Perceptron_Indepth.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d95eff00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:95% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "%config Completer.use_jedi = False\n",
    "\n",
    "display(HTML(\"<style>.container { width:95% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9488f22",
   "metadata": {},
   "source": [
    "# Tasks \n",
    "\n",
    "- [x] Explain Simple and Optimized Implementations of the Algorithm Step by Step"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9ead432",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "413bee76",
   "metadata": {},
   "outputs": [],
   "source": [
    "from drawdata import draw_scatter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import math\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cae903a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c14c2a9",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Create Dummy Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5fe8bc17",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"550\"\n",
       "            src=\"https://drawdata.xyz/scatter.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x16fff401850>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# draw_scatter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c4cb9d16",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# df = pd.read_clipboard(sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb311fec",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('D:/amit/ml_indepth/perceptron')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Path.cwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "79c93ea7",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# df.to_csv('dummy__perceptron.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f5143b67",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('dummy__perceptron.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6841e1f5",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df.z = df.z.apply(lambda x: 1 if x=='a' else -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "75bcf9b4",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    393\n",
       "-1    277\n",
       "Name: z, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.z.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af17b993",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Data Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "64d260cb",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_train_dummy = df[['x', 'y']].values\n",
    "y_train_dummy = df.z.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "eddfc9ce",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((670, 2), (670,))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_dummy.shape, y_train_dummy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0be06831",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_train_dummy = np.hstack((X_train_dummy, np.ones((X_train_dummy.shape[0],1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e6fb23a9",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(670, 3)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_dummy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2e074f85",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([541.18323885, 494.08355305,   1.        ])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_dummy[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e92d1b68",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_dummy[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "858dd350",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Basic Perceptron Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d4356b70",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# initialize the w\n",
    "w = np.zeros((1,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "42976d1c",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4b26c124",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_dummy[0].dot(w.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b5881a5a",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dfx = pd.DataFrame(X_train_dummy, columns=['x', 'y', 'b'])\n",
    "dfx['class'] = y_train_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9dc55694",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fig_unscaled = px.scatter(data_frame=dfx, x='x', y='y', color='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd0834ec",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2a4723d1",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "std_scaler = StandardScaler()\n",
    "normalizer = Normalizer()\n",
    "mm_scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5b5f80c6",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_train_dummy_std_scaled = std_scaler.fit_transform(X_train_dummy)\n",
    "X_train_dummy_normalized = normalizer.fit_transform(X_train_dummy)\n",
    "X_train_dummy_mm_scaled = mm_scaler.fit_transform(X_train_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "87aeb5a2",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dfx_std_scaled = pd.DataFrame(X_train_dummy_std_scaled,\n",
    "                              columns=['x', 'y', 'b'])\n",
    "dfx_std_scaled['class'] = y_train_dummy\n",
    "fig_std_scaled = px.scatter(data_frame=dfx_std_scaled,\n",
    "                            x='x',\n",
    "                            y='y',\n",
    "                            color='class')\n",
    "\n",
    "dfx_normalized = pd.DataFrame(X_train_dummy_normalized,\n",
    "                              columns=['x', 'y', 'b'])\n",
    "dfx_normalized['class'] = y_train_dummy\n",
    "fig_normalized = px.scatter(data_frame=dfx_normalized,\n",
    "                            x='x',\n",
    "                            y='y',\n",
    "                            color='class')\n",
    "\n",
    "dfx_mm_scaled = pd.DataFrame(X_train_dummy_mm_scaled, columns=['x', 'y', 'b'])\n",
    "dfx_mm_scaled['class'] = y_train_dummy\n",
    "fig_mm_scaled = px.scatter(data_frame=dfx_mm_scaled,\n",
    "                           x='x',\n",
    "                           y='y',\n",
    "                           color='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c4e09d31",
   "metadata": {
    "hidden": true
   },
   "outputs": [
