tutorial_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# mlrose_hiive Tutorial Examples - Genevieve Hayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mlrose_hiive is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces. This notebook contains the examples used in the mlrose_hiive tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlrose_hiive\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 1: 8-Queens Using Pre-Defined Fitness Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize fitness function object using pre-defined class\n",
    "fitness = mlrose_hiive.Queens()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define optimization problem object\n",
    "problem = mlrose_hiive.DiscreteOpt(length = 8, fitness_fn = fitness, maximize=False, max_val=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define decay schedule\n",
    "schedule = mlrose_hiive.ExpDecay()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solve using simulated annealing - attempt 1         \n",
    "init_state = np.array([0, 1, 2, 3, 4, 5, 6, 7])\n",
    "best_state, best_fitness, _ = mlrose_hiive.simulated_annealing(problem, schedule = schedule, max_attempts = 10, \n",
    "                                                      max_iters = 1000, init_state = init_state,\n",
    "                                                      random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best state found is:  [6 4 7 3 6 2 5 1]\n"
     ]
    }
   ],
   "source": [
    "print('The best state found is: ', best_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The fitness at the best state is:  2.0\n"
     ]
    }
   ],
   "source": [
    "print('The fitness at the best state is: ', best_fitness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solve using simulated annealing - attempt 2\n",
    "best_state, best_fitness, _ = mlrose_hiive.simulated_annealing(problem, schedule = schedule, max_attempts = 100, \n",
    "                                                      max_iters = 1000, init_state = init_state,\n",
    "                                                      random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 1 3 5 7 2 0 6]\n"
     ]
    }
   ],
   "source": [
    "print(best_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(best_fitness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 2: 8-Queens Using Custom Fitness Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define alternative N-Queens fitness function for maximization problem\n",
    "def queens_max(state):\n",
    "    \n",
    "    # Initialize counter\n",
    "    fitness = 0\n",
    "    \n",
    "    # For all pairs of queens\n",
    "    for i in range(len(state) - 1):\n",
    "        for j in range(i + 1, len(state)):\n",
    "            \n",
    "            # Check for horizontal, diagonal-up and diagonal-down attacks\n",
    "            if (state[j] != state[i]) \\\n",
    "                and (state[j] != state[i] + (j - i)) \\\n",
    "                and (state[j] != state[i] - (j - i)):\n",
    "                \n",
    "                # If no attacks, then increment counter\n",
    "                fitness += 1\n",
    "\n",
    "    return fitness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check function is working correctly\n",
    "state = np.array([1, 4, 1, 3, 5, 5, 2, 7])\n",
    "\n",
    "# The fitness of this state should be 22\n",
    "queens_max(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize custom fitness function object\n",
    "fitness_cust = mlrose_hiive.CustomFitness(queens_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define optimization problem object\n",
    "problem_cust = mlrose_hiive.DiscreteOpt(length = 8, fitness_fn = fitness_cust, maximize = True, max_val = 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solve using simulated annealing - attempt 1\n",
    "best_state, best_fitness, _ = mlrose_hiive.simulated_annealing(problem_cust, schedule = schedule, \n",
    "                                                      max_attempts = 10, max_iters = 1000, \n",
    "                                                      init_state = init_state, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 4 7 3 6 2 5 1]\n"
     ]
    }
   ],
   "source": [
    "print(best_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.0\n"
     ]
    }
   ],
   "source": [
    "print(best_fitness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solve using simulated annealing - attempt 2\n",
    "best_state, best_fitness, _ = mlrose_hiive.simulated_annealing(problem_cust, schedule = schedule, \n",
