8-Hyper-Parameter Tuning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper-Parameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter Notebook uses hyper-parameter tuning to optimize model performance, with Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbor models used for classification and performance evaluated using cross-validation scoring.  Parameters were tuned using a GridSearch estimator.\n",
    "\n",
    "## Approach\n",
    "* Evaluations of classification performed were performed using 2 sample datasets:\n",
    "    * sklearn's Iris sample dataset, consisting of 150 sample data points for 3 varieties of iris flowers\n",
    "    * voice dataset, containing 3168 sample data points of male and female speakers\n",
    "\n",
    "* Hyper-parameter tuning was performed using a GridSearch estimator for each classification model:\n",
    "    \n",
    "* Parameters providing the best scores and associated scores were plotted\n",
    "    \n",
    "## Results\n",
    "* Refer to the Figures and Table of results provided below\n",
    "* Hyperparameter tuning provided by sklearn GridSearchCV provided a convenient method of evaluating many classification model parameter sets in an efficient way.  The blue dots represent the optimal parameter sets selected for each Dataset/Classification Model, and the grey dots represent suboptimal parameter sets.\n",
    "* For the Iris dataset, the Support Vector Machine SVC model provided the  combination of best score of 0.964 and Mean Fit Time of 0.200 ms, with Random Forest and Decision Tree classifiers providing close performance.\n",
    "* For the Voice dataset, the SVC classifer also had the hightest Best Score at 0.980, which was slightly above the performance of the Random Forest classifier.  With SVC, the fit time was singificantly higher for the Voice dataset (40.6 ms) than for the Iris dataset (0.2 ms), which is expected given the larger number of features associated with the Voice dataset (20) vs. the Iris dataset (4)\n",
    "* It's interesting to note that the K-Nearest Neighbors classifier, while performing lower amongst these models, operated with fast Mean Fit Time (3.2 ms) for Voice, which might make using KNN a good choice vs. other more calculation-intensive options in cases where lower fit time is more important than optimum accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Figure: Tuned Classifier Performance: Best Score vs. Mean Fit Time (ms) - All Datasets/Classifiers | Figure: Tuned Classifier Performance: Best Score vs. Mean Fit Time (ms) including Suboptimal Results - All Datasets/Classifiers |\n",
    "| :----------: | :----------: |\n",
    "| ![Figure: Tuned Classifier Performance: Best Score vs. Mean Fit Time (ms) - All Datasets/Classifiers is Loading...](docs/8-Figure-Hyper_Parameter_Tuning-Combined.png \"8-Figure: Tuned Classifier Performance: Best Score vs. Mean Fit Time (ms) - All Datasets/Classifiers\") | ![Figure: Tuned Classifier Performance: Best Score vs. Mean Fit Time (ms) including Suboptimal Results - All Datasets/Classifiers is Loading...](docs/8-Figure-Hyper_Parameter_Tuning-Including_Suboptimal-Combined.png \"8-Figure: Tuned Classifier Performance: Best Score vs. Mean Fit Time (ms) including Suboptimal Results - All Datasets/Classifiers\") |\n",
    "\n",
    "| Figure: Tuned Classifier Performance: Best Score vs. F1 Score - All Datasets/Classifiers | Figure: Tuned Classifier Performance: Precision vs. Recall - All Datasets/Classifiers |\n",
    "| :----------: | :----------: |\n",
    "| ![Figure: Tuned Classifier Performance: Best Score vs. F1 Score - All Datasets/Classifiers is Loading...](docs/8-Figure-Hyper_Parameter_Tuning-BestScore_vs_F1-Combined.png \"8-Figure: uned Classifier Performance: Best Score vs. F1 Score - All Datasets/Classifiers\") | ![Figure: Tuned Classifier Performance: Precision vs. Recall - All Datasets/Classifiers is Loading...](docs/8-Figure-Hyper_Parameter_Tuning-Precision_vs_Recall-Combined.png \"8-Figure: Tuned Classifier Performance: Precision vs. Recall - All Datasets/Classifiers\") |\n",
    "\n",
    "| Figure: Tuned Classifier Performance - Subplots |\n",
    "| :----------: |\n",
    "| ![Figure: Tuned Classifier Performance - Subplots is Loading...](docs/8-Figure-Hyper_Parameter_Tuning-Subplots.png \"8-Figure: Tuned Classifier Performance - Subplots\") |\n",
    "\n",
    "| Table: Tuned Classifier Performance |\n",
    "| :----------: |\n",
    "| ![Table: Tuned Classifier Performance is Loading...](docs/8-Table-Hyper_Parameter_Tuning.png \"8-Table: Tuned Classifier Performance\") |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbpresent": {
     "id": "828c2e12-b1c6-4994-8f55-ce86373b6c97"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# %matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D    # Support 3D graphing\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.plotting import table\n",
    "import math\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "# Visualization\n",
    "import graphviz\n",
    "import pydotplus\n",
    "from IPython.display import Image\n",
    "\n",
    "# Machine Learning - Linear Models - Regression\n",
    "from sklearn.linear_model import LinearRegression  # TBD\n",
    "from sklearn.linear_model import Lasso             # TBD\n",
    "from sklearn.linear_model import Ridge             # TBD\n",
    "from sklearn.linear_model import ElasticNet        # TBD\n",
    "\n",
    "# Machine Learning - Linear Models - Classification\n",
    "from sklearn.linear_model import LogisticRegression   # Logistic Regression Classifier\n",
    "\n",
    "# Machine Learning - Decision Trees and Random Forests - Classification\n",
    "from sklearn import tree                             # Decision Tree Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier  # Random Forest Classifier\n",
    "\n",
    "# Machine Learning - Support Vector Machines - Classification\n",
    "from sklearn import svm                              # Support Vector Machine Classifier\n",
    "\n",
    "\n",
    "# Machine Leardning - K-Nearest Neighbors - Classification\n",
    "from sklearn.neighbors import KNeighborsClassifier   # K-Nearest Neighbors (KNN)\n",
    "\n",
    "# Machine Learning - GridSearch for hyper-parameter tuning\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "# Machine Learning - Data Preparation and Pre-Processing\n",
    "from sklearn.model_selection import train_test_split # Split data into training and testing samples\n",
    "from sklearn.model_selection import cross_val_score  # Score a model using k-fold or other cross validation\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder   # Convert categorical integer features (X) to One-Hot encoded values\n",
    "from sklearn.preprocessing import LabelEncoder    # Convert categorical labeled values to categorical integer values\n",
    "from sklearn.preprocessing import LabelBinarizer  # Convert categorical labeled values to Binary encoded values\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler  # Scale numerical features to standard normal distribution\n",
    "from sklearn.preprocessing import MinMaxScaler    # Scale numerical values based upon mix/max values\n",
    "\n",
    "# Machine Learning - Quantify Model Performance\n",
    "from sklearn.metrics import mean_squared_error    # Mean Squared Error (MSE) metric\n",
    "from sklearn.metrics import r2_score              # R-squared (Coefficient of Determination) metric\n",
    "from sklearn.metrics import confusion_matrix      # Generate a confusion matrix (actual vs. predicted counts)\n",
    "from sklearn.metrics import classification_report # Calculate metrics for prediction performance\n",
    "from sklearn.metrics import precision_score       # Calculate the precision: Tp / (Tp + Fp) => Ability to avoid false negatives\n",
    "from sklearn.metrics import recall_score          # Calculate the recall: Tp / (Tp + Fn) => Ability to find all positive samples\n",
    "from sklearn.metrics import f1_score              # Calculate the F1 score: 2*(precision*recall)/(precision+recall)\n",
    "\n",
    "# Machine Learning - Dataset Generation\n",
    "from sklearn.datasets import make_regression     # Generate linear data\n",
    "from sklearn.datasets import make_s_curve        # Generate nonlinear data\n",
    "from sklearn.datasets import make_blobs          # Generate blobs for classification\n",
    "from sklearn.datasets import make_circles        # Generate circles for classification\n",
    "from sklearn.datasets import load_iris           # Sample multi-class dataset for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper-Parameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to Fit Model, Evaluate Performance, and Tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to tune and evaluate a model, calculate predictions, and create a residuals chart\n",
    "def tune_eval_classifier(a_ds_name, a_model, a_param_grid):\n",
    "    # Get the model information\n",
    "    m_info = str(a_model)\n",
    "    \n",
    "    # Get the name of this model\n",
    "    m_name = m_info.split(\"(\",1)[0]    \n",
    "    \n",
    "    # Get the GridSearch estimator using the specified classifier model\n",
    "    # and parameter grid and score the results uging using 5-fold cross-validation\n",
    "    tuning_classifier = GridSearchCV( a_model, param_grid=a_param_grid, cv=5, return_train_score=False, n_jobs=-1)\n",
    "    \n",
    "    # Find the parameters providing the best performance \n",
    "    # Note: Not using scaled y in this case\n",
    "    tuning_classifier.fit(X_train_scaled, y_train.reshape(-1) )\n",
    "    \n",
    "    # Calculate the Precision Score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score)\n",
    "    m_precision_score = precision_score( y_test, tuning_classifier.predict(X_test_scaled), average='micro' )\n",
    "\n",
    "    # Calculate the Recall Score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score)\n",
    "    m_recall_score = recall_score( y_test, tuning_classifier.predict(X_test_scaled), average='micro' )\n",
    "\n",
    "    # Calculate the F1 Score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score)\n",
    "    # Note: Could also have just used f1 = 2*(precision_score * recall_score) / (precision_score + recall_score)\n",
    "    m_f1_score = f1_score( y_test, tuning_classifier.predict(X_test_scaled), average='micro' )\n",
    "\n",
    "    # Print a separator\n",
    "    print(\"*\"*80)\n",
    "    \n",
    "    print(f\"\\nModel: {m_name}, Best Score: {tuning_classifier.best_score_}\")\n",