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       "            Plotly.purge(gd);\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_mm_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "413ba894",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "while True:\n",
    "    m = 0\n",
    "    for x, y in zip(X_train_dummy, y_train_dummy):\n",
    "        if y * (x.reshape(1,3).dot(w.T)) <= 0:\n",
    "            w += y*x.reshape(1,3)\n",
    "            m += 1\n",
    "    if m == 0 :\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "825fffeb",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.44547961e+00,  2.96368580e+01, -9.54600000e+03]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7cce8a05",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def predict(x):\n",
    "    if x.reshape(1,3).dot(w.T) > 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return -1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ebf507a0",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train_dummy[10]), y_train_dummy[10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45536349",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Perceptron Other Implementations Using Step Transfer Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2c3b5161",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Model():\n",
    "    def __init__(self):\n",
    "        self.num_input_features = None\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        \"\"\" Fit the model.\n",
    "        Args:\n",
    "            X: A compressed sparse row matrix of floats with shape\n",
    "                [num_examples, num_features].\n",
    "            y: A dense array of ints with shape [num_examples].\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\" Predict.\n",
    "        Args:\n",
    "            X: A compressed sparse row matrix of floats with shape\n",
    "                [num_examples, num_features].\n",
    "\n",
    "        Returns:\n",
    "            A dense array of ints with shape [num_examples].\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e3557d3e",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class CustomPerceptron1(Model):\n",
    "    \n",
    "    def __init__(self, max_iter, n):\n",
    "        super().__init__()\n",
    "        self.max_iter = max_iter\n",
    "        self.n = n \n",
    "        \n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        self.ne, self.nf = np.shape(X)\n",
    "        self.w = np.zeros(self.nf)\n",
    "        \n",
    "#         for i in range(0, self.max_iter):\n",
    "        while True:\n",
    "            m = 0\n",
    "            for e, l in zip(X,y):\n",
    "                if l*np.dot(self.w, e) <= 0:\n",
    "                    self.w += l * e\n",
    "                    m += 1\n",
    "            if m == 0:\n",
    "                break\n",
    "                    \n",
    "    def predict(self, X):\n",
    "        ne, nf = np.shape(X)\n",
    "        if nf != self.nf:\n",
    "            X = X[:, :self.nf]\n",
    "        \n",
    "        predicted_Y = np.empty([ne])\n",
    "        \n",
    "        for i in range(ne):\n",
    "            if np.dot(self.w, X[i])>0:                           \n",
    "                predicted_Y[i] = 1\n",
    "            else:\n",
    "                predicted_Y[i] = -1\n",
    "            \n",
    "        return predicted_Y\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "32d0ecef",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model = Perceptron(10000, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "acd97356",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 7.16 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(X_train_dummy_std_scaled, y_train_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7d9edfec",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.13486593,  3.18068067,  0.        ])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cf04356f",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
       "       -1., -1., -1., -1., -1., -1., -1.])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_train_dummy_std_scaled[-20:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1a8f19a7",
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": true,
    "hidden": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-61-1224bf81250e>\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m     15\u001b[0m             \u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m                 \u001b[1;32mif\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mw\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m                     \u001b[0mm\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Took too long to converge\n",
    "# %%time \n",
    "# model.fit(X_train_dummy_mm_scaled, y_train_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2083e8d8",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.67654049, -0.44843048,  0.        ])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model.w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3a7f801b",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# model.predict(X_train_dummy_mm_scaled[-20:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c705a92e",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "model.fit(X_train_dummy_normalized, y_train_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "508ca25a",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.96666055e-02,  1.14696090e+00, -2.88355625e+02])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f82a58b6",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
       "       -1., -1., -1., -1., -1., -1., -1.])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_train_dummy_normalized[-20:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b8b4cf87",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 21.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(X_train_dummy, y_train_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e5733bab",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.37993956e+00,  1.84097266e+02, -4.43150000e+04])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e1c7e736",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
