object[1].set_yticklabels(new_ticks)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I. Regression [Maximum Temperature Forecasting for Dallas City] \n",
    "We are simulating the weather forecasting competition for maximum temperature forecasting. \n",
    "\n",
    "https://www.wxchallenge.com/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: netcdf4 in /home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages (1.6.2)\n",
      "Requirement already satisfied: numpy>=1.9 in /home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages (from netcdf4) (1.22.3)\n",
      "Requirement already satisfied: cftime in /home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages (from netcdf4) (1.6.2)\n",
      "Requirement already satisfied: imbalanced-learn in /home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages (0.12.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages (from imbalanced-learn) (1.22.3)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages (from imbalanced-learn) (1.10.1)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in /home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages (from imbalanced-learn) (1.0.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages (from imbalanced-learn) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages (from imbalanced-learn) (3.2.0)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score, learning_curve, validation_curve\n",
    "import sklearn.model_selection as model_selection\n",
    "from sklearn import tree\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "import optuna\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "import gdown\n",
    "! pip install netcdf4\n",
    "! pip install imbalanced-learn\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#precent auto-scrolling\n",
    "#The next cell prevents output in the notebook from being nested in a scroll box\n",
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-01-28 07:23:56--  https://raw.github.com/ekrell/ams_ai_shortcourse_2024/main/data/kdfw_processed_sub_data.csv\n",
      "Resolving raw.github.com (raw.github.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n",
      "Connecting to raw.github.com (raw.github.com)|185.199.109.133|:443... connected.\n",
      "HTTP request sent, awaiting response... "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "301 Moved Permanently\n",
      "Location: https://raw.githubusercontent.com/ekrell/ams_ai_shortcourse_2024/main/data/kdfw_processed_sub_data.csv [following]\n",
      "--2024-01-28 07:23:56--  https://raw.githubusercontent.com/ekrell/ams_ai_shortcourse_2024/main/data/kdfw_processed_sub_data.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4400996 (4.2M) [text/plain]\n",
      "Saving to: ‘kdfw_processed_sub_data.csv’\n",
      "\n",
      "100%[======================================>] 4,400,996   --.-K/s   in 0.1s    \n",
      "\n",
      "2024-01-28 07:23:57 (39.3 MB/s) - ‘kdfw_processed_sub_data.csv’ saved [4400996/4400996]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.github.com/ekrell/ams_ai_shortcourse_2024/main/data/kdfw_processed_sub_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "total_dataset = pd.read_csv('kdfw_processed_sub_data.csv',index_col=0).sort_values(by='date') ##kdfw - Dallas-Ft Worth, TX\n",
    "total_dataset = total_dataset.replace('********', np.nan).replace(np.inf, np.nan).dropna(how='any',axis=1)\n",
    "total_dataset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3143, 200)\n",
      "(3143, 4)\n",
      "Index(['OBS_tmpf_max', 'OBS_tmpf_min', 'OBS_sknt_max', 'OBS_phour'], dtype='object')\n",
      "(3143, 196)\n",
      "Index(['date', 'GFS0_pmsl_max', 'GFS0_pmsl_min', 'GFS0_pmsl_avg',\n",
      "       'GFS0_pres_max', 'GFS0_pres_min', 'GFS0_pres_avg', 'GFS0_sktc_max',\n",
      "       'GFS0_sktc_min', 'GFS0_sktc_avg',\n",
      "       ...\n",
      "       'GFS0_qpf_accum_avg', 'GFS0_wagl_max', 'GFS0_wagl_min', 'GFS0_wagl_avg',\n",
      "       'GFS0_frz_rain_max', 'GFS0_frz_rain_min', 'GFS0_frz_rain_avg',\n",
      "       'GFS0_sleet_max', 'GFS0_sleet_min', 'GFS0_sleet_avg'],\n",
      "      dtype='object', length=196)\n"
     ]
    }
   ],
   "source": [
    "#first, we seperate the observation (true value) from the features/predictors\n",
    "#Observation data\n",
    "total_label_data = total_dataset.filter(like='OBS')\n",
    "\n",
    "#remove obs data and date from the feature set\n",
    "dropCols = list(total_label_data.columns) # + ['date']\n",
    "total_feature_data = total_dataset.copy(deep=True)\n",
    "total_feature_data = total_feature_data.drop(dropCols,axis=1)\n",
    "\n",
    "#look at the shape of each array again\n",
    "print(total_dataset.shape)\n",
    "print(total_label_data.shape)\n",
    "print(total_label_data.columns)\n",
    "print(total_feature_data.shape)\n",
    "print(total_feature_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Max Temperature between Jan 2011 to Aug 2019')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "labels = total_dataset['date'].values\n",
    "data_label_pair = [('Max', total_label_data['OBS_tmpf_max'])]\n",
    "\n",
    "for label,temp in data_label_pair:\n",
    "  plt.plot(np.arange(len(labels)),temp,label=label)\n",
    "\n",
    "ax.set_xticks(np.arange(len(labels))[::500]) \n",
    "ax.set_xticklabels(labels[::500])\n",
    "plt.ylabel(\"Temperature $\\circ$F\",fontsize=14)\n",
    "plt.xlabel(\"Date\",fontsize=14)\n",
    "plt.legend(loc='upper right',fontsize=18)\n",
    "plt.title('Max Temperature between Jan 2011 to Aug 2019', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# However, the number of features is too big for our dataset. \n",
    "# For now, let's try with only 50 features from GFS model \n",
    "subsample_feature = total_feature_data.iloc[:,:51] #date + 50 features\n",
    "\n",
    "# we only need maximum temperature from obs\n",
    "subsample_label = total_label_data['OBS_tmpf_max'][:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Randomly splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape: (2514, 50)\n",
      "Observation data shape: (2514,)\n",
      "Testing data shape: (629, 50)\n"
     ]
    }
   ],
   "source": [
    "# randomly split 80% data for training set and 20% data for testing set\n",
    "random_train_x, random_test_x, random_train_y, random_test_y = train_test_split(\n",
    "    subsample_feature, \n",
    "    subsample_label, \n",
    "    train_size = 0.8\n",
    "    )\n",
    "\n",
    "random_train_x = random_train_x.drop(['date'], axis=1)\n",
    "random_test_x = random_test_x.drop(['date'], axis=1)\n",
    "print(f'Training data shape: {random_train_x.shape}')\n",
    "print(f'Observation data shape: {random_train_y.shape}')\n",
    "print(f'Testing data shape: {random_test_x.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: GFS0_pmsl_max\n",
      "range: 41.60000000000002\n",
      "max: 1044.5\n",
      "min: 1002.9\n",
      "\n",
      "Feature: GFS0_sktc_max\n",
      "range: 52.2\n",
      "max: 53.64\n",
      "min: 1.44\n"
     ]
    }
   ],
   "source": [
    "# see the ranges of features: GFS0_pmsl_max GFS0_sktc_max\n",
    "# PMSL - Mean sea level pressure (hPa)\n",
    "max1=random_train_x['GFS0_pmsl_max'].max(axis=0)\n",
    "min1=random_train_x['GFS0_pmsl_max'].min(axis=0)\n",
    "range1=max1-min1\n",
    "print(\"Feature: GFS0_pmsl_max\")\n",
    "print(\"range: \"+str(range1))\n",
    "print(\"max: \"+str(max1))\n",
    "print(\"min: \"+str(min1))\n",
    "\n",
    "print(\"\")\n",
    "\n",
    "# SKTC - Skin temperature (C)\n",
    "max2=random_train_x['GFS0_sktc_max'].max(axis=0)\n",
    "min2=random_train_x['GFS0_sktc_max'].min(axis=0)\n",
    "range2=max2-min2\n",
    "print(\"Feature: GFS0_sktc_max\")\n",
    "print(\"range: \"+str(range2))\n",
    "print(\"max: \"+str(max2))\n",
    "print(\"min: \"+str(min2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_data(train_x, test_x):\n",
    "    \"\"\"\n",
    "    Scales training and testing data using MinMaxScaler.\n",
    "\n",
    "    Parameters:\n",
    "    train_x (array-like): Training data features.\n",
    "    test_x (array-like): Testing data features.\n",
    "\n",
    "    Returns:\n",
    "    tuple: Scaled training and testing data.\n",
    "    \"\"\"\n",
    "\n",
    "    # Fit/find minimum and maximum value for each input variable\n",
    "    min_max_model = MinMaxScaler().fit(train_x)\n",
    "\n",
    "    # This saves the values to apply or 'transform' testing data\n",
    "    train_x_minmax = min_max_model.transform(train_x)\n",
    "    #print('Training data range \\nMax:', np.nanmax(train_x_minmax), ', Min:', np.nanmin(train_x_minmax))\n",
    "\n",
    "    # Apply the min/max values to testing data\n",
    "    test_x_minmax = min_max_model.transform(test_x)\n",
    "    #print('\\nTesting data range \\nMax:', np.nanmax(test_x_minmax), ', Min:', np.nanmin(test_x_minmax))\n",
    "\n",
    "    return train_x_minmax, test_x_minmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_random_train_x, scaled_random_test_x = scale_data(random_train_x, random_test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.20 | RMSE = 4.39\n",
      "Test Evaluation  =========>  R^2 = 0.91 | MAE = 3.36 | RMSE = 5.09\n"
     ]
    }
   ],
   "source": [
    "# Train and evaluate the model to predict max temperature\n",
    "linear_regression_model = LinearRegression()\n",
    "\n",
    "#Train\n",
    "linear_regression_model.fit(scaled_random_train_x, random_train_y.values)\n",
    "\n",
    "# score on training set\n",
    "train_y_pred = linear_regression_model.predict(scaled_random_train_x)\n",
    "R2 = r_squared(random_train_y.values, train_y_pred)\n",
    "MAE = mean_absolute_error(random_train_y.values, train_y_pred)\n",
    "RMSE = root_mean_squared_error(random_train_y.values, train_y_pred)\n",
    "print(f\"Train Evaluation =========>  R^2 = {R2:.2f} | MAE = {MAE:.2f} | RMSE = {RMSE:.2f}\")\n",
    "\n",
    "# score on testing set\n",
    "test_y_pred = linear_regression_model.predict(scaled_random_test_x)\n",
    "R2_test = r_squared(random_test_y.values, test_y_pred)\n",
    "MAE_test = mean_absolute_error(random_test_y.values, test_y_pred)\n",
    "RMSE_test = root_mean_squared_error(random_test_y.values, test_y_pred)\n",
    "print(f\"Test Evaluation  =========>  R^2 = {R2_test:.2f} | MAE = {MAE_test:.2f} | RMSE = {RMSE_test:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split by Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data_by_date(features, labels, date_ranges):\n",
    "    \"\"\"\n",
    "    Splits a DataFrame into multiple DataFrames based on specified date ranges.\n",
    "\n",
    "    Args:\n",
    "    df (pd.DataFrame): The original DataFrame.\n",
    "    time_col (str): The name of the column containing time data.\n",
    "    date_ranges (list of tuples): A list of tuples where each tuple contains start and end dates as strings.\n",
    "                                  Example: [('2009-01-01', '2012-12-31'), ('2013-01-01', '2017-12-31')]\n",
    "\n",
    "    Returns:\n",
    "    list: A list containing the split DataFrames.\n",
    "    \"\"\"\n",
    "\n",
    "    features_list = pd.to_datetime(features['date'])\n",
    "    date_mask = (features_list > date_ranges[0]) & (features_list <= date_ranges[1])\n",
    "    out_data = features.loc[date_mask,:].drop(['date'], axis=1)\n",
    "    out_labels = labels.loc[date_mask]\n",
    "\n",
    "    return out_data, out_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train dataset samples: (2558, 50) | test dataset samples: (583, 50)\n"
     ]
    }
   ],
   "source": [
    "date_ranges = [('2011-01-01','2017-12-31'), ('2018-01-01','2019-12-31')]\n",
    "time_train_x, time_train_y = split_data_by_date(subsample_feature, subsample_label,  date_ranges[0])\n",
    "time_test_x, time_test_y = split_data_by_date(subsample_feature, subsample_label, date_ranges[1])\n",
    "print(f\"train dataset samples: {time_train_x.shape} | test dataset samples: {time_test_x.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_time_train_x, scaled_time_test_x = scale_data(time_train_x, time_test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.18 | RMSE = 4.57\n",
      "Test Evaluation  =========>  R^2 = 0.85 | MAE = 4.47 | RMSE = 6.65\n"
     ]
    }
   ],
   "source": [
    "# Train and evaluate the model to predict max temperature\n",
    "linear_regression_model = LinearRegression()\n",
    "#Train\n",
    "linear_regression_model.fit(scaled_time_train_x, time_train_y.values)\n",
    "\n",
    "# score on training set\n",
    "train_y_pred = linear_regression_model.predict(scaled_time_train_x)\n",
    "R2 = r_squared(time_train_y.values, train_y_pred)\n",
    "MAE = mean_absolute_error(time_train_y.values, train_y_pred)\n",
    "RMSE = root_mean_squared_error(time_train_y.values, train_y_pred)\n",
    "print(f\"Train Evaluation =========>  R^2 = {R2:.2f} | MAE = {MAE:.2f} | RMSE = {RMSE:.2f}\")\n",
    "\n",
    "# score on testing set\n",
    "test_y_pred = linear_regression_model.predict(scaled_time_test_x)\n",
    "R2_test = r_squared(time_test_y.values, test_y_pred)\n",
    "MAE_test = mean_absolute_error(time_test_y.values, test_y_pred)\n",
    "RMSE_test = root_mean_squared_error(time_test_y.values, test_y_pred)\n",
    "print(f\"Test Evaluation  =========>  R^2 = {R2_test:.2f} | MAE = {MAE_test:.2f} | RMSE = {RMSE_test:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011 Holdout\n",
      "Train Evaluation =========>  R^2 = 0.94 | MAE = 3.12 | RMSE = 3.98\n",
      "Test Evaluation  =========>  R^2 = 0.86 | MAE = 4.11 | RMSE = 7.55\n",
      "==================================================================\n",
      "2012 Holdout\n",
      "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.22 | RMSE = 4.59\n",
      "Test Evaluation  =========>  R^2 = 0.93 | MAE = 3.45 | RMSE = 4.14\n",
      "==================================================================\n",
      "2013 Holdout\n",
      "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.27 | RMSE = 4.62\n",
      "Test Evaluation  =========>  R^2 = 0.95 | MAE = 2.85 | RMSE = 3.83\n",
      "==================================================================\n",
      "2014 Holdout\n",
      "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.23 | RMSE = 4.59\n",
      "Test Evaluation  =========>  R^2 = 0.94 | MAE = 3.14 | RMSE = 4.10\n",
      "==================================================================\n",
      "2015 Holdout\n",
      "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.15 | RMSE = 4.53\n",
      "Test Evaluation  =========>  R^2 = 0.92 | MAE = 3.77 | RMSE = 4.74\n",
      "==================================================================\n",
      "2016 Holdout\n",
      "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.25 | RMSE = 4.62\n",
      "Test Evaluation  =========>  R^2 = 0.94 | MAE = 3.09 | RMSE = 3.77\n",
      "==================================================================\n",
      "2017 Holdout\n",
      "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.23 | RMSE = 4.58\n",
      "Test Evaluation  =========>  R^2 = 0.91 | MAE = 3.30 | RMSE = 4.20\n",
      "==================================================================\n",
      "2018 Holdout\n",
      "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.25 | RMSE = 4.61\n",
      "Test Evaluation  =========>  R^2 = 0.34 | MAE = 4.52 | RMSE = 13.78\n",
      "==================================================================\n",
      "2019 Holdout\n",
      "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.21 | RMSE = 4.53\n",
      "Test Evaluation  =========>  R^2 = 0.80 | MAE = 4.08 | RMSE = 7.69\n",
      "==================================================================\n"
     ]
    }
   ],
   "source": [
    "# Train and evaluate the model to predict max temperature\n",
    "linear_regression_model = LinearRegression()\n",
    "# Store the results\n",
    "results = []\n",
    "years = [year for year in range(2011, 2020)]\n",
    "for year in years:\n",
    "    date_ranges = (f'{year}-01-01',f'{year}-12-31')\n",
    "    cv_test_x, cv_test_y = split_data_by_date(subsample_feature, subsample_label, date_ranges)\n",
    "\n",
    "    cv_train_x = subsample_feature[~subsample_feature.index.isin(cv_test_x.index)]\n",
    "    cv_train_x = cv_train_x.drop(['date'], axis=1)\n",
    "    cv_train_y = subsample_label[~subsample_label.index.isin(cv_test_y.index)]\n",
    "\n",
    "    scaled_cv_train_x, scaled_cv_test_x = scale_data(cv_train_x, cv_test_x)\n",
    "\n",
    "    #Train\n",