    "    print(f\"Best Parameters: {tuning_classifier.best_params_}\")\n",
    "    print(f\"F1: {m_f1_score}, Precision: {m_precision_score}, Recall: {m_recall_score}\")\n",
    "    \n",
    "    # Return the results\n",
    "    a_result = {\n",
    "        'Dataset': a_ds_name,\n",
    "        'Classifier': m_name,\n",
    "        'Best Score': tuning_classifier.best_score_,\n",
    "        'Best Parameters': tuning_classifier.best_params_,\n",
    "        'F1': m_f1_score,\n",
    "        'Precision': m_precision_score,\n",
    "        'Recall': m_recall_score,\n",
    "        'Mean Fit Time (ms)': 1000.0*tuning_classifier.cv_results_['mean_fit_time'][ tuning_classifier.best_index_ ],\n",
    "        'Std Dev Fit Time (ms)': 1000.0*tuning_classifier.cv_results_['std_fit_time'][ tuning_classifier.best_index_ ],\n",
    "\n",
    "        'Best Index': tuning_classifier.best_index_,\n",
    "        'CV Results': tuning_classifier.cv_results_,\n",
    "    }\n",
    "    \n",
    "    return a_result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare to gather summary results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize list of dictionary of results for summary in a dataframe later\n",
    "model_results = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iris Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])\n",
      "Feature Names: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "Target Names: ['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "# Import the Iris sample dataset as a dictionary\n",
    "iris = load_iris()\n",
    "\n",
    "print( f\"Iris {iris.keys()}\" )\n",
    "print( f\"Feature Names: {iris.feature_names}\" )\n",
    "print( f\"Target Names: {iris.target_names}\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4) (150,)\n"
     ]
    }
   ],
   "source": [
    "# Assign data and target variables\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "print( X.shape, y.shape )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data using train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(112, 1) (38, 1)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the outputs to provide a N by 1 vector (vs. array of size N)\n",
    "y_train = y_train.reshape(-1,1)\n",
    "y_test = y_test.reshape(-1,1)\n",
    "print(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the training data to create a scaler to standard normal distributions for each numerical feature and output\n",
    "X_scaler = StandardScaler().fit(X_train)\n",
    "y_scaler = StandardScaler().fit(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the training and test data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)\n",
    "\n",
    "y_train_scaled = y_scaler.transform(y_train)\n",
    "y_test_scaled = y_scaler.transform(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate classifiers - Iris Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jeff\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\Jeff\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\Jeff\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "\n",
      "Model: LogisticRegression, Best Score: 0.9196428571428571\n",
      "Best Parameters: {'C': 2.0, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "F1: 0.9473684210526315, Precision: 0.9473684210526315, Recall: 0.9473684210526315\n",
      "********************************************************************************\n",
      "\n",
      "Model: DecisionTreeClassifier, Best Score: 0.9464285714285714\n",
      "Best Parameters: {'max_depth': None, 'max_features': 'log2'}\n",
      "F1: 0.9473684210526315, Precision: 0.9473684210526315, Recall: 0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jeff\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\Jeff\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "\n",
      "Model: RandomForestClassifier, Best Score: 0.9553571428571429\n",
      "Best Parameters: {'max_depth': 1, 'max_features': None}\n",
      "F1: 0.9736842105263158, Precision: 0.9736842105263158, Recall: 0.9736842105263158\n",
      "********************************************************************************\n",
      "\n",
      "Model: SVC, Best Score: 0.9642857142857143\n",
      "Best Parameters: {'C': 2.0, 'kernel': 'linear', 'max_iter': 100}\n",
      "F1: 0.9736842105263158, Precision: 0.9736842105263158, Recall: 0.9736842105263158\n",
      "********************************************************************************\n",
      "\n",
      "Model: KNeighborsClassifier, Best Score: 0.9464285714285714\n",
      "Best Parameters: {'algorithm': 'auto', 'n_neighbors': 5}\n",
      "F1: 0.9736842105263158, Precision: 0.9736842105263158, Recall: 0.9736842105263158\n",
      "4.98 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jeff\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\Jeff\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 4.98 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -n1 -r1 -o  # Time the overall time required to evaluate all classifiers\n",
    "\n",
    "# Create a list of classifers and associated parameter grid for hyperparameter tuning\n",
    "classifier_dict = [\n",
    "    {\n",
    "        # LogisticRegression\n",
    "        'cls': LogisticRegression(),\n",
    "        'cls_param_grid': {'solver': [\"liblinear\", \"newton-cg\", \"lbfgs\", \"sag\", \"saga\"],\n",
    "                           'max_iter': [ 100, 500, 1000, 5000, 10000 ],\n",
    "                           'C': [1.0, 2.0, 5.0, 10.0, 20.0 ]}\n",
    "    },\n",
    "    {\n",
    "        # Decision Tree\n",
    "        'cls': tree.DecisionTreeClassifier(),\n",
    "        'cls_param_grid': {'max_depth': [ None, 16, 8, 4, 2, 1 ],\n",
    "                         'max_features': [ None, \"sqrt\", \"log2\" ]}\n",
    "    },\n",
    "    {\n",
    "        # Random Forest\n",
    "        'cls': RandomForestClassifier(),\n",
    "        'cls_param_grid': {'max_depth': [ None, 16, 8, 4, 2, 1 ],\n",
    "                         'max_features': [ None, \"sqrt\", \"log2\" ]}\n",
    "    },\n",
    "    {\n",
    "        # Support Vector Machine\n",
    "        'cls': svm.SVC(),\n",
    "        'cls_param_grid': {'kernel': [ \"linear\", \"poly\", \"rbf\", \"sigmoid\" ],\n",
    "                         'max_iter': [ 100, 500, 1000, 5000, 10000 ],\n",
    "                         'C': [1.0, 2.0, 5.0, 10.0, 20.0 ]}\n",
    "    },\n",
    "\n",
    "    {\n",
    "        # K-Nearest Neighbors\n",
    "        'cls': KNeighborsClassifier(),\n",
    "        'cls_param_grid': { 'n_neighbors': [ 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 ],\n",
    "                            'algorithm': [ 'auto', 'ball_tree', 'kd_tree', 'brute' ]\n",
    "                          }\n",
    "    }\n",
    "]\n",
    "\n",
    "\n",
    "# Evaluate each classifier with hyperparameter tuning\n",
    "for c in classifier_dict:\n",
    "    # Evaluate the classifer performance, create a plot    \n",
    "    model_result_item = tune_eval_classifier('Iris', c['cls'], c['cls_param_grid'])\n",
    "\n",
    "    # Add results for this dataset and model\n",
    "    model_results.append( model_result_item )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>Best Score</th>\n",
       "      <th>Best Parameters</th>\n",
       "      <th>F1</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Mean Fit Time (ms)</th>\n",
       "      <th>Std Dev Fit Time (ms)</th>\n",
       "      <th>Best Index</th>\n",
       "      <th>CV Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Iris</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.919643</td>\n",
       "      <td>{'C': 2.0, 'max_iter': 100, 'solver': 'newton-...</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>17.999935</td>\n",
       "      <td>0.632636</td>\n",
       "      <td>26</td>\n",
       "      <td>{'mean_fit_time': [0.0112060546875, 0.01900496...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Iris</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.946429</td>\n",
       "      <td>{'max_depth': None, 'max_features': 'log2'}</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.400114</td>\n",
       "      <td>0.490038</td>\n",
       "      <td>2</td>\n",
       "      <td>{'mean_fit_time': [0.0010003089904785157, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Iris</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.955357</td>\n",
       "      <td>{'max_depth': 1, 'max_features': None}</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>18.800640</td>\n",
       "      <td>2.482848</td>\n",
       "      <td>15</td>\n",
       "      <td>{'mean_fit_time': [0.021199560165405272, 0.026...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Iris</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>{'C': 2.0, 'kernel': 'linear', 'max_iter': 100}</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.200272</td>\n",
       "      <td>0.400543</td>\n",
       "      <td>20</td>\n",
       "      <td>{'mean_fit_time': [0.0009999275207519531, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Iris</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.946429</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 5}</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>1.000071</td>\n",
       "      <td>0.010132</td>\n",
       "      <td>2</td>\n",
       "      <td>{'mean_fit_time': [0.000800180435180664, 0.000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dataset              Classifier  Best Score  \\\n",
       "0    Iris      LogisticRegression    0.919643   \n",
       "1    Iris  DecisionTreeClassifier    0.946429   \n",
       "2    Iris  RandomForestClassifier    0.955357   \n",
       "3    Iris                     SVC    0.964286   \n",
       "4    Iris    KNeighborsClassifier    0.946429   \n",
       "\n",
       "                                     Best Parameters        F1  Precision  \\\n",
       "0  {'C': 2.0, 'max_iter': 100, 'solver': 'newton-...  0.947368   0.947368   \n",
       "1        {'max_depth': None, 'max_features': 'log2'}  0.947368   0.947368   \n",
       "2             {'max_depth': 1, 'max_features': None}  0.973684   0.973684   \n",
       "3    {'C': 2.0, 'kernel': 'linear', 'max_iter': 100}  0.973684   0.973684   \n",
       "4            {'algorithm': 'auto', 'n_neighbors': 5}  0.973684   0.973684   \n",
       "\n",
       "     Recall  Mean Fit Time (ms)  Std Dev Fit Time (ms)  Best Index  \\\n",
       "0  0.947368           17.999935               0.632636          26   \n",