    "                                                      max_attempts = 100, max_iters = 1000, \n",
    "                                                      init_state = init_state, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 1 3 5 7 2 0 6]\n"
     ]
    }
   ],
   "source": [
    "print(best_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.0\n"
     ]
    }
   ],
   "source": [
    "print(best_fitness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Example 3: Travelling Salesperson Using Coordinate-Defined Fitness Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of city coordinates\n",
    "coords_list = [(1, 1), (4, 2), (5, 2), (6, 4), (4, 4), (3, 6), (1, 5), (2, 3)]\n",
    "\n",
    "# Initialize fitness function object using coords_list\n",
    "fitness_coords = mlrose_hiive.TravellingSales(coords = coords_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define optimization problem object\n",
    "problem_fit = mlrose_hiive.TSPOpt(length = 8, fitness_fn = fitness_coords, maximize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solve using genetic algorithm - attempt 1\n",
    "best_state, best_fitness, _ = mlrose_hiive.genetic_alg(problem_fit, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 3 4 5 6 7 0 2]\n"
     ]
    }
   ],
   "source": [
    "print(best_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.89580466036301\n"
     ]
    }
   ],
   "source": [
    "print(best_fitness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solve using genetic algorithm - attempt 2\n",
    "best_state, best_fitness, _ = mlrose_hiive.genetic_alg(problem_fit, mutation_prob = 0.2, max_attempts = 100,\n",
    "                                              random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 6 5 4 3 2 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(best_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.34261754766733\n"
     ]
    }
   ],
   "source": [
    "print(best_fitness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 4: Travelling Salesperson Using Distance-Defined Fitness Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of distances between pairs of cities\n",
    "dist_list = [(0, 1, 3.1623), (0, 2, 4.1231), (0, 3, 5.8310), (0, 4, 4.2426), (0, 5, 5.3852),\n",
    "             (0, 6, 4.0000), (0, 7, 2.2361), (1, 2, 1.0000), (1, 3, 2.8284), (1, 4, 2.0000),\n",
    "             (1, 5, 4.1231), (1, 6, 4.2426), (1, 7, 2.2361), (2, 3, 2.2361), (2, 4, 2.2361),\n",
    "             (2, 5, 4.4721), (2, 6, 5.0000), (2, 7, 3.1623), (3, 4, 2.0000), (3, 5, 3.6056),\n",
    "             (3, 6, 5.0990), (3, 7, 4.1231), (4, 5, 2.2361), (4, 6, 3.1623), (4, 7, 2.2361),\n",
    "             (5, 6, 2.2361), (5, 7, 3.1623), (6, 7, 2.2361)]\n",
    "\n",
    "# Initialize fitness function object using dist_list\n",
    "fitness_dists = mlrose_hiive.TravellingSales(distances = dist_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define optimization problem object\n",
    "problem_fit2 = mlrose_hiive.TSPOpt(length = 8, fitness_fn = fitness_dists, maximize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solve using genetic algorithm\n",
    "best_state, best_fitness, _ = mlrose_hiive.genetic_alg(problem_fit2, mutation_prob = 0.2, max_attempts = 100,\n",
    "                                              random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 6 5 4 3 2 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(best_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.3428\n"
     ]
    }
   ],
   "source": [
    "print(best_fitness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 5: Travelling Salesperson Defining Fitness Function as Part of Optimization Problem Definition Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of city coordinates\n",
    "coords_list = [(1, 1), (4, 2), (5, 2), (6, 4), (4, 4), (3, 6), (1, 5), (2, 3)]\n",
    "\n",
    "# Define optimization problem object\n",
    "problem_no_fit = mlrose_hiive.TSPOpt(length = 8, coords = coords_list, maximize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solve using genetic algorithm\n",
    "best_state, best_fitness, _ = mlrose_hiive.genetic_alg(problem_no_fit, mutation_prob = 0.2, max_attempts = 100, \n",
    "                                              random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 6 5 4 3 2 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(best_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.34261754766733\n"
     ]
    }
   ],
   "source": [
    "print(best_fitness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 6: Fitting a Neural Network to the Iris Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Iris dataset\n",
