FlowerPrediction.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make classification with Linear regression with iris dataset from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import module\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#dataframe module setup\n",
    "irish = load_iris()\n",
    "\n",
    "#features target split\n",
    "dir(irish)\n",
    "xtr, xte, ytr, yte = train_test_split(irish.data, irish.target, test_size=.3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do gridsearch directly, because datasets from sklearn ready to train in machine learning algorithm, so we dont need to clean it first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.980000 using {'C': 50, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.977778 (0.031427) with: {'C': 70, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.977778 (0.031427) with: {'C': 70, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.971111 (0.041036) with: {'C': 70, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.000000 (0.000000) with: {'C': 70, 'penalty': 'l1', 'solver': 'newton-cg'}\n",
      "0.000000 (0.000000) with: {'C': 70, 'penalty': 'l1', 'solver': 'lbfgs'}\n",
      "0.962222 (0.037251) with: {'C': 70, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.977778 (0.031427) with: {'C': 100, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.977778 (0.031427) with: {'C': 100, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.968889 (0.041216) with: {'C': 100, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.000000 (0.000000) with: {'C': 100, 'penalty': 'l1', 'solver': 'newton-cg'}\n",
      "0.000000 (0.000000) with: {'C': 100, 'penalty': 'l1', 'solver': 'lbfgs'}\n",
      "0.960000 (0.040734) with: {'C': 100, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.977778 (0.031427) with: {'C': 60, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.977778 (0.031427) with: {'C': 60, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.971111 (0.041036) with: {'C': 60, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.000000 (0.000000) with: {'C': 60, 'penalty': 'l1', 'solver': 'newton-cg'}\n",
      "0.000000 (0.000000) with: {'C': 60, 'penalty': 'l1', 'solver': 'lbfgs'}\n",
      "0.962222 (0.037251) with: {'C': 60, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.977778 (0.031427) with: {'C': 50, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.980000 (0.030551) with: {'C': 50, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.971111 (0.041036) with: {'C': 50, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.000000 (0.000000) with: {'C': 50, 'penalty': 'l1', 'solver': 'newton-cg'}\n",
      "0.000000 (0.000000) with: {'C': 50, 'penalty': 'l1', 'solver': 'lbfgs'}\n",
      "0.962222 (0.037251) with: {'C': 50, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.977778 (0.031427) with: {'C': 20, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.977778 (0.031427) with: {'C': 20, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.968889 (0.037450) with: {'C': 20, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.000000 (0.000000) with: {'C': 20, 'penalty': 'l1', 'solver': 'newton-cg'}\n",
      "0.000000 (0.000000) with: {'C': 20, 'penalty': 'l1', 'solver': 'lbfgs'}\n",
      "0.962222 (0.037251) with: {'C': 20, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.977778 (0.031427) with: {'C': 30, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.980000 (0.030551) with: {'C': 30, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.968889 (0.041216) with: {'C': 30, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.000000 (0.000000) with: {'C': 30, 'penalty': 'l1', 'solver': 'newton-cg'}\n",
      "0.000000 (0.000000) with: {'C': 30, 'penalty': 'l1', 'solver': 'lbfgs'}\n",
      "0.962222 (0.037251) with: {'C': 30, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.973333 (0.032660) with: {'C': 10, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.973333 (0.032660) with: {'C': 10, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.966667 (0.037515) with: {'C': 10, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.000000 (0.000000) with: {'C': 10, 'penalty': 'l1', 'solver': 'newton-cg'}\n",
      "0.000000 (0.000000) with: {'C': 10, 'penalty': 'l1', 'solver': 'lbfgs'}\n",
      "0.957778 (0.040307) with: {'C': 10, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.964444 (0.041216) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.964444 (0.041216) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.962222 (0.041036) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.000000 (0.000000) with: {'C': 1.0, 'penalty': 'l1', 'solver': 'newton-cg'}\n",
      "0.000000 (0.000000) with: {'C': 1.0, 'penalty': 'l1', 'solver': 'lbfgs'}\n",
      "0.960000 (0.036918) with: {'C': 1.0, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.957778 (0.050136) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.957778 (0.050136) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.842222 (0.071974) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.000000 (0.000000) with: {'C': 0.1, 'penalty': 'l1', 'solver': 'newton-cg'}\n",
      "0.000000 (0.000000) with: {'C': 0.1, 'penalty': 'l1', 'solver': 'lbfgs'}\n",
      "0.804444 (0.066518) with: {'C': 0.1, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.873333 (0.083178) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.873333 (0.083178) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.666667 (0.000000) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.000000 (0.000000) with: {'C': 0.01, 'penalty': 'l1', 'solver': 'newton-cg'}\n",
      "0.000000 (0.000000) with: {'C': 0.01, 'penalty': 'l1', 'solver': 'lbfgs'}\n",
      "0.333333 (0.000000) with: {'C': 0.01, 'penalty': 'l1', 'solver': 'liblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hilla\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# define models and parameters\n",
    "model = LogisticRegression()\n",
    "solvers = ['newton-cg', 'lbfgs', 'liblinear']\n",
    "penalty = ['l2', \"l1\"]\n",
    "c_values = [70, 100, 60, 50, 20, 30, 10, 1.0, 0.1, 0.01]\n",
    "\n",
    "# define grid search\n",
    "grid = dict(solver=solvers,penalty=penalty,C=c_values)\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy',error_score=0)\n",
    "grid_result = grid_search.fit(irish.data, irish.target)\n",
    "\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "make logistic regression algorithm and set best parameters tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hilla\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#module\n",
    "lrmulti = LogisticRegression(C=50, penalty=\"l2\", solver=\"lbfgs\", random_state=10)\n",
    "lrmulti.fit(xtr, ytr)\n",
    "lrmulti.predict(xte)\n",
    "lrmulti.score(xte, yte)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algorithm report - confussion matrix and classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        16\n",
      "           1       1.00      1.00      1.00        16\n",
      "           2       1.00      1.00      1.00        13\n",
      "\n",
      "    accuracy                           1.00        45\n",
      "   macro avg       1.00      1.00      1.00        45\n",
      "weighted avg       1.00      1.00      1.00        45\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#needed module\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "#cm\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(confusion_matrix(lrmulti.predict(xte), yte), annot=True)\n",
    "plt.xlabel(\"prediction\")\n",
    "plt.ylabel(\"truth value\")\n",
    "\n",
    "#classification report\n",
    "print(classification_report(yte, lrmulti.predict(xte)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "2c0848194679cc856cdb22afd850d5f70cd56543c051b91d6fd67c617f7ae4ab"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}