       "1  0.947368            0.400114               0.490038           2   \n",
       "2  0.973684           18.800640               2.482848          15   \n",
       "3  0.973684            0.200272               0.400543          20   \n",
       "4  0.973684            1.000071               0.010132           2   \n",
       "\n",
       "                                          CV Results  \n",
       "0  {'mean_fit_time': [0.0112060546875, 0.01900496...  \n",
       "1  {'mean_fit_time': [0.0010003089904785157, 0.00...  \n",
       "2  {'mean_fit_time': [0.021199560165405272, 0.026...  \n",
       "3  {'mean_fit_time': [0.0009999275207519531, 0.00...  \n",
       "4  {'mean_fit_time': [0.000800180435180664, 0.000...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pd.DataFrame(model_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voice Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meanfreq</th>\n",
       "      <th>sd</th>\n",
       "      <th>median</th>\n",
       "      <th>Q25</th>\n",
       "      <th>Q75</th>\n",
       "      <th>IQR</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurt</th>\n",
       "      <th>sp.ent</th>\n",
       "      <th>sfm</th>\n",
       "      <th>...</th>\n",
       "      <th>centroid</th>\n",
       "      <th>meanfun</th>\n",
       "      <th>minfun</th>\n",
       "      <th>maxfun</th>\n",
       "      <th>meandom</th>\n",
       "      <th>mindom</th>\n",
       "      <th>maxdom</th>\n",
       "      <th>dfrange</th>\n",
       "      <th>modindx</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.059781</td>\n",
       "      <td>0.064241</td>\n",
       "      <td>0.032027</td>\n",
       "      <td>0.015071</td>\n",
       "      <td>0.090193</td>\n",
       "      <td>0.075122</td>\n",
       "      <td>12.863462</td>\n",
       "      <td>274.402906</td>\n",
       "      <td>0.893369</td>\n",
       "      <td>0.491918</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059781</td>\n",
       "      <td>0.084279</td>\n",
       "      <td>0.015702</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.066009</td>\n",
       "      <td>0.067310</td>\n",
       "      <td>0.040229</td>\n",
       "      <td>0.019414</td>\n",
       "      <td>0.092666</td>\n",
       "      <td>0.073252</td>\n",
       "      <td>22.423285</td>\n",
       "      <td>634.613855</td>\n",
       "      <td>0.892193</td>\n",
       "      <td>0.513724</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066009</td>\n",
       "      <td>0.107937</td>\n",
       "      <td>0.015826</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.009014</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.054688</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.077316</td>\n",
       "      <td>0.083829</td>\n",
       "      <td>0.036718</td>\n",
       "      <td>0.008701</td>\n",
       "      <td>0.131908</td>\n",
       "      <td>0.123207</td>\n",
       "      <td>30.757155</td>\n",
       "      <td>1024.927705</td>\n",
       "      <td>0.846389</td>\n",
       "      <td>0.478905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077316</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>0.015656</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.007990</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.046512</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.151228</td>\n",
       "      <td>0.072111</td>\n",
       "      <td>0.158011</td>\n",
       "      <td>0.096582</td>\n",
       "      <td>0.207955</td>\n",
       "      <td>0.111374</td>\n",
       "      <td>1.232831</td>\n",
       "      <td>4.177296</td>\n",
       "      <td>0.963322</td>\n",
       "      <td>0.727232</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151228</td>\n",
       "      <td>0.088965</td>\n",
       "      <td>0.017798</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.201497</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.554688</td>\n",
       "      <td>0.247119</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.135120</td>\n",
       "      <td>0.079146</td>\n",
       "      <td>0.124656</td>\n",
       "      <td>0.078720</td>\n",
       "      <td>0.206045</td>\n",
       "      <td>0.127325</td>\n",
       "      <td>1.101174</td>\n",
       "      <td>4.333713</td>\n",
       "      <td>0.971955</td>\n",
       "      <td>0.783568</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135120</td>\n",
       "      <td>0.106398</td>\n",
       "      <td>0.016931</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.712812</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>5.484375</td>\n",
       "      <td>5.476562</td>\n",
       "      <td>0.208274</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   meanfreq        sd    median       Q25       Q75       IQR       skew  \\\n",
       "0  0.059781  0.064241  0.032027  0.015071  0.090193  0.075122  12.863462   \n",
       "1  0.066009  0.067310  0.040229  0.019414  0.092666  0.073252  22.423285   \n",
       "2  0.077316  0.083829  0.036718  0.008701  0.131908  0.123207  30.757155   \n",
       "3  0.151228  0.072111  0.158011  0.096582  0.207955  0.111374   1.232831   \n",
       "4  0.135120  0.079146  0.124656  0.078720  0.206045  0.127325   1.101174   \n",
       "\n",
       "          kurt    sp.ent       sfm  ...  centroid   meanfun    minfun  \\\n",
       "0   274.402906  0.893369  0.491918  ...  0.059781  0.084279  0.015702   \n",
       "1   634.613855  0.892193  0.513724  ...  0.066009  0.107937  0.015826   \n",
       "2  1024.927705  0.846389  0.478905  ...  0.077316  0.098706  0.015656   \n",
       "3     4.177296  0.963322  0.727232  ...  0.151228  0.088965  0.017798   \n",
       "4     4.333713  0.971955  0.783568  ...  0.135120  0.106398  0.016931   \n",
       "\n",
       "     maxfun   meandom    mindom    maxdom   dfrange   modindx  label  \n",
       "0  0.275862  0.007812  0.007812  0.007812  0.000000  0.000000   male  \n",
       "1  0.250000  0.009014  0.007812  0.054688  0.046875  0.052632   male  \n",
       "2  0.271186  0.007990  0.007812  0.015625  0.007812  0.046512   male  \n",
       "3  0.250000  0.201497  0.007812  0.562500  0.554688  0.247119   male  \n",
       "4  0.266667  0.712812  0.007812  5.484375  5.476562  0.208274   male  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Import Data and Pre-Process: Voice Dataset\n",
    "voice = pd.read_csv('resources/voice.csv')\n",
    "voice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check datatypes to ensure numerical values are floats vs. objects -- CONFIRMED\n",
    "# voice.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3168, 20) (3168,)\n"
     ]
    }
   ],
   "source": [
    "# Assign X (data) and y (target)\n",
    "X = voice.drop(\"label\", axis=1)\n",
    "y = voice[\"label\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the output strings using a Categorical Binarizer\n",
    "label_encoder = LabelBinarizer()\n",
    "y_encoded = label_encoder.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data using train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, random_state=1, stratify=y_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the outputs to provide a N by 1 vector (vs. array of size N)\n",
    "# y_train = y_train.reshape(-1,1)\n",
    "# y_test = y_test.reshape(-1,1)\n",
    "# print(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the training data to create a scaler to standard normal distributions for each numerical feature and output\n",
    "X_scaler = StandardScaler().fit(X_train)\n",
    "y_scaler = StandardScaler().fit(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the training and test data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)\n",
    "\n",
    "y_train_scaled = y_scaler.transform(y_train)\n",
    "y_test_scaled = y_scaler.transform(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate classifiers - Voice Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "\n",
      "Model: LogisticRegression, Best Score: 0.9705387205387206\n",
      "Best Parameters: {'C': 1.0, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "F1: 0.9772727272727273, Precision: 0.9772727272727273, Recall: 0.9772727272727273\n",
      "********************************************************************************\n",
      "\n",
      "Model: DecisionTreeClassifier, Best Score: 0.9650673400673401\n",
      "Best Parameters: {'max_depth': 4, 'max_features': None}\n",
      "F1: 0.9734848484848485, Precision: 0.9734848484848485, Recall: 0.9734848484848485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jeff\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "\n",
      "Model: RandomForestClassifier, Best Score: 0.9776936026936027\n",
      "Best Parameters: {'max_depth': 8, 'max_features': 'sqrt'}\n",
      "F1: 0.9797979797979798, Precision: 0.9797979797979798, Recall: 0.9797979797979798\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jeff\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\sklearn\\svm\\base.py:241: ConvergenceWarning: Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "\n",
      "Model: SVC, Best Score: 0.9797979797979798\n",
      "Best Parameters: {'C': 2.0, 'kernel': 'rbf', 'max_iter': 500}\n",
      "F1: 0.9873737373737373, Precision: 0.9873737373737373, Recall: 0.9873737373737373\n",
      "********************************************************************************\n",
      "\n",
      "Model: KNeighborsClassifier, Best Score: 0.9675925925925926\n",
      "Best Parameters: {'algorithm': 'auto', 'n_neighbors': 1}\n",
      "F1: 0.9823232323232324, Precision: 0.9823232323232324, Recall: 0.9823232323232324\n",
      "18.1 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 18.1 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -n1 -r1 -o  # Time the overall time required to evaluate all classifiers\n",
    "\n",
    "# Create a list of classifers and associated parameter grid for hyperparameter tuning\n",
    "classifier_dict = [\n",
    "    {\n",
    "        # LogisticRegression\n",
    "        'cls': LogisticRegression(),\n",
    "        'cls_param_grid': {'solver': [\"liblinear\", \"newton-cg\", \"lbfgs\", \"sag\", \"saga\"],\n",
    "                           'max_iter': [ 100, 500, 1000, 5000, 10000 ],\n",
    "                           'C': [1.0, 2.0, 5.0, 10.0, 20.0 ]}\n",
    "    },\n",
    "    {\n",
    "        # Decision Tree\n",
    "        'cls': tree.DecisionTreeClassifier(),\n",
    "        'cls_param_grid': {'max_depth': [ None, 16, 8, 4, 2, 1 ],\n",