    "data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1 3.5 1.4 0.2]\n"
     ]
    }
   ],
   "source": [
    "# Get feature values of first observation\n",
    "print(data.data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "# Get feature names\n",
    "print(data.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Get target value of first observation\n",
    "print(data.target[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "# Get target name of first observation\n",
    "print(data.target_names[data.target[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.3 2.  1.  0.1]\n"
     ]
    }
   ],
   "source": [
    "# Get minimum feature values\n",
    "print(np.min(data.data, axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.9 4.4 6.9 2.5]\n"
     ]
    }
   ],
   "source": [
    "# Get maximum feature values\n",
    "print(np.max(data.data, axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n"
     ]
    }
   ],
   "source": [
    "# Get unique target values\n",
    "print(np.unique(data.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size = 0.2, \n",
    "                                                    random_state = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize feature data\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ghaye\\AppData\\Local\\conda\\conda\\envs\\mlrose_test\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:368: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# One hot encode target values\n",
    "one_hot = OneHotEncoder()\n",
    "\n",
    "y_train_hot = one_hot.fit_transform(y_train.reshape(-1, 1)).todense()\n",
    "y_test_hot = one_hot.transform(y_test.reshape(-1, 1)).todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize neural network object and fit object - attempt 1\n",
    "nn_model1 = mlrose_hiive.NeuralNetwork(hidden_nodes = [2], activation ='relu', \n",
    "                                 algorithm ='random_hill_climb', \n",
    "                                 max_iters = 1000, bias = True, is_classifier = True, \n",
    "                                 learning_rate = 0.0001, early_stopping = True, \n",
    "                                 clip_max = 5, max_attempts = 100, random_state = 3)\n",
    "\n",
    "nn_model1.fit(X_train_scaled, y_train_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.45\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for train set and assess accuracy\n",
    "y_train_pred = nn_model1.predict(X_train_scaled)\n",
    "\n",
    "y_train_accuracy = accuracy_score(y_train_hot, y_train_pred)\n",
    "\n",
    "print(y_train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5333333333333333\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for test set and assess accuracy\n",
    "y_test_pred = nn_model1.predict(X_test_scaled)\n",
    "\n",
    "y_test_accuracy = accuracy_score(y_test_hot, y_test_pred)\n",
    "\n",
    "print(y_test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize neural network object and fit object - attempt 2\n",
    "nn_model2 = mlrose_hiive.NeuralNetwork(hidden_nodes = [2], activation = 'relu', \n",
    "                                 algorithm = 'gradient_descent', \n",
    "                                 max_iters = 1000, bias = True, is_classifier = True, \n",
    "                                 learning_rate = 0.0001, early_stopping = True, \n",
    "                                 clip_max = 5, max_attempts = 100, random_state = 3)\n",
    "\n",
    "nn_model2.fit(X_train_scaled, y_train_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.625\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for train set and assess accuracy\n",
    "y_train_pred = nn_model2.predict(X_train_scaled)\n",
    "\n",
    "y_train_accuracy = accuracy_score(y_train_hot, y_train_pred)\n",
    "\n",
    "print(y_train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5666666666666667\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for test set and assess accuracy\n",
    "y_test_pred = nn_model2.predict(X_test_scaled)\n",
    "\n",
    "y_test_accuracy = accuracy_score(y_test_hot, y_test_pred)\n",
    "\n",
    "print(y_test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 7: Fitting a Logistic Regression to the Iris Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize logistic regression object and fit object - attempt 1\n",
    "lr_model1 = mlrose_hiive.LogisticRegression(algorithm = 'random_hill_climb', max_iters = 1000, \n",
    "                                      bias = True, learning_rate = 0.0001, \n",
    "                                      early_stopping = True, clip_max = 5, max_attempts = 100, \n",