       "       -1., -1., -1., -1., -1., -1., -1.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_train_dummy[-20:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9f8a32f8",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_dummy[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "8beefb0c",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3,), (686, 3))"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.w.shape, X_train_dummy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ba4419bb",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "769.5062505018591"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.norm(X_train_dummy[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8c468a57",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.835660334057529"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.norm(X_train_dummy_scaled[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11aeb829",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Perceptron using Stochastic Gradient Descent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3afe9a6e",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 1. Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "2945334e",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.48191319, -0.56077897, -1.        ],\n",
       "       [-0.98930089, -0.56096287, -1.        ],\n",
       "       [-0.79511501, -0.3625388 , -1.        ],\n",
       "       [-1.28252626, -0.24761745, -1.        ],\n",
       "       [-1.1359904 , -0.45413882, -1.        ],\n",
       "       [-0.74719578, -0.45130754, -1.        ],\n",
       "       [-0.31248589, -0.66825231, -1.        ],\n",
       "       [ 0.38781868, -0.22366354, -1.        ],\n",
       "       [ 0.18600259, -0.29907397, -1.        ],\n",
       "       [ 0.12436777, -0.66394631, -1.        ]])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial_set = np.concatenate((X_train_dummy_std_scaled[-10:,:-1], y_train_dummy[-10:].reshape(-1,1)), axis=1)\n",
    "trial_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "6506ef0e",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def predict(x, weights):\n",
    "    activation = weights[0]\n",
    "    for i in range(len(x)-1):\n",
    "        activation += weights[i+1] * x[i]\n",
    "    return 1. if activation>=0.0 else 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "01f00c11",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "weights = [-0.1, 0.20653640140000007, -0.23418117710000003]\n",
    "# setup is bias first [bias, w1, w2]\n",
    "# activation = (w1*x1) + (w2*x2) + bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "dc03eabf",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected=-1, Predicted=1\n",
      "Expected=-1, Predicted=1\n",
      "Expected=-1, Predicted=1\n",
      "Expected=-1, Predicted=1\n",
      "Expected=-1, Predicted=1\n",
      "Expected=-1, Predicted=1\n",
      "Expected=-1, Predicted=1\n",
      "Expected=-1, Predicted=1\n",
      "Expected=-1, Predicted=1\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(trial_set)-1):\n",
    "    prediction = predict(trial_set[i], weights)\n",
    "    print(\"Expected=%d, Predicted=%d\" % (trial_set[i][-1], prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eaaba35d",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "674041e8",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 2. Training Network Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "14b9ee6f",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def train_weights(train, l_rate, n_epoch):\n",
    "    weights = [0.0 for i in range(len(train[0]))]\n",
    "    for epoch in range(n_epoch):\n",
    "        sum_error = 0.0\n",
    "        for row in train:\n",
    "            prediction = predict(row[:-1], weights)\n",
    "            error = row[-1] - prediction \n",
    "            sum_error += error**2\n",
    "            weights[0] =  weights[0] + l_rate * error\n",
    "            for i in range(len(row)-1):\n",
    "                weights[i+1] = weights[i+1] + l_rate * error * row[i]\n",
    "        print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error))\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "644ae427",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">epoch=0, lrate=0.100, error=13.000\n",
      ">epoch=1, lrate=0.100, error=10.000\n",
      ">epoch=2, lrate=0.100, error=10.000\n",
      ">epoch=3, lrate=0.100, error=10.000\n",
      ">epoch=4, lrate=0.100, error=10.000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[-5.099999999999998, 3.171360511923467, 2.3022181866516562]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_rate = 0.1\n",
    "n_epoch = 5\n",
    "weights = train_weights(trial_set, l_rate, n_epoch)\n",
    "weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f8b51b",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Testing on Sonar Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "bfe97195",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from random import seed\n",
    "from random import randrange\n",
    "from csv import reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "c49adfc4",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# load csv file\n",
    "def load_csv(filename):\n",
    "    dataset = list()\n",
    "    with open(filename, 'r') as file:\n",
    "        csv_reader = reader(file)\n",
    "        for row in csv_reader:\n",
    "            if not row:\n",
    "                continue\n",
    "            dataset.append(row)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "a0aa68e5",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# convert string to float\n",
    "def str_column_to_float(dataset, columns):\n",
    "    for row in dataset:\n",
    "        row[columns] = float(row[columns].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "a487adaa",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Convert string column to integer\n",
    "def str_column_to_int(dataset, column):\n",
    "\tclass_values = [row[column] for row in dataset]\n",
    "\tunique = set(class_values)\n",
    "\tlookup = dict()\n",
    "\tfor i, value in enumerate(unique):\n",
    "\t\tlookup[value] = i\n",
    "\tfor row in dataset:\n",
    "\t\trow[column] = lookup[row[column]]\n",
    "\treturn lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "9f9821b3",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Split a dataset into k folds\n",
    "def cross_validation_split(dataset, n_folds):\n",
    "\tdataset_split = list()\n",
    "\tdataset_copy = list(dataset)\n",
    "\tfold_size = int(len(dataset) / n_folds)\n",