    "    linear_regression_model.fit(scaled_cv_train_x, cv_train_y.values)\n",
    "    cv_train_y_pred = linear_regression_model.predict(scaled_cv_train_x)\n",
    "    R2 = r_squared(cv_train_y.values, cv_train_y_pred)\n",
    "    MAE = mean_absolute_error(cv_train_y.values, cv_train_y_pred)\n",
    "    RMSE = root_mean_squared_error(cv_train_y.values, cv_train_y_pred)\n",
    "    print(f\"{year} Holdout\")\n",
    "    print(f\"Train Evaluation =========>  R^2 = {R2:.2f} | MAE = {MAE:.2f} | RMSE = {RMSE:.2f}\")\n",
    "\n",
    "    # score on testing set\n",
    "    cv_test_y_pred = linear_regression_model.predict(scaled_cv_test_x)\n",
    "    R2_test = r_squared(cv_test_y.values, cv_test_y_pred)\n",
    "    MAE_test = mean_absolute_error(cv_test_y.values, cv_test_y_pred)\n",
    "    RMSE_test = root_mean_squared_error(cv_test_y.values, cv_test_y_pred)\n",
    "    print(f\"Test Evaluation  =========>  R^2 = {R2_test:.2f} | MAE = {MAE_test:.2f} | RMSE = {RMSE_test:.2f}\")\n",
    "\n",
    "    print(\"==================================================================\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter_plot(ytrue, ypred):\n",
    "\n",
    "    R2 = r_squared(ytrue, ypred)\n",
    "    MAE = mean_absolute_error(ytrue, ypred)\n",
    "    RMSE = root_mean_squared_error(ytrue, ypred)\n",
    "    g = sns.jointplot(x = ytrue, \n",
    "                        y = ypred,\n",
    "                        height = 6, \n",
    "                        ratio = 3)\n",
    "\n",
    "    g.ax_joint.plot([0, 110], [0, 110], '--r', linewidth=2)\n",
    "\n",
    "    plt.xlabel('Measured (F)')\n",
    "    plt.ylabel('Predicted (F)')\n",
    "    plt.grid(False)\n",
    "\n",
    "    scores = (r'R^2={:.2f}' + '\\n' + r'MAE={:.2f}' + '\\n' + r'RMSE={:.2f}').format(\n",
    "        R2, MAE, RMSE)\n",
    "\n",
    "    plt.text(1, 115.7, scores, bbox=dict(facecolor = 'white', edgecolor = 'gray', boxstyle = 'round, pad=0.2'),\n",
    "            fontsize = 12, ha='left', va = 'top')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_plot(cv_test_y.values, cv_test_y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II. Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Binarization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Binarize the 'temperature' column\n",
    "subsample_label_class = pd.DataFrame()\n",
    "subsample_label_class['class'] = np.where(subsample_label > 100, 1, 0)\n",
    "# Binarize the 'temperature' column and label classes\n",
    "subsample_label_class['class_name'] = np.where(subsample_label > 100, 'Severe Days', 'Common Days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the frequency of each class\n",
    "class_counts = subsample_label_class['class_name'].value_counts()\n",
    "\n",
    "# Plotting\n",
    "plt.bar(class_counts.index, class_counts.values)\n",
    "# Adding count labels on top of each bar\n",
    "for index, value in enumerate(class_counts.values):\n",
    "    plt.text(index, value, str(value), ha='center')\n",
    "plt.title('Temperature Classification')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data Splitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "subsample_feature_ = subsample_feature.drop(['date'], axis=1) \n",
    "cls_xtrain, cls_xtest, cls_ytrain, cls_ytest = model_selection.train_test_split(subsample_feature_, \n",
    "                                                                 subsample_label_class['class'].values, \n",
    "                                                                 test_size=0.2, \n",
    "                                                                 random_state=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_cls_xtrain, scaled_cls_xtest = scale_data(cls_xtrain, cls_xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the frequency of each class\n",
    "class_counts = np.array([np.count_nonzero(cls_ytest == 0), np.count_nonzero(cls_ytest == 1)])\n",
    "# Plotting\n",
    "\n",
    "# Plotting for Random Over-Sampling data\n",
    "plt.bar(['Class 0', 'Class 1'], class_counts)\n",
    "plt.title('Class Distribution')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Frequency')\n",
    "# Adding text labels on top of each bar\n",
    "for index, value in enumerate(class_counts):\n",
    "    plt.text(index, value, str(value), ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=50, max_depth=6, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.97 | Precision: 0.75 | Recall = 0.50 | F1_score = 0.60\n",
      "POD = 0.50 | F: 0.01 | FAR = 0.25 | CSI = 0.43 | HSS = 0.59 | PSS = 0.49\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf.fit(scaled_cls_xtrain, cls_ytrain)\n",
    "\n",
    "# Make predictions on the test set\n",
    "cls_y_pred = rf.predict(scaled_cls_xtest)\n",
    "\n",
    "# Compute the confusion matrix\n",
    "conf_matrix = confusion_matrix(cls_ytest, cls_y_pred)\n",
    "\n",
    "results = evaluate_binary_classification(cls_ytest, cls_y_pred)\n",
    "print(f\"Accuracy = {results['accuracy']:.2f} | Precision: {results['precision']:.2f} | Recall = {results['recall']:.2f} | F1_score = {results['f1_score']:.2f}\")\n",
    "\n",
    "skr = evaluation_skill_metrics(cls_ytest, cls_y_pred)\n",
    "print(f\"POD = {skr['POD']:.2f} | F: {skr['F']:.2f} | FAR = {skr['FAR']:.2f} | CSI = {skr['CSI']:.2f} | HSS = {skr['HSS']:.2f} | PSS = {skr['PSS']:.2f}\")\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='g')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix of the Best Random Forest Model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cls_ytest_probs = rf.predict_proba(scaled_cls_xtest)[:, 1]\n",
    "reliability_diagram(cls_ytest, cls_ytest_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_curve(cls_ytest, cls_ytest_probs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Learning Curve\n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://zahidhasan.github.io/images/learning_curve2.png\" alt=\"Alt text for the image\" width=\"800px\" />\n",
    "    </p>\n",
    "</figure>\n",
    "\n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://zahidhasan.github.io/images/learning_curve3.png\" alt=\"Alt text for the image\" width=\"800px\" />\n",
    "</figure>\n",
    "Fixing High Bias: <br>\n",
    "- When training and testing errors converge and are high\n",
    "- No matter how much data we feed the model, the model cannot represent the underlying relationship and has high systematic error\n",
    "- Poor fit\n",
    "- Poor generalization\n",
    "\n",
    "SOLUTIONS: \n",
    "\n",
    "- find more features\n",
    "- add polynomial features\n",
    "- increase parameters (more hidden layer neurons, for example)\n",
    "- decrease regularization\n",
    "\n",
    "\n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://zahidhasan.github.io/images/learning_curve4.png\" alt=\"Alt text for the image\" width=\"800px\" />\n",
    "        <figcaption> Bias-Variance Trade-off. Figure Credit: <a href=\"https://zahidhasan.github.io/2020/10/13/bias-variance-trade-off-and-learning-curve.html\">Link</a></figcaption>\n",
    "    </p>\n",
    "</figure>\n",
    "Fixing High Variance:  <br>\n",
    "- When there is a large gap between the errors\n",
    "- Require data to improve\n",
    "- Can simplify the model with fewer or less complex features\n",
    "SOLUTIONS: \n",
    "- more training data\n",
    "- reduce number of features, manually or using a model selection algorithm\n",
    "- increase regularization\n",
    "\n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://zahidhasan.github.io/images/bias_variance7.png\" alt=\"Alt text for the image\" width=\"800px\" />\n",
    "        <figcaption> Bias-Variance Trade-off. Figure Credit: <a href=\"https://zahidhasan.github.io/2020/10/13/bias-variance-trade-off-and-learning-curve.html\">Link</a></figcaption>\n",
    "    </p>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "# Compute the learning curve\n",
    "train_sizes, train_scores, val_scores = learning_curve(rf, scaled_cls_xtrain, cls_ytrain, cv=5, n_jobs=-1, \n",
    "                                                       train_sizes=np.linspace(0.1, 1.0, 10))\n",
    "\n",
    "# Calculate mean and standard deviation for training set scores\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "\n",
    "# Calculate mean and standard deviation for test set scores\n",
    "val_mean = np.mean(val_scores, axis=1)\n",
    "val_std = np.std(val_scores, axis=1)\n",
    "\n",
    "# Plot the learning curves\n",
    "plt.plot(train_sizes, train_mean, label='Training score', color='blue', marker='o')\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color='blue', alpha=0.15)\n",
    "\n",
    "plt.plot(train_sizes, val_mean, label='Validation score', color='green', marker='o')\n",
    "plt.fill_between(train_sizes, val_mean - val_std, val_mean + val_std, color='green', alpha=0.15)\n",
    "\n",
    "plt.title('Learning Curve')\n",
    "plt.xlabel('Training Data Size')\n",
    "plt.ylabel('Model Score')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### III. Imbalance Learning\n",
    "\n",
    "##### Data-level approaches\n",
    "- under-sampling \n",
    "- Over-sampling\n",
    "- Hybrid approach\n",
    "##### Algorithm-level approaches\n",
    "- cost-sensitive cost functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "\n",
    "# Over-Sampling\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_ros, y_ros = ros.fit_resample(scaled_cls_xtrain, cls_ytrain)\n",
    "\n",
    "# SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "X_smote, y_smote = smote.fit_resample(scaled_cls_xtrain, cls_ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the frequency of each class\n",
    "class_counts_ros = np.array([np.count_nonzero(y_ros == 0), np.count_nonzero(y_ros == 1)])\n",
    "class_counts_smote = np.array([np.count_nonzero(y_smote == 0), np.count_nonzero(y_smote == 1)])\n",
    "\n",
    "# Creating subplots\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n",
    "\n",
    "# Plotting for Random Over-Sampling data\n",
    "axes[0].bar(['Class 0', 'Class 1'], class_counts_ros)\n",
    "axes[0].set_title('ROS Class Distribution')\n",
    "axes[0].set_xlabel('Class')\n",
    "axes[0].set_ylabel('Frequency')\n",
    "# Adding text labels on top of each bar\n",
    "for index, value in enumerate(class_counts_ros):\n",
    "    axes[0].text(index, value, str(value), ha='center')\n",
    "\n",
    "# Plotting for SMOTE data\n",
    "axes[1].bar(['Class 0', 'Class 1'], class_counts_smote)\n",
    "axes[1].set_title('SMOTE Class Distribution')\n",
    "axes[1].set_xlabel('Class')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "# Adding text labels on top of each bar\n",
    "for index, value in enumerate(class_counts_smote):\n",
    "    axes[1].text(index, value, str(value), ha='center')\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=6, n_estimators=50, random_state=42)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_ros = RandomForestClassifier(n_estimators=50, max_depth=6, random_state=42)\n",
    "rf_ros.fit(X_ros, y_ros)\n",
    "\n",
    "rf_smote = RandomForestClassifier(n_estimators=50, max_depth=6, random_state=42)\n",
    "rf_smote.fit(X_smote, y_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.92 | Precision: 0.32 | Recall = 0.96 | F1_score = 0.48\n",
      " POD = 0.96 | F: 0.08 | FAR = 0.68 | CSI = 0.32 | HSS = 0.45 | PSS = 0.88\n"
     ]
    },
    {
     "data": {
      "image/png": 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SokmTJikyMlIRERGaPHmy2rRp47o7papINgAAMM0Lt74eOXJEt99+u44dO6b69evruuuu0/bt29W4cWNJ0pQpU1RUVKRx48YpPz9fSUlJ2rhxo0JDQ11rzJ07V/7+/ho0aJCKioqUnJysjIwM+fn5VSsWm2VZlke/3QWg5NgBb4cAXJCuaPFHb4cAXHByjv/H+DWK3lvikXWCuo/yyDo1jcoGAACmeaGNciEh2QAAwDRexAYAAGAOlQ0AAEyjjQIAAIyijQIAAGAOlQ0AAEyjjQIAAIzy8WSDNgoAADCKygYAAKb5+AZRkg0AAEzz8TYKyQYAAKb5eGWDPRsAAMAoKhsAAJhGGwUAABhFGwUAAMAcKhsAAJhGGwUAABjl48kGbRQAAGAUlQ0AAEyzLG9H4FUkGwAAmEYbBQAAwBwqGwAAmObjlQ2SDQAATPPxh3qRbAAAYJqPVzbYswEAAIyisgEAgGnc+goAAIyijQIAAGAOlQ0AAEzz8coGyQYAAKb5+K2vtFEAAIBRVDYAADDMKuNuFAAAYJKP79mgjQIAAIyisgEAgGk+vkGUZAMAANPYswEAAIxizwYAAIA5VDYAADDNxysbJBsAAJjm4299pY0CAACMorKBanl26SotXPaS21hkRD1lvfmyJGna40/pjfXvuJ1v26qFXl48T5L0be536n3biArXfuqxB9X7xs4ejxm4EIxNHakpD92nZYtW6bFpf5MkBYcEaerDqep5c3fVqxeuI4ePKuOFl/XS8v/zcrTwONooQPVc3qSxlsyf6fr5kkvcC2Q3XNdBjz94v+vngIAA1//HRkfp/bXuycr/vbFey17+hzpf18FQxIB3tW3fWrffeZs+2/uF2/hDjz+g6264Rvff86COHDqqLt076tG/Pag8x/fatP597wQLM3z81lfaKKg2Pz8/RUVGuI6Iepe6nQ8MCHA7Hx4WWulnoyIj9O4HW3VTchcFBwfV8DcBzAsOCdK8RelKu/8RFfx4wu1c+2vaaXXmm/roXzv07eGjeuXF1/TZ3i/V5qrWXooWMMOrycaRI0c0bdo0de/eXS1btlSrVq3UvXt3TZs2TYcPH/ZmaDiHQ0e+VfcBw9T7thGa/HC6Dn+b63Y+e9cn6tJ3iPoOGaXpT8zX8fwfK11r3+f79fn+A7q1X2/DUQPe8ejsB7V50wf6V9ZH5c7t2L5LyX26KqZBtCTpuhuuUZPLG+uDzVtrOkyYZpV55rhIea2NsmXLFvXp00fx8fHq1auXevXqJcuylJeXp9dff13PPPOM1q9fr+uvv95bIaICbVu10My/TFbjRg11/Icf9fyKV/TneybpjVWLdGl4mG64roN63dhZcbHR+vaoQ88sXqmUCf9Pry57WoGBgeXWW/3W22p6Wbzat2nlhW8DmNXvjzepdduWuqXH0ArPP5L2hNLnTdf2vZtUUlKisjJLaamPaMdHu2o4Uhjn420UryUb999/v0aNGqW5c+dWej41NVXZ2dnnXMfpdMrpdLqNXeJ0ym63eyxW/Ffnjtf894dmUruEluozaKTeWP+Ohg+5VX16dHWdbt70MrW+8gr1/NNwZW3NVs9u7onjT06n1m16X2NG3F5T4QM1pkFcjKbPnKI7b7tHxc7iCueMuHuo2ndoq1FD/1ffHj6qazsl/rJn47vvK6yEABcrr7VR9u7dq3vuuafS82PGjNHevXt/c5309HSFh4e7HbPmL/JkqDiH4KA6at70Mn1z+NsKz9ePilBcbLQOHSl/fuN7W1T0k1MDbko2HSZQ4xKuaqWo6Eit3fyK9n+3U/u/26nrbrhGI+4eqv3f7VRQcJAm/+V/9fhfntS7b2fp80/368Ulmfrnmrc1evxwb4cPD7PKyjxyXKy8Vtlo0KCBtm7dqhYtWlR4ftu2bWrQoMFvrpOWlqaJEye6jV1ysuK/+OB5xcXFyvnmkBLbVbyh7ceCE3Lkfa+oyIhy51a/9ba635BUboMpUBts/eAj9b7+T25jsxc8ogP7D2rR/OXy87tEgYEBKjvrL5DS0rJyd3ihFqCN4h2TJ0/WPffco507d6pnz56KiYmRzWaTw+HQpk2btGTJEs2bN+8317Hb7eVaJiXFxwxFjb8tWKxu1yepQUy0fsj/Zc/GqcLTuuXmHjp9ukjPLlulnt1uUP3ICH2b+53mP5+heuFh6tGlk9s6h44c1c7de7XwyUe99E0AswpPndaXn3/lNlZUWKT8H350jW/fkq20Rybqp5+c+vZwrpKuT9Stg/vp8Yee9EbIMOki3tzpCV5LNsaNG6fIyEjNnTtXzz//vEpLSyX9cmtkYmKiXnzxRQ0aNMhb4aES3+Ud05Tps5RfcEIRl4arbesr9fILcxUXG6OfnE7t//qg3lz/rk6cKlT9yAhde3VbPflomkJCgt3WWf3WRkXXj1Sna6/20jcBvG/C6Kma8tB9mvd8ui69NEzfHsnVk39dwEO9UOvYLMv7D2wvKSnRsWO/VCOioqLcHgJ1XusdO+CJsIBa54oWf/R2CMAFJ+f4f4xfo/DRYR5ZJ+Thl3570gXogniCaEBAQJX2ZwAAcFG6iDd3egK7kAAAgFEkGwAAmFZmeeb4HdLT02Wz2ZSamuoasyxLM2bMUFxcnIKCgtStWzft27fP7XNOp1MTJkxQVFSUQkJCNGDAAB05cqRa1ybZAADANC8/rjw7O1svvPCC2rZt6zY+e/ZszZkzRwsWLFB2drZiY2PVs2dPnTx50jUnNTVVa9asUWZmprZs2aJTp06pX79+rhs7qoJkAwCAWuzUqVMaNmyYFi9erHr16rnGLcvSvHnzNG3aNN16661KSEjQihUrdPr0ab388suSpIKCAi1dulRPPfWUevToofbt22vVqlXas2eP3nnnnSrHQLIBAIBpHmqjOJ1OnThxwu04+5UdZxs/frz69u2rHj16uI3n5OTI4XCoV69erjG73a6uXbtq69ZfXga4c+dOlZSUuM2Ji4tTQkKCa05VkGwAAGCYpx5XXtErOtLT0yu9bmZmpj7++OMK5zgcDklSTEyM23hMTIzrnMPhUGBgoFtF5Ow5VXFB3PoKAAB+W0Wv6KjsxaOHDx/Wfffdp40bN6pOnTqVrmmz2dx+tiyr3NjZqjLn16hsAABgmofaKHa7XWFhYW5HZcnGzp07lZeXp8TERPn7+8vf319ZWVl6+umn5e/v76ponF2hyMvLc52LjY1VcXGx8vPzK51TFSQbAACY5oVbX5OTk7Vnzx7t3r3bdXTo0EHDhg3T7t271bRpU8XGxmrTpk2uzxQXFysrK0udOv3yPqvExEQFBAS4zcnNzdXevXtdc6qCNgoAAKZ54UVsoaGhSkhIcBsLCQlRZGSkazw1NVUzZ85U8+bN1bx5c82cOVPBwcEaOnSoJCk8PFwpKSmaNGmSIiMjFRERocmTJ6tNmzblNpyeC8kGAAA+asqUKSoqKtK4ceOUn5+vpKQkbdy4UaGhoa45c+fOlb+/vwYNGqSioiIlJycrIyNDfn5+Vb7OBfEiNk/jRWxAxXgRG1BeTbyI7dTEAR5Zp+6ctR5Zp6ZR2QAAwDDrdz5q/GLHBlEAAGAUlQ0AAEzz8coGyQYAAKaV1fzdKBcS2igAAMAoKhsAAJhGGwUAABjl48kGbRQAAGAUlQ0AAAyrhc/PrBaSDQAATPPxNgrJBgAApvl4ssGeDQAAYBSVDQAADPP1d6OQbAAAYJqPJxu0UQAAgFFUNgAAMM23X41CsgEAgGm+vmeDNgoAADCKygYAAKb5eGWDZAMAANN8fM8GbRQAAGAUlQ0AAAzz9Q2iJBsAAJjm420Ukg0AAAzz9coGezYAAIBRVDYAADCNNgoAADDJ8vFkgzYKAAAwisoGAACm+Xhlg2QDAADDaKMAAAAYRGUDAADTfLyyQbIBAIBhvt5GIdkAAMAwX0822LMBAACMorIBAIBhvl7ZINkAAMA0y+btCLyKNgoAADCKygYAAIbRRgEAAEZZZbRRAAAAjKGyAQCAYbRRAACAURZ3owAAAJhDZQMAAMNoowAAAKN8/W4Ukg0AAAyzLG9H4F3s2QAAAEZR2QAAwDDaKAAAwChfTzZoowAAAKOobAAAYJivbxAl2QAAwDDaKAAAAAZR2QAAwDBffzcKyQYAAIb5+uPKaaMAAFALLVy4UG3btlVYWJjCwsLUsWNHrV+/3nXesizNmDFDcXFxCgoKUrdu3bRv3z63NZxOpyZMmKCoqCiFhIRowIABOnLkSLVjIdkAAMCwMsvmkaM6/vCHP+iJJ57Qjh07tGPHDt1444265ZZbXAnF7NmzNWfOHC1YsEDZ2dmKjY1Vz549dfLkSdcaqampWrNmjTIzM7VlyxadOnVK/fr1U2lpabVisVlW7bshp+TYAW+HAFyQrmjxR2+HAFxwco7/x/g1vriyj0fWafH5+t+edA4RERH629/+ppEjRyouLk6pqamaOnWqpF+qGDExMZo1a5bGjBmjgoIC1a9fXytXrtTgwYMlSUePHlV8fLzWrVun3r17V/m6VDYAADDMKrN55HA6nTpx4oTb4XQ6f/P6paWlyszMVGFhoTp27KicnBw5HA716tXLNcdut6tr167aunWrJGnnzp0qKSlxmxMXF6eEhATXnKoi2QAA4CKRnp6u8PBwtyM9Pb3S+Xv27FHdunVlt9t1zz33aM2aNWrVqpUcDockKSYmxm1+TEyM65zD4VBgYKDq1atX6ZyqOq9kY+XKlbr++usVFxenb775RpI0b948vfHGG+ezHAAAtZpleeZIS0tTQUGB25GWllbpdVu0aKHdu3dr+/btGjt2rIYPH65PP/3Udd5mc98HYllWubHy3+W355yt2snGwoULNXHiRN1888368ccfXZtELr30Us2bN6+6ywEAUOt5qo1it9tdd5ecOex2e6XXDQwM1OWXX64OHTooPT1d7dq10/z58xUbGytJ5SoUeXl5rmpHbGysiouLlZ+fX+mcqqp2svHMM89o8eLFmjZtmvz8/FzjHTp00J49e6q7HAAAqCGWZcnpdKpJkyaKjY3Vpk2bXOeKi4uVlZWlTp06SZISExMVEBDgNic3N1d79+51zamqaj/UKycnR+3bty83brfbVVhYWN3lAACo9ap726onPPjgg+rTp4/i4+N18uRJZWZm6v3339eGDRtks9mUmpqqmTNnqnnz5mrevLlmzpyp4OBgDR06VJIUHh6ulJQUTZo0SZGRkYqIiNDkyZPVpk0b9ejRo1qxVDvZaNKkiXbv3q3GjRu7ja9fv16tWrWq7nIAANR63nhc+Xfffac77rhDubm5Cg8PV9u2bbVhwwb17NlTkjRlyhQVFRVp3Lhxys/PV1JSkjZu3KjQ0FDXGnPnzpW/v78GDRqkoqIiJScnKyMjw62zURXVfs7G8uXL9dBDD+mpp55SSkqKlixZoq+//lrp6elasmSJhgwZUq0ATOA5G0DFeM4GUF5NPGdjT5P+HlmnTc6bHlmnplW7snHXXXfp559/1pQpU3T69GkNHTpUDRs21Pz58y+IRAMAgAtN7Xt8ZvWc14vYRo8erdGjR+vYsWMqKytTdHS0p+MCAKDW8MaejQvJ73rra1RUlKfiAAAAtdR5bRA918M8DhxgvwQAAL/mjQ2iF5JqJxupqaluP5eUlGjXrl3asGGDHnjgAU/FBQBArcGejWq67777Khx/9tlntWPHjt8dEAAAtY2v79nw2IvY+vTpo9dee81TywEAgFrid20Q/bV//OMfioiI8NRyv0tQXGdvhwBckEIDg7wdAuCT2LNRTe3bt3fbIGpZlhwOh77//ns999xzHg0OAIDawNfbKNVONgYOHOj28yWXXKL69eurW7duuvLKKz0VFwAAqCWqlWz8/PPPuuyyy9S7d2/X62kBAMC5+fjNKNXbIOrv76+xY8fK6XSaigcAgFqnzLJ55LhYVftulKSkJO3atctELAAAoBaq9p6NcePGadKkSTpy5IgSExMVEhLidr5t27YeCw4AgNrA1+9GqfIr5keOHKl58+bp0ksvLb+IzSbLsmSz2VRaWurpGKvNP7Cht0MALkjc+gqUl3/qK+PX+DD2No+s09nxD4+sU9OqnGz4+fkpNzdXRUVF55zXuHFjjwT2e5BsABUj2QDKI9kwr8ptlDM5yYWQTAAAcDGx5NttlGrt2TjX214BAEDFynz83tdqJRtXXHHFbyYcP/zww+8KCACA2qaMykbVPfLIIwoPDzcVCwAAqIWqlWwMGTJE0dHRpmIBAKBWYs9GFbFfAwCA81Pm7QC8rMpPEK3iHbIAAABuqlzZKCvz9bwMAIDzQxsFAAAY5ev/XK/2i9gAAACqg8oGAACG+Xplg2QDAADDfH3PBm0UAABgFJUNAAAMK/PtwgbJBgAApvFuFAAAYJSvPxaTPRsAAMAoKhsAABjGra8AAMCoMh9/mSltFAAAYBSVDQAADPP1DaIkGwAAGObrezZoowAAAKOobAAAYBhPEAUAAEb5+hNEaaMAAACjqGwAAGAYd6MAAACj2LMBAACM4tZXAAAAg6hsAABgGHs2AACAUb6+Z4M2CgAAMIrKBgAAhvn6BlGSDQAADPP1ZIM2CgAAMIrKBgAAhlk+vkGUZAMAAMNoowAAABhEsgEAgGFlHjqqIz09Xddcc41CQ0MVHR2tgQMH6osvvnCbY1mWZsyYobi4OAUFBalbt27at2+f2xyn06kJEyYoKipKISEhGjBggI4cOVKtWEg2AAAwzPLQUR1ZWVkaP368tm/frk2bNunnn39Wr169VFhY6Joze/ZszZkzRwsWLFB2drZiY2PVs2dPnTx50jUnNTVVa9asUWZmprZs2aJTp06pX79+Ki0trXIsNsuyat1TVP0DG3o7BOCCFBoY5O0QgAtO/qmvjF9jfqM/e2Sd+w6tOu/Pfv/994qOjlZWVpa6dOkiy7IUFxen1NRUTZ06VdIvVYyYmBjNmjVLY8aMUUFBgerXr6+VK1dq8ODBkqSjR48qPj5e69atU+/evat0bSobAABcJJxOp06cOOF2OJ3OKn22oKBAkhQRESFJysnJkcPhUK9evVxz7Ha7unbtqq1bt0qSdu7cqZKSErc5cXFxSkhIcM2pCpINAAAM89SejfT0dIWHh7sd6enpv3l9y7I0ceJE3XDDDUpISJAkORwOSVJMTIzb3JiYGNc5h8OhwMBA1atXr9I5VcGtrwAAGOapW1/T0tI0ceJEtzG73f6bn7v33nv1ySefaMuWLeXO2WzuDwGxLKvc2NmqMufXqGwAAHCRsNvtCgsLczt+K9mYMGGC1q5dq/fee09/+MMfXOOxsbGSVK5CkZeX56p2xMbGqri4WPn5+ZXOqQqSDQAADPPG3SiWZenee+/V6tWrtXnzZjVp0sTtfJMmTRQbG6tNmza5xoqLi5WVlaVOnTpJkhITExUQEOA2Jzc3V3v37nXNqQraKAAAGFbmhceVjx8/Xi+//LLeeOMNhYaGuioY4eHhCgoKks1mU2pqqmbOnKnmzZurefPmmjlzpoKDgzV06FDX3JSUFE2aNEmRkZGKiIjQ5MmT1aZNG/Xo0aPKsZBsAABQCy1cuFCS1K1bN7fx5cuXa8SIEZKkKVOmqKioSOPGjVN+fr6SkpK0ceNGhYaGuubPnTtX/v7+GjRokIqKipScnKyMjAz5+flVORaeswH4EJ6zAZRXE8/ZeKKxZ56z8f++Of/nbHgTlQ0AAAyrdf+qryY2iAIAAKOobAAAYFiZj9c2SDYAADDMUw/1uliRbAAAYJhv1zXYswEAAAyjsgEAgGG0UQAAgFHeeILohYQ2CgAAMIrKBgAAhnHrKwAAMMq3Uw3aKAAAwDAqGwAAGMbdKAAAwChf37NBGwUAABhFZQMAAMN8u65BsgEAgHHs2QAAAEaxZwMAAMAgKhsAABjm23UNkg0AAIzz9T0btFEAAIBRVDYAADDM8vFGCskGAACG0UYBAAAwiMoGAACG+fpzNkg2AAAwzLdTDdooAADAMJINGNH5hiS9viZDhw7u1M/F32rAgN7eDgmoUfdPukfvZq3Wodzd+jLnI616ZaEub97Ebc7UB/9XH338to5894lyDu/UmjdXKLFDOy9FDJPKZHnkuFiRbMCIkJBgffLJp/rf1L94OxTAKzrdcK2WvLBKvW78H93af7j8/f20+o0MBQcHueZ8vT9HUyY+ouuT+qpPryE69M23Wv1GhiKjIrwYOUwo89BxsbJZlnXxpkqV8A9s6O0Q8Cs/F3+rW28bqbVr3/Z2KD4vNDDotyfBiMioCH118N/q2/t2bf1XdoVzQkPr6lDubt3S7w598P62Go7Qd+Wf+sr4NUZddptH1lly8B8eWaemUdkAgBoQFhYqScrP/7HC8wEBARp+12AV/HhCe/d8XoORAeZd0MnG4cOHNXLkyHPOcTqdOnHihNtRC4s1AC5yf01/UNu2ZuuzT/e7jfe+qbsOO/4jx/F9GnvvXfrjgOH64Xi+l6KEKb7eRrmgk40ffvhBK1asOOec9PR0hYeHux1W2ckaihAAftvf5sxQ64QWGjXi/nLnPvxgu7p0GqDeyYP07qYPtfzFpxVVnz0btY3lof8uVl59zsbatWvPef7AgQO/uUZaWpomTpzoNlYv8srfFRcAeMqsJx9Wn5uTdXPv23X0qKPc+dOni5Rz4BvlHPhGO7J3a8fud3THnYM096lFXogWMMOrycbAgQNls9nO2faw2WznXMNut8tut1frMwBQE2Y/NV19+/dU/z7DdOibI1X6jM1mU6A90HBkqGkXcwvEE7zaRmnQoIFee+01lZWVVXh8/PHH3gwPv0NISLDatWutdu1aS5KaXNZI7dq1Vnx8nJcjA2rGk3Mf0aDBt2j0yIk6dbJQ0dFRio6OUp06v/zjKDg4SA9Nn6QO11yl+Pg4tW3XWvMXzFRcw1i9sWa9l6OHp5VZlkeOi5VXKxuJiYn6+OOPNXDgwArP/1bVAxeuDont9O47/71F66knZ0iSVrz4qlJGle9bA7VNyuhhkqR/bnjZbXzcmCl65aXVKi0tVfMWTTVk2B8VGRmhH37I166de3RzryH6/LP9FS0JXLS8mmw88MADKiwsrPT85Zdfrvfee68GI4KnZH2wjeedwKfVq3v5Oc87ncW6c+j4GooG3ubr/2z2arLRuXPnc54PCQlR165daygaAADMuJgfNe4JF/StrwAA4OLHK+YBADDsYn5GhieQbAAAYJiv3/pKsgEAgGHs2QAAADCIygYAAIaxZwMAABjl63s2aKMAAACjqGwAAGCYr796g2QDAADDuBsFAADAICobAAAY5usbREk2AAAwzNdvfaWNAgAAjKKyAQCAYb6+QZRkAwAAw3z91lfaKAAAGFbmoaO6PvjgA/Xv319xcXGy2Wx6/fXX3c5blqUZM2YoLi5OQUFB6tatm/bt2+c2x+l0asKECYqKilJISIgGDBigI0eOVCsOkg0AAGqpwsJCtWvXTgsWLKjw/OzZszVnzhwtWLBA2dnZio2NVc+ePXXy5EnXnNTUVK1Zs0aZmZnasmWLTp06pX79+qm0tLTKcdisWljb8Q9s6O0QgAtSaGCQt0MALjj5p74yfo1e8Td5ZJ2Nhzec92dtNpvWrFmjgQMHSvqlqhEXF6fU1FRNnTpV0i9VjJiYGM2aNUtjxoxRQUGB6tevr5UrV2rw4MGSpKNHjyo+Pl7r1q1T7969q3RtKhsAABhWJssjh9Pp1IkTJ9wOp9N5XjHl5OTI4XCoV69erjG73a6uXbtq69atkqSdO3eqpKTEbU5cXJwSEhJcc6qCZAMAgItEenq6wsPD3Y709PTzWsvhcEiSYmJi3MZjYmJc5xwOhwIDA1WvXr1K51QFd6MAAGCYp3YspKWlaeLEiW5jdrv9d61ps9ncfrYsq9zY2aoy59eobAAAYJin2ih2u11hYWFux/kmG7GxsZJUrkKRl5fnqnbExsaquLhY+fn5lc6pCpINAAB8UJMmTRQbG6tNmza5xoqLi5WVlaVOnTpJkhITExUQEOA2Jzc3V3v37nXNqQraKAAAGOatd6OcOnVKX33137ttcnJytHv3bkVERKhRo0ZKTU3VzJkz1bx5czVv3lwzZ85UcHCwhg4dKkkKDw9XSkqKJk2apMjISEVERGjy5Mlq06aNevToUeU4SDYAADCszEtPmdixY4e6d+/u+vnMfo/hw4crIyNDU6ZMUVFRkcaNG6f8/HwlJSVp48aNCg0NdX1m7ty58vf316BBg1RUVKTk5GRlZGTIz8+vynHwnA3Ah/CcDaC8mnjORpeGyR5Z54Nv3/XIOjWNygYAAIbVun/VVxPJBgAAhvHWVwAAYJSvJxvc+goAAIyisgEAgGG18F6MaiHZAADAMNooAAAABlHZAADAMG89QfRCQbIBAIBhvr5ngzYKAAAwisoGAACG+foGUZINAAAMo40CAABgEJUNAAAMo40CAACM4tZXAABgVBl7NgAAAMyhsgEAgGG0UQAAgFG0UQAAAAyisgEAgGG0UQAAgFG0UQAAAAyisgEAgGG0UQAAgFG0UQAAAAyisgEAgGG0UQAAgFGWVebtELyKZAMAAMN8/RXz7NkAAABGUdkAAMAwy8fvRiHZAADAMNooAAAABlHZAADAMNooAADAKJ4gCgAAYBCVDQAADOMJogAAwChf37NBGwUAABhFZQMAAMN8/TkbJBsAABjm620Ukg0AAAzj1lcAAACDqGwAAGAYbRQAAGCUr28QpY0CAACMorIBAIBhtFEAAIBR3I0CAABgEJUNAAAM40VsAADAKNooAAAABlHZAADAMO5GAQAARrFnAwAAGOXrlQ32bAAAAKOobAAAYJivVzZINgAAMMy3Uw3aKAAAwDCb5eu1HRjjdDqVnp6utLQ02e12b4cDXDD4swFfQ7IBY06cOKHw8HAVFBQoLCzM2+EAFwz+bMDX0EYBAABGkWwAAACjSDYAAIBRJBswxm63a/r06WyAA87Cnw34GjaIAgAAo6hsAAAAo0g2AACAUSQbAADAKJINAABgFMkGjHnuuefUpEkT1alTR4mJifrwww+9HRLgVR988IH69++vuLg42Ww2vf76694OCagRJBsw4u9//7tSU1M1bdo07dq1S507d1afPn106NAhb4cGeE1hYaHatWunBQsWeDsUoEZx6yuMSEpK0tVXX62FCxe6xlq2bKmBAwcqPT3di5EBFwabzaY1a9Zo4MCB3g4FMI7KBjyuuLhYO3fuVK9evdzGe/Xqpa1bt3opKgCAt5BswOOOHTum0tJSxcTEuI3HxMTI4XB4KSoAgLeQbMAYm83m9rNlWeXGAAC1H8kGPC4qKkp+fn7lqhh5eXnlqh0AgNqPZAMeFxgYqMTERG3atMltfNOmTerUqZOXogIAeIu/twNA7TRx4kTdcccd6tChgzp27KgXXnhBhw4d0j333OPt0ACvOXXqlL766ivXzzk5Odq9e7ciIiLUqFEjL0YGmMWtrzDmueee0+zZs5Wbm6uEhATNnTtXXbp08XZYgNe8//776t69e7nx4cOHKyMjo+YDAmoIyQYAADCKPRsAAMAokg0AAGAUyQYAADCKZAMAABhFsgEAAIwi2QAAAEaRbAAAAKNINoBaaMaMGbrqqqtcP48YMUIDBw6s8TgOHjwom82m3bt31/i1AVw4SDaAGjRixAjZbDbZbDYFBASoadOmmjx5sgoLC41ed/78+VV+QiUJAgBP490oQA276aabtHz5cpWUlOjDDz/UqFGjVFhYqIULF7rNKykpUUBAgEeuGR4e7pF1AOB8UNkAapjdbldsbKzi4+M1dOhQDRs2TK+//rqr9bFs2TI1bdpUdrtdlmWpoKBAd999t6KjoxUWFqYbb7xR//nPf9zWfOKJJxQTE6PQ0FClpKTop59+cjt/dhulrKxMs2bN0uWXXy673a5GjRrpr3/9qySpSZMmkqT27dvLZrOpW7durs8tX75cLVu2VJ06dXTllVfqueeec7vOv//9b7Vv31516tRRhw4dtGvXLg/+5gBcrKhsAF4WFBSkkpISSdJXX32lV199Va+99pr8/PwkSX379lVERITWrVun8PBwPf/880pOTtaXX36piIgIvfrqq5o+fbqeffZZde7cWStXrtTTTz+tpk2bVnrNtLQ0LV68WHPnztUNN9yg3Nxcff7555J+SRiuvfZavfPOO2rdurUCAwMlSYsXL9b06dO1YMECtW/fXrt27dLo0aMVEhKi4cOHq7CwUP369dONN96oVatWKScnR/fdd5/h3x6Ai4IFoMYMHz7cuuWWW1w/f/TRR1ZkZKQ1aNAga/r06VZAQICVl5fnOv/uu+9aYWFh1k8//eS2TrNmzaznn3/esizL6tixo3XPPfe4nU9KSrLatWtX4XVPnDhh2e12a/HixRXGmJOTY0mydu3a5TYeHx9vvfzyy25jjz32mNWxY0fLsizr+eeftyIiIqzCwkLX+YULF1a4FgDfQhsFqGFvvfWW6tatqzp16qhjx47q0qWLnnnmGUlS48aNVb9+fdfcnTt36tSpU4qMjFTdunVdR05Ojr7++mtJ0meffaaOHTu6XePsn3/ts88+k9PpVHJycpVj/v7773X48GGlpKS4xfH444+7xdGuXTsFBwdXKQ4AvoM2ClDDunfvroULFyogIEBxcXFum0BDQkLc5paVlalBgwZ6//33y61z6aWXntf1g4KCqv2ZsrIySb+0UpKSktzOnWn3WJZ1XvEAqP1INoAaFhISossvv7xKc6+++mo5HA75+/vrsssuq3BOy5YttX37dt15552use3bt1e6ZvPmzRUUFKR3331Xo0aNKnf+zB6N0tJS11hMTIwaNmyoAwcOaNiwYRWu26pVK61cuVJFRUWuhOZccQDwHbRRgAtYjx491LFjRw0cOFBvv/22Dh48qK1bt+ovf/mLduzYIUm67777tGzZMi1btkxffvmlpk+frn379lW6Zp06dTR16lRNmTJFL774or7++mtt375dS5culSRFR0crKChIGzZs0HfffaeCggJJvzwoLD09XfPnz9eXX36pPXv2aPny5ZozZ44kaejQobrkkkuUkpKiTz/9VOvWrdOTTz5p+DcE4GJAsgFcwGw2m9atW6cuXbpo5MiRuuKKKzRkyBAdPHhQMTExkqTBgwfr4Ycf1tSpU5WYmKhvvvlGY8eOPee6Dz30kCZNmqSHH35YLVu21ODBg5WXlydJ8vf319NPP63nn39ecXFxuuWWWyRJo0aN0pIlS5SRkaE2bdqoa9euysjIcN0qW7duXb355pv69NNP1b59e02bNk2zZs0y+NsBcLGwWTRaAQCAQVQ2AACAUSQbAADAKJINAABgFMkGAAAwimQDAAAYRbIBAACMItkAAABGkWwAAACjSDYAAIBRJBsAAMAokg0AAGAUyQYAADDq/wMbeF+r7HCVcgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.93 | Precision: 0.34 | Recall = 1.00 | F1_score = 0.51\n",
      " POD = 1.00 | F: 0.08 | FAR = 0.66 | CSI = 0.34 | HSS = 0.48 | PSS = 0.92\n"
     ]
    },
    {
     "data": {
      "image/png": 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BAwdKkkJCQjR06FBNmDBBYWFhCg0N1cSJE9WyZUvn3SnlRbIBAEAVdOTIEd199906duyYateurRtvvFHbtm1Tw4YNJUmTJk1SQUGBRo8erdzcXMXFxWnjxo0KCgpyrjFnzhz5+vqqX79+KigoUGJiolJTU+Xj41OhWGyWZVlu/XWXgKJj33k6BOCSdE3TOzwdAnDJyTr+mfFrFLy/1C3rBCQ+6JZ1KhuVDQAATONFbAAAAOZQ2QAAwLQ/eCfJ5Y5kAwAA02ijAAAAmENlAwAA02ijAAAAo7w82aCNAgAAjKKyAQCAaV6+QZRkAwAA07y8jUKyAQCAaV5e2WDPBgAAMIrKBgAAptFGAQAARtFGAQAAMIfKBgAAptFGAQAARnl5skEbBQAAGEVlAwAA0yzL0xF4FMkGAACm0UYBAAAwh8oGAACmeXllg2QDAADTvPyhXiQbAACY5uWVDfZsAAAAo6hsAABgGre+AgAAo2ijAAAAmENlAwAA07y8skGyAQCAaV5+6yttFAAAYBSVDQAADLNKuBsFAACY5OV7NmijAAAAo6hsAABgmpdvECXZAADANPZsAAAAo9izAQAAYA6VDQAATPPyygbJBgAApnn5W19powAAAKOobKBCFqxYq0UrX3QZCwutpYy3X5IkTXn6Wb357nsu51s1b6qXls11fj505Hs9s2C5dn6+V4WFRbr5xnZKfniUwkNrGY8f8JRRSUM06bFxWrl4rZ6a8jfneONrovV/jyfphptiVa1aNX3z5bd6aMgj+v6/OR6MFm5HGwWomKujG2r5vOnOz9WquRbIbr6xnZ5+9GHnZz8/P+c/ny74RQ8+PEVNr26kFc/NkCTNX7ZGD02appeWzim1FlAVtGrTQnfff5f27fnKZbzBVVfq7/9M1atr12nOzEU6eeKkrr6mkRyOQg9FCmO49RWoGB8fH4WHhZZ53t/Pr8zzOz/fq+9zjuq11PmqGRgoSXrq0Yd1U49++mTHZ4q/vo2RmAFPqREYoLmLU5T88BN6aPxwl3MTp4zVh+9t1own5jrHDh/8byVHCJjn0f+MPHLkiKZMmaLOnTurWbNmat68uTp37qwpU6bo8OHDngwNF3DoyH/Vuc896n7XYE18PEWH/5vtcj5z5+fq0HOAeg4Ypqkz5ul47s/Oc0VFRbLZfk1IzrLb/VWtWjV9+vneyvoJQKV5ctaj2pT+kf6d8YnLuM1mU+du7ZW1/6BW/32RMr/8QOs2rlXX2zp7KFIYZZW457hMeSzZ2Lx5s5o1a6Z169apdevWuv/++3XvvfeqdevWeuONN9SiRQv9+9//9lR4KEOr5k01/S8TtWTO05o2eZyO/ZSre0dO0M95JyT92kKZMXWSVjw/Q488NEx79n2toWP/T4WFv5aFW7W4VgHVq2v2wpUq+OUXnS74Rc/OX6GSkhIdO/6TJ38a4Ha97rhVLVo106ynnit1Lqx2qGrWDNTIcUOU8f6/df9dI/Wvf27S4tWzFZcQ64FoYVSJ5Z7jMuWxNsrDDz+sYcOGac6cOWWeT0pKUmZm5gXXcTgccjgcLmPVHA7Z7Xa3xYr/aR9//f8+NJZaxzRTj35D9Oa772nQgDvVo0tH5+kmja5Si2uvUdc/DVLGlkx17XSTQmtdoWefelRPPTNfL772lqpVs6lHl05q3vRq9mugSqkbFaGp0yfp/rtGqvA8ezDO/v89/d0PtHLxWknSvj1fKfb61ho4+M/6ZMuOSo0XMMljycaePXu0du3aMs+PGDFCixcv/t11UlJS9MQTT7iM/eWR/6fHJ437wzHi99UIqK4mja7SwcPn7zPXDg9VVGQdHTryv/M3xcVqw99XKffnPPn4+Cg4qKY69h6oW+tGVlbYgHEx1zVXeJ0wvbXpZeeYr6+vbkiI1f3DBqhF/RtVVFSk/V9/5/K9/d9kqV3cdZUcLUyzuBvFM+rWrastW7aoadOm5z2/detW1a1b93fXSU5O1vjx413Gqp1kg1VlKSwsVNbBQ4pt3eK853/OO6Gcoz+ed8NorStCJEmf7Niln3J/VuebbzQaK1CZtnz0ibrf9CeXsVnzn9B33xzQ4nmrVFhYpM937lWjq69ymRPduKH+e9h1HxSqgMu4BeIOHks2Jk6cqJEjR2rHjh3q2rWrIiIiZLPZlJOTo/T0dC1fvlxz58793XXsdnuplklR4TFDUeNv85ep001xqhtRRz/l/qwlq1/WqfzTuv22Ljp9ukALVq5V1043q3ZYqP6b/YPmLUlVrZBgdemQ4Fxj3T83qlHD+qp1RYg+2/ulZsxdrPv736Hohld68JcB7pV/6rS+/nK/y1hBfoFyf/rZOb50/mo9v3yW/rNlh7ZuzlTHxJuU2L2D7u4zzBMhw6TLeHOnO3gs2Rg9erTCwsI0Z84cLVmyRMXFxZJ+va0yNjZWL7zwgvr16+ep8FCGH44e06SpM5Wbd0KhV4SoVYtr9dLSOYqKjNAvDoe++faA3n73fZ04la/aYaG6oW0rPfNksgIDazjXOHDoiOYuTlXeiZOqVzdCDw4aoPv73+HBXwV4xsZ/btJfJjytUUlDNDVlsr7bf0CjB0/Q9k92ejo0wK1sluX5B7YXFRXp2LFfqxHh4eEuD4G6qPWOfff7kwAvdE1TkjrgXFnHPzN+jfwn73HLOoGPv/j7ky5Bl8RDvfz8/Mq1PwMAgMuSl28Q5V5DAABg1CVR2QAAoErz8rtRqGwAAGDaJfC48pSUFNlsNiUlJf0vLMvStGnTFBUVpYCAAHXq1El797q+OsLhcGjs2LEKDw9XYGCg+vTpoyNHjlTo2iQbAABUcZmZmVq6dKlatWrlMj5r1izNnj1b8+fPV2ZmpiIjI9W1a1edPHnSOScpKUnr1q1TWlqaNm/erFOnTqlXr17Ou0jLg2QDAADTPPhulFOnTumee+7RsmXLVKtWLee4ZVmaO3eupkyZojvvvFMxMTFavXq1Tp8+rZdeekmSlJeXpxUrVujZZ59Vly5d1KZNG61du1a7d+/We++9V+4YSDYAADDMKilxy+FwOHTixAmX49z3g51rzJgx6tmzp7p06eIynpWVpZycHHXr1s05Zrfb1bFjR23ZskWStGPHDhUVFbnMiYqKUkxMjHNOeZBsAABwmUhJSVFISIjLkZKSUub8tLQ0ffrpp+edk5OTI0mKiIhwGY+IiHCey8nJkb+/v0tF5Nw55cHdKAAAmOamu1HO9z6wst5yfvjwYY0bN04bN25U9erVy1zTZrO5fLYsq9TYucoz57eobAAAYJqb9mzY7XYFBwe7HGUlGzt27NDRo0cVGxsrX19f+fr6KiMjQ88995x8fX2dFY1zKxRHjx51nouMjFRhYaFyc3PLnFMeJBsAAJjmgVtfExMTtXv3bu3atct5tGvXTvfcc4927dqlRo0aKTIyUunp6c7vFBYWKiMjQwkJv748MzY2Vn5+fi5zsrOztWfPHuec8qCNAgBAFRQUFKSYmBiXscDAQIWFhTnHk5KSNH36dDVp0kRNmjTR9OnTVaNGDQ0cOFCSFBISoqFDh2rChAkKCwtTaGioJk6cqJYtW5bacHohJBsAAJh2iT5BdNKkSSooKNDo0aOVm5uruLg4bdy4UUFBQc45c+bMka+vr/r166eCggIlJiYqNTVVPj4+5b7OJfHWV3fjra/A+fHWV6C0ynjr68mk3m5ZJ2ju225Zp7KxZwMAABhFGwUAANMu0TZKZSHZAADAtJI/9hK1yx1tFAAAYBSVDQAATKONAgAAjPLyZIM2CgAAMIrKBgAAhlXBR1pVCMkGAACmeXkbhWQDAADTvDzZYM8GAAAwisoGAACGWV5e2SDZAADANC9PNmijAAAAo6hsAABgmne/GoVkAwAA07x9zwZtFAAAYBSVDQAATPPyygbJBgAApnn5ng3aKAAAwCgqGwAAGObtG0RJNgAAMM3L2ygkGwAAGObtlQ32bAAAAKOobAAAYBptFAAAYJLl5ckGbRQAAGAUlQ0AAEzz8soGyQYAAIbRRgEAADCIygYAAKZ5eWWDZAMAAMO8vY1CsgEAgGHenmywZwMAABhFZQMAAMO8vbJBsgEAgGmWzdMReBRtFAAAYBSVDQAADKONAgAAjLJKaKMAAAAYQ2UDAADDaKMAAACjLO5GAQAAMIfKBgAAhtFGAQAARnn73SgkGwAAGGZZno7As9izAQAAjKKyAQCAYbRRAACAUd6ebNBGAQAARlHZAADAMG/fIEqyAQCAYbRRAAAADKKyAQCAYd7+bhSSDQAADPP2x5XTRgEAoApatGiRWrVqpeDgYAUHBys+Pl7vvvuu87xlWZo2bZqioqIUEBCgTp06ae/evS5rOBwOjR07VuHh4QoMDFSfPn105MiRCsdCsgEAgGElls0tR0VceeWVmjFjhrZv367t27frlltu0e233+5MKGbNmqXZs2dr/vz5yszMVGRkpLp27aqTJ08610hKStK6deuUlpamzZs369SpU+rVq5eKi4srFIvNsqreDTlFx77zdAjAJemapnd4OgTgkpN1/DPj1/jq2h5uWafpl+/+/qQLCA0N1d/+9jcNGTJEUVFRSkpK0uTJkyX9WsWIiIjQzJkzNWLECOXl5al27dpas2aN+vfvL0n6/vvvVb9+fa1fv17du3cv93WpbAAAYJhVYnPL4XA4dOLECZfD4XD87vWLi4uVlpam/Px8xcfHKysrSzk5OerWrZtzjt1uV8eOHbVlyxZJ0o4dO1RUVOQyJyoqSjExMc455UWyAQDAZSIlJUUhISEuR0pKSpnzd+/erZo1a8put2vkyJFat26dmjdvrpycHElSRESEy/yIiAjnuZycHPn7+6tWrVplzimvi0o21qxZo5tuuklRUVE6ePCgJGnu3Ll68803L2Y5AACqNMtyz5GcnKy8vDyXIzk5uczrNm3aVLt27dK2bds0atQoDRo0SF988YXzvM3mug/EsqxSY6V/y+/POVeFk41FixZp/Pjxuu222/Tzzz87N4lcccUVmjt3bkWXAwCgynNXG8VutzvvLjl72O32Mq/r7++vq6++Wu3atVNKSopat26tefPmKTIyUpJKVSiOHj3qrHZERkaqsLBQubm5Zc4prwonG88//7yWLVumKVOmyMfHxznerl077d69u6LLAQCASmJZlhwOh6KjoxUZGan09HTnucLCQmVkZCghIUGSFBsbKz8/P5c52dnZ2rNnj3NOeVX4oV5ZWVlq06ZNqXG73a78/PyKLgcAQJVX0dtW3eHRRx9Vjx49VL9+fZ08eVJpaWn68MMPtWHDBtlsNiUlJWn69Olq0qSJmjRpounTp6tGjRoaOHCgJCkkJERDhw7VhAkTFBYWptDQUE2cOFEtW7ZUly5dKhRLhZON6Oho7dq1Sw0bNnQZf/fdd9W8efOKLgcAQJXniceV//DDD7rvvvuUnZ2tkJAQtWrVShs2bFDXrl0lSZMmTVJBQYFGjx6t3NxcxcXFaePGjQoKCnKuMWfOHPn6+qpfv34qKChQYmKiUlNTXTob5VHh52ysWrVKjz32mJ599lkNHTpUy5cv17fffquUlBQtX75cAwYMqFAAJvCcDeD8eM4GUFplPGdjd3Rvt6zTMuttt6xT2Spc2XjggQd05swZTZo0SadPn9bAgQNVr149zZs375JINAAAuNRUvcdnVsxFvYht+PDhGj58uI4dO6aSkhLVqVPH3XEBAFBleGLPxqXkD731NTw83F1xAACAKuqiNohe6GEe333HfgkAAH7LExtELyUVTjaSkpJcPhcVFWnnzp3asGGDHnnkEXfFBQBAlcGejQoaN27ceccXLFig7du3/+GAAACoarx9z4bbXsTWo0cP/eMf/3DXcgAAoIr4QxtEf+u1115TaGiou5b7QwKi2ns6BOCSFOQf4OkQAK/Eno0KatOmjcsGUcuylJOTox9//FELFy50a3AAAFQF3t5GqXCy0bdvX5fP1apVU+3atdWpUydde+217ooLAABUERVKNs6cOaOrrrpK3bt3d76eFgAAXJiX34xSsQ2ivr6+GjVqlBwOh6l4AACockosm1uOy1WF70aJi4vTzp07TcQCAACqoArv2Rg9erQmTJigI0eOKDY2VoGBgS7nW7Vq5bbgAACoCrz9bpRyv2J+yJAhmjt3rq644orSi9hssixLNptNxcXF7o6xwnz963k6BOCSxK2vQGm5p/Ybv8bHkXe5ZZ32Oa+5ZZ3KVu5kw8fHR9nZ2SooKLjgvIYNG7olsD+CZAM4P5INoDSSDfPK3UY5m5NcCskEAACXE0ve3Uap0J6NC73tFQAAnF+Jl9/7WqFk45prrvndhOOnn376QwEBAFDVlFDZKL8nnnhCISEhpmIBAABVUIWSjQEDBqhOnTqmYgEAoEpiz0Y5sV8DAICLU+LpADys3E8QLecdsgAAAC7KXdkoKfH2vAwAgItDGwUAABjl7f+5XuEXsQEAAFQElQ0AAAzz9soGyQYAAIZ5+54N2igAAMAoKhsAABhW4t2FDZINAABM490oAADAKG9/LCZ7NgAAgFFUNgAAMIxbXwEAgFElXv4yU9ooAADAKCobAAAY5u0bREk2AAAwzNv3bNBGAQAARlHZAADAMJ4gCgAAjPL2J4jSRgEAAEZR2QAAwDDuRgEAAEaxZwMAABjFra8AAAAGUdkAAMAw9mwAAACjvH3PBm0UAABgFJUNAAAM8/YNoiQbAAAY5u3JBm0UAABgFJUNAAAMs7x8gyjJBgAAhtFGAQAAMIhkAwAAw0rcdFRESkqKrr/+egUFBalOnTrq27evvvrqK5c5lmVp2rRpioqKUkBAgDp16qS9e/e6zHE4HBo7dqzCw8MVGBioPn366MiRIxWKhWQDAADDLDcdFZGRkaExY8Zo27ZtSk9P15kzZ9StWzfl5+c758yaNUuzZ8/W/PnzlZmZqcjISHXt2lUnT550zklKStK6deuUlpamzZs369SpU+rVq5eKi4vLHYvNsqwq9xRVX/96ng4BuCQF+Qd4OgTgkpN7ar/xa8xrcK9b1hl3aO1Ff/fHH39UnTp1lJGRoQ4dOsiyLEVFRSkpKUmTJ0+W9GsVIyIiQjNnztSIESOUl5en2rVra82aNerfv78k6fvvv1f9+vW1fv16de/evVzXprIBAMBlwuFw6MSJEy6Hw+Eo13fz8vIkSaGhoZKkrKws5eTkqFu3bs45drtdHTt21JYtWyRJO3bsUFFRkcucqKgoxcTEOOeUB8kGAACGuWvPRkpKikJCQlyOlJSU372+ZVkaP368br75ZsXExEiScnJyJEkREREucyMiIpzncnJy5O/vr1q1apU5pzy49RUAAMPcdetrcnKyxo8f7zJmt9t/93sPPfSQPv/8c23evLnUOZvN9SEglmWVGjtXeeb8FpUNAAAuE3a7XcHBwS7H7yUbY8eO1VtvvaUPPvhAV155pXM8MjJSkkpVKI4ePeqsdkRGRqqwsFC5ubllzikPkg0AAAzzxN0olmXpoYce0uuvv65NmzYpOjra5Xx0dLQiIyOVnp7uHCssLFRGRoYSEhIkSbGxsfLz83OZk52drT179jjnlAdtFAAADCvxwOPKx4wZo5deeklvvvmmgoKCnBWMkJAQBQQEyGazKSkpSdOnT1eTJk3UpEkTTZ8+XTVq1NDAgQOdc4cOHaoJEyYoLCxMoaGhmjhxolq2bKkuXbqUOxaSDQAAqqBFixZJkjp16uQyvmrVKg0ePFiSNGnSJBUUFGj06NHKzc1VXFycNm7cqKCgIOf8OXPmyNfXV/369VNBQYESExOVmpoqHx+fcsfCczYAL8JzNoDSKuM5GzMauuc5G/938OKfs+FJVDYAADCsyv1XfQWxQRQAABhFZQMAAMNKvLy2QbIBAIBh7nqo1+WKZAMAAMO8u67Bng0AAGAYlQ0AAAyjjQIAAIzyxBNELyW0UQAAgFFUNgAAMIxbXwEAgFHenWrQRgEAAIZR2QAAwDDuRgEAAEZ5+54N2igAAMAoKhsAABjm3XUNkg0AAIxjzwYAADCKPRsAAAAGUdkAAMAw765rkGwAAGCct+/ZoI0CAACMorIBAIBhlpc3Ukg2AAAwjDYKAACAQVQ2AAAwzNufs0GyAQCAYd6datBGAQAAhpFswJiRIwbpm6+26tSJb/XJtnd18003eDokoNI8PGGk3s94XYeyd+nrrE+09uVFurpJdJnz5zz3lHJP7dfI0YMrL0hUmhJZbjkuVyQbMOLPf+6j2c9OU8qM59Tuhu7avPk/eufttapfP8rToQGVIuHmG7R86Vp1u+XPurP3IPn6+uj1N1NVo0ZAqbm39eqi2Hat9f33OR6IFJWhxE3H5YpkA0Y8PG64Vq5K08pVL+vLL/drwsSpOnzke40ccb+nQwMqxZ/vGKKXX3xdX+77Rnv2fKkxo/5P9RvU03VtYlzm1a0boVnPTtODQyfoTNEZD0UL0yw3/e9yRbIBt/Pz81Pbtq2U/l6Gy3h6eobib2znoagAzwoODpIk5eb+7Byz2WxavPwZPT9vmb7c942HIgPMu6STjcOHD2vIkCEXnONwOHTixAmXw7Iu3+yvKggPD5Wvr6+O/nDMZfzo0WOKiKzjoagAz/pryqPauiVT+774X1KRNH6Ezpwp1pKFqz0YGSoDbZRL2E8//aTVqy/8lzAlJUUhISEuh1VyspIixIWcm/TZbDYSQXilv82ephYxTTVs8MPOsdbXtdCI0YM0ZsQkD0aGyuLtbRSPPmfjrbfeuuD577777nfXSE5O1vjx413GaoVd+4fiwh9z7NhPOnPmjCIia7uM164dpqM//OihqADPmPnM4+pxW6Ju6363ywbQ+ITrVbt2mHZ/+ZFzzNfXV0+nJGvUmMFq3aKTB6IFzPBostG3b9/f/a9dm812wTXsdrvsdnuFvgOzioqK9Omnn6tLYge9+eYG53iXLh309tv/8mBkQOWa9exU9ezdVb173KNDB4+4nHsl7Q1lfPhvl7HX3lilV19+Uy+ufa0yw0QluJxbIO7g0WSjbt26WrBggfr27Xve87t27VJsbGzlBgW3mDNvmVavmqcdOz7Ttk92aPjQe9Wgfj0tWbrG06EBleKZOU/orj/31sABI3XqZL7q1AmXJJ04cVK//OJQ7k8/K/enn12+c6bojH744Uft/ybLAxHDpBIvbyF7NNmIjY3Vp59+WmayQY//8vX3v7+lsNBa+suUh1W3bh3t2fuVeve5T4cO/dfToQGVYujweyRJ/9zwksv46BGT9PKLr3siJMBjbJYH/23+8ccfKz8/X7feeut5z+fn52v79u3q2LFjhdb19a/njvCAKifIv/QDpQBvl3tqv/Fr3NvwTress/bg5ZmoerSy0b59+wueDwwMrHCiAQDApeZyftS4O1zSt74CAIDLH6+YBwDAsMv5GRnuQLIBAIBh3PoKAACMYs8GAACAQVQ2AAAwjD0bAADAKG/fs0EbBQAAGEVlAwAAw7z91RskGwAAGMbdKAAAAAZR2QAAwDBv3yBKsgEAgGHefusrbRQAAGAUlQ0AAAxjgygAADDKsiy3HBX10UcfqXfv3oqKipLNZtMbb7xRKq5p06YpKipKAQEB6tSpk/bu3esyx+FwaOzYsQoPD1dgYKD69OmjI0eOVCgOkg0AAAwrcdNRUfn5+WrdurXmz59/3vOzZs3S7NmzNX/+fGVmZioyMlJdu3bVyZMnnXOSkpK0bt06paWlafPmzTp16pR69eql4uLicsdhs6rgk0Z8/et5OgTgkhTkH+DpEIBLTu6p/cav0b1+D7es86/D7170d202m9atW6e+fftK+rWqERUVpaSkJE2ePFnSr1WMiIgIzZw5UyNGjFBeXp5q166tNWvWqH///pKk77//XvXr19f69evVvXv3cl2bygYAAIZZbvqfO2VlZSknJ0fdunVzjtntdnXs2FFbtmyRJO3YsUNFRUUuc6KiohQTE+OcUx5sEAUAwDB3bRB1OBxyOBwuY3a7XXa7vcJr5eTkSJIiIiJcxiMiInTw4EHnHH9/f9WqVavUnLPfLw8qGwAAXCZSUlIUEhLicqSkpPyhNW02m8tny7JKjZ2rPHN+i2QDAADD3HU3SnJysvLy8lyO5OTki4opMjJSkkpVKI4ePeqsdkRGRqqwsFC5ubllzikPkg0AAAwrkeWWw263Kzg42OW4mBaKJEVHRysyMlLp6enOscLCQmVkZCghIUGSFBsbKz8/P5c52dnZ2rNnj3NOebBnAwCAKurUqVPav/9/d9tkZWVp165dCg0NVYMGDZSUlKTp06erSZMmatKkiaZPn64aNWpo4MCBkqSQkBANHTpUEyZMUFhYmEJDQzVx4kS1bNlSXbp0KXccJBsAABjmqXejbN++XZ07d3Z+Hj9+vCRp0KBBSk1N1aRJk1RQUKDRo0crNzdXcXFx2rhxo4KCgpzfmTNnjnx9fdWvXz8VFBQoMTFRqamp8vHxKXccPGcD8CI8ZwMorTKes9GhXqJb1vnov++7ZZ3Kxp4NAABgFG0UAAAMq3IthAoi2QAAwDBvf+sryQYAAIZ5e7LBng0AAGAUlQ0AAAyrgjd+VgjJBgAAhtFGAQAAMIjKBgAAhnnqCaKXCpINAAAM8/Y9G7RRAACAUVQ2AAAwzNs3iJJsAABgGG0UAAAAg6hsAABgGG0UAABgFLe+AgAAo0rYswEAAGAOlQ0AAAyjjQIAAIyijQIAAGAQlQ0AAAyjjQIAAIyijQIAAGAQlQ0AAAyjjQIAAIyijQIAAGAQlQ0AAAyjjQIAAIyyrBJPh+BRJBsAABjm7a+YZ88GAAAwisoGAACGWV5+NwrJBgAAhtFGAQAAMIjKBgAAhtFGAQAARvEEUQAAAIOobAAAYBhPEAUAAEZ5+54N2igAAMAoKhsAABjm7c/ZINkAAMAwb2+jkGwAAGAYt74CAAAYRGUDAADDaKMAAACjvH2DKG0UAABgFJUNAAAMo40CAACM4m4UAAAAg6hsAABgGC9iAwAARtFGAQAAMIjKBgAAhnE3CgAAMMrb92zQRgEAwDDLstxyXIyFCxcqOjpa1atXV2xsrD7++GM3/7rfR7IBAEAV9corrygpKUlTpkzRzp071b59e/Xo0UOHDh2q1DhsVhVsJPn61/N0CMAlKcg/wNMhAJec3FP7jV/Dz03/Xioq/G+F5sfFxalt27ZatGiRc6xZs2bq27evUlJS3BJTeVDZAADAMMtNR0UUFhZqx44d6tatm8t4t27dtGXLlov+LReDDaIAAFwmHA6HHA6Hy5jdbpfdbi8199ixYyouLlZERITLeEREhHJycozGea4qmWycqWCZCWY4HA6lpKQoOTn5vH8RAG/F3w3v465/L02bNk1PPPGEy9jUqVM1bdq0Mr9js9lcPluWVWrMtCq5ZwOXhhMnTigkJER5eXkKDg72dDjAJYO/G7hYFalsFBYWqkaNGvr73/+uO+64wzk+btw47dq1SxkZGcbjPYs9GwAAXCbsdruCg4NdjrKqY/7+/oqNjVV6errLeHp6uhISEiojXKcq2UYBAADS+PHjdd9996ldu3aKj4/X0qVLdejQIY0cObJS4yDZAACgiurfv7+OHz+uJ598UtnZ2YqJidH69evVsGHDSo2DZAPG2O12TZ06lQ1wwDn4u4HKNHr0aI0ePdqjMbBBFAAAGMUGUQAAYBTJBgAAMIpkAwAAGEWyAQAAjCLZgDELFy5UdHS0qlevrtjYWH388ceeDgnwqI8++ki9e/dWVFSUbDab3njjDU+HBFQKkg0Y8corrygpKUlTpkzRzp071b59e/Xo0UOHDh3ydGiAx+Tn56t169aaP3++p0MBKhW3vsKIuLg4tW3bVosWLXKONWvWTH379lVKSooHIwMuDTabTevWrVPfvn09HQpgHJUNuF1hYaF27Nihbt26uYx369ZNW7Zs8VBUAABPIdmA2x07dkzFxcWKiIhwGY+IiFBOTo6HogIAeArJBoyx2Wwuny3LKjUGAKj6SDbgduHh4fLx8SlVxTh69GipagcAoOoj2YDb+fv7KzY2Vunp6S7j6enpSkhI8FBUAABP4a2vMGL8+PG677771K5dO8XHx2vp0qU6dOiQRo4c6enQAI85deqU9u/f7/yclZWlXbt2KTQ0VA0aNPBgZIBZ3PoKYxYuXKhZs2YpOztbMTExmjNnjjp06ODpsACP+fDDD9W5c+dS44MGDVJqamrlBwRUEpINAABgFHs2AACAUSQbAADAKJINAABgFMkGAAAwimQDAAAYRbIBAACMItkAAABGkWwAVdC0adN03XXXOT8PHjxYffv2rfQ4Dhw4IJvNpl27dlX6tQFcOkg2gEo0ePBg2Ww22Ww2+fn5qVGjRpo4caLy8/ONXnfevHnlfkIlCQIAd+PdKEAlu/XWW7Vq1SoVFRXp448/1rBhw5Sfn69Fixa5zCsqKpKfn59brhkSEuKWdQDgYlDZACqZ3W5XZGSk6tevr4EDB+qee+7RG2+84Wx9rFy5Uo0aNZLdbpdlWcrLy9ODDz6oOnXqKDg4WLfccos+++wzlzVnzJihiIgIBQUFaejQofrll19czp/bRikpKdHMmTN19dVXy263q0GDBvrrX/8qSYqOjpYktWnTRjabTZ06dXJ+b9WqVWrWrJmqV6+ua6+9VgsXLnS5zn/+8x+1adNG1atXV7t27bRz5043/skBuFxR2QA8LCAgQEVFRZKk/fv369VXX9U//vEP+fj4SJJ69uyp0NBQrV+/XiEhIVqyZIkSExP19ddfKzQ0VK+++qqmTp2qBQsWqH379lqzZo2ee+45NWrUqMxrJicna9myZZozZ45uvvlmZWdn68svv5T0a8Jwww036L333lOLFi3k7+8vSVq2bJmmTp2q+fPnq02bNtq5c6eGDx+uwMBADRo0SPn5+erVq5duueUWrV27VllZWRo3bpzhPz0AlwULQKUZNGiQdfvttzs/f/LJJ1ZYWJjVr18/a+rUqZafn5919OhR5/n333/fCg4Otn755ReXdRo3bmwtWbLEsizLio+Pt0aOHOlyPi4uzmrduvV5r3vixAnLbrdby5YtO2+MWVlZliRr586dLuP169e3XnrpJZexp556yoqPj7csy7KWLFlihYaGWvn5+c7zixYtOu9aALwLbRSgkr3zzjuqWbOmqlevrvj4eHXo0EHPP/+8JKlhw4aqXbu2c+6OHTt06tQphYWFqWbNms4jKytL3377rSRp3759io+Pd7nGuZ9/a9++fXI4HEpMTCx3zD/++KMOHz6soUOHusTx9NNPu8TRunVr1ahRo1xxAPAetFGASta5c2ctWrRIfn5+ioqKctkEGhgY6DK3pKREdevW1YcfflhqnSuuuOKirh8QEFDh75SUlEj6tZUSFxfncu5su8eyrIuKB0DVR7IBVLLAwEBdffXV5Zrbtm1b5eTkyNfXV1ddddV55zRr1kzbtm3T/fff7xzbtm1bmWs2adJEAQEBev/99zVs2LBS58/u0SguLnaORUREqF69evruu+90zz33nHfd5s2ba82aNSooKHAmNBeKA4D3oI0CXMK6dOmi+Ph49e3bV//617904MABbdmyRX/5y1+0fft2SdK4ceO0cuVKrVy5Ul9//bWmTp2qvXv3lrlm9erVNXnyZE2aNEkvvPCCvv32W23btk0rVqyQJNWpU0cBAQHasGGDfvjhB+Xl5Un69UFhKSkpmjdvnr7++mvt3r1bq1at0uzZsyVJAwcOVLVq1TR06FB98cUXWr9+vZ555hnDf0IALgckG8AlzGazaf369erQoYOGDBmia665RgMGDNCBAwcUEREhSerfv78ef/xxTZ48WbGxsTp48KBGjRp1wXUfe+wxTZgwQY8//riaNWum/v376+jRo5IkX19fPffcc1qyZImioqJ0++23S5KGDRum5cuXKzU1VS1btlTHjh2VmprqvFW2Zs2aevvtt/XFF1+oTZs2mjJlimbOnGnwTwfA5cJm0WgFAAAGUdkAAABGkWwAAACjSDYAAIBRJBsAAMAokg0AAGAUyQYAADCKZAMAABhFsgEAAIwi2QAAAEaRbAAAAKNINgAAgFEkGwAAwKj/D/Vhyf6At2YuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(model, X_test, y_test):\n",
    "    y_pred = model.predict(X_test)\n",
    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    results = evaluate_binary_classification(y_test, y_pred)\n",
    "    print(f\"Accuracy = {results['accuracy']:.2f} | Precision: {results['precision']:.2f} | Recall = {results['recall']:.2f} | F1_score = {results['f1_score']:.2f}\")\n",
    "\n",
    "    skr = evaluation_skill_metrics(y_test, y_pred)\n",
    "    print(f\" POD = {skr['POD']:.2f} | F: {skr['F']:.2f} | FAR = {skr['FAR']:.2f} | CSI = {skr['CSI']:.2f} | HSS = {skr['HSS']:.2f} | PSS = {skr['PSS']:.2f}\")\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='g')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.show()\n",
    "\n",
    "# For Over-Sampled data\n",
    "plot_confusion_matrix(rf_ros, scaled_cls_xtest, cls_ytest)\n",
    "\n",
    "# For SMOTE data\n",
    "plot_confusion_matrix(rf_smote, scaled_cls_xtest, cls_ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \n",
    "<figure>\n",
    "    <p align=\"center\">\n",
    "        <img src=\"https://files.oaiusercontent.com/file-3yzQp4CtW2ZC2gI1BWPWUYi0?se=2024-01-26T14%3A47%3A51Z&sp=r&sv=2021-08-06&sr=b&rscc=max-age%3D31536000%2C%20immutable&rscd=attachment%3B%20filename%3De8d408f1-18ca-4dd6-89ab-d3659dca0187.webp&sig=atbM8qwxb5bhRzzx76FWtF1eGFqFAVgwkY31QQ2/h30%3D\" alt=\"Alt text for the image\" width=\"500px\" />\n",
    "    </p>\n",
    "</figure>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_probs_smote = rf_smote.predict_proba(scaled_cls_xtest)[:, 1]\n",
    "reliability_diagram(cls_ytest, y_test_probs_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IV. Hyper-parameter Tunning and Model Selection\n",
    "\n",
    "There are several solution: \n",
    "- Manual search\n",
    "- Grid search\n",
    "- Tools: \n",
    "    - Scikit-Optimize\n",
    "    - Keras-Tunner\n",
    "    - Optuna\n",
    "    - Hyperopt\n",
    "    - Ray.tune\n",
    "    - Talos \n",
    "    - BaysianOptimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_rf(trial):\n",
    "    n_estimators = trial.suggest_int(\"n_estimators\", 10, 100)\n",
    "    max_depth = trial.suggest_int(\"max_depth\", 2, 32, log=True)\n",
    "\n",
    "    clf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42)\n",
    "    return np.mean(cross_val_score(clf, scaled_cls_xtrain, cls_ytrain, cv=3, n_jobs=-1))\n",
    "\n",
    "def objective_mlp(trial):\n",
    "    hidden_layer_sizes = trial.suggest_categorical(\"hidden_layer_sizes\", [(50,), (100,), (50, 50), (100, 50)])\n",
    "    learning_rate_init = trial.suggest_float(\"learning_rate_init\", 1e-4, 1e-2, log=True)\n",
    "\n",
    "    clf = MLPClassifier(hidden_layer_sizes=hidden_layer_sizes, learning_rate_init=learning_rate_init, random_state=42)\n",
    "    return np.mean(cross_val_score(clf, scaled_cls_xtrain, cls_ytrain, cv=3, n_jobs=-1))\n",
    "\n",
    "def objective_lr(trial):\n",
    "    C = trial.suggest_float(\"C\", 1e-4, 1e4, log=True)\n",
    "\n",
    "    clf = LogisticRegression(C=C, random_state=42)\n",
    "    return np.mean(cross_val_score(clf, scaled_cls_xtrain, cls_ytrain, cv=3, n_jobs=-1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-01-26 09:40:26,257] A new study created in memory with name: no-name-4869aa82-c51f-40c3-9127-d3220bbddba8\n",
      "[I 2024-01-26 09:40:26,518] Trial 0 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 49, 'max_depth': 16}. Best is trial 0 with value: 0.9681782020684168.\n",
      "[I 2024-01-26 09:40:26,783] Trial 1 finished with value: 0.9665871121718377 and parameters: {'n_estimators': 75, 'max_depth': 12}. Best is trial 0 with value: 0.9681782020684168.\n",
      "[I 2024-01-26 09:40:27,000] Trial 2 finished with value: 0.968178202068417 and parameters: {'n_estimators': 89, 'max_depth': 10}. Best is trial 2 with value: 0.968178202068417.\n",
      "[I 2024-01-26 09:40:27,099] Trial 3 finished with value: 0.9649960222752586 and parameters: {'n_estimators': 22, 'max_depth': 11}. Best is trial 2 with value: 0.968178202068417.\n",
      "[I 2024-01-26 09:40:27,425] Trial 4 finished with value: 0.9665871121718377 and parameters: {'n_estimators': 92, 'max_depth': 28}. Best is trial 2 with value: 0.968178202068417.\n",
      "[I 2024-01-26 09:40:27,640] Trial 5 finished with value: 0.9661893396976929 and parameters: {'n_estimators': 75, 'max_depth': 19}. Best is trial 2 with value: 0.968178202068417.\n",
      "[I 2024-01-26 09:40:27,862] Trial 6 finished with value: 0.9657915672235481 and parameters: {'n_estimators': 93, 'max_depth': 5}. Best is trial 2 with value: 0.968178202068417.\n",
      "[I 2024-01-26 09:40:28,134] Trial 7 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 81, 'max_depth': 12}. Best is trial 2 with value: 0.968178202068417.\n",
      "[I 2024-01-26 09:40:28,389] Trial 8 finished with value: 0.967780429594272 and parameters: {'n_estimators': 53, 'max_depth': 14}. Best is trial 2 with value: 0.968178202068417.\n",
      "[I 2024-01-26 09:40:28,454] Trial 9 finished with value: 0.9431185361972951 and parameters: {'n_estimators': 22, 'max_depth': 2}. Best is trial 2 with value: 0.968178202068417.\n",
      "[I 2024-01-26 09:40:28,642] Trial 10 finished with value: 0.9649960222752586 and parameters: {'n_estimators': 40, 'max_depth': 5}. Best is trial 2 with value: 0.968178202068417.\n",
      "[I 2024-01-26 09:40:28,867] Trial 11 finished with value: 0.9693715194908511 and parameters: {'n_estimators': 56, 'max_depth': 7}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:29,107] Trial 12 finished with value: 0.9689737470167065 and parameters: {'n_estimators': 65, 'max_depth': 7}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:29,322] Trial 13 finished with value: 0.9498806682577565 and parameters: {'n_estimators': 68, 'max_depth': 3}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:29,491] Trial 14 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 38, 'max_depth': 6}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:29,683] Trial 15 finished with value: 0.9502784407319013 and parameters: {'n_estimators': 63, 'max_depth': 3}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:29,917] Trial 16 finished with value: 0.967780429594272 and parameters: {'n_estimators': 61, 'max_depth': 8}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:30,041] Trial 17 finished with value: 0.9590294351630867 and parameters: {'n_estimators': 39, 'max_depth': 4}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:30,228] Trial 18 finished with value: 0.9685759745425616 and parameters: {'n_estimators': 48, 'max_depth': 8}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:30,300] Trial 19 finished with value: 0.9661893396976929 and parameters: {'n_estimators': 12, 'max_depth': 23}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:30,427] Trial 20 finished with value: 0.9431185361972951 and parameters: {'n_estimators': 61, 'max_depth': 2}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:30,639] Trial 21 finished with value: 0.9689737470167064 and parameters: {'n_estimators': 47, 'max_depth': 8}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:30,794] Trial 22 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 31, 'max_depth': 7}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:31,038] Trial 23 finished with value: 0.9689737470167065 and parameters: {'n_estimators': 54, 'max_depth': 9}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:31,236] Trial 24 finished with value: 0.9606205250596659 and parameters: {'n_estimators': 56, 'max_depth': 4}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:31,498] Trial 25 finished with value: 0.9669848846459824 and parameters: {'n_estimators': 69, 'max_depth': 6}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:31,786] Trial 26 finished with value: 0.968178202068417 and parameters: {'n_estimators': 82, 'max_depth': 9}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:32,018] Trial 27 finished with value: 0.967780429594272 and parameters: {'n_estimators': 55, 'max_depth': 6}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:32,245] Trial 28 finished with value: 0.9598249801113763 and parameters: {'n_estimators': 69, 'max_depth': 4}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:32,450] Trial 29 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 44, 'max_depth': 16}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:32,601] Trial 30 finished with value: 0.9482895783611774 and parameters: {'n_estimators': 31, 'max_depth': 3}. Best is trial 11 with value: 0.9693715194908511.\n",
      "[I 2024-01-26 09:40:32,826] Trial 31 finished with value: 0.9693715194908513 and parameters: {'n_estimators': 50, 'max_depth': 9}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:33,044] Trial 32 finished with value: 0.967780429594272 and parameters: {'n_estimators': 53, 'max_depth': 14}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:33,306] Trial 33 finished with value: 0.9685759745425617 and parameters: {'n_estimators': 64, 'max_depth': 9}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:33,536] Trial 34 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 58, 'max_depth': 10}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:33,792] Trial 35 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 75, 'max_depth': 7}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:33,997] Trial 36 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 50, 'max_depth': 11}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:34,273] Trial 37 finished with value: 0.9645982498011137 and parameters: {'n_estimators': 82, 'max_depth': 5}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:34,483] Trial 38 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 45, 'max_depth': 15}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:34,652] Trial 39 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 34, 'max_depth': 12}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:34,931] Trial 40 finished with value: 0.9661893396976929 and parameters: {'n_estimators': 76, 'max_depth': 20}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:35,153] Trial 41 finished with value: 0.9685759745425616 and parameters: {'n_estimators': 49, 'max_depth': 8}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:35,496] Trial 42 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 100, 'max_depth': 10}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:35,747] Trial 43 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 59, 'max_depth': 7}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:35,927] Trial 44 finished with value: 0.967780429594272 and parameters: {'n_estimators': 43, 'max_depth': 6}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:36,168] Trial 45 finished with value: 0.9657915672235481 and parameters: {'n_estimators': 66, 'max_depth': 5}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:36,400] Trial 46 finished with value: 0.9677804295942721 and parameters: {'n_estimators': 51, 'max_depth': 12}. Best is trial 31 with value: 0.9693715194908513.\n",