    "                         'max_features': [ None, \"sqrt\", \"log2\" ]}\n",
    "    },\n",
    "    {\n",
    "        # Random Forest\n",
    "        'cls': RandomForestClassifier(),\n",
    "        'cls_param_grid': {'max_depth': [ None, 16, 8, 4, 2, 1 ],\n",
    "                         'max_features': [ None, \"sqrt\", \"log2\" ]}\n",
    "    },\n",
    "    {\n",
    "        # Support Vector Machine\n",
    "        'cls': svm.SVC(),\n",
    "        'cls_param_grid': {'kernel': [ \"linear\", \"poly\", \"rbf\", \"sigmoid\" ],\n",
    "                         'max_iter': [ 100, 500, 1000, 5000, 10000 ],\n",
    "                         'C': [1.0, 2.0, 5.0, 10.0, 20.0 ]}\n",
    "    },\n",
    "\n",
    "    {\n",
    "        # K-Nearest Neighbors\n",
    "        'cls': KNeighborsClassifier(),\n",
    "        'cls_param_grid': { 'n_neighbors': [ 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 ],\n",
    "                            'algorithm': [ 'auto', 'ball_tree', 'kd_tree', 'brute' ]\n",
    "                          }\n",
    "    }\n",
    "]\n",
    "\n",
    "\n",
    "# Evaluate each classifier with hyperparameter tuning\n",
    "for c in classifier_dict:\n",
    "    # Evaluate the classifer performance, create a plot    \n",
    "    model_result_item = tune_eval_classifier('Voice', c['cls'], c['cls_param_grid'])\n",
    "\n",
    "    # Add results for this dataset and model\n",
    "    model_results.append( model_result_item )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>Best Score</th>\n",
       "      <th>Best Parameters</th>\n",
       "      <th>F1</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Mean Fit Time (ms)</th>\n",
       "      <th>Std Dev Fit Time (ms)</th>\n",
       "      <th>Best Index</th>\n",
       "      <th>CV Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Iris</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.919643</td>\n",
       "      <td>{'C': 2.0, 'max_iter': 100, 'solver': 'newton-...</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>17.999935</td>\n",
       "      <td>0.632636</td>\n",
       "      <td>26</td>\n",
       "      <td>{'mean_fit_time': [0.0112060546875, 0.01900496...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Iris</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.946429</td>\n",
       "      <td>{'max_depth': None, 'max_features': 'log2'}</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.400114</td>\n",
       "      <td>0.490038</td>\n",
       "      <td>2</td>\n",
       "      <td>{'mean_fit_time': [0.0010003089904785157, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Iris</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.955357</td>\n",
       "      <td>{'max_depth': 1, 'max_features': None}</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>18.800640</td>\n",
       "      <td>2.482848</td>\n",
       "      <td>15</td>\n",
       "      <td>{'mean_fit_time': [0.021199560165405272, 0.026...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Iris</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>{'C': 2.0, 'kernel': 'linear', 'max_iter': 100}</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.200272</td>\n",
       "      <td>0.400543</td>\n",
       "      <td>20</td>\n",
       "      <td>{'mean_fit_time': [0.0009999275207519531, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Iris</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.946429</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 5}</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>1.000071</td>\n",
       "      <td>0.010132</td>\n",
       "      <td>2</td>\n",
       "      <td>{'mean_fit_time': [0.000800180435180664, 0.000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Voice</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.970539</td>\n",
       "      <td>{'C': 1.0, 'max_iter': 100, 'solver': 'newton-...</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>78.004026</td>\n",
       "      <td>4.939072</td>\n",
       "      <td>1</td>\n",
       "      <td>{'mean_fit_time': [0.008799409866333008, 0.078...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Voice</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.965067</td>\n",
       "      <td>{'max_depth': 4, 'max_features': None}</td>\n",
       "      <td>0.973485</td>\n",
       "      <td>0.973485</td>\n",
       "      <td>0.973485</td>\n",
       "      <td>19.804573</td>\n",
       "      <td>0.749373</td>\n",
       "      <td>9</td>\n",
       "      <td>{'mean_fit_time': [0.03040037155151367, 0.0083...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Voice</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.977694</td>\n",
       "      <td>{'max_depth': 8, 'max_features': 'sqrt'}</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>66.199589</td>\n",
       "      <td>9.764994</td>\n",
       "      <td>7</td>\n",
       "      <td>{'mean_fit_time': [0.2124000072479248, 0.06420...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Voice</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>{'C': 2.0, 'kernel': 'rbf', 'max_iter': 500}</td>\n",
       "      <td>0.987374</td>\n",
       "      <td>0.987374</td>\n",
       "      <td>0.987374</td>\n",
       "      <td>40.601683</td>\n",
       "      <td>1.354254</td>\n",
       "      <td>31</td>\n",
       "      <td>{'mean_fit_time': [0.010400342941284179, 0.031...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Voice</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.967593</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 1}</td>\n",
       "      <td>0.982323</td>\n",
       "      <td>0.982323</td>\n",
       "      <td>0.982323</td>\n",
       "      <td>3.199911</td>\n",
       "      <td>0.400616</td>\n",
       "      <td>0</td>\n",
       "      <td>{'mean_fit_time': [0.003199911117553711, 0.003...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dataset              Classifier  Best Score  \\\n",
       "0    Iris      LogisticRegression    0.919643   \n",
       "1    Iris  DecisionTreeClassifier    0.946429   \n",
       "2    Iris  RandomForestClassifier    0.955357   \n",
       "3    Iris                     SVC    0.964286   \n",
       "4    Iris    KNeighborsClassifier    0.946429   \n",
       "5   Voice      LogisticRegression    0.970539   \n",
       "6   Voice  DecisionTreeClassifier    0.965067   \n",
       "7   Voice  RandomForestClassifier    0.977694   \n",
       "8   Voice                     SVC    0.979798   \n",
       "9   Voice    KNeighborsClassifier    0.967593   \n",
       "\n",
       "                                     Best Parameters        F1  Precision  \\\n",
       "0  {'C': 2.0, 'max_iter': 100, 'solver': 'newton-...  0.947368   0.947368   \n",
       "1        {'max_depth': None, 'max_features': 'log2'}  0.947368   0.947368   \n",
       "2             {'max_depth': 1, 'max_features': None}  0.973684   0.973684   \n",
       "3    {'C': 2.0, 'kernel': 'linear', 'max_iter': 100}  0.973684   0.973684   \n",
       "4            {'algorithm': 'auto', 'n_neighbors': 5}  0.973684   0.973684   \n",
       "5  {'C': 1.0, 'max_iter': 100, 'solver': 'newton-...  0.977273   0.977273   \n",
       "6             {'max_depth': 4, 'max_features': None}  0.973485   0.973485   \n",
       "7           {'max_depth': 8, 'max_features': 'sqrt'}  0.979798   0.979798   \n",
       "8       {'C': 2.0, 'kernel': 'rbf', 'max_iter': 500}  0.987374   0.987374   \n",
       "9            {'algorithm': 'auto', 'n_neighbors': 1}  0.982323   0.982323   \n",
       "\n",
       "     Recall  Mean Fit Time (ms)  Std Dev Fit Time (ms)  Best Index  \\\n",
       "0  0.947368           17.999935               0.632636          26   \n",
       "1  0.947368            0.400114               0.490038           2   \n",
       "2  0.973684           18.800640               2.482848          15   \n",
       "3  0.973684            0.200272               0.400543          20   \n",
       "4  0.973684            1.000071               0.010132           2   \n",
       "5  0.977273           78.004026               4.939072           1   \n",
       "6  0.973485           19.804573               0.749373           9   \n",
       "7  0.979798           66.199589               9.764994           7   \n",
       "8  0.987374           40.601683               1.354254          31   \n",
       "9  0.982323            3.199911               0.400616           0   \n",
       "\n",
       "                                          CV Results  \n",
       "0  {'mean_fit_time': [0.0112060546875, 0.01900496...  \n",
       "1  {'mean_fit_time': [0.0010003089904785157, 0.00...  \n",
       "2  {'mean_fit_time': [0.021199560165405272, 0.026...  \n",
       "3  {'mean_fit_time': [0.0009999275207519531, 0.00...  \n",
       "4  {'mean_fit_time': [0.000800180435180664, 0.000...  \n",
       "5  {'mean_fit_time': [0.008799409866333008, 0.078...  \n",
       "6  {'mean_fit_time': [0.03040037155151367, 0.0083...  \n",
       "7  {'mean_fit_time': [0.2124000072479248, 0.06420...  \n",
       "8  {'mean_fit_time': [0.010400342941284179, 0.031...  \n",
       "9  {'mean_fit_time': [0.003199911117553711, 0.003...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(model_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary - Iris and Voice Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>Best Score</th>\n",
       "      <th>F1</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Mean Fit Time (ms)</th>\n",
       "      <th>Std Dev Fit Time (ms)</th>\n",
       "      <th>Best Parameters</th>\n",
       "      <th>Best Index</th>\n",
       "      <th>CV Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Iris</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.919643</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>17.999935</td>\n",
       "      <td>0.632636</td>\n",
       "      <td>{'C': 2.0, 'max_iter': 100, 'solver': 'newton-...</td>\n",
       "      <td>26</td>\n",
       "      <td>{'mean_fit_time': [0.0112060546875, 0.01900496...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Iris</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.946429</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.400114</td>\n",
       "      <td>0.490038</td>\n",
       "      <td>{'max_depth': None, 'max_features': 'log2'}</td>\n",
       "      <td>2</td>\n",
       "      <td>{'mean_fit_time': [0.0010003089904785157, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Iris</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.955357</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>18.800640</td>\n",