    "\tfor i in range(n_folds):\n",
    "\t\tfold = list()\n",
    "\t\twhile len(fold) < fold_size:\n",
    "\t\t\tindex = randrange(len(dataset_copy))\n",
    "\t\t\tfold.append(dataset_copy.pop(index))\n",
    "\t\tdataset_split.append(fold)\n",
    "\treturn dataset_split\n",
    "\n",
    "# Calculate accuracy percentage\n",
    "def accuracy_metric(actual, predicted):\n",
    "\tcorrect = 0\n",
    "\tfor i in range(len(actual)):\n",
    "\t\tif actual[i] == predicted[i]:\n",
    "\t\t\tcorrect += 1\n",
    "\treturn correct / float(len(actual)) * 100.0\n",
    " \n",
    "# Evaluate an algorithm using a cross validation split\n",
    "def evaluate_algorithm(dataset, algorithm, n_folds, *args):\n",
    "\tfolds = cross_validation_split(dataset, n_folds)\n",
    "\tscores = list()\n",
    "\tfor fold in folds:\n",
    "\t\ttrain_set = list(folds)\n",
    "\t\ttrain_set.remove(fold)\n",
    "\t\ttrain_set = sum(train_set, [])\n",
    "\t\ttest_set = list()\n",
    "\t\tfor row in fold:\n",
    "\t\t\trow_copy = list(row)\n",
    "\t\t\ttest_set.append(row_copy)\n",
    "\t\t\trow_copy[-1] = None\n",
    "\t\tpredicted = algorithm(train_set, test_set, *args)\n",
    "\t\tactual = [row[-1] for row in fold]\n",
    "\t\taccuracy = accuracy_metric(actual, predicted)\n",
    "\t\tscores.append(accuracy)\n",
    "\treturn scores\n",
    " \n",
    "# Make a prediction with weights\n",
    "def predict(row, weights):\n",
    "\tactivation = weights[0]\n",
    "\tfor i in range(len(row)-1):\n",
    "\t\tactivation += weights[i + 1] * row[i]\n",
    "\treturn 1.0 if activation >= 0.0 else 0.0\n",
    " \n",
    "# Estimate Perceptron weights using stochastic gradient descent\n",
    "def train_weights(train, l_rate, n_epoch):\n",
    "\tweights = [0.0 for i in range(len(train[0]))]\n",
    "\tfor epoch in range(n_epoch):\n",
    "\t\tfor row in train:\n",
    "\t\t\tprediction = predict(row, weights)\n",
    "\t\t\terror = row[-1] - prediction\n",
    "\t\t\tweights[0] = weights[0] + l_rate * error\n",
    "\t\t\tfor i in range(len(row)-1):\n",
    "\t\t\t\tweights[i + 1] = weights[i + 1] + l_rate * error * row[i]\n",
    "\treturn weights\n",
    " \n",
    "# Perceptron Algorithm With Stochastic Gradient Descent\n",
    "def perceptron(train, test, l_rate, n_epoch):\n",
    "\tpredictions = list()\n",
    "\tweights = train_weights(train, l_rate, n_epoch)\n",
    "\tfor row in test:\n",
    "\t\tprediction = predict(row, weights)\n",
    "\t\tpredictions.append(prediction)\n",
    "\treturn(predictions)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "d43aec29",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [76.81159420289855, 69.56521739130434, 72.46376811594203]\n",
      "Mean Accuracy: 72.947%\n"
     ]
    }
   ],
   "source": [
    "# Test the Perceptron algorithm on the sonar dataset\n",
    "seed(1)\n",
    "# load and prepare data\n",
    "filename = 'sonar.all-data.csv'\n",
    "dataset = load_csv(filename)\n",
    "for i in range(len(dataset[0])-1):\n",
    "\tstr_column_to_float(dataset, i)\n",
    "# convert string class to integers\n",
    "str_column_to_int(dataset, len(dataset[0])-1)\n",
    "# evaluate algorithm\n",
    "n_folds = 3\n",
    "l_rate = 0.01\n",
    "n_epoch = 500\n",
    "scores = evaluate_algorithm(dataset, perceptron, n_folds, l_rate, n_epoch)\n",
    "print('Scores: %s' % scores)\n",
    "print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e108219d",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Perceptron Implementation 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d1e40a9c",
   "metadata": {
    "code_folding": [],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class CustomPerceptron(object):\n",
    "    def __init__(self, n_inputs, threshold=1000, learning_rate=0.1):\n",
    "        self.threshold = threshold\n",
    "        self.learning_rate = learning_rate\n",
    "        self.weights = np.zeros(n_inputs+1)\n",
    "        \n",
    "    def predict(self, inputs):\n",
    "        summationn = np.dot(inputs, self.weights[1:]) + self.weights[0]\n",
    "        if summationn > 0:\n",
    "            activation = 1\n",
    "        else:\n",
    "            activation = 0\n",
    "        return activation\n",
    "    \n",
    "    def train(self, training_inputs, labels):\n",
    "        for _ in range(self.threshold):\n",
    "            for inputs, label in zip(training_inputs, labels):\n",
    "                prediction = self.predict(inputs)\n",
    "                self.weights[1:] += self.learning_rate * (label-prediction) * inputs\n",
    "                self.weights[0] += self.learning_rate * (label-prediction)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "84e29809",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_train_dummy = [1 if i == 1 else 0 for i in y_train_dummy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5eac7b89",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model = CustomPerceptron(n_inputs=2, threshold=10000, learning_rate=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a2707bd7",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model.train(X_train_dummy_std_scaled[:,:-1], y_train_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "49727551",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1       , 0.0923207 , 0.28477031])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "41bb6a43",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.07833151, 1.29151418, 0.        ])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_dummy_std_scaled[:,:][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ca7a86f4",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_train_dummy_std_scaled[:,:-1][-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8039d444",
   "metadata": {},
   "source": [
    "# SkLearn Implementation and Comparision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e8fd3ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "import sklearn.datasets as datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8002e542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('D:/amit/ml_indepth/perceptron')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Path.cwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db23d9dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = Path('D:/amit/ml_indepth/datasets/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77453ceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('D:/amit/ml_indepth/datasets/diabetes.xlsx'),\n",
