tutorials/ionosphere_supervised-FRUFS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get all imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "import time\n",
    "from FRUFS import FRUFS\n",
    "from sklearn.cluster import KMeans\n",
    "from matplotlib.pyplot import figure\n",
    "from sklearn.metrics.cluster import normalized_mutual_info_score\n",
    "from lightgbm import LGBMRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "   0   1        2        3        4        5        6        7        8   \\\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape:  (351, 34) Target Variable shape:  (351,)\n"
     ]
    }
   ],
   "source": [
    "# Load the data into a dataframe\n",
    "df = pd.read_csv(\"../data/ionosphere/ionosphere.data\", header=None)\n",
    "# Pre-process the data\n",
    "df[34] = df[34].str.replace(\"g\", '1').replace(\"b\", '0')\n",
    "# Lets check it out\n",
    "display(df.head())\n",
    "# Split the data into input features and target variable\n",
    "data, Y = df.drop([34], axis=1), df[34].values\n",
    "# Lets check out the shape of our data\n",
    "print(\"Data shape: \", data.shape, \"Target Variable shape: \", Y.shape)\n",
    "\n",
    "num_classes = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling with Decision Tree without FRUFS (no feature selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.9436619718309859\n",
      "Score: 0.9142857142857143\n",
      "Score: 0.8285714285714286\n",
      "Score: 0.8428571428571429\n",
      "Score: 0.8714285714285714\n",
      "\n",
      "\n",
      "Average Accuracy:  0.8801609657947687\n",
      "\n",
      "\n",
      "Total Time Required (in seconds):  0.027924776077270508\n"
     ]
    }
   ],
   "source": [
    "# We want to time our algorithm\n",
    "start = time.time()\n",
    "# Use KFold for understanding the performance of PyImpetus\n",
    "kfold = KFold(n_splits=5, random_state=27, shuffle=True)\n",
    "# This will hold all the accuracy scores\n",
    "scores = list()\n",
    "# Perform CV\n",
    "for train, test in kfold.split(data):\n",
    "    # Split data into train and test based on folds\n",
    "    x_train, x_test = data.iloc[train], data.iloc[test]\n",
    "    y_train, y_test = Y[train], Y[test]\n",
    "    \n",
    "    # Convert the data into numpy arrays\n",
    "    x_train, x_test = x_train.values, x_test.values\n",
    "    \n",
    "    model_dt = DecisionTreeClassifier(random_state=27)\n",
    "    model_dt.fit(x_train, y_train)\n",
    "    preds = model_dt.predict(x_test)\n",
    "\n",
    "    # We are going to use the NMI metric to measure the quality/performance of the clustering \n",
    "    score = accuracy_score(y_test, preds)\n",
    "    print(\"Score:\", score)\n",
    "    scores.append(score)\n",
    "# Compute average score\n",
    "print(\"\\n\\nAverage Accuracy: \", sum(scores)/len(scores))\n",
    "# Finally, check out the total time taken\n",
    "end = time.time()\n",
    "print(\"\\n\\nTotal Time Required (in seconds): \", end-start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling with FRUFS (feature selection is applied)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Progress bar: 100%|████████| 34/34 [00:04<00:00,  7.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.9154929577464789\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Progress bar: 100%|███████| 34/34 [00:00<00:00, 741.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.9857142857142858\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Progress bar: 100%|███████| 34/34 [00:00<00:00, 897.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Progress bar: 100%|███████| 34/34 [00:00<00:00, 897.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.8857142857142857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Progress bar: 100%|███████| 34/34 [00:00<00:00, 873.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.8857142857142857\n",
      "\n",
      "\n",
      "Average Accuracy:  0.9145271629778673\n",
      "\n",
      "\n",
      "Total Time Required (in seconds):  4.612658977508545\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# We want to time our algorithm\n",
    "start = time.time()\n",
    "# Use KFold for understanding the performance of PyImpetus\n",
    "kfold = KFold(n_splits=5, random_state=27, shuffle=True)\n",
    "# This will hold all the accuracy scores\n",
    "scores = list()\n",
    "# Perform CV\n",
    "for train, test in kfold.split(data):\n",
    "    # Split data into train and test based on folds\n",
    "    x_train, x_test = data.iloc[train], data.iloc[test]\n",
    "    y_train, y_test = Y[train], Y[test]\n",
    "    \n",
    "    # Convert the data into numpy arrays\n",
    "    x_train, x_test = x_train.values, x_test.values\n",
    "    \n",
    "    # Initialize the FRUFS object with your supervised algorithm of choice\n",
    "    model = FRUFS(model_r=DecisionTreeRegressor(random_state=27), k=24, n_jobs=-1, verbose=0, random_state=27)\n",
    "    # Train the FRUFS model and use it to downsize your data\n",
    "    x_train = model.fit_transform(x_train)\n",
    "    x_test = model.transform(x_test)\n",
    "    \n",
    "    # Finally, classify on selected features\n",
    "    model_dt = DecisionTreeClassifier(random_state=27)\n",
    "    model_dt.fit(x_train, y_train)\n",
    "    preds = model_dt.predict(x_test)\n",
    "\n",
    "    # We are going to use the NMI metric to measure the quality/performance of the clustering \n",
    "    score = accuracy_score(y_test, preds)\n",
    "    print(\"Score:\", score)\n",
    "    scores.append(score)\n",
    "# Compute average score\n",
    "print(\"\\n\\nAverage Accuracy: \", sum(scores)/len(scores))\n",
    "# Finally, check out the total time taken\n",
    "end = time.time()\n",
    "print(\"\\n\\nTotal Time Required (in seconds): \", end-start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Results (Accuracy is used so higher is better)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Accuracy using all features: 88.02 (0.028 seconds)\n",
    "### Final Accuracy using PyImpetus recommended features: 91.45 (4.613 seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(8, 20), dpi=100)\n",
    "model.feature_importance()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}