    "                                      random_state = 3)\n",
    "\n",
    "lr_model1.fit(X_train_scaled, y_train_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19166666666666668\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for train set and assess accuracy\n",
    "y_train_pred = lr_model1.predict(X_train_scaled)\n",
    "\n",
    "y_train_accuracy = accuracy_score(y_train_hot, y_train_pred)\n",
    "\n",
    "print(y_train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06666666666666667\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for test set and assess accuracy\n",
    "y_test_pred = lr_model1.predict(X_test_scaled)\n",
    "\n",
    "y_test_accuracy = accuracy_score(y_test_hot, y_test_pred)\n",
    "\n",
    "print(y_test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize logistic regression object and fit object - attempt 2\n",
    "\n",
    "lr_model2 = mlrose_hiive.LogisticRegression(algorithm = 'random_hill_climb', max_iters = 1000, \n",
    "                                      bias = True, learning_rate = 0.01, \n",
    "                                      early_stopping = True, clip_max = 5, max_attempts = 100,\n",
    "                                      random_state = 3)\n",
    "\n",
    "lr_model2.fit(X_train_scaled, y_train_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6833333333333333\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for train set and assess accuracy\n",
    "y_train_pred = lr_model2.predict(X_train_scaled)\n",
    "\n",
    "y_train_accuracy = accuracy_score(y_train_hot, y_train_pred)\n",
    "\n",
    "print(y_train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for test set and assess accuracy\n",
    "y_test_pred = lr_model2.predict(X_test_scaled)\n",
    "\n",
    "y_test_accuracy = accuracy_score(y_test_hot, y_test_pred)\n",
    "\n",
    "print(y_test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 8: Fitting a Logistic Regression to the Iris Data using the NeuralNetwork() class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize neural network object and fit object - attempt 1\n",
    "lr_nn_model1 = mlrose_hiive.NeuralNetwork(hidden_nodes = [], activation = 'sigmoid', \n",
    "                                    algorithm = 'random_hill_climb', \n",
    "                                    max_iters = 1000, bias = True, is_classifier = True, \n",
    "                                    learning_rate = 0.0001, early_stopping = True, \n",
    "                                    clip_max = 5, max_attempts = 100, random_state = 3)\n",
    "\n",
    "lr_nn_model1.fit(X_train_scaled, y_train_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19166666666666668\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for train set and assess accuracy\n",
    "y_train_pred = lr_nn_model1.predict(X_train_scaled)\n",
    "\n",
    "y_train_accuracy = accuracy_score(y_train_hot, y_train_pred)\n",
    "\n",
    "print(y_train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06666666666666667\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for test set and assess accuracy\n",
    "y_test_pred = lr_nn_model1.predict(X_test_scaled)\n",
    "\n",
    "y_test_accuracy = accuracy_score(y_test_hot, y_test_pred)\n",
    "\n",
    "print(y_test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize neural network object and fit object - attempt 2\n",
    "lr_nn_model2 = mlrose_hiive.NeuralNetwork(hidden_nodes = [], activation = 'sigmoid', \n",
    "                                    algorithm = 'random_hill_climb', \n",
    "                                    max_iters = 1000, bias = True, is_classifier = True, \n",
    "                                    learning_rate = 0.01, early_stopping = True, \n",
    "                                    clip_max = 5, max_attempts = 100, random_state = 3)\n",
    "\n",
    "lr_nn_model2.fit(X_train_scaled, y_train_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6833333333333333\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for train set and assess accuracy\n",
    "y_train_pred = lr_nn_model2.predict(X_train_scaled)\n",
    "\n",
    "y_train_accuracy = accuracy_score(y_train_hot, y_train_pred)\n",
    "\n",
    "print(y_train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7\n"
     ]
    }
   ],
   "source": [
    "# Predict labels for test set and assess accuracy\n",
    "y_test_pred = lr_nn_model2.predict(X_test_scaled)\n",
    "\n",
    "y_test_accuracy = accuracy_score(y_test_hot, y_test_pred)\n",
    "\n",
    "print(y_test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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