Please cite us if you use the software
" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Example-4 (File)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Environment check" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Checking that the notebook is running on Google Colab or not." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "try:\n", " import google.colab\n", " !{sys.executable} -m pip -q -q install pycm\n", "except:\n", " pass" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Run" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from pycm import ConfusionMatrix\n", "import numpy as np\n", "import os\n", "if \"Example4_files\" not in os.listdir():\n", " os.mkdir(\"Example4_files\")\n", "y_test = np.array([600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200])\n", "y_pred = np.array([100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pycm.ConfusionMatrix(classes: [100, 200, 500, 600])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cm=ConfusionMatrix(y_test, y_pred)\n", "cm" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predict 100 200 500 600 \n", "Actual\n", "100 0 0 0 0 \n", "\n", "200 9 6 1 0 \n", "\n", "500 1 1 1 0 \n", "\n", "600 1 0 0 0 \n", "\n", "\n", "\n", "\n", "\n", "Overall Statistics : \n", "\n", "95% CI (0.14096,0.55904)\n", "ACC Macro 0.675\n", "ARI 0.02298\n", "AUNP None\n", "AUNU None\n", "Bangdiwala B 0.31356\n", "Bennett S 0.13333\n", "CBA 0.17708\n", "CSI None\n", "Chi-Squared None\n", "Chi-Squared DF 9\n", "Conditional Entropy 1.23579\n", "Cramer V None\n", "Cross Entropy 1.70995\n", "F1 Macro 0.23043\n", "F1 Micro 0.35\n", "FNR Macro None\n", "FNR Micro 0.65\n", "FPR Macro 0.21471\n", "FPR Micro 0.21667\n", "Gwet AC1 0.19505\n", "Hamming Loss 0.65\n", "Joint Entropy 2.11997\n", "KL Divergence None\n", "Kappa 0.07801\n", "Kappa 95% CI (-0.2185,0.37453)\n", "Kappa No Prevalence -0.3\n", "Kappa Standard Error 0.15128\n", "Kappa Unbiased -0.12554\n", "Krippendorff Alpha -0.0974\n", "Lambda A 0.0\n", "Lambda B 0.0\n", "Mutual Information 0.10088\n", "NIR 0.8\n", "NPV Macro 0.76741\n", "NPV Micro 0.78333\n", "Overall ACC 0.35\n", "Overall CEN 0.3648\n", "Overall J (0.60294,0.15074)\n", "Overall MCC 0.12642\n", "Overall MCEN 0.37463\n", "Overall RACC 0.295\n", "Overall RACCU 0.4225\n", "P-Value 1.0\n", "PPV Macro None\n", "PPV Micro 0.35\n", "Pearson C None\n", "Phi-Squared None\n", "RCI 0.11409\n", "RR 5.0\n", "Reference Entropy 0.88418\n", "Response Entropy 1.33667\n", "SOA1(Landis & Koch) Slight\n", "SOA2(Fleiss) Poor\n", "SOA3(Altman) Poor\n", "SOA4(Cicchetti) Poor\n", "SOA5(Cramer) None\n", "SOA6(Matthews) Negligible\n", "SOA7(Lambda A) None\n", "SOA8(Lambda B) None\n", "SOA9(Krippendorff Alpha) Low\n", "SOA10(Pearson C) None\n", "Scott PI -0.12554\n", "Standard Error 0.10665\n", "TNR Macro 0.78529\n", "TNR Micro 0.78333\n", "TPR Macro None\n", "TPR Micro 0.35\n", "Zero-one Loss 13\n", "\n", "Class Statistics :\n", "\n", "Classes 100 200 500 600 \n", "ACC(Accuracy) 0.45 0.45 0.85 0.95 \n", "AGF(Adjusted F-score) 0.0 0.33642 0.56659 0.0 \n", "AGM(Adjusted geometric mean) None 0.56694 0.7352 0 \n", "AM(Difference between automatic and manual classification) 11 -9 -1 -1 \n", "AUC(Area under the ROC curve) None 0.5625 0.63725 0.5 \n", "AUCI(AUC value interpretation) None Poor Fair Poor \n", "AUPR(Area under