       "      <td>2.482848</td>\n",
       "      <td>{'max_depth': 1, 'max_features': None}</td>\n",
       "      <td>15</td>\n",
       "      <td>{'mean_fit_time': [0.021199560165405272, 0.026...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Iris</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.200272</td>\n",
       "      <td>0.400543</td>\n",
       "      <td>{'C': 2.0, 'kernel': 'linear', 'max_iter': 100}</td>\n",
       "      <td>20</td>\n",
       "      <td>{'mean_fit_time': [0.0009999275207519531, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Iris</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.946429</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>1.000071</td>\n",
       "      <td>0.010132</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 5}</td>\n",
       "      <td>2</td>\n",
       "      <td>{'mean_fit_time': [0.000800180435180664, 0.000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Voice</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.970539</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>78.004026</td>\n",
       "      <td>4.939072</td>\n",
       "      <td>{'C': 1.0, 'max_iter': 100, 'solver': 'newton-...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'mean_fit_time': [0.008799409866333008, 0.078...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Voice</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.965067</td>\n",
       "      <td>0.973485</td>\n",
       "      <td>0.973485</td>\n",
       "      <td>0.973485</td>\n",
       "      <td>19.804573</td>\n",
       "      <td>0.749373</td>\n",
       "      <td>{'max_depth': 4, 'max_features': None}</td>\n",
       "      <td>9</td>\n",
       "      <td>{'mean_fit_time': [0.03040037155151367, 0.0083...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Voice</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.977694</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>66.199589</td>\n",
       "      <td>9.764994</td>\n",
       "      <td>{'max_depth': 8, 'max_features': 'sqrt'}</td>\n",
       "      <td>7</td>\n",
       "      <td>{'mean_fit_time': [0.2124000072479248, 0.06420...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Voice</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>0.987374</td>\n",
       "      <td>0.987374</td>\n",
       "      <td>0.987374</td>\n",
       "      <td>40.601683</td>\n",
       "      <td>1.354254</td>\n",
       "      <td>{'C': 2.0, 'kernel': 'rbf', 'max_iter': 500}</td>\n",
       "      <td>31</td>\n",
       "      <td>{'mean_fit_time': [0.010400342941284179, 0.031...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Voice</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.967593</td>\n",
       "      <td>0.982323</td>\n",
       "      <td>0.982323</td>\n",
       "      <td>0.982323</td>\n",
       "      <td>3.199911</td>\n",
       "      <td>0.400616</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 1}</td>\n",
       "      <td>0</td>\n",
       "      <td>{'mean_fit_time': [0.003199911117553711, 0.003...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dataset              Classifier  Best Score        F1  Precision    Recall  \\\n",
       "0    Iris      LogisticRegression    0.919643  0.947368   0.947368  0.947368   \n",
       "1    Iris  DecisionTreeClassifier    0.946429  0.947368   0.947368  0.947368   \n",
       "2    Iris  RandomForestClassifier    0.955357  0.973684   0.973684  0.973684   \n",
       "3    Iris                     SVC    0.964286  0.973684   0.973684  0.973684   \n",
       "4    Iris    KNeighborsClassifier    0.946429  0.973684   0.973684  0.973684   \n",
       "5   Voice      LogisticRegression    0.970539  0.977273   0.977273  0.977273   \n",
       "6   Voice  DecisionTreeClassifier    0.965067  0.973485   0.973485  0.973485   \n",
       "7   Voice  RandomForestClassifier    0.977694  0.979798   0.979798  0.979798   \n",
       "8   Voice                     SVC    0.979798  0.987374   0.987374  0.987374   \n",
       "9   Voice    KNeighborsClassifier    0.967593  0.982323   0.982323  0.982323   \n",
       "\n",
       "   Mean Fit Time (ms)  Std Dev Fit Time (ms)  \\\n",
       "0           17.999935               0.632636   \n",
       "1            0.400114               0.490038   \n",
       "2           18.800640               2.482848   \n",
       "3            0.200272               0.400543   \n",
       "4            1.000071               0.010132   \n",
       "5           78.004026               4.939072   \n",
       "6           19.804573               0.749373   \n",
       "7           66.199589               9.764994   \n",
       "8           40.601683               1.354254   \n",
       "9            3.199911               0.400616   \n",
       "\n",
       "                                     Best Parameters  Best Index  \\\n",
       "0  {'C': 2.0, 'max_iter': 100, 'solver': 'newton-...          26   \n",
       "1        {'max_depth': None, 'max_features': 'log2'}           2   \n",
       "2             {'max_depth': 1, 'max_features': None}          15   \n",
       "3    {'C': 2.0, 'kernel': 'linear', 'max_iter': 100}          20   \n",
       "4            {'algorithm': 'auto', 'n_neighbors': 5}           2   \n",
       "5  {'C': 1.0, 'max_iter': 100, 'solver': 'newton-...           1   \n",
       "6             {'max_depth': 4, 'max_features': None}           9   \n",
       "7           {'max_depth': 8, 'max_features': 'sqrt'}           7   \n",
       "8       {'C': 2.0, 'kernel': 'rbf', 'max_iter': 500}          31   \n",
       "9            {'algorithm': 'auto', 'n_neighbors': 1}           0   \n",
       "\n",
       "                                          CV Results  \n",
       "0  {'mean_fit_time': [0.0112060546875, 0.01900496...  \n",
       "1  {'mean_fit_time': [0.0010003089904785157, 0.00...  \n",
       "2  {'mean_fit_time': [0.021199560165405272, 0.026...  \n",
       "3  {'mean_fit_time': [0.0009999275207519531, 0.00...  \n",
       "4  {'mean_fit_time': [0.000800180435180664, 0.000...  \n",
       "5  {'mean_fit_time': [0.008799409866333008, 0.078...  \n",
       "6  {'mean_fit_time': [0.03040037155151367, 0.0083...  \n",
       "7  {'mean_fit_time': [0.2124000072479248, 0.06420...  \n",
       "8  {'mean_fit_time': [0.010400342941284179, 0.031...  \n",
       "9  {'mean_fit_time': [0.003199911117553711, 0.003...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare a dataframe of the results\n",
    "summary_df = pd.DataFrame( model_results,\n",
    "                         columns = [ 'Dataset', 'Classifier',\n",
    "                                    'Best Score', \"F1\", 'Precision', 'Recall',\n",
    "                                    'Mean Fit Time (ms)', 'Std Dev Fit Time (ms)',\n",
    "                                    'Best Parameters',\n",
    "                                    'Best Index', 'CV Results' ])\n",
    "\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Best Score                0.959283\n",
       "F1                        0.971604\n",
       "Precision                 0.971604\n",
       "Recall                    0.971604\n",
       "Mean Fit Time (ms)       24.621081\n",
       "Std Dev Fit Time (ms)     2.122451\n",
       "Best Index               11.300000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to Plot Classifier Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function: Create a plot of classifier performance\n",
    "def plot_summary(a_ax, a_df, a_plot_suboptimal = True, a_title = None, a_x_col = \"Mean Fit Time (ms)\", a_y_col = \"Best Score\"):\n",
    "    \n",
    "    # Set the title\n",
    "    if a_title is None:\n",
    "        # If no title specified, use the Dataset and Classifier values\n",
    "        # from the first data point\n",
    "        title_text = f\"Tuned Classifier Performance - {a_df['Dataset'][0]} / {a_df['Classifier'][0]}\"\n",
    "        title_text += f\"\\n( {a_y_col} vs. {a_x_col} )\"\n",
    "        a_ax.set_title( title_text )\n",
    "    else:\n",
    "        a_ax.set_title(a_title)\n",
    "          \n",
    "    # Ensure that flag controling plot of suboptimal results is valid\n",
    "    if (a_plot_suboptimal is not None) and (a_plot_suboptimal == True):\n",
    "        plot_subopt = True\n",
    "    else:\n",
    "        plot_subopt = False\n",
    "    \n",
    "    # If x and y axis limits have been provided, then use them\n",
    "    # If not, then use default limits based upon the data\n",
    "    x_min = 0.9*min(a_df[a_x_col])\n",
    "    x_max = 1.1*max(a_df[a_x_col])\n",
    "    y_min = 0.99*min(a_df[a_y_col])\n",
    "    y_max = min(1.01, 1.02*max(a_df[a_y_col]))\n",
    "        \n",
    "    # Set the y-axis range\n",
    "    a_ax.set_ylim( y_min,y_max )\n",
    "    a_ax.set_ylabel(a_y_col)\n",
    "\n",
    "    # Set the x-axis range\n",
    "    a_ax.set_xlim( x_min, x_max )\n",
    "    a_ax.set_xlabel(a_x_col)\n",
    "    \n",
    "    # Plot suboptimal values if the flag is set\n",
    "    if plot_subopt:\n",
    "\n",
    "        # Plot the results of all parameters tested for this dataset/classifier\n",
    "        # (as background for the plots of the \"best\" results below)\n",
    "        # Define a dictionary to map the overall plot dataframe headings to the CV Results dictionary headings\n",
    "        fieldmap_dict = {\n",
    "            'Best Score': { 'field': 'mean_test_score', 'scale': 1.0 },\n",
    "            'Mean Fit Time (ms)': { 'field': 'mean_fit_time', 'scale': 1000.0 },\n",
    "            'Std Dev Fit Time (ms)': { 'field': 'std_fit_time', 'scale': 1000.0 }\n",
    "        }\n",
    "\n",
    "        # If the mapped x and y fields are valid, attempt to plot them\n",
    "        try:\n",
    "            mapped_x_col = fieldmap_dict[ a_x_col ]['field']\n",
    "            mapped_y_col = fieldmap_dict[ a_y_col ]['field']\n",
    "\n",
    "            try:\n",
    "                # Readjust the max and min axis limits to consider all of the optimal and non-optimal results\n",
    "                for cv_item in a_df['CV Results']:\n",
    "                    x_min = min(x_min, 0.9 * fieldmap_dict[ a_x_col ]['scale'] * min(cv_item[mapped_x_col]) )\n",
    "                    x_max = max(x_max, 1.1 * fieldmap_dict[ a_x_col ]['scale'] * max(cv_item[mapped_x_col]) )\n",
    "                    y_min = min(y_min, 0.99 * fieldmap_dict[ a_y_col ]['scale'] * min(cv_item[mapped_y_col]) )\n",