      "[I 2024-01-26 09:40:36,622] Trial 47 finished with value: 0.9697692919649961 and parameters: {'n_estimators': 47, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:36,866] Trial 48 finished with value: 0.9693715194908513 and parameters: {'n_estimators': 56, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:37,129] Trial 49 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 72, 'max_depth': 17}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:37,312] Trial 50 finished with value: 0.9685759745425616 and parameters: {'n_estimators': 35, 'max_depth': 11}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:37,557] Trial 51 finished with value: 0.9689737470167065 and parameters: {'n_estimators': 59, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:37,790] Trial 52 finished with value: 0.9693715194908513 and parameters: {'n_estimators': 55, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:37,980] Trial 53 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 41, 'max_depth': 13}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:38,220] Trial 54 finished with value: 0.9693715194908513 and parameters: {'n_estimators': 63, 'max_depth': 7}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:38,452] Trial 55 finished with value: 0.9689737470167064 and parameters: {'n_estimators': 56, 'max_depth': 8}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:38,663] Trial 56 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 52, 'max_depth': 10}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:38,915] Trial 57 finished with value: 0.967780429594272 and parameters: {'n_estimators': 62, 'max_depth': 6}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:39,113] Trial 58 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 45, 'max_depth': 29}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:39,315] Trial 59 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 48, 'max_depth': 7}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:39,465] Trial 60 finished with value: 0.9661893396976929 and parameters: {'n_estimators': 25, 'max_depth': 8}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:39,707] Trial 61 finished with value: 0.9661893396976929 and parameters: {'n_estimators': 64, 'max_depth': 10}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:39,934] Trial 62 finished with value: 0.9685759745425616 and parameters: {'n_estimators': 60, 'max_depth': 7}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:40,210] Trial 63 finished with value: 0.967780429594272 and parameters: {'n_estimators': 71, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:40,434] Trial 64 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 56, 'max_depth': 6}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:40,707] Trial 65 finished with value: 0.9669848846459824 and parameters: {'n_estimators': 66, 'max_depth': 13}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:40,993] Trial 66 finished with value: 0.9645982498011137 and parameters: {'n_estimators': 78, 'max_depth': 5}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:41,216] Trial 67 finished with value: 0.9689737470167065 and parameters: {'n_estimators': 51, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:41,443] Trial 68 finished with value: 0.9665871121718377 and parameters: {'n_estimators': 57, 'max_depth': 11}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:41,670] Trial 69 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 54, 'max_depth': 8}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:41,898] Trial 70 finished with value: 0.9685759745425616 and parameters: {'n_estimators': 62, 'max_depth': 7}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:42,134] Trial 71 finished with value: 0.9689737470167065 and parameters: {'n_estimators': 54, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:42,305] Trial 72 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 46, 'max_depth': 11}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:42,573] Trial 73 finished with value: 0.9665871121718377 and parameters: {'n_estimators': 66, 'max_depth': 10}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:42,776] Trial 74 finished with value: 0.9685759745425616 and parameters: {'n_estimators': 48, 'max_depth': 8}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:43,020] Trial 75 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 59, 'max_depth': 6}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:43,188] Trial 76 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 41, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:43,434] Trial 77 finished with value: 0.9689737470167064 and parameters: {'n_estimators': 53, 'max_depth': 7}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:43,697] Trial 78 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 72, 'max_depth': 13}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:43,881] Trial 79 finished with value: 0.9665871121718377 and parameters: {'n_estimators': 37, 'max_depth': 10}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:44,127] Trial 80 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 64, 'max_depth': 8}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:44,354] Trial 81 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 57, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:44,591] Trial 82 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 60, 'max_depth': 12}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:44,848] Trial 83 finished with value: 0.9673826571201273 and parameters: {'n_estimators': 67, 'max_depth': 11}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:45,063] Trial 84 finished with value: 0.9693715194908513 and parameters: {'n_estimators': 50, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:45,277] Trial 85 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 50, 'max_depth': 8}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:45,481] Trial 86 finished with value: 0.967780429594272 and parameters: {'n_estimators': 43, 'max_depth': 6}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:45,717] Trial 87 finished with value: 0.9693715194908513 and parameters: {'n_estimators': 55, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:45,913] Trial 88 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 51, 'max_depth': 7}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:46,097] Trial 89 finished with value: 0.964200477326969 and parameters: {'n_estimators': 47, 'max_depth': 5}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:46,312] Trial 90 finished with value: 0.9685759745425617 and parameters: {'n_estimators': 55, 'max_depth': 10}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:46,526] Trial 91 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 52, 'max_depth': 8}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:46,763] Trial 92 finished with value: 0.9685759745425617 and parameters: {'n_estimators': 62, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:47,020] Trial 93 finished with value: 0.9669848846459824 and parameters: {'n_estimators': 58, 'max_depth': 26}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:47,233] Trial 94 finished with value: 0.967780429594272 and parameters: {'n_estimators': 49, 'max_depth': 12}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:47,468] Trial 95 finished with value: 0.967780429594272 and parameters: {'n_estimators': 54, 'max_depth': 14}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:47,729] Trial 96 finished with value: 0.9661893396976929 and parameters: {'n_estimators': 70, 'max_depth': 10}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:47,952] Trial 97 finished with value: 0.9681782020684168 and parameters: {'n_estimators': 56, 'max_depth': 11}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:48,145] Trial 98 finished with value: 0.9669848846459824 and parameters: {'n_estimators': 43, 'max_depth': 8}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:48,411] Trial 99 finished with value: 0.9685759745425617 and parameters: {'n_estimators': 64, 'max_depth': 9}. Best is trial 47 with value: 0.9697692919649961.\n",
      "[I 2024-01-26 09:40:48,412] A new study created in memory with name: no-name-0ab7087b-b1d7-4852-9b0b-46302f588fd3\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:40:49,877] Trial 0 finished with value: 0.9554494828957836 and parameters: {'hidden_layer_sizes': (50,), 'learning_rate_init': 0.005569212078755028}. Best is trial 0 with value: 0.9554494828957836.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:40:52,248] Trial 1 finished with value: 0.9514717581543358 and parameters: {'hidden_layer_sizes': (100,), 'learning_rate_init': 0.00032709029176805626}. Best is trial 0 with value: 0.9554494828957836.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:40:53,716] Trial 2 finished with value: 0.9550517104216388 and parameters: {'hidden_layer_sizes': (50,), 'learning_rate_init': 0.001644935564524318}. Best is trial 0 with value: 0.9554494828957836.\n",
      "[I 2024-01-26 09:40:55,622] Trial 3 finished with value: 0.9550517104216388 and parameters: {'hidden_layer_sizes': (100,), 'learning_rate_init': 0.00630461694825219}. Best is trial 0 with value: 0.9554494828957836.\n",
      "[I 2024-01-26 09:40:58,408] Trial 4 finished with value: 0.9534606205250596 and parameters: {'hidden_layer_sizes': (50, 50), 'learning_rate_init': 0.006313742775374513}. Best is trial 0 with value: 0.9554494828957836.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:40:59,934] Trial 5 finished with value: 0.956642800318218 and parameters: {'hidden_layer_sizes': (50,), 'learning_rate_init': 0.0024891499071455817}. Best is trial 5 with value: 0.956642800318218.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:41:02,388] Trial 6 finished with value: 0.9431185361972951 and parameters: {'hidden_layer_sizes': (100,), 'learning_rate_init': 0.0001538714228547223}. Best is trial 5 with value: 0.956642800318218.\n",
      "[I 2024-01-26 09:41:04,656] Trial 7 finished with value: 0.9570405727923629 and parameters: {'hidden_layer_sizes': (100,), 'learning_rate_init': 0.005581566801685862}. Best is trial 7 with value: 0.9570405727923629.\n",
      "[I 2024-01-26 09:41:06,765] Trial 8 finished with value: 0.9574383452665076 and parameters: {'hidden_layer_sizes': (100,), 'learning_rate_init': 0.005084649325504859}. Best is trial 8 with value: 0.9574383452665076.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:41:09,643] Trial 9 finished with value: 0.958631662688942 and parameters: {'hidden_layer_sizes': (50, 50), 'learning_rate_init': 0.0008414911616010238}. Best is trial 9 with value: 0.958631662688942.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:41:12,606] Trial 10 finished with value: 0.9574383452665076 and parameters: {'hidden_layer_sizes': (50, 50), 'learning_rate_init': 0.000574268428861}. Best is trial 9 with value: 0.958631662688942.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:41:17,463] Trial 11 finished with value: 0.9598249801113763 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.000967119847734019}. Best is trial 11 with value: 0.9598249801113763.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:41:22,493] Trial 12 finished with value: 0.960222752585521 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0007863660510098703}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:41:27,547] Trial 13 finished with value: 0.9566428003182179 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.00037686538783263183}. Best is trial 12 with value: 0.960222752585521.\n",
      "[I 2024-01-26 09:41:31,868] Trial 14 finished with value: 0.9590294351630867 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0015225648420773325}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:41:36,827] Trial 15 finished with value: 0.9502784407319013 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.00014029697369503135}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:41:42,259] Trial 16 finished with value: 0.960222752585521 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0007652058006992081}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:41:47,254] Trial 17 finished with value: 0.9522673031026253 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0002646333520444003}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:41:52,427] Trial 18 finished with value: 0.9582338902147972 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0006610437247370387}. Best is trial 12 with value: 0.960222752585521.\n",
      "[I 2024-01-26 09:41:55,152] Trial 19 finished with value: 0.9534606205250596 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0030186287961685983}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:00,220] Trial 20 finished with value: 0.9594272076372315 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.001519639137933539}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:05,164] Trial 21 finished with value: 0.9574383452665076 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0010303368090328162}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:10,225] Trial 22 finished with value: 0.9558472553699283 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0005069892663858892}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:15,191] Trial 23 finished with value: 0.9578361177406522 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.000994755744325826}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:20,307] Trial 24 finished with value: 0.9518695306284805 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.00024259078133068637}. Best is trial 12 with value: 0.960222752585521.\n",
      "[I 2024-01-26 09:42:22,881] Trial 25 finished with value: 0.9538583929992045 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0022326056024277716}. Best is trial 12 with value: 0.960222752585521.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:28,086] Trial 26 finished with value: 0.9618138424821003 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0007786022157771006}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:33,121] Trial 27 finished with value: 0.9570405727923627 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0004284983720463582}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:36,099] Trial 28 finished with value: 0.9578361177406522 and parameters: {'hidden_layer_sizes': (50, 50), 'learning_rate_init': 0.0008159669078521858}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:37,575] Trial 29 finished with value: 0.9518695306284805 and parameters: {'hidden_layer_sizes': (50,), 'learning_rate_init': 0.0012603047810593251}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:39,090] Trial 30 finished with value: 0.9494828957836118 and parameters: {'hidden_layer_sizes': (50,), 'learning_rate_init': 0.0006853184719794912}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:44,121] Trial 31 finished with value: 0.9582338902147972 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0011822527760371575}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:49,207] Trial 32 finished with value: 0.960222752585521 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0007657123254043589}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:54,095] Trial 33 finished with value: 0.9566428003182179 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0004386765286384279}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:42:59,001] Trial 34 finished with value: 0.954653937947494 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0003128404655291309}. Best is trial 26 with value: 0.9618138424821003.\n",
      "[I 2024-01-26 09:43:03,983] Trial 35 finished with value: 0.9574383452665076 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.001935724826003808}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:43:05,525] Trial 36 finished with value: 0.9490851233094669 and parameters: {'hidden_layer_sizes': (50,), 'learning_rate_init': 0.0006536121007589927}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:43:10,394] Trial 37 finished with value: 0.9526650755767702 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.00021421740592889244}. Best is trial 26 with value: 0.9618138424821003.\n",
