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https://github.com/20kaushik02/CSE515_MWDB_Project.git
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task 3
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pranavbrkr
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Phase 3/task_3.ipynb
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Phase 3/task_3.ipynb
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Phase 3/task_3_sklearn_decision_tree.ipynb
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Phase 3/task_3_sklearn_decision_tree.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils import *\n",
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"warnings.filterwarnings('ignore')\n",
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"%matplotlib inline\n",
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"from statistics import mode\n",
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"import pickle\n",
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"from sklearn.metrics import precision_recall_fscore_support\n",
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"from sklearn.decomposition import PCA\n",
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"from sklearn.tree import DecisionTreeClassifier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"selected_feature_model = \"fc_fd\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"all_images = list(fd_collection.find())\n",
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"all_images = sorted(all_images, key = lambda x: x[\"image_id\"])\n",
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"\n",
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"odd_image_ids = [img[\"image_id\"] for img in all_images if img[\"image_id\"] % 2 == 0]\n",
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"\n",
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"even_image_labels = [img[\"true_label\"] for img in all_images if img[\"image_id\"] % 2 == 0]\n",
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"odd_image_labels = [img[\"true_label\"] for img in all_images if img[\"image_id\"] % 2 != 0]\n",
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"\n",
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"feature_vectors = [np.array(img[selected_feature_model]).flatten() for img in all_images]\n",
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"\n",
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"total_len = len(feature_vectors)\n",
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"even_feature_vectors = []\n",
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"odd_feature_vectors = []\n",
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"\n",
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"for i in range(total_len):\n",
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" if i % 2 == 0:\n",
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" even_feature_vectors.append(feature_vectors[i])\n",
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" else:\n",
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" odd_feature_vectors.append(feature_vectors[i])\n",
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"\n",
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"even_feature_vectors = np.array(even_feature_vectors)\n",
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"odd_feature_vectors = np.array(odd_feature_vectors)\n",
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"\n",
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"odd_len = odd_feature_vectors.shape[0]\n",
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"even_len = even_feature_vectors.shape[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"max_depth = 10\n",
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"reduced_dimensionality = 150\n",
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"\n",
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"# pca = PCA(n_components = reduced_dimensionality)\n",
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"# even_feature_vectors = pca.fit_transform(even_feature_vectors)\n",
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"# odd_feature_vectors = pca.fit_transform(odd_feature_vectors)\n",
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"\n",
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"\n",
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"clf = DecisionTreeClassifier()\n",
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"clf.fit(even_feature_vectors, even_image_labels)\n",
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"predictions = clf.predict(odd_feature_vectors)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Class 0: Precision=0.7946428571428571, Recall=0.8202764976958525, F1-score=0.8072562358276645\n",
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"Class 1: Precision=0.8195121951219512, Recall=0.7706422018348624, F1-score=0.7943262411347518\n",
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"Class 2: Precision=0.9361702127659575, Recall=0.88, F1-score=0.9072164948453608\n",
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"Class 3: Precision=0.9897172236503856, Recall=0.9649122807017544, F1-score=0.9771573604060915\n",
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"Class 4: Precision=1.0, Recall=0.9259259259259259, F1-score=0.9615384615384615\n",
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"Class 5: Precision=0.9503722084367245, Recall=0.9575, F1-score=0.9539227895392279\n",
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"Class 6: Precision=0.09523809523809523, Recall=0.09523809523809523, F1-score=0.09523809523809523\n",
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"Class 7: Precision=0.875, Recall=0.6666666666666666, F1-score=0.7567567567567567\n",
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"Class 8: Precision=0.9473684210526315, Recall=0.75, F1-score=0.8372093023255814\n",
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"Class 9: Precision=0.5652173913043478, Recall=0.48148148148148145, F1-score=0.52\n",
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"Class 10: Precision=0.7307692307692307, Recall=0.8260869565217391, F1-score=0.7755102040816326\n",
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"Class 11: Precision=1.0, Recall=0.875, F1-score=0.9333333333333333\n",
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"Class 12: Precision=0.8524590163934426, Recall=0.8125, F1-score=0.8319999999999999\n",
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"Class 13: Precision=0.7272727272727273, Recall=0.8163265306122449, F1-score=0.7692307692307693\n",
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"Class 14: Precision=0.32, Recall=0.36363636363636365, F1-score=0.3404255319148936\n",