       " WindowsPath('D:/amit/ml_indepth/datasets/sonar.all-data.csv')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data_path.glob('*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50b65949",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(data_path/'diabetes.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "289cdb4f",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>72</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.283</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0.930</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>56</td>\n",
       "      <td>29</td>\n",
       "      <td>152</td>\n",
       "      <td>28.7</td>\n",
       "      <td>0.801</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>0.207</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>4</td>\n",
       "      <td>144</td>\n",
       "      <td>58</td>\n",
       "      <td>28</td>\n",
       "      <td>140</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.287</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "103            1       81             72             18       40  26.6   \n",
       "104            2       85             65              0        0  39.6   \n",
       "105            1      126             56             29      152  28.7   \n",
       "106            1       96            122              0        0  22.4   \n",
       "107            4      144             58             28      140  29.5   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "103                     0.283   24        0  \n",
       "104                     0.930   27        0  \n",
       "105                     0.801   21        0  \n",
       "106                     0.207   27        0  \n",
       "107                     0.287   37        0  \n",
       "\n",
       "[108 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d4960c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1].values\n",
    "y = df.Outcome.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e5750acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Perceptron(random_state=42)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Perceptron(random_state=42)\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3fdabc6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.676\n"
     ]
    }
   ],
   "source": [
    "print(\"%0.3f\" % model.score(X, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "da7e6baf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = CustomPerceptron(n_inputs=X.shape[-1], threshold=100000, learning_rate=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6bd14e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.train(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "fa2c29b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-8285.70000001,   161.2       ,    22.9       ,   -32.3       ,\n",
       "          15.3       ,   -11.4       ,    55.81      ,   638.0266    ,\n",
       "          43.2       ])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8d9f8a46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 142.   , -119.   , -278.   , -237.   ,  203.   ,  100.3  ,\n",
       "          10.677,  208.   ]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "0252d5c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = []\n",
    "for x in X:\n",
    "    predictions.append(model1.predict(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6489b41b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e3cbc690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6481481481481481"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([True if x==y else False for x,y in zip(y, predictions)])/len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "957bfb88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.648148\n",
       "1    0.351852\n",
       "Name: Outcome, dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Outcome.value_counts()/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "17038114",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = CustomPerceptron1(100000, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "82fb14ba",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-131-c93c6a2d9250>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-129-15f260d92852>\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m     15\u001b[0m             \u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m                 \u001b[1;32mif\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mw\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m                     \u001b[0mm\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Runs forever so our linear separation assumtion fails\n",
    "model2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "878e6d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2de9e43f",
   "metadata": {},
   "source": [
    "# Real World Use Case 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "399ea845",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import cross_val_score, RepeatedStratifiedKFold\n",
    "from sklearn.linear_model import Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e114f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=1000, n_features=10, n_informative=10, n_redundant=0, n_classes=2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14159da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Perceptron()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c4b73911",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d1ab05d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f17a7f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accuracy: 0.763 (0.060)\n"
     ]
    }
   ],
   "source": [
    "print('Mean Accuracy: %.3f (%.3f)' % (np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7848013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.86, 0.84, 0.71, 0.74, 0.58, 0.77, 0.79, 0.74, 0.8 , 0.84, 0.8 ,\n",
       "       0.75, 0.71, 0.75, 0.8 , 0.76, 0.79, 0.72, 0.68, 0.8 , 0.78, 0.79,\n",
       "       0.81, 0.79, 0.66, 0.72, 0.77, 0.7 , 0.86, 0.78])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a66f3062",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ac87165",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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