    "                    y_max = max(y_max, min(1.01, 1.02 * fieldmap_dict[ a_y_col ]['scale'] * max(cv_item[mapped_y_col])) )\n",
    "\n",
    "                    # print(f\"Readjusting Limits - [x_min: {x_min:.4f} to x_max: {x_max:.4f}], [y_min: {y_min:.4f} to y_max: {y_max:.4f}]\")\n",
    "\n",
    "                # Set the y-axis range\n",
    "                a_ax.set_ylim( y_min,y_max )\n",
    "                a_ax.set_ylabel(a_y_col)\n",
    "\n",
    "                # Set the x-axis range\n",
    "                a_ax.set_xlim( x_min, x_max )\n",
    "                a_ax.set_xlabel(a_x_col)\n",
    "\n",
    "                # For all results in this dataframe, plot all of the optimal and non-optimal results\n",
    "                for cv_item in a_df['CV Results']:\n",
    "\n",
    "                    # Plot the set of points associated with this dataframe for this result\n",
    "                    a_ax.scatter( fieldmap_dict[ a_x_col ]['scale']*cv_item[mapped_x_col],\n",
    "                                  fieldmap_dict[ a_y_col ]['scale']*cv_item[mapped_y_col],\n",
    "                                  c=\"#D0D0D080\" )\n",
    "\n",
    "            except:\n",
    "                error_text = f\"NOTE: Could not plot optimal/non-optimal background results\"\n",
    "                error_text += f\"\\nmapped_x_col = {mapped_x_col}, mapped_y_col = {mapped_y_col}\"\n",
    "                print(error_text)\n",
    "\n",
    "        except:\n",
    "            error_text = f\"NOTE: Could not map Dataframe fields to CV Results fields\"\n",
    "            error_text += f\"\\na_x_col = {a_x_col}, a_y_col = {a_y_col}\"\n",
    "            print(error_text)\n",
    "            \n",
    "    # Plot the primary (i.e., optimized/tuned) data points in a distinctive color\n",
    "    a_ax.scatter( a_df[a_x_col], a_df[a_y_col], c=\"b\" )\n",
    "    \n",
    "    # Add grid lines\n",
    "    a_ax.grid(c='k', linestyle='-', linewidth=0.2)\n",
    "    \n",
    "    # Add text to key datapoints\n",
    "    # If the number of points is small then add a note for all of them\n",
    "    if len(a_df.index) < 8:\n",
    "        # Get the set of all indices - should already be unique since they are indices\n",
    "        # but for consistency will still make this a set\n",
    "        i_set = set(a_df.index)\n",
    "    \n",
    "    else:\n",
    "        # Otherwise, if the number of points is larger then annotate only key points\n",
    "        \n",
    "        # Find the index of the column a_y_col value with the lowest value\n",
    "        i_y_lowest = a_df[a_y_col].idxmin()\n",
    "        \n",
    "\n",
    "        # Find the index of the column a_y_col value with the highest value\n",
    "        i_y_highest = a_df[a_y_col].idxmax()\n",
    "\n",
    "        # Find the index of the column a_x_col value with the lowest value\n",
    "        i_x_lowest = a_df[a_x_col].idxmin()\n",
    "        \n",
    "\n",
    "        # Find the index of the column a_x_col value with the highest value\n",
    "        i_x_highest = a_df[a_x_col].idxmax()\n",
    "\n",
    "        # Get the unique indices for the key datapoints using a Python Set\n",
    "        # to avoid duplicate entries\n",
    "        i_set = set( [i_x_lowest, i_x_highest, i_y_lowest, i_y_highest] )\n",
    "\n",
    "    # Plot a text note for each of the key datapoints\n",
    "    for i in i_set:\n",
    "\n",
    "        # Add text to the datapoint\n",
    "        point_text = f\"({a_df[a_x_col][i]:0.4f}, {a_df[a_y_col][i]:0.4f})\"\n",
    "\n",
    "        try:\n",
    "            point_text += f\"\\n{a_df['Dataset'][i]} / {a_df['Classifier'][i]}\"\n",
    "#             point_text += f\"\\nBest Score: {a_df['Best Score'][i]:0.4f}\"\n",
    "#             point_text += f\"\\nMean Fit Time (ms): {a_df['Mean Fit Time (ms)'][i]:0.2f}\"\n",
    "#             point_text += f\"\\nStd Dev Fit Time (ms): {a_df['Std Dev Fit Time (ms)'][i]:0.2f}\"\n",
    "#             point_text += f\"\\nBest Parameters: \\n{a_df['Best Parameters'][i]}\"\n",
    "            \n",
    "        except:\n",
    "            pass\n",
    "\n",
    "        # Place the text at an offset from the datapoint\n",
    "        # x: No offset\n",
    "        x_offset = 0\n",
    "        x_offset = (x_max-x_min)/20 * (.25)\n",
    "#         x_offset = (x_max-x_min)/20 * ( 1 if i % 2 == 0 else -5 )\n",
    "        \n",
    "        # y: Offset by a fraction of the y-axis max-min range,\n",
    "        #    and positive for even indices, negative for odd indices\n",
    "        #    (to try to avoid overlap between adjacent datapoints)\n",
    "        y_offset = 0\n",
    "#         y_offset = (y_max-y_min)/20 * ( 1 if i % 2 == 0 else -6 )\n",
    "\n",
    "        # Plot the text\n",
    "        a_ax.text( a_df[a_x_col][i] + x_offset, a_df[a_y_col][i] + y_offset,\n",
    "                   point_text, c=\"#0000FFFF\" )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot each Dataset/Classifer combination on the same plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Classification Performance results: Best Score vs. Mean Fit Time (ms)\n",
    "fig2 = plt.figure(figsize=(8,8))\n",
    "\n",
    "# Create a single plot of all results\n",
    "ax = fig2.add_subplot( 1,1,1 )\n",
    "\n",
    "# Create the plot\n",
    "plot_summary(ax, summary_df, False, \"Tuned Classifier Performance - All Datasets/Classifiers\")\n",
    "\n",
    "# Save the image - use it for Summary of Analysis at the top of the notebook\n",
    "plt.savefig('docs/Figure-Hyper_Parameter_Tuning-Combined.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Classification Performance results: Best Score vs. Mean Fit Time (ms)\n",
    "fig2 = plt.figure(figsize=(10,10))\n",
    "\n",
    "# Create a single plot of all results\n",
    "ax = fig2.add_subplot( 1,1,1 )\n",
    "\n",
    "# Create the plot\n",
    "plot_summary(ax, summary_df, True, \"Tuned Classifier Performance - All Datasets/Classifiers\")\n",
    "\n",
    "# Save the image - use it for Summary of Analysis at the top of the notebook\n",
    "plt.savefig('docs/Figure-Hyper_Parameter_Tuning-Including_Suboptimal-Combined.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: Could not map Dataframe fields to CV Results fields\n",
      "a_x_col = F1, a_y_col = Best Score\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Classification Performance: Best Score vs. F1 results\n",
    "fig3 = plt.figure(figsize=(8,8))\n",
    "\n",
    "# Create a single plot of all results\n",
    "ax = fig3.add_subplot( 1,1,1 )\n",
    "\n",
    "# Create the plot\n",
    "plot_summary(ax, summary_df, True,\n",
    "             \"Tuned Classifier Best Score vs. F1 - All Datasets/Classifiers\",\n",
    "             \"F1\", \"Best Score\")\n",
    "\n",
    "# Save the image - use it for Summary of Analysis at the top of the notebook\n",
    "plt.savefig('docs/Figure-Hyper_Parameter_Tuning-BestScore_vs_F1-Combined.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: Could not map Dataframe fields to CV Results fields\n",
      "a_x_col = Recall, a_y_col = Precision\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Classification Performance: Precision vs Recall results\n",
    "fig3 = plt.figure(figsize=(8,8))\n",
    "\n",
    "# Create a single plot of all results\n",
    "ax = fig3.add_subplot( 1,1,1 )\n",
    "\n",
    "# Create the plot\n",
    "plot_summary(ax, summary_df, True,\n",
    "             \"Tuned Classifier Precision vs. Recall - All Datasets/Classifiers\",\n",
    "             \"Recall\", \"Precision\")\n",
    "\n",
    "# Save the image - use it for Summary of Analysis at the top of the notebook\n",
    "plt.savefig('docs/Figure-Hyper_Parameter_Tuning-Precision_vs_Recall-Combined.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot each Dataset/Classifer combination on separate subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x2520 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Classification Performance results\n",
    "fig1 = plt.figure(figsize=(15,35))\n",
    "\n",
    "# Create subplots for each Dataset/Classifier combination\n",
    "ds_list = summary_df['Dataset'].unique()\n",
    "cls_list = summary_df['Classifier'].unique()\n",
    "\n",
    "# Create subplots for (# Dataset) columns x (# Classifier) rows\n",
    "ax_list = []\n",
    "i_plot = 0\n",
    "for i_ds in range(len(ds_list)):\n",
    "    for i_cls in range(len(cls_list)):\n",
    "        \n",
    "        # Create the subplot axis and append it to the list of plots\n",
    "        # Rows: Classifiers, Columns: Datasets\n",
    "        # Position of a Plot: (Classifier index) * (size of row) + (Dataset index) + 1        \n",
    "        ax_list.append( fig1.add_subplot(len(cls_list), len(ds_list), i_cls*len(ds_list) + i_ds + 1  ) )\n",
    "\n",
    "        # Get the relevant subset of the dataframe for this subplot\n",
    "        target_df = summary_df[ (summary_df['Dataset'] == ds_list[i_ds])\n",
    "                               & (summary_df['Classifier'] == cls_list[i_cls]) ].reset_index(drop=True)\n",
    "\n",
    "        # Create the plot\n",
    "        plot_summary(ax_list[i_plot], target_df, True)\n",
    "\n",
    "        # Increment the plot counter\n",
    "        i_plot += 1\n",
    "        \n",
    "# Save the image - use it for Summary of Analysis at the top of the notebook\n",
    "plt.savefig('docs/Figure-Hyper_Parameter_Tuning-Subplots.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>Best Score</th>\n",
       "      <th>F1</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Mean Fit Time (ms)</th>\n",
       "      <th>Std Dev Fit Time (ms)</th>\n",
       "      <th>Best Parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Iris</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.9643</td>\n",
       "      <td>0.9737</td>\n",
       "      <td>0.9737</td>\n",
       "      <td>0.9737</td>\n",
       "      <td>0.2003</td>\n",
       "      <td>0.4005</td>\n",
       "      <td>{'C': 2.0, 'kernel': 'linear', 'max_iter': 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Iris</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.9554</td>\n",
       "      <td>0.9737</td>\n",
       "      <td>0.9737</td>\n",
       "      <td>0.9737</td>\n",
       "      <td>18.8006</td>\n",
       "      <td>2.4828</td>\n",
       "      <td>{'max_depth': 1, 'max_features': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Iris</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.9464</td>\n",