      "[I 2024-01-26 09:43:12,079] Trial 38 finished with value: 0.9514717581543358 and parameters: {'hidden_layer_sizes': (100,), 'learning_rate_init': 0.009565199816841414}. Best is trial 26 with value: 0.9618138424821003.\n",
      "[I 2024-01-26 09:43:14,978] Trial 39 finished with value: 0.9582338902147972 and parameters: {'hidden_layer_sizes': (50, 50), 'learning_rate_init': 0.002808554099516275}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:43:17,463] Trial 40 finished with value: 0.9506762132060462 and parameters: {'hidden_layer_sizes': (100,), 'learning_rate_init': 0.0005467753677624048}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:43:22,346] Trial 41 finished with value: 0.9598249801113763 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0008190551881149974}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:43:27,337] Trial 42 finished with value: 0.9594272076372315 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0009841817212749345}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:43:32,259] Trial 43 finished with value: 0.958631662688942 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0012269878280749833}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:43:37,278] Trial 44 finished with value: 0.9590294351630867 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0007436616987047546}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:43:42,135] Trial 45 finished with value: 0.9554494828957836 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0005135574433646532}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:43:43,709] Trial 46 finished with value: 0.9538583929992045 and parameters: {'hidden_layer_sizes': (50,), 'learning_rate_init': 0.0014951178951328747}. Best is trial 26 with value: 0.9618138424821003.\n",
      "[I 2024-01-26 09:43:45,341] Trial 47 finished with value: 0.9550517104216388 and parameters: {'hidden_layer_sizes': (50, 50), 'learning_rate_init': 0.003661172914612496}. Best is trial 26 with value: 0.9618138424821003.\n",
      "[I 2024-01-26 09:43:49,725] Trial 48 finished with value: 0.9554494828957836 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0018840333631623802}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:43:52,159] Trial 49 finished with value: 0.9506762132060462 and parameters: {'hidden_layer_sizes': (100,), 'learning_rate_init': 0.0003777757684400049}. Best is trial 26 with value: 0.9618138424821003.\n",
      "[I 2024-01-26 09:43:56,310] Trial 50 finished with value: 0.9594272076372315 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0009030477917883693}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:01,241] Trial 51 finished with value: 0.9550517104216388 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0006058290364905736}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:06,321] Trial 52 finished with value: 0.9610182975338106 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0007998226423399591}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:11,228] Trial 53 finished with value: 0.9614160700079554 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0013202323473395351}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:16,177] Trial 54 finished with value: 0.9578361177406522 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0011896999152429461}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:21,368] Trial 55 finished with value: 0.9606205250596659 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0007656765610156907}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:26,480] Trial 56 finished with value: 0.9590294351630867 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0013513126203381493}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:31,405] Trial 57 finished with value: 0.9590294351630867 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0010652128786027015}. Best is trial 26 with value: 0.9618138424821003.\n",
      "[I 2024-01-26 09:44:35,748] Trial 58 finished with value: 0.9558472553699283 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.001844914248213698}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:38,845] Trial 59 finished with value: 0.9570405727923627 and parameters: {'hidden_layer_sizes': (50, 50), 'learning_rate_init': 0.0004875181032617427}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:43,856] Trial 60 finished with value: 0.9558472553699285 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0006062787457249043}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:48,786] Trial 61 finished with value: 0.9586316626889418 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0007599196001802677}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:44:53,631] Trial 62 finished with value: 0.9618138424821003 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0008239309971074154}. Best is trial 26 with value: 0.9618138424821003.\n",
      "[I 2024-01-26 09:44:58,480] Trial 63 finished with value: 0.9594272076372315 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0009062035566697244}. Best is trial 26 with value: 0.9618138424821003.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:45:03,526] Trial 64 finished with value: 0.9598249801113763 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0011062971486157404}. Best is trial 26 with value: 0.9618138424821003.\n",
      "[I 2024-01-26 09:45:08,252] Trial 65 finished with value: 0.962211614956245 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0013662281605384516}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:45:12,961] Trial 66 finished with value: 0.9618138424821002 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0014123173527917402}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:45:17,660] Trial 67 finished with value: 0.9594272076372315 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0016711329795345948}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:45:20,196] Trial 68 finished with value: 0.954653937947494 and parameters: {'hidden_layer_sizes': (100,), 'learning_rate_init': 0.0022192990967740455}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:45:21,702] Trial 69 finished with value: 0.9530628480509149 and parameters: {'hidden_layer_sizes': (50,), 'learning_rate_init': 0.0013936871555354667}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:45:26,605] Trial 70 finished with value: 0.9618138424821003 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0016559963316810946}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:45:30,976] Trial 71 finished with value: 0.9554494828957836 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.001632815533071547}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:45:33,711] Trial 72 finished with value: 0.9570405727923627 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.002318180531276558}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:45:38,547] Trial 73 finished with value: 0.9514717581543358 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0001044836965793153}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:45:43,390] Trial 74 finished with value: 0.9594272076372315 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0009000747297320536}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:45:47,520] Trial 75 finished with value: 0.9534606205250596 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.002661324436921854}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:45:52,325] Trial 76 finished with value: 0.9594272076372315 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0013565623871400036}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:45:57,229] Trial 77 finished with value: 0.9594272076372315 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0010844298184844766}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:02,142] Trial 78 finished with value: 0.9582338902147972 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0006669842181272458}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:07,118] Trial 79 finished with value: 0.9614160700079554 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0016864131594288051}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:46:09,221] Trial 80 finished with value: 0.9522673031026253 and parameters: {'hidden_layer_sizes': (50, 50), 'learning_rate_init': 0.002075942703969729}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:46:11,854] Trial 81 finished with value: 0.9526650755767702 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.003487378644222549}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:16,853] Trial 82 finished with value: 0.9578361177406522 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0016843157130282886}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:21,801] Trial 83 finished with value: 0.9582338902147972 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0015229135176601362}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:26,841] Trial 84 finished with value: 0.9558472553699283 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.001311220953441737}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:46:31,446] Trial 85 finished with value: 0.9574383452665076 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0009943639716184218}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:32,962] Trial 86 finished with value: 0.9510739856801909 and parameters: {'hidden_layer_sizes': (50,), 'learning_rate_init': 0.0011305641306455499}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:37,799] Trial 87 finished with value: 0.9614160700079554 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0008697060083048835}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:42,830] Trial 88 finished with value: 0.9598249801113763 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0008697946642483008}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:45,279] Trial 89 finished with value: 0.956642800318218 and parameters: {'hidden_layer_sizes': (100,), 'learning_rate_init': 0.0019451407225978168}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:50,231] Trial 90 finished with value: 0.9570405727923627 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0015056647339858503}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:46:55,232] Trial 91 finished with value: 0.9582338902147972 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0006753344908789291}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:46:59,856] Trial 92 finished with value: 0.9586316626889418 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0012080922493250955}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:47:04,763] Trial 93 finished with value: 0.9614160700079556 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0008117090465419546}. Best is trial 65 with value: 0.962211614956245.\n",
      "[I 2024-01-26 09:47:09,370] Trial 94 finished with value: 0.9594272076372315 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0009671314025198786}. Best is trial 65 with value: 0.962211614956245.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:47:14,218] Trial 95 finished with value: 0.9630071599045347 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0008271961894392654}. Best is trial 95 with value: 0.9630071599045347.\n",
      "[I 2024-01-26 09:47:18,580] Trial 96 finished with value: 0.9562450278440732 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0017476660677591186}. Best is trial 95 with value: 0.9630071599045347.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:47:23,444] Trial 97 finished with value: 0.9570405727923627 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0005499515115157212}. Best is trial 95 with value: 0.9630071599045347.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "[I 2024-01-26 09:47:28,400] Trial 98 finished with value: 0.9594272076372315 and parameters: {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0007325249908035245}. Best is trial 95 with value: 0.9630071599045347.\n",
      "[I 2024-01-26 09:47:31,027] Trial 99 finished with value: 0.9610182975338106 and parameters: {'hidden_layer_sizes': (50, 50), 'learning_rate_init': 0.0014065237597957238}. Best is trial 95 with value: 0.9630071599045347.\n",
      "[I 2024-01-26 09:47:31,028] A new study created in memory with name: no-name-d5efb32f-8650-4157-a4a3-a1c3f495bfcd\n",
      "[I 2024-01-26 09:47:31,092] Trial 0 finished with value: 0.9530628480509149 and parameters: {'C': 1.322230979367869}. Best is trial 0 with value: 0.9530628480509149.\n",
      "[I 2024-01-26 09:47:31,175] Trial 1 finished with value: 0.9534606205250596 and parameters: {'C': 2.2364249303969226}. Best is trial 1 with value: 0.9534606205250596.\n",
      "[I 2024-01-26 09:47:31,229] Trial 2 finished with value: 0.9486873508353222 and parameters: {'C': 0.5768678840721839}. Best is trial 1 with value: 0.9534606205250596.\n",
      "[I 2024-01-26 09:47:31,325] Trial 3 finished with value: 0.9542561654733492 and parameters: {'C': 4.3412574041042795}. Best is trial 3 with value: 0.9542561654733492.\n",
      "[I 2024-01-26 09:47:31,359] Trial 4 finished with value: 0.9431185361972951 and parameters: {'C': 0.0005677795686901535}. Best is trial 3 with value: 0.9542561654733492.\n",
      "[I 2024-01-26 09:47:31,394] Trial 5 finished with value: 0.9431185361972951 and parameters: {'C': 0.0003053529280755008}. Best is trial 3 with value: 0.9542561654733492.\n",
      "[I 2024-01-26 09:47:31,433] Trial 6 finished with value: 0.9431185361972951 and parameters: {'C': 0.00016201279866286754}. Best is trial 3 with value: 0.9542561654733492.\n",
      "[I 2024-01-26 09:47:31,471] Trial 7 finished with value: 0.9431185361972951 and parameters: {'C': 0.0009214857524843075}. Best is trial 3 with value: 0.9542561654733492.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:31,566] Trial 8 finished with value: 0.9630071599045347 and parameters: {'C': 88.89333137607679}. Best is trial 8 with value: 0.9630071599045347.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:31,681] Trial 9 finished with value: 0.9630071599045346 and parameters: {'C': 135.65861619192364}. Best is trial 8 with value: 0.9630071599045347.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:31,811] Trial 10 finished with value: 0.9657915672235481 and parameters: {'C': 1728.1356460265151}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:31,943] Trial 11 finished with value: 0.9645982498011137 and parameters: {'C': 6929.554205932919}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:32,054] Trial 12 finished with value: 0.9645982498011137 and parameters: {'C': 6000.095526131067}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:32,176] Trial 13 finished with value: 0.964200477326969 and parameters: {'C': 9534.412464007419}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:32,305] Trial 14 finished with value: 0.9634049323786794 and parameters: {'C': 391.35682565166627}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:32,438] Trial 15 finished with value: 0.9653937947494033 and parameters: {'C': 1042.7333633336132}. Best is trial 10 with value: 0.9657915672235481.\n",
      "[I 2024-01-26 09:47:32,476] Trial 16 finished with value: 0.9431185361972951 and parameters: {'C': 0.03644802743318935}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:32,566] Trial 17 finished with value: 0.9645982498011137 and parameters: {'C': 920.1059246629242}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:32,684] Trial 18 finished with value: 0.9590294351630867 and parameters: {'C': 15.693804235126454}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:32,804] Trial 19 finished with value: 0.9614160700079554 and parameters: {'C': 27.54949936184673}. Best is trial 10 with value: 0.9657915672235481.\n",
      "[I 2024-01-26 09:47:32,842] Trial 20 finished with value: 0.9431185361972951 and parameters: {'C': 0.08820727168939486}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:32,951] Trial 21 finished with value: 0.9649960222752586 and parameters: {'C': 1419.1804123947334}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:33,040] Trial 22 finished with value: 0.9645982498011137 and parameters: {'C': 1045.9304514322382}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:33,148] Trial 23 finished with value: 0.964200477326969 and parameters: {'C': 1698.3724190454911}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:33,226] Trial 24 finished with value: 0.962211614956245 and parameters: {'C': 93.14743188269537}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:33,345] Trial 25 finished with value: 0.9626093874303899 and parameters: {'C': 293.00029606305367}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:33,424] Trial 26 finished with value: 0.9638027048528243 and parameters: {'C': 2604.364942280406}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:33,541] Trial 27 finished with value: 0.9610182975338106 and parameters: {'C': 20.11824545997621}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:33,644] Trial 28 finished with value: 0.964200477326969 and parameters: {'C': 439.26198221802366}. Best is trial 10 with value: 0.9657915672235481.\n",
