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Merge pull request #1 from viral98/master
Fixed files to support Python 3, Added python notebook
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,227 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "import numpy as np\n", | ||
| "import pickle\n", | ||
| "import sklearn.ensemble as ske\n", | ||
| "from sklearn import tree, linear_model\n", | ||
| "from sklearn.model_selection import train_test_split\n", | ||
| "from sklearn.feature_selection import SelectFromModel\n", | ||
| "from sklearn.externals import joblib\n", | ||
| "from sklearn.naive_bayes import GaussianNB\n", | ||
| "from sklearn.metrics import confusion_matrix\n", | ||
| "\n", | ||
| "data = pd.read_csv('data.csv', sep='|')\n", | ||
| "X = data.drop(['Name', 'md5', 'legitimate'], axis=1).values\n", | ||
| "y = data['legitimate'].values\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Researching important feature based on 54 total features\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "c:\\users\\tagdi\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", | ||
| " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "print('Researching important feature based on %i total features\\n' % X.shape[1])\n", | ||
| "\n", | ||
| "# Feature selection using Trees Classifier\n", | ||
| "fsel = ske.ExtraTreesClassifier().fit(X, y)\n", | ||
| "model = SelectFromModel(fsel, prefit=True)\n", | ||
| "X_new = model.transform(X)\n", | ||
| "nb_features = X_new.shape[1]\n", | ||
| "\n", | ||
| "X_train, X_test, y_train, y_test = train_test_split(X_new, y ,test_size=0.2)\n", | ||
| "\n", | ||
| "features = []\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 9, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "12 features identified as important:\n", | ||
| "1. feature Characteristics (0.174459)\n", | ||
| "2. feature Machine (0.153884)\n", | ||
| "3. feature DllCharacteristics (0.132542)\n", | ||
| "4. feature SectionsMaxEntropy (0.074540)\n", | ||
| "5. feature Subsystem (0.055532)\n", | ||
| "6. feature SizeOfStackReserve (0.047293)\n", | ||
| "7. feature ImageBase (0.046986)\n", | ||
| "8. feature SectionsNb (0.038547)\n", | ||
| "9. feature MajorSubsystemVersion (0.033042)\n", | ||
| "10. feature VersionInformationSize (0.031814)\n", | ||
| "11. feature MajorOperatingSystemVersion (0.022208)\n", | ||
| "12. feature SectionsMinEntropy (0.019704)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "print('%i features identified as important:' % nb_features)\n", | ||
| "\n", | ||
| "indices = np.argsort(fsel.feature_importances_)[::-1][:nb_features]\n", | ||
| "for f in range(nb_features):\n", | ||
| " print(\"%d. feature %s (%f)\" % (f + 1, data.columns[2+indices[f]], fsel.feature_importances_[indices[f]]))\n", | ||
| "\n", | ||
| "# XXX : take care of the feature order\n", | ||
| "for f in sorted(np.argsort(fsel.feature_importances_)[::-1][:nb_features]):\n", | ||
| " features.append(data.columns[2+f])\n", | ||
| "\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 10, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "\n", | ||
| "Now testing algorithms\n", | ||
| "DecisionTree : 99.036581 %\n", | ||
| "RandomForest : 99.398769 %\n", | ||
| "GradientBoosting : 98.772184 %\n", | ||
| "AdaBoost : 98.678015 %\n", | ||
| "GNB : 70.449113 %\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "#Algorithm comparison\n", | ||
| "algorithms = {\n", | ||
| " \"DecisionTree\": tree.DecisionTreeClassifier(max_depth=10),\n", | ||
| " \"RandomForest\": ske.RandomForestClassifier(n_estimators=50),\n", | ||
| " \"GradientBoosting\": ske.GradientBoostingClassifier(n_estimators=50),\n", | ||
| " \"AdaBoost\": ske.AdaBoostClassifier(n_estimators=100),\n", | ||
| " \"GNB\": GaussianNB()\n", | ||
| " }\n", | ||
| "\n", | ||
| "results = {}\n", | ||
| "print(\"\\nNow testing algorithms\")\n", | ||
| "for algo in algorithms:\n", | ||
| " clf = algorithms[algo]\n", | ||
| " clf.fit(X_train, y_train)\n", | ||
| " score = clf.score(X_test, y_test)\n", | ||
| " print(\"%s : %f %%\" % (algo, score*100))\n", | ||
| " results[algo] = score\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 11, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "\n", | ||
| "Winner algorithm is RandomForest with a 99.398769 % success\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "winner = max(results, key=results.get)\n", | ||
| "print('\\n Algorithm with highest accuracy on train/test is %s with a %f %% success' % (winner, results[winner]*100))\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 13, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "\n", | ||
| "# print('Saving algorithm and feature list in classifier directory...')\n", | ||
| "# joblib.dump(algorithms[winner], 'classifier/classifier.pkl')\n", | ||
| "# open('classifier/features.pkl', 'w').write(pickle.dumps(features))\n", | ||
| "# print('Saved')\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 14, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "False positive rate : 0.462701 %\n", | ||
| "False negative rate : 0.931487 %\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Identify false and true positive rates\n", | ||
| "clf = algorithms[winner]\n", | ||
| "res = clf.predict(X_test)\n", | ||
| "mt = confusion_matrix(y_test, res)\n", | ||
| "print(\"False positive rate : %f %%\" % ((mt[0][1] / float(sum(mt[0])))*100))\n", | ||
| "print('False negative rate : %f %%' % ( (mt[1][0] / float(sum(mt[1]))*100)))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "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": "3.7.4" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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