       "      <td>0.9737</td>\n",
       "      <td>0.9737</td>\n",
       "      <td>0.9737</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Iris</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.9464</td>\n",
       "      <td>0.9474</td>\n",
       "      <td>0.9474</td>\n",
       "      <td>0.9474</td>\n",
       "      <td>0.4001</td>\n",
       "      <td>0.4900</td>\n",
       "      <td>{'max_depth': None, 'max_features': 'log2'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Iris</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.9196</td>\n",
       "      <td>0.9474</td>\n",
       "      <td>0.9474</td>\n",
       "      <td>0.9474</td>\n",
       "      <td>17.9999</td>\n",
       "      <td>0.6326</td>\n",
       "      <td>{'C': 2.0, 'max_iter': 100, 'solver': 'newton-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Voice</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.9798</td>\n",
       "      <td>0.9874</td>\n",
       "      <td>0.9874</td>\n",
       "      <td>0.9874</td>\n",
       "      <td>40.6017</td>\n",
       "      <td>1.3543</td>\n",
       "      <td>{'C': 2.0, 'kernel': 'rbf', 'max_iter': 500}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Voice</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.9777</td>\n",
       "      <td>0.9798</td>\n",
       "      <td>0.9798</td>\n",
       "      <td>0.9798</td>\n",
       "      <td>66.1996</td>\n",
       "      <td>9.7650</td>\n",
       "      <td>{'max_depth': 8, 'max_features': 'sqrt'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Voice</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.9705</td>\n",
       "      <td>0.9773</td>\n",
       "      <td>0.9773</td>\n",
       "      <td>0.9773</td>\n",
       "      <td>78.0040</td>\n",
       "      <td>4.9391</td>\n",
       "      <td>{'C': 1.0, 'max_iter': 100, 'solver': 'newton-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Voice</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.9676</td>\n",
       "      <td>0.9823</td>\n",
       "      <td>0.9823</td>\n",
       "      <td>0.9823</td>\n",
       "      <td>3.1999</td>\n",
       "      <td>0.4006</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Voice</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.9651</td>\n",
       "      <td>0.9735</td>\n",
       "      <td>0.9735</td>\n",
       "      <td>0.9735</td>\n",
       "      <td>19.8046</td>\n",
       "      <td>0.7494</td>\n",
       "      <td>{'max_depth': 4, 'max_features': None}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dataset              Classifier  Best Score      F1  Precision  Recall  \\\n",
       "0    Iris                     SVC      0.9643  0.9737     0.9737  0.9737   \n",
       "1    Iris  RandomForestClassifier      0.9554  0.9737     0.9737  0.9737   \n",
       "2    Iris    KNeighborsClassifier      0.9464  0.9737     0.9737  0.9737   \n",
       "3    Iris  DecisionTreeClassifier      0.9464  0.9474     0.9474  0.9474   \n",
       "4    Iris      LogisticRegression      0.9196  0.9474     0.9474  0.9474   \n",
       "5   Voice                     SVC      0.9798  0.9874     0.9874  0.9874   \n",
       "6   Voice  RandomForestClassifier      0.9777  0.9798     0.9798  0.9798   \n",
       "7   Voice      LogisticRegression      0.9705  0.9773     0.9773  0.9773   \n",
       "8   Voice    KNeighborsClassifier      0.9676  0.9823     0.9823  0.9823   \n",
       "9   Voice  DecisionTreeClassifier      0.9651  0.9735     0.9735  0.9735   \n",
       "\n",
       "   Mean Fit Time (ms)  Std Dev Fit Time (ms)  \\\n",
       "0              0.2003                 0.4005   \n",
       "1             18.8006                 2.4828   \n",
       "2              1.0001                 0.0101   \n",
       "3              0.4001                 0.4900   \n",
       "4             17.9999                 0.6326   \n",
       "5             40.6017                 1.3543   \n",
       "6             66.1996                 9.7650   \n",
       "7             78.0040                 4.9391   \n",
       "8              3.1999                 0.4006   \n",
       "9             19.8046                 0.7494   \n",
       "\n",
       "                                     Best Parameters  \n",
       "0    {'C': 2.0, 'kernel': 'linear', 'max_iter': 100}  \n",
       "1             {'max_depth': 1, 'max_features': None}  \n",
       "2            {'algorithm': 'auto', 'n_neighbors': 5}  \n",
       "3        {'max_depth': None, 'max_features': 'log2'}  \n",
       "4  {'C': 2.0, 'max_iter': 100, 'solver': 'newton-...  \n",
       "5       {'C': 2.0, 'kernel': 'rbf', 'max_iter': 500}  \n",
       "6           {'max_depth': 8, 'max_features': 'sqrt'}  \n",
       "7  {'C': 1.0, 'max_iter': 100, 'solver': 'newton-...  \n",
       "8            {'algorithm': 'auto', 'n_neighbors': 1}  \n",
       "9             {'max_depth': 4, 'max_features': None}  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe to summarize overall results\n",
    "table_df = summary_df[['Dataset', 'Classifier', 'Best Score', 'F1', 'Precision', 'Recall',\n",
    "                       'Mean Fit Time (ms)', 'Std Dev Fit Time (ms)',\n",
    "                       'Best Parameters']]\n",
    "\n",
    "# Sort the results by Dataset and descending Best Score results\n",
    "table_df = table_df.sort_values(\n",
    "    by=['Dataset', 'Best Score', 'F1', 'Precision', 'Recall', 'Mean Fit Time (ms)', 'Std Dev Fit Time (ms)'],\n",
    "    ascending=[True, False, False, False, False, True, True], inplace=False).reset_index(drop=True, inplace=False)\n",
    "\n",
    "# Round the numerical fields\n",
    "table_df = table_df.round( decimals = {\n",
    "    'Best Score': 4,'F1': 4, 'Precision': 4, 'Recall': 4, 'Mean Fit Time (ms)': 4, 'Std Dev Fit Time (ms)': 4\n",
    "})\n",
    "\n",
    "# Convert the Best Parameters dictionary to a string\n",
    "table_df['Best Parameters'] = table_df['Best Parameters'].map(lambda x: str(x) )\n",
    "table_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Table: Performance results\n",
    "# fig_table_1 = plt.figure(figsize=(15,12))\n",
    "# fig_table_1 = plt.figure(figsize=(15,10))\n",
    "fig_table_1 = plt.figure()\n",
    "\n",
    "# Create axes for this table, but with no visible frame\n",
    "ax_table = fig_table_1.add_subplot(111, frame_on=False)\n",
    "\n",
    "# Hide the x and y axes\n",
    "ax_table.xaxis.set_visible(False)\n",
    "ax_table.yaxis.set_visible(False)\n",
    "\n",
    "# Create the table\n",
    "results_table = plt.table(\n",
    "            cellText=table_df.values,\n",
    "#             colWidths = [0.2]*len(table_df.columns),\n",
    "#             rowLabels=table_df.index,\n",
    "            colLabels=table_df.columns,\n",
    "            cellLoc = 'center',\n",
    "            rowLoc = 'center',\n",
    "            loc='top')\n",
    "\n",
    "# Set the fontsize to something readable\n",
    "results_table.auto_set_font_size(False)\n",
    "results_table.set_fontsize(12)\n",
    "\n",
    "# Set the column widths automatically\n",
    "results_table.auto_set_column_width(True)\n",
    "\n",
    "# Scale the table\n",
    "results_table.scale(4,4)\n",
    "\n",
    "# Show the resulting table\n",
    "plt.show()\n",
    "        \n",
    "# Save the image - use it for Summary of Analysis at the top of the notebook\n",
    "fig_table_1.savefig('docs/Table-Hyper_Parameter_Tuning.png', bbox_inches='tight') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.9"
  },
  "nbpresent": {
   "slides": {
    "03210a56-863e-4749-b7ba-ed75bfceceee": {
     "id": "03210a56-863e-4749-b7ba-ed75bfceceee",
     "prev": "86b3b05f-6e9a-49dc-8a83-97f72c348c5f",
     "regions": {
      "2be9481c-cff7-4f32-b835-90f2a2cb989a": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "46550b81-7dd8-4efc-b710-0f10002c9f2b",
        "part": "whole"
       },
       "id": "2be9481c-cff7-4f32-b835-90f2a2cb989a"
      },
      "3f484569-30ec-4529-8d3c-ac88b8c6dbfb": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "2b83c32a-d7e2-4db6-b45f-afbbf028fe90",
        "part": "whole"
       },
       "id": "3f484569-30ec-4529-8d3c-ac88b8c6dbfb"
      }
     }
    },
    "22531930-3fc8-45ff-a4f1-32e94c1d1455": {
     "id": "22531930-3fc8-45ff-a4f1-32e94c1d1455",
     "prev": "8eb735f6-11af-4f77-a4ed-b637fb18ac08",
     "regions": {
      "1407c818-c9fd-470e-8260-6b6904d888de": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "664c64ef-5517-4725-95cb-874bbc7711c7",
        "part": "whole"
       },
       "id": "1407c818-c9fd-470e-8260-6b6904d888de"
      }
     }
    },
    "29c55765-6994-4dbf-b82f-117b4f1e0cee": {
     "id": "29c55765-6994-4dbf-b82f-117b4f1e0cee",
     "prev": "b7c25bc1-4dd7-421b-9951-0c0c403c176d",
     "regions": {
      "44583f15-ec4f-4aec-8e7c-f01652e954fc": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "7c23961d-86fe-4d07-bc58-6d61f03c3731",
        "part": "whole"
       },
       "id": "44583f15-ec4f-4aec-8e7c-f01652e954fc"
      }
     }
    },
    "39c5764b-ab2c-4aae-8500-dc39319e4d38": {
     "id": "39c5764b-ab2c-4aae-8500-dc39319e4d38",
     "prev": "22531930-3fc8-45ff-a4f1-32e94c1d1455",
     "regions": {
      "d0237596-8095-44dc-81c6-6d9c6b204684": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "348e164e-a000-4def-92c0-db93f9a617a1",
        "part": "whole"
       },
       "id": "d0237596-8095-44dc-81c6-6d9c6b204684"
      }
     }
    },
    "39e950f1-f2c4-422a-8f45-835c5c61e7e0": {
     "id": "39e950f1-f2c4-422a-8f45-835c5c61e7e0",
     "prev": "4b5f1824-db4a-4ef3-9515-a88d3f2276c1",
     "regions": {
      "0a72cc48-c38f-4e8c-94d3-3198eccde68a": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "c3820b22-bf55-4dca-ba8b-f08060567b6e",
        "part": "whole"
       },
       "id": "0a72cc48-c38f-4e8c-94d3-3198eccde68a"
      },
      "b49c6eca-318f-41b8-a7a5-a620d18f639c": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "aeeeb44d-7825-4050-b7ba-70813bd3396b",
        "part": "whole"
       },
       "id": "b49c6eca-318f-41b8-a7a5-a620d18f639c"
      }
     }
    },
    "4792c0cc-ccd4-48bf-9b0e-a3c1f0f3008d": {
     "id": "4792c0cc-ccd4-48bf-9b0e-a3c1f0f3008d",
     "prev": "39c5764b-ab2c-4aae-8500-dc39319e4d38",
     "regions": {
      "d4adae26-ff2f-4a81-ad91-fd3a9bfd965c": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "4d6b8af6-438e-4034-bda1-1f977cf12441",
        "part": "whole"
       },
       "id": "d4adae26-ff2f-4a81-ad91-fd3a9bfd965c"
      },
      "d75a8261-ee89-4f4d-b388-2f429e112f44": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "a3b6e99c-2c85-47da-b29b-3b53c3c309b8",