      "[I 2024-01-26 09:47:33,704] Trial 29 finished with value: 0.9466984884645983 and parameters: {'C': 0.35659703998693976}. Best is trial 10 with value: 0.9657915672235481.\n",
      "[I 2024-01-26 09:47:33,733] Trial 30 finished with value: 0.9431185361972951 and parameters: {'C': 0.004125502091954726}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:33,852] Trial 31 finished with value: 0.9653937947494033 and parameters: {'C': 4016.7330182299324}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:33,961] Trial 32 finished with value: 0.964200477326969 and parameters: {'C': 2247.6388790627857}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:34,072] Trial 33 finished with value: 0.9645982498011137 and parameters: {'C': 2523.5679690344746}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:34,183] Trial 34 finished with value: 0.9638027048528243 and parameters: {'C': 496.25386719890577}. Best is trial 10 with value: 0.9657915672235481.\n",
      "[I 2024-01-26 09:47:34,313] Trial 35 finished with value: 0.954653937947494 and parameters: {'C': 3.782992752644213}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:34,394] Trial 36 finished with value: 0.9630071599045346 and parameters: {'C': 45.03183885847963}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:34,495] Trial 37 finished with value: 0.962211614956245 and parameters: {'C': 256.2637826527166}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:34,623] Trial 38 finished with value: 0.9645982498011137 and parameters: {'C': 3926.5551403244663}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:34,735] Trial 39 finished with value: 0.9649960222752586 and parameters: {'C': 886.5336850420342}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:34,854] Trial 40 finished with value: 0.9630071599045346 and parameters: {'C': 122.81531484873483}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:34,973] Trial 41 finished with value: 0.9634049323786794 and parameters: {'C': 1053.2889752880483}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:35,091] Trial 42 finished with value: 0.9574383452665076 and parameters: {'C': 8.027337970562836}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:35,221] Trial 43 finished with value: 0.9653937947494033 and parameters: {'C': 4583.94281184982}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:35,343] Trial 44 finished with value: 0.9649960222752586 and parameters: {'C': 8100.493543501699}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:35,462] Trial 45 finished with value: 0.9638027048528243 and parameters: {'C': 3878.8853037971317}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:35,574] Trial 46 finished with value: 0.9634049323786794 and parameters: {'C': 7994.8501928715295}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:35,701] Trial 47 finished with value: 0.9626093874303899 and parameters: {'C': 203.65657757612556}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:35,835] Trial 48 finished with value: 0.9657915672235481 and parameters: {'C': 1453.6458073794058}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:35,957] Trial 49 finished with value: 0.9638027048528243 and parameters: {'C': 629.3356289543615}. Best is trial 10 with value: 0.9657915672235481.\n",
      "[I 2024-01-26 09:47:36,050] Trial 50 finished with value: 0.9514717581543358 and parameters: {'C': 1.1053203664780997}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:36,180] Trial 51 finished with value: 0.9638027048528243 and parameters: {'C': 4195.696315721467}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:36,334] Trial 52 finished with value: 0.9638027048528243 and parameters: {'C': 1402.5198961141532}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:36,446] Trial 53 finished with value: 0.9630071599045346 and parameters: {'C': 44.390154368777374}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:36,585] Trial 54 finished with value: 0.9645982498011137 and parameters: {'C': 1464.1590926218516}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:36,714] Trial 55 finished with value: 0.9645982498011137 and parameters: {'C': 9441.895764174627}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:36,808] Trial 56 finished with value: 0.9657915672235481 and parameters: {'C': 3490.6027375252556}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:36,932] Trial 57 finished with value: 0.9653937947494033 and parameters: {'C': 3901.6991142461648}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:37,073] Trial 58 finished with value: 0.9638027048528243 and parameters: {'C': 530.636014769334}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:37,207] Trial 59 finished with value: 0.9626093874303899 and parameters: {'C': 160.06278932610638}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:37,320] Trial 60 finished with value: 0.9645982498011137 and parameters: {'C': 2536.2865965306005}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:37,428] Trial 61 finished with value: 0.9657915672235481 and parameters: {'C': 4456.481839488397}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:37,537] Trial 62 finished with value: 0.9649960222752586 and parameters: {'C': 4610.996763114591}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:37,656] Trial 63 finished with value: 0.964200477326969 and parameters: {'C': 643.1394642171972}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:37,744] Trial 64 finished with value: 0.9645982498011137 and parameters: {'C': 2454.070189752077}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:37,833] Trial 65 finished with value: 0.9649960222752586 and parameters: {'C': 5994.6700647306325}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:37,943] Trial 66 finished with value: 0.9653937947494033 and parameters: {'C': 944.7384445971708}. Best is trial 10 with value: 0.9657915672235481.\n",
      "[I 2024-01-26 09:47:37,991] Trial 67 finished with value: 0.9431185361972951 and parameters: {'C': 0.008047879092574846}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:38,099] Trial 68 finished with value: 0.9638027048528243 and parameters: {'C': 1610.0320023333904}. Best is trial 10 with value: 0.9657915672235481.\n",
      "[I 2024-01-26 09:47:38,148] Trial 69 finished with value: 0.9431185361972951 and parameters: {'C': 0.00010437223403231915}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:38,250] Trial 70 finished with value: 0.9638027048528243 and parameters: {'C': 290.3537779484523}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:38,368] Trial 71 finished with value: 0.9638027048528243 and parameters: {'C': 3622.2692586341363}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:38,495] Trial 72 finished with value: 0.964200477326969 and parameters: {'C': 5437.23092785837}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:38,614] Trial 73 finished with value: 0.9649960222752586 and parameters: {'C': 2881.6591675318864}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:38,714] Trial 74 finished with value: 0.9645982498011137 and parameters: {'C': 1695.578805559952}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:38,842] Trial 75 finished with value: 0.9653937947494033 and parameters: {'C': 893.6542849415708}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:38,970] Trial 76 finished with value: 0.9653937947494033 and parameters: {'C': 5996.888992836971}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:39,098] Trial 77 finished with value: 0.9649960222752586 and parameters: {'C': 9992.072038905462}. Best is trial 10 with value: 0.9657915672235481.\n",
      "[I 2024-01-26 09:47:39,186] Trial 78 finished with value: 0.9486873508353222 and parameters: {'C': 0.6301760520991272}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:39,321] Trial 79 finished with value: 0.9638027048528243 and parameters: {'C': 2046.9526735503248}. Best is trial 10 with value: 0.9657915672235481.\n",
      "[I 2024-01-26 09:47:39,389] Trial 80 finished with value: 0.9431185361972951 and parameters: {'C': 0.1364272747394824}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:39,518] Trial 81 finished with value: 0.964200477326969 and parameters: {'C': 777.5926953502054}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:39,640] Trial 82 finished with value: 0.9649960222752586 and parameters: {'C': 1213.8472993231035}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:39,768] Trial 83 finished with value: 0.962211614956245 and parameters: {'C': 65.7880765976864}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:39,880] Trial 84 finished with value: 0.9653937947494033 and parameters: {'C': 4013.543777900516}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:39,990] Trial 85 finished with value: 0.9645982498011137 and parameters: {'C': 382.02296657886336}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:40,101] Trial 86 finished with value: 0.9630071599045347 and parameters: {'C': 416.6489436693545}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:40,218] Trial 87 finished with value: 0.9634049323786794 and parameters: {'C': 1046.3366370403055}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:40,309] Trial 88 finished with value: 0.9638027048528243 and parameters: {'C': 3336.0138642507663}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:40,418] Trial 89 finished with value: 0.9657915672235481 and parameters: {'C': 6631.71189439819}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:40,540] Trial 90 finished with value: 0.9649960222752586 and parameters: {'C': 6222.851089888445}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:40,662] Trial 91 finished with value: 0.964200477326969 and parameters: {'C': 1842.6111764631837}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:40,775] Trial 92 finished with value: 0.9657915672235481 and parameters: {'C': 2404.5122555584558}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:40,897] Trial 93 finished with value: 0.9653937947494033 and parameters: {'C': 3084.323013252283}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:41,019] Trial 94 finished with value: 0.964200477326969 and parameters: {'C': 9752.16712317985}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:41,142] Trial 95 finished with value: 0.9653937947494033 and parameters: {'C': 5267.662314819356}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:41,254] Trial 96 finished with value: 0.964200477326969 and parameters: {'C': 2239.751016707367}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:41,351] Trial 97 finished with value: 0.9649960222752586 and parameters: {'C': 599.5726533246292}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:41,471] Trial 98 finished with value: 0.9649960222752586 and parameters: {'C': 7123.1097103595985}. Best is trial 10 with value: 0.9657915672235481.\n",
      "/home/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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/hkamangir/anaconda3/envs/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "[I 2024-01-26 09:47:41,624] Trial 99 finished with value: 0.9653937947494033 and parameters: {'C': 1393.8676694084477}. Best is trial 10 with value: 0.9657915672235481.\n"
     ]
    }
   ],
   "source": [
    "study_rf = optuna.create_study(direction=\"maximize\")\n",
    "study_rf.optimize(objective_rf, n_trials=100)\n",
    "\n",
    "study_mlp = optuna.create_study(direction=\"maximize\")\n",
    "study_mlp.optimize(objective_mlp, n_trials=100)\n",
    "\n",
    "study_lr = optuna.create_study(direction=\"maximize\")\n",
    "study_lr.optimize(objective_lr, n_trials=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Random Forest Model:  {'n_estimators': 47, 'max_depth': 9}\n",
      "Best MLP Model:  {'hidden_layer_sizes': (100, 50), 'learning_rate_init': 0.0008271961894392654}\n",
      "Best Logistic Regression Model:  {'C': 1728.1356460265151}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Random Forest Model: \", study_rf.best_params)\n",
    "print(\"Best MLP Model: \", study_mlp.best_params)\n",
    "print(\"Best Logistic Regression Model: \", study_lr.best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.98 | Precision: 0.78 | Recall = 0.58 | F1_score = 0.67\n",
      "POD = 0.58 | F: 0.01 | FAR = 0.22 | CSI = 0.50 | HSS = 0.66 | PSS = 0.58\n"
     ]
    },
    {
     "data": {
      "image/png": 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dOnVKcXFxTr+DvLw83XPPPUpKSnJ0q915551asGCBnn/+eW3btu2KXRHXa+DAgdqxY4f27NmjuXPnqlatWmrTpk2BekU5h139rj/99FNduHBBDz74oNPnULp0acXGxnpkJtyt6oYPNsLCwuTv76/k5GSX6p88eVLSxQvA5SIjIx3r84WGhhaoZ7fblZmZeR2tLVytWrW0fv16VahQQUOHDlWtWrVUq1YtTZ8+/arbnTx58orvI3/9pS5/L/njW4ryXmw2mx566CEtXrxYr7/+umrXrq3WrVsXWnf79u3q1KmTpIuzhb788kslJSVp7NixRT5uYe/zam0cMGCAzp8/r4iICJfGaqSlpckYU6TP0x38/f1VunRppzK73a7z5887Xh87dky//fabfH195ePj47SkpKToxIkTLh2rcePGat68ue644w517dpV77//vm677TYNHTrU6VjffPNNgeMEBgbKGOM4Vps2bbRixQrHhbhy5cpq0KCB03ie6zVp0iQlJSVp48aNGjt2rI4dO6aePXs6jWMp6m/LlfP45MmTioiIKFDv8rKiXkNCQkIK1PP19S1Q7uvrK0lO3/3VVK5cWc2bN3da8meEFfXacHnd/PEx999/f4HfwqRJk2SMcdxOYOnSpYqLi9Obb76pFi1aKCQkRA8++KBSUlJceh/X0qZNG0VFRemNN97QokWLNHDgwELHfBTlHHb1u87/HO64444Cn8PSpUtdPvdwbTf8bBQvLy+1b99eH3/8sQ4fPnzNqaH5F52jR48WqHvkyBHHaH13yP9PJCsry2ngamE/0NatW6t169bKzc3Vjh07NGPGDMXHxys8PPyKA6FCQ0N19OjRAuVHjhyRJLe+l0sNGDBA48aN0+uvv64XXnjhivWWLFkiHx8frVq1yuk/1Cvdk+JqijJ//ejRoxo6dKiaNGmiffv2aeTIkXrllVeuuk25cuVUqlQpj3ye1xIWFqbQ0NArDh683inHpUqV0u233673339fqampqlChgsLCwuTn51fo4Mn8tuS77777dN999ykrK0vbtm1TYmKi+vbtq+rVq6tFixbX1SZJqlmzpmNQaJs2beTn56dnn31WM2bM0MiRIyW597eVLzQ0tND/IC8vK85ryPUq6rXh8vMrf/2MGTOuOOMlPysQFhamadOmadq0aTp06JBWrlypv/3tb0pNTXXbgNeHHnpIzz77rGw2m+Li4gqtU5Rz2NXvOr/+Bx98oGrVqv2u94Cru+EzG5I0ZswYGWP0yCOPKDs7u8D6nJwcffTRR5Kku+++W5K0ePFipzpJSUn67rvv3DqVMX+WwjfffONUnt+Wwnh5eSkmJkavvvqqJOnrr7++Yt327dvr888/d5xI+RYuXCh/f3/LpoVWqlRJTz/9tLp3737FE1+SY/qcl5eXoywzM1OLFi0qUNdd2aLc3Fw98MADstls+vjjj5WYmKgZM2Zo2bJlV90uICBAMTExWrZsmVM78vLytHjxYlWuXFm1a9cucnuuJ3t0uW7duunkyZPKzc0t8Jds8+bNr3iPj2vJzc3Vnj17ZLfbHVOZu3Xrpp9//lmhoaGFHquwmTd2u12xsbGaNGmSJDlG6bvjvUvSqFGjdNttt+nFF1/UmTNnJBXtt+Wqdu3a6bPPPnOa9ZKbm6ulS5c61SvOa8j1+r3XhlatWqls2bL69ttvC/0dNG/e3JGJuVTVqlX1xBNPqGPHjk7Xrt97fsfFxal79+56+umnValSpULrFOUcdvW77ty5s7y9vfXzzz9f8XOAe9zwmQ1JatGihWbNmqUhQ4YoOjpajz/+uG6//XbHTYFmz56tBg0aqHv37qpTp44effRRzZgxQ6VKlVKXLl104MAB/f3vf1eVKlX01FNPua1d9957r0JCQjRo0CA999xz8vb21oIFC/TLL7841Xv99df1+eefq2vXrqpatarOnz/v+MuyQ4cOV9z/+PHjtWrVKrVr107jxo1TSEiI3n77ba1evVqTJ092TG20wosvvnjNOl27dtWUKVPUt29fPfroozp58qReeumlQqcnN2zYUEuWLNHSpUtVs2ZNlS5d+prjLAozfvx4bdq0SWvXrlVERIRGjBihjRs3atCgQWratKlq1KhxxW0TExPVsWNHtWvXTiNHjpSvr69ee+017d27V+++++513R2wVq1a8vPz09tvv6169eqpTJkyioyMdOqrv5Y+ffro7bff1r333qsnn3xSd955p3x8fHT48GF98cUXuu+++/THP/7xmvvZuXOn4zdx7NgxzZs3T//973/11FNPObID8fHx+vDDD9WmTRs99dRTatSokeMmYGvXrtWIESMUExOjcePG6fDhw2rfvr0qV66s3377TdOnT5ePj49iY2Pd9t6li1NzExIS1KtXL02fPl3PPvtskX5brnr22We1cuVK3X333Ro3bpz8/f316quvFpjyW5zXkOv1e68NZcqU0YwZMxQXF6dTp07p/vvvV4UKFXT8+HH95z//0fHjxzVr1iylp6erXbt26tu3r+rWravAwEAlJSXpk08+0Z/+9CfH/ho2bKhly5Zp1qxZio6OVqlSpYr0H3VkZKRLWStXz2FXv+vq1avrueee09ixY7V//37dc889KleunI4dO6bt27crICCAe7+4i0eHpxbR7t27TVxcnKlatarx9fU1AQEBpmnTpmbcuHEmNTXVUS83N9dMmjTJ1K5d2/j4+JiwsDDz17/+tcC0vEtHRF8qLi6uwMhqFTIbxRhjtm/fblq2bGkCAgJMpUqVzPjx482bb77pNOJ569at5o9//KOpVq2asdvtJjQ01MTGxpqVK1cWOMals1GMMWbPnj2me/fuJjg42Pj6+prGjRsXGP1/pSmF+aParzVb4NLZKFdT2IySefPmmTp16hi73W5q1qxpEhMTzdy5cwtMLztw4IDp1KmTCQwMNJIcn++V2n7puvzZKGvXrjWlSpUq8BmdPHnSVK1a1dxxxx0mKyvrqu9h06ZN5u677zYBAQHGz8/P3HXXXeajjz5yqlOU2SjGXJxNUrduXePj4+P0HcbFxZmAgIAC9Qsb1Z6Tk2Neeukl07hxY1O6dGlTpkwZU7duXTN48GDz448/XvX4hc1GCQkJMTExMWbevHkmNzfXqf7Zs2fNs88+a+rUqWN8fX1NcHCwadiwoXnqqacco/dXrVplunTpYipVqmR8fX1NhQoVzL333ms2bdrk0nsvzNW+a2MuTnksV66cYyaFq7+tatWqma5duxbYX2xsbIHf65dffumYghoREWGefvppM3v27AL7/L3XkCu16UrXkUu5+vv7PdeGfBs3bjRdu3Y1ISEhxsfHx1SqVMl07drVUf/8+fPmscceM40aNTJBQUHGz8/P1KlTx4wfP95kZGQ49nPq1Clz//33m7JlyxqbzXbNWRtX+twudaXZTq6cw8a4/l0bY8yKFStMu3btTFBQkLHb7aZatWrm/vvvd5oCzGyU38dmTCE3pgcAAHCTm2LMBgAAuHkRbAAAAEsRbAAAAEsRbAAAcIv69ddf9de//lWhoaHy9/dXkyZNtHPnTsd6Y4wmTJigyMhI+fn5qW3bttq3b5/TPrKysjRs2DCFhYUpICBAPXr0KPBQwmsh2AAA4BaUlpamVq1aycfHRx9//LG+/fZbvfzyy06PBpg8ebKmTJmimTNnKikpSREREerYsaPjnjfSxSnzy5cv15IlS7R582adPXtW3bp1U25ursttYTYKAAC3oL/97W/68ssvtWnTpkLXG2MUGRmp+Ph