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"Class 15: Precision=0.5102040816326531, Recall=0.5952380952380952, F1-score=0.5494505494505494\n",
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"Class 16: Precision=0.5471698113207547, Recall=0.6304347826086957, F1-score=0.5858585858585859\n",
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"Class 17: Precision=0.7931034482758621, Recall=0.92, F1-score=0.851851851851852\n",
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"Class 18: Precision=0.75, Recall=0.5714285714285714, F1-score=0.6486486486486486\n",
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"Class 19: Precision=0.9824561403508771, Recall=0.9032258064516129, F1-score=0.9411764705882352\n",
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"Class 20: Precision=0.9090909090909091, Recall=0.43478260869565216, F1-score=0.5882352941176471\n",
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"Class 21: Precision=0.84, Recall=0.7, F1-score=0.7636363636363636\n",
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"Class 22: Precision=0.7083333333333334, Recall=0.5483870967741935, F1-score=0.6181818181818182\n",
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"Class 23: Precision=0.5254237288135594, Recall=0.5849056603773585, F1-score=0.5535714285714286\n",
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"Class 24: Precision=0.64, Recall=0.6666666666666666, F1-score=0.6530612244897959\n",
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"Class 25: Precision=0.6, Recall=0.7058823529411765, F1-score=0.6486486486486486\n",
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"Class 26: Precision=0.78125, Recall=0.6756756756756757, F1-score=0.7246376811594203\n",
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"Class 27: Precision=0.5227272727272727, Recall=0.6571428571428571, F1-score=0.5822784810126581\n",
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"Class 28: Precision=0.5, Recall=0.48, F1-score=0.4897959183673469\n",
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"Class 29: Precision=0.6086956521739131, Recall=0.56, F1-score=0.5833333333333334\n",
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"Class 30: Precision=0.7241379310344828, Recall=0.7241379310344828, F1-score=0.7241379310344829\n",
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"Class 31: Precision=1.0, Recall=0.9696969696969697, F1-score=0.9846153846153847\n",
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"Class 32: Precision=0.7272727272727273, Recall=0.6153846153846154, F1-score=0.6666666666666667\n",
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"Class 33: Precision=0.875, Recall=0.6363636363636364, F1-score=0.7368421052631579\n",
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"Class 34: Precision=0.5609756097560976, Recall=0.6764705882352942, F1-score=0.6133333333333334\n",
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"Class 35: Precision=0.7027027027027027, Recall=0.7027027027027027, F1-score=0.7027027027027027\n",
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"Class 36: Precision=0.9259259259259259, Recall=0.78125, F1-score=0.847457627118644\n",
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"Class 37: Precision=0.7096774193548387, Recall=0.8148148148148148, F1-score=0.7586206896551724\n",
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"Class 38: Precision=0.9285714285714286, Recall=0.8125, F1-score=0.8666666666666666\n",
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"Class 39: Precision=0.8541666666666666, Recall=0.9761904761904762, F1-score=0.9111111111111111\n",
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"Class 40: Precision=0.8888888888888888, Recall=0.9411764705882353, F1-score=0.9142857142857143\n",
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"Class 41: Precision=0.7647058823529411, Recall=0.7878787878787878, F1-score=0.7761194029850745\n",
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"Class 42: Precision=0.5, Recall=0.5217391304347826, F1-score=0.5106382978723404\n",
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"Class 43: Precision=0.8888888888888888, Recall=0.47058823529411764, F1-score=0.6153846153846153\n",
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"Class 44: Precision=0.8333333333333334, Recall=0.8823529411764706, F1-score=0.8571428571428571\n",
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"Class 45: Precision=0.2926829268292683, Recall=0.48, F1-score=0.3636363636363636\n",
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"Class 46: Precision=1.0, Recall=0.98, F1-score=0.98989898989899\n",
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"Class 47: Precision=0.96, Recall=0.96, F1-score=0.96\n",
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"Class 48: Precision=0.5416666666666666, Recall=0.6190476190476191, F1-score=0.5777777777777778\n",
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"Class 49: Precision=0.92, Recall=0.8518518518518519, F1-score=0.8846153846153846\n",
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"Class 50: Precision=0.3333333333333333, Recall=0.36363636363636365, F1-score=0.34782608695652173\n",
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"Class 51: Precision=0.8461538461538461, Recall=0.825, F1-score=0.8354430379746836\n",
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"Class 52: Precision=0.5217391304347826, Recall=0.8, F1-score=0.6315789473684211\n",
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"Class 53: Precision=0.68, Recall=0.53125, F1-score=0.5964912280701754\n",
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"Class 54: Precision=0.9545454545454546, Recall=0.9767441860465116, F1-score=0.9655172413793104\n",
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"Class 55: Precision=0.8392857142857143, Recall=0.8245614035087719, F1-score=0.8318584070796461\n",
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"Class 56: Precision=0.6774193548387096, Recall=0.6774193548387096, F1-score=0.6774193548387096\n",
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"Class 57: Precision=0.8604651162790697, Recall=0.925, F1-score=0.891566265060241\n",
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"Class 58: Precision=0.8787878787878788, Recall=0.7435897435897436, F1-score=0.8055555555555556\n",
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"Class 59: Precision=0.5333333333333333, Recall=0.38095238095238093, F1-score=0.4444444444444444\n",
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"Class 60: Precision=0.6206896551724138, Recall=0.5454545454545454, F1-score=0.5806451612903226\n",
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"Class 61: Precision=0.36, Recall=0.42857142857142855, F1-score=0.391304347826087\n",