        "part": "whole"
       },
       "id": "d75a8261-ee89-4f4d-b388-2f429e112f44"
      }
     }
    },
    "4b5f1824-db4a-4ef3-9515-a88d3f2276c1": {
     "id": "4b5f1824-db4a-4ef3-9515-a88d3f2276c1",
     "prev": "e92a9020-6631-400a-aa50-46c1fa0a5f0c",
     "regions": {
      "2bd81133-11e3-48df-bf12-2d0a57949bca": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "69fab88f-0864-47cc-933b-eaffb638cbf3",
        "part": "whole"
       },
       "id": "2bd81133-11e3-48df-bf12-2d0a57949bca"
      },
      "d3322861-28ab-4f1e-8a1f-3a7f4d3da8c1": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "3810c2af-fdbe-4f01-bc9f-2bb1a8ae70c6",
        "part": "whole"
       },
       "id": "d3322861-28ab-4f1e-8a1f-3a7f4d3da8c1"
      }
     }
    },
    "57992fb9-2f74-4e8e-8772-7b9f04606a56": {
     "id": "57992fb9-2f74-4e8e-8772-7b9f04606a56",
     "prev": "595093b4-3f86-406c-abb5-aea78e1241b3",
     "regions": {
      "3b3c8191-9f7e-4ab2-afac-b2b47008484a": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "a6d0bdff-9bba-472e-b354-da47b8761dbe",
        "part": "whole"
       },
       "id": "3b3c8191-9f7e-4ab2-afac-b2b47008484a"
      }
     }
    },
    "5907f515-df93-4e86-82c6-b8721b6e6346": {
     "id": "5907f515-df93-4e86-82c6-b8721b6e6346",
     "prev": "39e950f1-f2c4-422a-8f45-835c5c61e7e0",
     "regions": {
      "22f9005a-ed7a-4399-b501-497565ada2da": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "2e255efc-6ae1-4554-b32f-9400b47007c9",
        "part": "whole"
       },
       "id": "22f9005a-ed7a-4399-b501-497565ada2da"
      },
      "82d64cef-48f7-413c-ad01-e7fa2921f7c0": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "711a40c8-c479-448b-a3bc-5c7ff8369838",
        "part": "whole"
       },
       "id": "82d64cef-48f7-413c-ad01-e7fa2921f7c0"
      },
      "d943d3ea-6cd3-4d4a-8914-7fea0231efda": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "9bfbb27b-cae0-4feb-809a-77718bb2e6a3",
        "part": "whole"
       },
       "id": "d943d3ea-6cd3-4d4a-8914-7fea0231efda"
      }
     }
    },
    "595093b4-3f86-406c-abb5-aea78e1241b3": {
     "id": "595093b4-3f86-406c-abb5-aea78e1241b3",
     "prev": "5907f515-df93-4e86-82c6-b8721b6e6346",
     "regions": {
      "3366a1dc-6c33-45f0-a97f-a2744ed0587f": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "e54b79a1-316d-4fd1-ad5f-6cd580b51f99",
        "part": "whole"
       },
       "id": "3366a1dc-6c33-45f0-a97f-a2744ed0587f"
      },
      "40b6dba8-5994-4fd0-9ce5-9d36f3bb9f93": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "3d860568-b048-40f2-a64d-f585211d6c1c",
        "part": "whole"
       },
       "id": "40b6dba8-5994-4fd0-9ce5-9d36f3bb9f93"
      }
     }
    },
    "76838eab-c2c3-4ff3-b09f-854a7c74c6d4": {
     "id": "76838eab-c2c3-4ff3-b09f-854a7c74c6d4",
     "prev": null,
     "regions": {
      "b0032c00-1d89-4c94-a7a0-488f243c381e": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "0dc0cdcb-d8df-4a29-a0d2-a3cf6cc889ce",
        "part": "whole"
       },
       "id": "b0032c00-1d89-4c94-a7a0-488f243c381e"
      }
     }
    },
    "7a25817a-e5bc-4021-9c6b-61bc74dbdb92": {
     "id": "7a25817a-e5bc-4021-9c6b-61bc74dbdb92",
     "prev": "b04719fc-934a-404b-a642-dc732a3d1589",
     "regions": {
      "38d56409-500a-45cf-8e79-722d68d8fc38": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "335f050d-99af-43b3-a5ec-69fe896e48e2",
        "part": "whole"
       },
       "id": "38d56409-500a-45cf-8e79-722d68d8fc38"
      },
      "c838cb6d-409c-4c17-a1eb-3d7dc6aab32b": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "19ca6a7e-4b5d-4b0f-88c5-e68634abb65d",
        "part": "whole"
       },
       "id": "c838cb6d-409c-4c17-a1eb-3d7dc6aab32b"
      }
     }
    },
    "86b3b05f-6e9a-49dc-8a83-97f72c348c5f": {
     "id": "86b3b05f-6e9a-49dc-8a83-97f72c348c5f",
     "prev": "57992fb9-2f74-4e8e-8772-7b9f04606a56",
     "regions": {
      "49a41ef6-224d-46c1-bb81-254b8a4cee88": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "d7df6fa0-ce96-4073-b95d-ce78b3f4fdf1",
        "part": "whole"
       },
       "id": "49a41ef6-224d-46c1-bb81-254b8a4cee88"
      },
      "7ce316ab-4799-4e15-b095-374da21bc1aa": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "4a5226b5-d431-4cbe-86a6-291952a45a37",
        "part": "whole"
       },
       "id": "7ce316ab-4799-4e15-b095-374da21bc1aa"
      },
      "a7d701c6-61e3-48e3-8e0d-a4b318aef7d8": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "5e425283-82de-4c48-b2ae-31a1d6983849",
        "part": "whole"
       },
       "id": "a7d701c6-61e3-48e3-8e0d-a4b318aef7d8"
      },
      "e180cf23-71a7-46a8-8598-86a079bd7d78": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "28aaab45-1e14-4db5-9d2c-63b239cafe13",
        "part": "whole"
       },
       "id": "e180cf23-71a7-46a8-8598-86a079bd7d78"
      }
     }
    },
    "8eb735f6-11af-4f77-a4ed-b637fb18ac08": {
     "id": "8eb735f6-11af-4f77-a4ed-b637fb18ac08",
     "prev": "7a25817a-e5bc-4021-9c6b-61bc74dbdb92",
     "regions": {
      "ca38ef02-01f9-42cb-9e35-d67f8d665597": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "69b638ed-ed32-4824-bb81-df958c215a33",
        "part": "whole"
       },
       "id": "ca38ef02-01f9-42cb-9e35-d67f8d665597"
      },
      "fc52547b-a8f8-40e2-9397-5b18ca2b71b3": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "7317e4f2-150e-4336-ac48-d82641441aa8",
        "part": "whole"
       },
       "id": "fc52547b-a8f8-40e2-9397-5b18ca2b71b3"
      }
     }
    },
    "af2f5933-b31d-4d30-b08a-e4c09432b9bb": {
     "id": "af2f5933-b31d-4d30-b08a-e4c09432b9bb",
     "prev": "29c55765-6994-4dbf-b82f-117b4f1e0cee",
     "regions": {
      "479b436f-50bb-4c3a-9a4e-748e85eacde6": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "6d53481c-69ff-41a0-937e-4e8a39e2b2b5",
        "part": "whole"
       },
       "id": "479b436f-50bb-4c3a-9a4e-748e85eacde6"
      },
      "a02a5bdc-cab5-4ddc-aa9d-8ee5e0db6bd4": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "b0f8400f-71e1-4892-9012-88e54c053a36",
        "part": "whole"
       },
       "id": "a02a5bdc-cab5-4ddc-aa9d-8ee5e0db6bd4"
      }
     }
    },
    "b04719fc-934a-404b-a642-dc732a3d1589": {
     "id": "b04719fc-934a-404b-a642-dc732a3d1589",
     "prev": "af2f5933-b31d-4d30-b08a-e4c09432b9bb",
     "regions": {
      "701cdc94-3399-40dc-8e45-10d6edbf0959": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "fa78fa75-54cf-40d3-b52c-4b51c5070d9a",
        "part": "whole"
       },
       "id": "701cdc94-3399-40dc-8e45-10d6edbf0959"
      },
      "81f01069-a0c6-4ab1-98d1-d465e96aac6d": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "fb58a52c-71e9-402d-af86-eab194cf8050",
        "part": "whole"
       },
       "id": "81f01069-a0c6-4ab1-98d1-d465e96aac6d"
      },
      "ae73fc9b-89cf-4405-ab54-34e6c8b73a19": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "241f356d-567a-421d-b27c-7ce2d146d1bb",
        "part": "whole"
       },
       "id": "ae73fc9b-89cf-4405-ab54-34e6c8b73a19"
      }
     }
    },
    "b7c25bc1-4dd7-421b-9951-0c0c403c176d": {
     "id": "b7c25bc1-4dd7-421b-9951-0c0c403c176d",
     "prev": "76838eab-c2c3-4ff3-b09f-854a7c74c6d4",
     "regions": {
      "47f524f5-68f8-49df-9a61-61eab6cab03c": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "966e050d-5739-4ef9-a2ae-9a4957e14f3e",
        "part": "whole"
       },
       "id": "47f524f5-68f8-49df-9a61-61eab6cab03c"
      },
      "4b3ba01a-29e7-4e67-ac78-988162f7dde9": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "91401f60-44eb-42b2-86ea-c82b5e955ace",
        "part": "whole"
       },
       "id": "4b3ba01a-29e7-4e67-ac78-988162f7dde9"
      },
      "a47d9662-0a49-4b84-aae0-908067552ebd": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "6de4dae8-c949-4927-89a3-9741aba83e8d",
        "part": "whole"
       },
       "id": "a47d9662-0a49-4b84-aae0-908067552ebd"
      },
      "e64668fe-aafe-4e24-a46d-6a6266eeee77": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "7068c96d-f8f0-42bb-bdfd-dcbf9335323c",
        "part": "whole"
       },
       "id": "e64668fe-aafe-4e24-a46d-6a6266eeee77"
      }
     }
    },
    "d3ecec0a-67fe-4558-a268-50695fd7fe1c": {
     "id": "d3ecec0a-67fe-4558-a268-50695fd7fe1c",
     "prev": "fcda7de6-cb93-4b63-9dea-d5f23fecd968",
     "regions": {
      "b268bb57-2543-4b91-b9c0-84aa3cf09d5c": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "67aab8fa-45cd-4ede-8285-e3dad22d8b16",
        "part": "whole"
       },
       "id": "b268bb57-2543-4b91-b9c0-84aa3cf09d5c"
      }
     }
    },
    "e92a9020-6631-400a-aa50-46c1fa0a5f0c": {
     "id": "e92a9020-6631-400a-aa50-46c1fa0a5f0c",
     "prev": "ee94ae9b-9e13-414c-a017-db1d8913aaa8",
     "regions": {
      "bb15b4ea-132f-4146-ab77-93a4969c2904": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "7d5d2002-5cae-4d5f-8e2e-d5eaf5cd17da",
        "part": "whole"
       },
       "id": "bb15b4ea-132f-4146-ab77-93a4969c2904"
      }
     }
    },
    "ee94ae9b-9e13-414c-a017-db1d8913aaa8": {
     "id": "ee94ae9b-9e13-414c-a017-db1d8913aaa8",
     "prev": "f8fce519-927e-4fd0-88d8-23ece4403794",
     "regions": {
      "0b578bb1-2cd1-480b-8d65-dde0861851cb": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "f1c699bc-6717-4eb9-8e6d-66d433ba4618",
        "part": "whole"
       },
       "id": "0b578bb1-2cd1-480b-8d65-dde0861851cb"
      }
     }
    },
    "f8fce519-927e-4fd0-88d8-23ece4403794": {
     "id": "f8fce519-927e-4fd0-88d8-23ece4403794",
     "prev": "4792c0cc-ccd4-48bf-9b0e-a3c1f0f3008d",
     "regions": {
      "7510ae01-fe22-48b0-92cb-29b880cc85fc": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "e61ba538-5828-4a5a-a2c5-4fcceac35889",
        "part": "whole"
       },
       "id": "7510ae01-fe22-48b0-92cb-29b880cc85fc"
      }
     }
    },
    "fcda7de6-cb93-4b63-9dea-d5f23fecd968": {
     "id": "fcda7de6-cb93-4b63-9dea-d5f23fecd968",
     "prev": "03210a56-863e-4749-b7ba-ed75bfceceee",
     "regions": {
      "43f5b3bd-9e0c-4a98-a18c-1b9f7beaa3b8": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "318099ba-93dd-4c2c-a3ee-fc6622c472c8",
        "part": "whole"
       },
       "id": "43f5b3bd-9e0c-4a98-a18c-1b9f7beaa3b8"
      },
      "dbab9920-f08a-457a-949d-2c8289707798": {
       "attrs": {
        "height": 0.4,
        "width": 0.8,
        "x": 0.1,
        "y": 0.5
       },
       "content": {
        "cell": "8bb35ef7-00cc-4ad9-9dd4-6fac480df38e",
        "part": "whole"
       },
       "id": "dbab9920-f08a-457a-949d-2c8289707798"
      }
     }
    }
   },
   "themes": {}
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}