4jR49WtLFLEZ4eLgmTZqkwYMHKz09XeXLl9eiRYscz5Y5cuSIqlSpojVr1qhz584utYXMBgAAt6CVK1eqefPm+stf/qIKFSqoadOmmjNnjmN9cnKyUlJSHM8hkv53x+AtW7ZIunizwJycHKc6kZGRatCggaOOK26KO4gWVc4J156qCpQ0fpGFP1QPKMkuZLvvqcZX4q7/l/ICKzk9tFC6GCAUdnfd/fv3a9asWRo+fLieeeYZbd++Xf/3f/8nu93u9DC9S5+Om/86/+nEKSkp8vX1Vbly5QrUKcrD+MhsAABwk0hMTFRwcLDTkpiYWGjdvLw8NWvWTAkJCWratKkGDx6sRx55RLNmzXKqd/mjGowx13x8gyt1LkWwAQCA1fJy3bKMGTNG6enpTsuYMWMKPWTFihVVv359p7J69erp0KFDkqSIiAhJBZ+Gm5qa6sh2REREKDs7W2lpaVes4wqCDQAArGby3LLkP8X50uVKDyhs1aqVvv/+e6eyH374QdWqVZMk1ahRQxEREVq3bp1jfXZ2tjZu3KiWLVtKkqKjo+Xj4+NU5+jRo9q7d6+jjituyTEbAADcUPLyiv2QTz31lFq2bOl4qvL27ds1e/ZszZ49W9LF7pP4+HglJCQoKipKUVFRSkhIkL+/v/r27StJCg4O1qBBgzRixAiFhoYqJCREI0eOVMOGDa/61PLLEWwAAHALuuOOO7R8+XKNGTNGzz33nGrUqKFp06apX79+jjqjRo1SZmamhgwZorS0NMXExGjt2rUKDAx01Jk6daq8vb3Vq1cvZWZmqn379lqwYIG8vLxcbssteZ8NZqMAhWM2ClBQccxGyT6y79qVXOAbebtb9lPcyGwAAGA1D3Sj3EgYIAoAACxFZgMAAKuZkp3ZINgAAMBqea4/tOxWRDcKAACwFJkNAACsRjcKAACwFLNRAAAArENmAwAAixm6UQAAgKVKeDcKwQYAAFYr4ZkNxmwAAABLkdkAAMBqJfymXgQbAABYjW4UAAAA65DZAADAasxGAQAAlqIbBQAAwDpkNgAAsBrdKAAAwErGlOypr3SjAAAAS5HZAADAaiV8gCjBBgAAVmPMBgAAsFQJz2wwZgMAAFiKzAYAAFbjQWwAAMBSdKMAAABYh8wGAABWYzYKAACwFN0oAAAA1iGzAQCA1ehGAQAAlirhwQbdKAAAwFJkNgAAsFhJf8Q8wQYAAFYr4d0oBBsAAFiNqa8AAADWIbMBAIDV6EYBAACWohsFAADAOmQ2AACwGt0oAADAUnSjAAAAWIfMBgAAVqMbBQAAWKqEBxt0owAAAEuR2QAAwGolfIAowQYAAFYr4d0oBBsAAFithGc2GLMBAAAsRbABAIDV8vLcsxTBhAkTZLPZnJaIiAjHemOMJkyYoMjISPn5+alt27bat2+f0z6ysrI0bNgwhYWFKSAgQD169NDhw4eL/PYJNgAAsJrJc89SRLfffruOHj3qWPbs2eNYN3nyZE2ZMkUzZ85UUlKSIiIi1LFjR505c8ZRJz4+XsuXL9eSJUu0efNmnT17Vt26dVNubm6R2sGYDQAAblHe3t5O2Yx8xhhNmzZNY8eO1Z/+9CdJ0ltvvaXw8HC98847Gjx4sNLT0zV37lwtWrRIHTp0kCQtXrxYVapU0fr169W5c2eX20FmAwAAq3mgG0WSfvzxR0VGRqpGjRrq06eP9u/fL0lKTk5WSkqKOnXq5Khrt9sVGxurLVu2SJJ27typnJwcpzqRkZFq0KCBo46ryGwAAGA1N019zcrKUlZWllOZ3W6X3W4vUDcmJkYLFy5U7dq1dezYMT3//PNq2bKl9u3bp5SUFElSeHi40zbh4eE6ePCgJCklJUW+vr4qV65cgTr527uKzAYAADeJxMREBQcHOy2JiYmF1u3SpYv+/Oc/q2HDhurQoYNWr14t6WJ3ST6bzea0jTGmQNnlXKlzOYINAACsZoxbljFjxig9Pd1pGTNmjEtNCAgIUMOGDfXjjz86xnFcnqFITU11ZDsiIiKUnZ2ttLS0K9ZxFcEGAABWc9OYDbvdrqCgIKelsC6UwmRlZem7775TxYoVVaNGDUVERGjdunWO9dnZ2dq4caNatmwpSYqOjpaPj49TnaNHj2rv3r2OOq5izAYAALegkSNHqnv37qpatapSU1P1/PPP6/Tp04qLi5PNZlN8fLwSEhIUFRWlqKgoJSQkyN/fX3379pUkBQcHa9CgQRoxYoRCQ0MVEhKikSNHOrplioJgAwAAq3ng2SiHDx/WAw88oBMnTqh8+fK66667tG3bNlWrVk2SNGrUKGVmZmrIkCFKS0tTTEyM1q5dq8DAQMc+pk6dKm9vb/Xq1UuZmZlq3769FixYIC8vryK1xWaMMW59dzeAnBP7Pd0E4IbkF9na000AbjgXsn+1/BiZi8e6ZT9+f33BLfspbmQ2AACwWgl/6isDRAEAgKXIbAAAYLVbb8RCkRBsAABgNbpRAAAArENmAwAAq5XwzAbBBgAAVjMlO9igGwUAAFiKzAYAABYzecxGAQAAVirhYzboRgEAAJYiswEAgNVK+ABRgg0AAKzGmA0AAGApxmwAAABYh8wGAABWK+GZDYINAACsVsKf+ko3CgAAsBTBBors2PETGj1xslp16aXmd/fUn+OGat9/f3SsN8bo1bmL1a5HP0W3u08Dnhiln/YfdNrH+/9aowFPjFJMxz+pQasuOn3mbHG/DcCjRo96Qheyf9XLL030dFNQHPLy3LPcpAg2UCTpp8+o/2Mj5OPtrddf/of+9fYbenrYwwosE+CoM+/t97VwyTI9M3yIlsydrrCQcnok/hllZJxz1Dl/Pkt/iGmuRx7s44m3AXhU8+jGenhQP/3nm2893RQUlzzjnuUmRbCBIpn39vuKqFBez48drob166hSxXDd1bypqlaOlHQxq7HovRV6NK6POrZtpaia1ZXw7Aidz8rS6nUbHPvp3/uPerh/LzW6va6H3gngGQEB/lq4cKYee3yUfkv7zdPNAYqFR4ONw4cPa+zYsWrXrp3q1aun+vXrq127dho7dqx++eUXTzYNV/DF5m26vW6Uhj/7gtp07aP7BwzVBys/dqw/fCRFJ06mqeWdzRxlvr6+at6koXbv4a84YMYrCfp4zWf67PNNnm4KipPJc89yk/LYbJTNmzerS5cuqlKlijp16qROnTrJGKPU1FStWLFCM2bM0Mcff6xWrVp5qokoxOEjKVq6YrUe7P0nPfJgb+359gclTn1dPj4+uq9LB504lSZJCi1Xzmm70JCyOpKS6okmAzeMXr16qGnTBrqrRVdPNwXF7SbuAnEHjwUbTz31lB5++GFNnTr1iuvj4+OVlJR01f1kZWUpKyvLqaxUVpbsdrvb2or/ycszur1ulOIfGyBJqlf7Nv2UfFDvLV+t+7p0cNSz2WxO2xlTsAwoSSpXjtTUl59Tl659C1yzgFudx7pR9u7dq8cee+yK6wcPHqy9e/decz+JiYkKDg52WiZNf92dTcUlyoeGqFb1qk5lNatX0dFjxyVJYSEXMxonTp1yqnMq7TeFlitbLG0EbkTNmjVUeHh5bd/2sc6fO6jz5w4qNralhj0xUOfPHVSpUgyhu5WZvDy3LDcrj2U2KlasqC1btqhOnTqFrt+6dasqVqx4zf2MGTNGw4cPdyordeZXt7QRBTVtVF8HDh12Kjt46FdVjKggSaocGaGw0HLamrRL9WrfJknKycnRjt179NTjA4u9vcCN4vPPN6tx07udyt6cM0Xff/+z/vnSq8q7if8jgQvoRvGMkSNH6rHHHtPOnTvVsWNHhYeHy2azKSUlRevWrdObb76padOmXXM/dru9QJdJTvYJi1qN/r17qv/gEZr91hLd076N9nz7vT5Y+bHGj/o/SRe7Svr36qk5C5eqauVIVatSSXMWLlVpu11dO7Z17OfEyVM6cTJNhw4fkST9+PMBBfj7qWJEBQUHBXrirQGWOns2Q/v2fe9Udi7jnE6eTCtQjlvQTTy40x08FmwMGTJEoaGhmjp1qt544w3l5uZKkry8vBQdHa2FCxeqV69enmoerqBhvTqalvh3TX99gV5f8I4qVYzQ6CcHq1vn//3FNrDfX3Q+K1vPv/yqTp85q0b162j2tBcUEODvqLN0xRrNmve243Xc0KclSc8/M1w9u3YsvjcEALCczRjP37A9JydHJ05czEaEhYXJx8fn9+3vxH53NAu45fhFtvZ0E4AbzoVs67veM57r55b9BIx7+9qVbkA3xIPYfHx8XBqfAQDATamEj8lh+DMAALDUDZHZAADglsZsFAAAYKkSPhuFbhQAAGApMhsAAFiNbhQAAGClm/lW4+5ANwoAALAUmQ0AAKxGNwoAALAUwQYAALAUU18BAACsQ2YDAACr0Y0CAACsZEp4sEE3CgAAsBSZDQAArFbCMxsEGwAAWI07iAIAAFiHzAYAAFajGwUAAFiqhAcbdKMAAABLkdkAAMBixpDZAAAAVsoz7ll+h8TERNlsNsXHxzvKjDGaMGGCIiMj5efnp7Zt22rfvn1O22VlZWnYsGEKCwtTQECAevToocOHDxfp2AQbAABYzcPBRlJSkmbPnq1GjRo5lU+ePFlTpkzRzJkzlZSUpIiICHXs2FFnzpxx1ImPj9fy5cu1ZMkSbd68WWfPnlW3bt2Um5vr8vEJNgAAuIWdPXtW/fr105w5c1SuXDlHuTFG06ZN09ixY/WnP/1JDRo00FtvvaVz587pnXfekSSlp6dr7ty5evnll9WhQwc1bdpUixcv1p49e7R+/XqX20CwAQCAxUyecctyPYYOHaquXbuqQ4cOTuXJyclKSUlRp06dHGV2u12xsbHasmWLJGnnzp3KyclxqhMZGakGDRo46riCAaIAAFjNTVNfs7KylJWV5VRmt9tlt9sLrb9kyRJ9/fXXSkpKKrAuJSVFkhQeHu5UHh4eroMHDzrq+Pr6OmVE8uvkb+8KMhsAANwkEhMTFRwc7LQkJiYWWveXX37Rk08+qcWLF6t06dJX3KfNZnN6bYwpUHY5V+pcimADAACr5blnGTNmjNLT052WMWPGFHrInTt3KjU1VdHR0fL29pa3t7c2btyoV155Rd7e3o6MxuUZitTUVMe6iIgIZWdnKy0t7Yp1XEGwAQCAxdw1ZsNutysoKMhpuVIXSvv27bVnzx7t3r3bsTRv3lz9+vXT7t27VbNmTUVERGjdunWObbKzs7Vx40a1bNlSkhQdHS0fHx+nOkePHtXevXsddVzBmA0AAG5BgYGBatCggVNZQECAQkNDHeXx8fFKSEhQVFSUoqKilJCQIH9/f/Xt21eSFBwcrEGDBmnEiBEKDQ1VSEiIRo4cqYYNGxYYcHo1BBsAAFjtBn02yqhRo5SZmakhQ4YoLS1NMTExWrt2rQIDAx11pk6dKm9vb/Xq1UuZmZlq3769FixYIC8vL5ePYzO34D1Uc07s93QTgBuSX2RrTzcBuOFcyP7V8mP81rudW/ZTdukXbtlPcWPMBgAAsBTdKAAAWOx6b8h1qyDYAADAanmeboBnEWwAAGCxkp7ZYMwGAACwFJkNAACsRjcKAACwkinhwQbdKAAAwFJkNgAAsFoJz2wQbAAAYDG6UQAAACxEZgMAAKuV8MwGwQYAABYr6d0oBBsAAFispAcbjNkAAACWIrMBAIDFSnpmg2ADAACrGZunW+BRdKMAAABLkdkAAMBidKMAAABLmTy6UQAAACxDZgMAAIvRjQIAACxlmI0CAABgHTIbAABYjG4UAABgqZI+G4VgAwAAixnj6RZ4FmM2AACApchsAABgMbpRAACApUp6sEE3CgAAsBSZDQAALFbSB4gSbAAAYDG6UQAAACxEZgMAAIuV9GejEGwAAGCxkn67crpRAACApchsAABgsTy6UQAAgJUYswEAACzF1FcAAAALXVewsWjRIrVq1UqRkZE6ePCgJGnatGn617/+5dbGAQBwKzDGPcvNqsjBxqxZszR8+HDde++9+u2335SbmytJKlu2rKZNm+bu9gEAcNMzeTa3LDerIgcbM2bM0Jw5czR27Fh5eXk5yps3b649e/a4tXEAAODmV+QBosnJyWratGmBcrvdroyMDLc0CgCAW0lJn/pa5MxGjRo1tHv37gLlH3/8serXr++ONgEAcEsxxuaW5WZV5MzG008/raFDh+r8+fMyxmj79u169913lZiYqDfffNOKNgIAgJtYkYONhx56SBcuXNCoUaN07tw59e3bV5UqVdL06dPVp08fK9oIAMBN7WaeSeIONmOu/yM4ceKE8vLyVKFCBXe26XfLObHf000Abkh+ka093QTghnMh+1fLj7G7Wg+37KfJwZVu2U9x+113EA0LC3NXOwAAwC2qyMFGjRo1ZLNdeZDK/v1kFQAAuNTNPLjTHYo8GyU+Pl5PPvmkYxkyZIhatGih9PR0Pfroo1a0EQCAm5on7iA6a9YsNWrUSEFBQQoKClKLFi308ccfX9ImowkTJigyMlJ+fn5q27at9u3b57SPrKwsDRs2TGFhYQoICFCPHj10+PDhIr//Imc2nnzyyULLX331Ve3YsaPIDQAA4FbniftsVK5cWS+++KJuu+02SdJbb72l++67T7t27dLtt9+uyZMna8qUKVqwYIFq166t559/Xh07dtT333+vwMBASRcTDB999JGWLFmi0NBQjRgxQt26ddPOnTudbux5Lb9rgOil9u/fryZNmuj06dPu2N3vwgBRoHAMEAUKKo4Bojsq93TLfpofXvG7tg8JCdE///lPDRw4UJGRkYqPj9fo0aMlXcxihIeHa9KkSRo8eLDS09NVvnx5LVq0SL1795YkHTlyRFWqVNGaNWvUuXNnl4/rtkfMf/DBBwoJCXHX7n6XgEptPN0E4IbkXcr1v0QAuI+7xmxkZWUpKyvLqcxut8tut191u9zcXL3//vvKyMhQixYtlJycrJSUFHXq1MlpP7GxsdqyZYsGDx6snTt3Kicnx6lOZGSkGjRooC1btlgbbDRt2tRpgKgxRikpKTp+/Lhee+21ou4OAIBbnru6URITEzVx4kSnsvHjx2vChAmF1t+zZ49atGih8+fPq0yZMlq+fLnq16+vLVu2SJLCw8Od6oeHhzue5p6SkiJfX1+VK1euQJ2UlJQitbvIwUbPnj2dXpcqVUrly5dX27ZtVbdu3aLuDgAAuGjMmDEaPny4U9nVshp16tTR7t279dtvv+nDDz9UXFycNm7c6Fh/+exSY8xVZ5y6WudyRQo2Lly4oOrVq6tz586KiIgo0oEAACip3HUDUVe6TC7l6+vrGCDavHlzJSUlafr06Y5xGikpKapYsaKjfmpqqiPbERERoezsbKWlpTllN1JTU9WyZcsitbtIU1+9vb31+OOPF+gvAgAAV5ZnbG5Zfi9jjLKyslSjRg1FRERo3bp1jnXZ2dnauHGjI5CIjo6Wj4+PU52jR49q7969RQ42ityNEhMTo127dqlatWpF3RQAABSTZ555Rl26dFGVKlV05swZLVmyRBs2bNAnn3wim82m+Ph4JSQkKCoqSlFRUUpISJC/v7/69u0rSQoODtagQYM0YsQIhYaGKiQkRCNHjlTDhg3VoUOHIrWlyMHGkCFDNGLECB0+fFjR0dEKCAhwWt+oUaOi7hIAgFuaJ+4geuzYMfXv319Hjx5VcHCwGjVqpE8++UQdO3aUJI0aNUqZmZkaMmSI0tLSFBMTo7Vr1zrusSFJU6dOlbe3t3r16qXMzEy1b99eCxYsKNI9NqQi3Gdj4MCBmjZtmsqWLVtwJzabY8BIbm5ukRpgBV97ZU83AbghlbIV+abBwC3v/PlDlh9jU8T9btlP65QP3LKf4uZysOHl5aWjR48qMzPzqvVuhO4Vgg2gcAQbQEEEG9ZzuRslPya5EYIJAABuJkYl+0FsRRqzUdR5tQAAQMpz19zXm1SRgo3atWtfM+A4derU72oQAAC3mjwyG66bOHGigoODrWoLAAC4BRUp2OjTp48qVKhgVVsAALglMWbDRYzXAADg+uR5ugEe5vI8OBdnyAIAADhxObORl1fS4zIAAK4P3SgAAMBSJf3PdW4nCAAALEVmAwAAi5X0zAbBBgAAFivpYzboRgEAAJYiswEAgMXySnZig2ADAACr8WwUAABgqZJ+W0zGbAAAAEuR2QAAwGJMfQUAAJbKK+EPM6UbBQAAWIrMBgAAFivpA0QJNgAAsFhJH7NBNwoAALAUmQ0AACzGHUQBAIClSvodROlGAQAAliKzAQCAxZiNAgAALMWYDQAAYCmmvgIAAFiIzAYAABZjzAYAALBUSR+zQTcKAACwFJkNAAAsVtIHiBJsAABgsZIebNCNAgAALEVmAwAAi5kSPkCUYAMAAIvRjQIAAGAhMhsAAFispGc2CDYAALAYdxAFAACW4g6iAAAAFiKzAQCAxRizAQAALFXSgw26UQAAgKXIbAAAYDFmowAAAEsxGwUAAMBCZDYAALAYA0QBAICljJuWokhMTNQdd9yhwMBAVahQQT179tT333/v3C5jNGHCBEVGRsrPz09t27bVvn37nOpkZWVp2LBhCgsLU0BAgHr06KHDhw8XqS0EGwAA3II2btyooUOHatu2bVq3bp0uXLigTp06KSMjw1Fn8uTJmjJlimbOnKmkpCRFRESoY8eOOnPmjKNOfHy8li9friVLlmjz5s06e/asunXrptzcXJfbYjPG3HKDZH3tlT3dBOCGVMrG3xfA5c6fP2T5MV6o1s8t+xl78O3r3vb48eOqUKGCNm7cqDZt2sgYo8jISMXHx2v06NGSLmYxwsPDNWnSJA0ePFjp6ekqX768Fi1apN69e0uSjhw5oipVqmjNmjXq3LmzS8fmygMAgMXy3LRkZWXp9OnTTktWVpZLbUhPT5ckhYSESJKSk5OVkpKiTp06OerY7XbFxsZqy5YtkqSdO3cqJyfHqU5kZKQaNGjgqOMKgg0AACzmrjEbiYmJCg4OdloSExOvfXxjNHz4cP3hD39QgwYNJEkpKSmSpPDwcKe64eHhjnUpKSny9fVVuXLlrljHFcxGAQDgJjFmzBgNHz7cqcxut19zuyeeeELffPONNm/eXGCdzeZ8ExBjTIGyy7lS51JkNgAAsJi7ulHsdruCgoKclmsFG8OGDdPKlSv1xRdfqHLl/41pjIiIkKQCGYrU1FRHtiMiIkLZ2dlKS0u7Yh1XEGwAAGCxPJt7lqIwxuiJJ57QsmXL9Pnnn6tGjRpO62vUqKGIiAitW7fOUZadna2NGzeqZcuWkqTo6Gj5+Pg41Tl69Kj27t3rqOMKulEAALgFDR06VO+8847+9a9/KTAw0JHBCA4Olp+fn2w2m+Lj45WQkKCoqChFRUUpISFB/v7+6tu3r6PuoEGDNGLECIWGhiokJEQjR45Uw4YN1aFDB5fbQrABAIDF8jzwKLZZs2ZJktq2betUPn/+fA0YMECSNGrUKGVmZmrIkCFKS0tTTEyM1q5dq8DAQEf9qVOnytvbW7169VJmZqbat2+vBQsWyMvLy+W2cJ8NoAThPhtAQcVxn42x1fu6ZT8vHHjHLfspblx5AACApehGAQDAYiX9QWwEGwAAWMwTYzZuJHSjAAAAS5HZAADAYiU7r0GwAQCA5RizAQAALMWYDQAAAAuR2QAAwGIlO69BsAEAgOVK+pgNulEAAIClyGwAAGAxU8I7Ugg2AACwGN0oAAAAFiKzAQCAxUr6fTYINgAAsFjJDjXoRgEAABYj2MDv9oc/xGj5svk6kLxD2VmH1aNH5wJ1/v7scB1I3qH0337SurXvq3692h5oKVC8/vCHO/Xhh/O0f3+Szp8/pO7dO12x7syZiTp//pCeeGJQMbYQxSVPxi3LzYpgA79bQIC/vvnmW8XH/73Q9SNHDNGTTz6i+Pi/q2XLrjp2LFVr1ryjMmUCirmlQPHy9/fXnj3f6qmnCj838nXv3kl33NFEv/6aUkwtQ3HLc9Nys2LMBn63Tz/9Qp9++sUV1w8bNkgvvjhDK/71sSRp4KCndPiXXerTp6fefPPt4momUOzWrt2gtWs3XLVOZGS4pk79h7p3768VK+YXT8NQ7Er6fTbIbMBSNWpUVcWK4Vq/fqOjLDs7W5s2bVOLu5p7sGWA59lsNs2bN01Tp76h7777wdPNASxzQwcbv/zyiwYOHHjVOllZWTp9+rTTYkzJjiBvJOHh5SVJx1JPOJUfSz2h8IjynmgScMMYOXKILlzI1auvzvN0U2Cxkt6NckMHG6dOndJbb7111TqJiYkKDg52WvJyzxRTC+GqywNAm2wiJkRJ1rRpQw0d+pAeeWSEp5uCYmDc9O9m5dExGytXrrzq+v37919zH2PGjNHw4cOdykLD6v2udsF9jh07LkmKCC+vlJRUR3mFCqFK/f/rgJKoVas7VaFCmH78caujzNvbW5MmPathwwaqTp1WHmwd4F4eDTZ69uwpm8121W4Pm8121X3Y7XbZ7fYibYPik5x8SEePHlP7Dm20+z/7JEk+Pj5q3fouPTM2wcOtAzznnXc+1Oefb3Iq++ijxXrnnWVauPA9D7UKVrmZu0DcwaPBRsWKFfXqq6+qZ8+eha7fvXu3oqOji7dRKLKAAH/dVqu643X16lXUuFF9nUr7Tb/8ckQzZszV6FFP6Kcfk/XTT8kaPXqYzp3L1JIlKzzWZqA4BAT4q9Zl50ajRvWV9v/PjVOnfnOqf+FCjo4dO64ff7x2Vhc3l7wS3m/s0WAjOjpaX3/99RWDjWtlPXBjiI5urPXr3ne8fumfEyRJCxe+p4cfGa6XXn5Nfn6l9corL6hcuWBt375bXbv209mzGR5qMVA8oqMbae3a/2Up/vnP8ZKkRYveZ6wGShSb8eD/5ps2bVJGRobuueeeQtdnZGRox44dio2NLdJ+fe2V3dE84JZTynZDjwkHPOL8+UOWH+Ov1f7klv0sPrjMLfspbh7NbLRu3fqq6wMCAoocaAAAcKO5mW817g78mQMAACzF7coBALDYzXyPDHcg2AAAwGJMfQUAAJZizAYAAICFyGwAAGAxxmwAAABLlfQxG3SjAAAAS5HZAADAYiX90RsEGwAAWIzZKAAAABYiswEAgMVK+gBRgg0AACxW0qe+0o0CAAAsRWYDAACLlfQBogQbAABYjKmvAADAUiV9gChjNgAAgKXIbAAAYLGSPhuFYAMAAIuV9AGidKMAAABLkdkAAMBiJX02CpkNAAAslifjlqWo/v3vf6t79+6KjIyUzWbTihUrnNYbYzRhwgRFRkbKz89Pbdu21b59+5zqZGVladiwYQoLC1NAQIB69Oihw4cPF6kdBBsAANyiMjIy1LhxY82cObPQ9ZMnT9aUKVM0c+ZMJSUlKSIiQh07dtSZM2ccdeLj47V8+XItWbJEmzdv1tmzZ9WtWzfl5ua63A6buQVzO772yp5uAnBDKmXj7wvgcufPH7L8GG0rd3DLfjYcXn/d29psNi1fvlw9e/aUdDGrERkZqfj4eI0ePVrSxSxGeHi4Jk2apMGDBys9PV3ly5fXokWL1Lt3b0nSkSNHVKVKFa1Zs0adO3d26dhceQAAsFieMW5ZsrKydPr0aaclKyvrutqUnJyslJQUderUyVFmt9sVGxurLVu2SJJ27typnJwcpzqRkZFq0KCBo44rCDYAALhJJCYmKjg42GlJTEy8rn2lpKRIksLDw53Kw8PDHetSUlLk6+urcuXKXbGOK5iNAgCAxdw1XmHMmDEaPny4U5ndbv9d+7TZbE6vjTEFyi7nSp1LkdkAAMBi7pqNYrfbFRQU5LRcb7AREREhSQUyFKmpqY5sR0REhLKzs5WWlnbFOq4g2AAAwGKemvp6NTVq1FBERITWrVvnKMvOztbGjRvVsmVLSVJ0dLR8fHyc6hw9elR79+511HEF3SgAANyizp49q59++snxOjk5Wbt371ZISIiqVq2q+Ph4JSQkKCoqSlFRUUpISJC/v7/69u0rSQoODtagQYM0YsQIhYaGKiQkRCNHjlTDhg3VoYPrM2wINgAAsJin7jKxY8cOtWvXzvE6f7xHXFycFixYoFGjRikzM1NDhgxRWlqaYmJitHbtWgUGBjq2mTp1qry9vdWrVy9lZmaqffv2WrBggby8vFxuB/fZAEoQ7rMBFFQc99m4MzLWLfvZfmSjW/ZT3LjyAAAAS9GNAgCAxUwJf8Q8wQYAABa7BUcsFAndKAAAwFJkNgAAsJi775FxsyHYAADAYnSjAAAAWIjMBgAAFqMbBQAAWIqprwAAwFJ5jNkAAACwDpkNAAAsRjcKAACwFN0oAAAAFiKzAQCAxehGAQAAlqIbBQAAwEJkNgAAsBjdKAAAwFJ0owAAAFiIzAYAABajGwUAAFjKmDxPN8GjCDYAALBYSX/EPGM2AACApchsAABgMVPCZ6MQbAAAYDG6UQAAACxEZgMAAIvRjQIAACzFHUQBAAAsRGYDAACLcQdRAABgqZI+ZoNuFAAAYCkyGwAAWKyk32eDYAMAAIuV9G4Ugg0AACzG1FcAAAALkdkAAMBidKMAAABLlfQBonSjAAAAS5HZAADAYnSjAAAASzEbBQAAwEJkNgAAsBgPYgMAAJaiGwUAAMBCZDYAALAYs1EAAIClGLMBAAAsVdIzG4zZAAAAliKzAQCAxUp6ZoNgAwAAi5XsUINuFAAAYDGbKem5HVgmKytLiYmJGjNmjOx2u6ebA9wwODdQ0hBswDKnT59WcHCw0tPTFRQU5OnmADcMzg2UNHSjAAAASxFsAAAASxFsAAAASxFswDJ2u13jx49nABxwGc4NlDQMEAUAAJYiswEAACxFsAEAACxFsAEAACxFsAEAACxFsAHLvPbaa6pRo4ZKly6t6Ohobdq0ydNNAjzq3//+t7p3767IyEjZbDatWLHC000CigXBBiyxdOlSxcfHa+zYsdq1a5dat26tLl266NChQ55uGuAxGRkZaty4sWbOnOnppgDFiqmvsERMTIyaNWumWbNmOcrq1aunnj17KjEx0YMtA24MNptNy5cvV8+ePT3dFMByZDbgdtnZ2dq5c6c6derkVN6pUydt2bLFQ60CAHgKwQbc7sSJE8rNzVV4eLhTeXh4uFJSUjzUKgCApxBswDI2m83ptTGmQBkA4NZHsAG3CwsLk5eXV4EsRmpqaoFsBwDg1kewAbfz9fVVdHS01q1b51S+bt06tWzZ0kOtAgB4irenG4Bb0/Dhw9W/f381b95cLVq00OzZs3Xo0CE99thjnm4a4DFnz57VTz/95HidnJys3bt3KyQkRFWrVvVgywBrMfUVlnnttdc0efJkHT16VA0aNNDUqVPVpk0bTzcL8JgNGzaoXbt2Bcrj4uK0YMGC4m8QUEwINgAAgKUYswEAACxFsAEAACxFsAEAACxFsAEAACxFsAEAACxFsAEAACxFsAEAACxFsAHcgiZMmKAmTZo4Xg8YMEA9e/Ys9nYcOHBANptNu3fvLvZjA7hxEGwAxWjAgAGy2Wyy2Wzy8fFRzZo1NXLkSGVkZFh63OnTp7t8h0oCBADuxrNRgGJ2zz33aP78+crJydGmTZv08MMPKyMjQ7NmzXKql5OTIx8fH7ccMzg42C37AYDrQWYDKGZ2u10RERGqUqWK+vbtq379+mnFihWOro958+apZs2astvtMsYoPT1djz76qCpUqKCgoCDdfffd+s9//uO0zxdffFHh4eEKDAzUoEGDdP78eaf1l3ej5OXladKkSbrttttkt9tVtWpVvfDCC5KkGjVqSJKaNm0qm82mtm3bOrabP3++6tWrp9KlS6tu3bp67bXXnI6zfft2NW3aVKVLl1bz5s21a9cuN35yAG5WZDYAD/Pz81NOTo4k6aefftJ7772nDz/8UF5eXpKkrl27KiQkRGvWrFFwcLDeeOMNtW/fXj/88INCQkL03nvvafz48Xr11VfVunVrLVq0SK+88opq1qx5xWOOGTNGc+bM0dSpU/WHP/xBR48e1X//+19JFwOGO++8U+vXr9ftt98uX19fSdKcOXM0fvx4zZw5U02bNtWuXbv0yCOPKCAgQHFxccrIyFC3bt109913a/HixUpOTtaTTz5p8acH4KZgABSbuLg4c9999zlef/XVVyY0NNT06tXLjB8/3vj4+JjU1FTH+s8++8wEBQWZ8+fPO+2nVq1a5o033jDGGNOiRQvz2GOPOa2PiYkxjRs3LvS4p0+fNna73cyZM6fQNiYnJxtJZteuXU7lVapUMe+8845T2T/+8Q/TokULY4wxb7zxhgkJCTEZGRmO9bNmzSp0XwBKFrpRgGK2atUqlSlTRqVLl1aLFi3Upk0bzZgxQ5JUrVo1lS9f3lF3586dOnv2rEJDQ1WmTBnHkpycrJ9//lmS9N1336lFixZOx7j89aW+++47ZWVlqX379i63+fjx4/rll180aNAgp3Y8//zzTu1o3Lix/P39XWoHgJKDbhSgmLVr106zZs2Sj4+PIiMjnQaBBgQEONXNy8tTxYoVtWHDhgL7KVu27HUd38/Pr8jb5OXlSbrYlRITE+O0Lr+7xxhzXe0BcOsj2ACKWUBAgG677TaX6jZr1kwpKSny9vZW9erVC61Tr149bdu2TQ8++KCjbNu2bVfcZ1RUlPz8/PTZZ5/p4YcfLrA+f4xGbm6uoyw8PFyVKlXS/v371a9fv0L3W79+fS1atEiZmZmOgOZq7QBQctCNAtzAOnTooBYtWqhnz5769NNPdeDAAW3ZskXPPvusduzYIUl68sknNW/ePM2bN08//PCDxo8fr3379l1xn6VLl9bo0aM1atQoLVy4UD///LO2bdumuXPnSpIqVKggPz8/ffLJJzp27JjS09MlXbxRWGJioqZPn64ffvhBe/bs0fz58zVlyhRJUt++fVWqVCkNGjRI3377rdasWaOXXnrJ4k8IwM2AYAO4gdlsNq1Zs0Zt2rTRwIEDVbt2bfXp00cHDhxQeHi4JKl3794aN26cRo8erejoaB08eFCPP/74Vff797//XSNGjNC4ceNUr1499e7dW6mpqZIkb29vvfLKK3rjjTcUGRmp++67T5L08MMP680339SCBQvUsGFDxcbGasGCBY6psmXKlNFHH32kb7/9Vk2bNtXYsWM1adIkCz8dADcLm6GjFQAAWIjMBgAAsBTBBgAAsBTBBgAAsBTBBgAAsBTBBgAAsBTBBgAAsBTBBgAAsBTBBgAAsBTBBgAAsBTBBgAAsBTBBgAAsBTBBgAAsNT/AzP+dHoXft9qAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example: Evaluate the best Random Forest model\n",
    "best_rf_params = study_rf.best_params\n",
    "best_rf = RandomForestClassifier(**best_rf_params, random_state=42).fit(scaled_cls_xtrain, cls_ytrain)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred_tune = best_rf.predict(scaled_cls_xtest)\n",
    "\n",
    "# Compute the confusion matrix\n",
    "conf_matrix = confusion_matrix(cls_ytest, y_pred_tune)\n",
    "\n",
    "results = evaluate_binary_classification(cls_ytest, y_pred_tune)\n",
    "print(f\"Accuracy = {results['accuracy']:.2f} | Precision: {results['precision']:.2f} | Recall = {results['recall']:.2f} | F1_score = {results['f1_score']:.2f}\")\n",
    "\n",
    "skr = evaluation_skill_metrics(cls_ytest, y_pred_tune)\n",
    "print(f\"POD = {skr['POD']:.2f} | F: {skr['F']:.2f} | FAR = {skr['FAR']:.2f} | CSI = {skr['CSI']:.2f} | HSS = {skr['HSS']:.2f} | PSS = {skr['PSS']:.2f}\")\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='g')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix of the Best Random Forest Model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_probs_tune = best_rf.predict_proba(scaled_cls_xtest)[:, 1]\n",
    "reliability_diagram(cls_ytest, y_test_probs_tune)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "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": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}