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"Class 62: Precision=0.5263157894736842, Recall=0.5, F1-score=0.5128205128205129\n",
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"Class 63: Precision=0.5625, Recall=0.6136363636363636, F1-score=0.5869565217391304\n",
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"Class 64: Precision=0.45454545454545453, Recall=0.3125, F1-score=0.3703703703703703\n",
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"Class 65: Precision=0.3090909090909091, Recall=0.4473684210526316, F1-score=0.3655913978494624\n",
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"Class 66: Precision=1.0, Recall=0.8888888888888888, F1-score=0.9411764705882353\n",
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"Class 67: Precision=0.2777777777777778, Recall=0.2777777777777778, F1-score=0.2777777777777778\n",
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"Class 68: Precision=0.6923076923076923, Recall=0.9473684210526315, F1-score=0.7999999999999999\n",
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"Class 69: Precision=0.6875, Recall=0.4583333333333333, F1-score=0.5499999999999999\n",
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"Class 70: Precision=0.9333333333333333, Recall=0.7368421052631579, F1-score=0.8235294117647058\n",
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"Class 71: Precision=0.85, Recall=0.7727272727272727, F1-score=0.8095238095238095\n",
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"Class 72: Precision=0.8076923076923077, Recall=0.7777777777777778, F1-score=0.7924528301886792\n",
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"Class 73: Precision=1.0, Recall=0.7647058823529411, F1-score=0.8666666666666666\n",
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"Class 74: Precision=0.3617021276595745, Recall=0.6071428571428571, F1-score=0.4533333333333333\n",
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"Class 75: Precision=1.0, Recall=1.0, F1-score=1.0\n",
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"Class 76: Precision=0.7, Recall=0.7, F1-score=0.7\n",
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"Class 77: Precision=0.7241379310344828, Recall=0.875, F1-score=0.7924528301886793\n",
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"Class 78: Precision=0.9090909090909091, Recall=1.0, F1-score=0.9523809523809523\n",
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"Class 79: Precision=0.7096774193548387, Recall=0.6875, F1-score=0.6984126984126984\n",
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"Class 80: Precision=0.2777777777777778, Recall=0.2631578947368421, F1-score=0.27027027027027023\n",
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"Class 81: Precision=0.803921568627451, Recall=0.9761904761904762, F1-score=0.8817204301075269\n",
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"Class 82: Precision=0.5294117647058824, Recall=0.3103448275862069, F1-score=0.391304347826087\n",
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"Class 83: Precision=0.4, Recall=0.35294117647058826, F1-score=0.37500000000000006\n",
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"Class 84: Precision=0.8181818181818182, Recall=0.84375, F1-score=0.8307692307692308\n",
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"Class 85: Precision=0.39285714285714285, Recall=0.4782608695652174, F1-score=0.4313725490196078\n",
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"Class 86: Precision=1.0, Recall=0.9534883720930233, F1-score=0.9761904761904763\n",
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"Class 87: Precision=0.7, Recall=0.7241379310344828, F1-score=0.711864406779661\n",
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"Class 88: Precision=0.8333333333333334, Recall=0.9375, F1-score=0.8823529411764706\n",
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"Class 89: Precision=0.8, Recall=0.8888888888888888, F1-score=0.8421052631578948\n",
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"Class 90: Precision=0.868421052631579, Recall=0.7857142857142857, F1-score=0.825\n",
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"Class 91: Precision=0.9615384615384616, Recall=1.0, F1-score=0.9803921568627451\n",
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"Class 92: Precision=0.8333333333333334, Recall=0.9302325581395349, F1-score=0.8791208791208791\n",
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"Class 93: Precision=0.9090909090909091, Recall=0.8108108108108109, F1-score=0.8571428571428571\n",
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"Class 94: Precision=0.925, Recall=0.925, F1-score=0.925\n",
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"Class 95: Precision=0.2857142857142857, Recall=0.2222222222222222, F1-score=0.25\n",
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"Class 96: Precision=0.53125, Recall=0.5666666666666667, F1-score=0.5483870967741935\n",
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"Class 97: Precision=0.5555555555555556, Recall=0.5882352941176471, F1-score=0.5714285714285715\n",
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"Class 98: Precision=0.78125, Recall=0.8928571428571429, F1-score=0.8333333333333334\n",
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"Class 99: Precision=0.3125, Recall=0.2631578947368421, F1-score=0.2857142857142857\n",
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"Class 100: Precision=0.6666666666666666, Recall=0.6666666666666666, F1-score=0.6666666666666666\n",
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"Accuracy: 77.82%\n"
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]
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}
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],
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"source": [
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"precision, recall, f1_score, _ = precision_recall_fscore_support(odd_image_labels, predictions, labels=range(101))\n",
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"\n",
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"for i in range(101):\n",
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" print(f\"Class {i}: Precision={precision[i]}, Recall={recall[i]}, F1-score={f1_score[i]}\")\n",
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"\n",
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"correct_predictions = sum(1 for actual, predicted in zip(odd_image_labels, predictions) if actual == predicted)\n",
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"accuracy = (correct_predictions / len(odd_image_labels)) * 100.0\n",
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"print(f\"Accuracy: {accuracy:.2f}%\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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