{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "968d35ea",
   "metadata": {},
   "source": [
    "# Logistic Regression\n",
    "\n",
    "**Logistic Regression** is a statistical method used for modeling the probability of a binary outcome. It's a type of generalized linear model (GLM) that predicts the probability that a given instance belongs to a particular category. Despite its name, logistic regression is used for binary classification problems, not regression problems.\n",
    "\n",
    "Key Points:\n",
    "1. **Sigmoid Function**: At its core, logistic regression uses the logistic (or sigmoid) function to squeeze the output of a linear equation between 0 and 1, which can then be interpreted as a probability.\n",
    "\n",
    "2. **Applications**: Logistic regression is widely used in fields like medicine (e.g., predicting whether a patient has a disease or not), finance (e.g., predicting loan default), and marketing (e.g., predicting customer churn).\n",
    "\n",
    "3. **Assumptions**: Logistic regression assumes linearity of independent variables and log odds, absence of multicollinearity, and that the outcome variable is binary.\n",
    "\n",
    "4. **Extensions**: For outcomes with more than two categories, extensions of logistic regression like multinomial and ordinal logistic regression are used.\n",
    "\n",
    "Logistic regression provides a simple yet powerful way to determine the effect of multiple predictors on a binary outcome, and it's a foundational algorithm in the world of machine learning and statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41eacf21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries/modules\n",
    "\n",
    "# Load the digits dataset\n",
    "from sklearn.datasets import load_digits\n",
    "\n",
    "# Split data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Import the logistic regression model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Import matplotlib for data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import seaborn for enhanced data visualization\n",
    "import seaborn as sns\n",
    "\n",
    "# Import metrics for evaluating the model\n",
    "from sklearn import metrics\n",
    "\n",
    "# Import numpy for numerical operations\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21db97e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image Data Shape (1797, 64)\n",
      "Label Data Shape (1797,)\n"
     ]
    }
   ],
   "source": [
    "# Load the digits dataset and store it in the 'digits' variable\n",
    "digits = load_digits()\n",
    "\n",
    "# Print the shape of the image data and label data\n",
    "print(\"Image Data Shape\", digits.data.shape)\n",
    "print(\"Label Data Shape\", digits.target.shape)\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d168f01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression(max_iter=10000)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Logistic Regression model instance\n",
    "logisticRegr = LogisticRegression(max_iter=10000)\n",
    "\n",
    "# Fit (train) the Logistic Regression model using the training data\n",
    "logisticRegr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9dde9c48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9733333333333334\n",
      "[[43  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 36  1  0  0  0  0  0  0  0]\n",
      " [ 0  0 38  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 44  0  1  0  0  1  0]\n",
      " [ 0  2  0  0 53  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 57  1  0  0  1]\n",
      " [ 0  0  0  0  0  1 44  0  0  0]\n",
      " [ 0  0  0  0  0  1  0 40  0  0]\n",
      " [ 0  0  0  0  0  1  0  0 37  0]\n",
      " [ 0  0  0  1  0  0  0  0  1 46]]\n"
     ]
    }
   ],
   "source": [
    "# Predict the label for a single test sample (reshaped to match model input shape)\n",
    "logisticRegr.predict(x_test[0].reshape(1, -1))\n",
    "\n",
    "# Predict labels for the first 10 test samples\n",
    "logisticRegr.predict(x_test[0:10])\n",
    "\n",
    "# Predict labels for all test samples and store them in the 'predictions' variable\n",
    "predictions = logisticRegr.predict(x_test)\n",
    "\n",
    "# Calculate the accuracy score of the model on the test data\n",
    "score = logisticRegr.score(x_test, y_test)\n",
    "\n",
    "# Print the accuracy score\n",
    "print(score)\n",
    "\n",
    "# Calculate the confusion matrix to evaluate model performance\n",
    "cm = metrics.confusion_matrix(y_test, predictions)\n",
    "\n",
    "# Print the confusion matrix\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9f0bb31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70000, 784)\n",
      "(70000,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "x_org, y = fetch_openml('mnist_784', version=1, return_X_y=True)\n",
    "\n",
    "# Print the shape of the data (images) and target (labels)\n",
    "print(x_org.shape)    # Shape of the data (images)\n",
    "print(y.shape)  # Shape of the target (labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "75f687c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the MNIST dataset into training and testing sets\n",
    "# `train_img` contains the training data (images)\n",
    "# `test_img` contains the testing data (images)\n",
    "# `train_lbl` contains the training labels\n",
    "# `test_lbl` contains the testing labels\n",
    "train_img, test_img, train_lbl, test_lbl = train_test_split(x_org, y, test_size=1/7.0, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b88b5dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2000x20000 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "def plot_digits(data):\n",
    "    num_plots = data.shape[0]\n",
    "    fig = plt.figure(figsize=(num_plots, 10.*num_plots))\n",
    "    grid = ImageGrid(fig, 111, nrows_ncols=(1, num_plots), axes_pad=0.1)  #makes a grid of images\n",
    "    for i in range(num_plots):\n",
    "        grid[i].imshow(data.iloc[i].values.reshape((28,28)))\n",
    "    plt.show()\n",
    "plot_digits(x_org[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e058a87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ashleygao/miniforge3/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: 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/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=200)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=200)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Logistic Regression model instance with the 'lbfgs' solver\n",
    "logisticRegr = LogisticRegression(solver='lbfgs', max_iter=200)\n",
    "\n",
    "# Fit (train) the Logistic Regression model using the training data\n",
    "logisticRegr.fit(train_img, train_lbl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1ee63023",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict labels for the first 10 test images\n",
    "# logisticRegr.predict(test_img[0:10])\n",
    "\n",
    "# Predict labels for all test images and store them in the 'predictions' variable\n",
    "predictions = logisticRegr.predict(test_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "599e9fae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.917\n"
     ]
    }
   ],
   "source": [
    "# Calculate the accuracy score of the model on the test data\n",
    "score = logisticRegr.score(test_img, test_lbl)\n",
    "\n",
    "# Print the accuracy score\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a84746f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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