docs/source/_notebooks/experiments/ml_cls_exp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/AtrCheema/ai4water_examples/blob/master/docs/source/_notebooks/experiments/ml_cls_exp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "\n",
    "# Machine learning algorithms for classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**********Tensorflow models could not be imported **********\n",
      "\n",
      "python 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:19:05) [MSC v.1916 64 bit (AMD64)]\n",
      "os nt\n",
      "ai4water 1.06\n",
      "easy_mpl 0.21.3\n",
      "SeqMetrics 1.3.3\n",
      "numpy 1.22.2\n",
      "pandas 1.4.0\n",
      "matplotlib 3.5.1\n",
      "h5py 3.6.0\n",
      "sklearn 1.2.1\n",
      "xarray 0.21.1\n",
      "netCDF4 1.5.8\n",
      "seaborn 0.12.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\experimental\\enable_hist_gradient_boosting.py:16: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from ai4water.datasets import MtropicsLaos\n",
    "from ai4water.experiments import MLClassificationExperiments\n",
    "from ai4water.utils.utils import get_version_info\n",
    "\n",
    "for lib, ver in get_version_info().items():\n",
    "    print(lib, ver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Not downloading the data since the directory \n",
      "    F:\\data\\MtropicsLaos already exists.\n",
      "    Use overwrite=True to remove previously saved files and download again\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\mytools\\AI4Water\\ai4water\\datasets\\mtropics.py:907: UserWarning: preprocessing of shapefiles can not be done because no fiona installation is found.\n",
      "  warnings.warn(\"preprocessing of shapefiles can not be done because no fiona installation is found.\")\n"
     ]
    }
   ],
   "source": [
    "dataset = MtropicsLaos(save_as_nc=True,  # if set to True, then netcdf must be installed\n",
    "                       convert_to_csv=False,\n",
    "                       path=\"F:\\\\data\\\\MtropicsLaos\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\mytools\\AI4Water\\ai4water\\datasets\\mtropics.py:576: FutureWarning: Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.\n",
      "  return wl.loc[st:en], spm.loc[st:en]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(258, 9)\n"
     ]
    }
   ],
   "source": [
    "data =  dataset.make_classification(lookback_steps=1)\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "air_temp        0\n",
       "rel_hum         0\n",
       "wind_speed      0\n",
       "sol_rad         0\n",
       "water_level     0\n",
       "pcp             0\n",
       "susp_pm         0\n",
       "Ecoli_source    0\n",
       "Ecoli_mpn100    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['air_temp', 'rel_hum', 'wind_speed', 'sol_rad', 'water_level', 'pcp', 'susp_pm', 'Ecoli_source']\n"
     ]
    }
   ],
   "source": [
    "inputs = data.columns.tolist()[0:-1]\n",
    "print(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ecoli_mpn100']\n"
     ]
    }
   ],
   "source": [
    "outputs = data.columns.tolist()[-1:]\n",
    "print(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = MLClassificationExperiments(\n",
    "    input_features=inputs,\n",
    "    output_features=outputs,\n",
    "    save=False\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Training *****\n",
      "input_x shape:  (144, 8)\n",
      "target shape:  (144, 1)\n",
      "***** Validation *****\n",
      "input_x shape:  (36, 8)\n",
      "target shape:  (36, 1)\n",
      "***** Test *****\n",
      "input_x shape:  (78, 8)\n",
      "target shape:  (78, 1)\n",
      "running  AdaBoostClassifier model\n",
      "running  BaggingClassifier model\n",
      "running  BernoulliNB model\n",
      "running  CalibratedClassifierCV model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\svm\\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\svm\\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\svm\\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\svm\\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\svm\\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\tree\\_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  DecisionTreeClassifier model\n",
      "running  DummyClassifier model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\tree\\_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  ExtraTreeClassifier model\n",
      "running  ExtraTreesClassifier model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  GaussianProcessClassifier model\n",
      "running  GradientBoostingClassifier model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  HistGradientBoostingClassifier model\n",
      "running  KNeighborsClassifier model\n",
      "running  LabelPropagation model\n",
      "running  LabelSpreading model\n",
      "running  LinearSVC model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\svm\\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  LogisticRegression model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  MLPClassifier model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  NearestCentroid model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\svm\\_base.py:299: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  NuSVC model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  PassiveAggressiveClassifier model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  Perceptron model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  QuadraticDiscriminantAnalysis model\n",
      "running  RandomForestClassifier model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  RidgeClassifier model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.46444e-32): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  RidgeClassifierCV model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  SGDClassifier model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  SVC model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:356: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _f1_score = 2 * (precision * recall)  / (precision + recall)\n",
      "D:\\C\\Anaconda3\\envs\\ai4w_dataset\\lib\\site-packages\\SeqMetrics\\_cls.py:286: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _precision =  TP / (TP + FP)\n"
     ]
    }
   ],
   "source": [
    "exp.fit(data=data,\n",
    "        exclude=['LinearDiscriminantAnalysis'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Following plot is only available when we perform cross validation\n",
    "# exp.plot_cv_scores(data=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "_ = exp.compare_precision_recall_curves(data[inputs].values, data[outputs].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "nbsphinx-thumbnail": {
     "tooltip": "Comparing Machine Learning Algorithms for Classification"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "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": [
    "_ = exp.compare_roc_curves(data[inputs].values, data[outputs].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}