the PR curve) None 0.61607 0.41667 None \n", "BB(Braun-Blanquet similarity) 0.0 0.375 0.33333 0.0 \n", "BCD(Bray-Curtis dissimilarity) 0.275 0.225 0.025 0.025 \n", "BM(Informedness or bookmaker informedness) None 0.125 0.27451 0.0 \n", "CEN(Confusion entropy) 0.33496 0.35708 0.53895 0.0 \n", "DOR(Diagnostic odds ratio) None 1.8 8.0 None \n", "DP(Discriminant power) None 0.14074 0.4979 None \n", "DPI(Discriminant power interpretation) None Poor Poor None \n", "ERR(Error rate) 0.55 0.55 0.15 0.05 \n", "F0.5(F0.5 score) 0.0 0.68182 0.45455 0.0 \n", "F1(F1 score - harmonic mean of precision and sensitivity) 0.0 0.52174 0.4 0.0 \n", "F2(F2 score) 0.0 0.42254 0.35714 0.0 \n", "FDR(False discovery rate) 1.0 0.14286 0.5 None \n", "FN(False negative/miss/type 2 error) 0 10 2 1 \n", "FNR(Miss rate or false negative rate) None 0.625 0.66667 1.0 \n", "FOR(False omission rate) 0.0 0.76923 0.11111 0.05 \n", "FP(False positive/type 1 error/false alarm) 11 1 1 0 \n", "FPR(Fall-out or false positive rate) 0.55 0.25 0.05882 0.0 \n", "G(G-measure geometric mean of precision and sensitivity) None 0.56695 0.40825 None \n", "GI(Gini index) None 0.125 0.27451 0.0 \n", "GM(G-mean geometric mean of specificity and sensitivity) None 0.53033 0.56011 0.0 \n", "HD(Hamming distance) 11 11 3 1 \n", "IBA(Index of balanced accuracy) None 0.17578 0.12303 0.0 \n", "ICSI(Individual classification success index) None 0.23214 -0.16667 None \n", "IS(Information score) None 0.09954 1.73697 None \n", "J(Jaccard index) 0.0 0.35294 0.25 0.0 \n", "LS(Lift score) None 1.07143 3.33333 None \n", "MCC(Matthews correlation coefficient) None 0.10483 0.32673 None \n", "MCCI(Matthews correlation coefficient interpretation) None Negligible Weak None \n", "MCEN(Modified confusion entropy) 0.33496 0.37394 0.58028 0.0 \n", "MK(Markedness) 0.0 0.08791 0.38889 None \n", "N(Condition negative) 20 4 17 19 \n", "NLR(Negative likelihood ratio) None 0.83333 0.70833 1.0 \n", "NLRI(Negative likelihood ratio interpretation) None Negligible Negligible Negligible \n", "NPV(Negative predictive value) 1.0 0.23077 0.88889 0.95 \n", "OC(Overlap coefficient) None 0.85714 0.5 None \n", "OOC(Otsuka-Ochiai coefficient) None 0.56695 0.40825 None \n", "OP(Optimized precision) None 0.11667 0.37308 -0.05 \n", "P(Condition positive or support) 0 16 3 1 \n", "PLR(Positive likelihood ratio) None 1.5 5.66667 None \n", "PLRI(Positive likelihood ratio interpretation) None Poor Fair None \n", "POP(Population) 20 20 20 20 \n", "PPV(Precision or positive predictive value) 0.0 0.85714 0.5 None \n", "PRE(Prevalence) 0.0 0.8 0.15 0.05 \n", "Q(Yule Q - coefficient of colligation) None 0.28571 0.77778 None \n", "QI(Yule Q interpretation) None Weak Strong None \n", "RACC(Random accuracy) 0.0 0.28 0.015 0.0 \n", "RACCU(Random accuracy unbiased) 0.07563 0.33062 0.01562 0.00063 \n", "TN(True negative/correct rejection) 9 3 16 19 \n", "TNR(Specificity or true negative rate) 0.45 0.75 0.94118 1.0 \n", "TON(Test outcome negative) 9 13 18 20 \n", "TOP(Test outcome positive) 11 7 2 0 \n", "TP(True positive/hit) 0 6 1 0 \n", "TPR(Sensitivity, recall, hit rate, or true positive rate) None 0.375 0.33333 0.0 \n", "Y(Youden index) None 0.125 0.27451 0.0 \n", "dInd(Distance index) None 0.67315 0.66926 1.0 \n", "sInd(Similarity index) None 0.52401 0.52676 0.29289 \n", "\n" ] } ], "source": [ "print(cm)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Save" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Message': 'D:\\\\For Asus Laptop\\\\projects\\\\pycm\\\\Document\\\\Example4_files\\\\cm.obj',\n", " 'Status': True}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cm.save_obj(os.path.join(\"Example4_files\", \"cm\"))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Open File" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Message': 'D:\\\\For Asus Laptop\\\\projects\\\\pycm\\\\Document\\\\Example4_files\\\\cm_stat.obj',\n", " 'Status': True}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cm.save_obj(os.path.join(\"Example4_files\", \"cm_stat\"), save_stat=True)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Open File" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Message': 'D:\\\\For Asus Laptop\\\\projects\\\\pycm\\\\Document\\\\Example4_files\\\\cm_no_vectors.obj',\n", " 'Status': True}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cm.save_obj(os.path.join(\"Example4_files\", \"cm_no_vectors\"), save_vector=False)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Open File" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Load" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pycm.ConfusionMatrix(classes: [100, 200, 500, 600])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cm_load = ConfusionMatrix(file=open(os.path.join(\"Example4_files\", \"cm.obj\"), \"r\"))\n", "cm" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predict 100 200 500 600 \n", "Actual\n", "100 0 0 0 0 \n", "\n", "200 9 6 1 0 \n", "\n", "500 1 1 1 0 \n", "\n", "600 1 0 0 0 \n", "\n", "\n", "\n", "\n", "\n", "Overall Statistics : \n", "\n", "95% CI (0.14096,0.55904)\n", "ACC Macro 0.675\n", "ARI 0.02298\n", "AUNP None\n", "AUNU None\n", "Bangdiwala B 0.31356\n", "Bennett S 0.13333\n", "CBA 0.17708\n", "CSI None\n", "Chi-Squared None\n", "Chi-Squared DF 9\n", "Conditional Entropy 1.23579\n", "Cramer V None\n", "Cross Entropy 1.70995\n", "F1 Macro 0.23043\n", "F1 Micro 0.35\n", "FNR Macro None\n", "FNR Micro 0.65\n", "FPR Macro 0.21471\n", "FPR Micro 0.21667\n", "Gwet AC1 0.19505\n", "Hamming Loss 0.65\n", "Joint Entropy 2.11997\n", "KL Divergence None\n", "Kappa 0.07801\n", "Kappa 95% CI (-0.2185,0.37453)\n", "Kappa No Prevalence -0.3\n", "Kappa Standard Error 0.15128\n", "Kappa Unbiased -0.12554\n", "Krippendorff Alpha -0.0974\n", "Lambda A 0.0\n", "Lambda B 0.0\n", "Mutual Information 0.10088\n", "NIR 0.8\n", "NPV Macro 0.76741\n", "NPV Micro 0.78333\n", "Overall ACC 0.35\n", "Overall CEN 0.3648\n", "Overall J (0.60294,0.15074)\n", "Overall MCC 0.12642\n", "Overall MCEN 0.37463\n", "Overall RACC 0.295\n", "Overall RACCU 0.4225\n", "P-Value 1.0\n", "PPV Macro None\n", "PPV Micro 0.35\n", "Pearson C None\n", "Phi-Squared None\n", "RCI 0.11409\n", "RR 5.0\n", "Reference Entropy 0.88418\n", "Response Entropy 1.33667\n", "SOA1(Landis & Koch) Slight\n", "SOA2(Fleiss) Poor\n", "SOA3(Altman) Poor\n", "SOA4(Cicchetti) Poor\n", "SOA5(Cramer) None\n", "SOA6(Matthews) Negligible\n", "SOA7(Lambda A) None\n", "SOA8(Lambda B) None\n", "SOA9(Krippendorff Alpha) Low\n", "SOA10(Pearson C) None\n", "Scott PI -0.12554\n", "Standard Error 0.10665\n", "TNR Macro 0.78529\n", "TNR Micro 0.78333\n", "TPR Macro None\n", "TPR Micro 0.35\n", "Zero-one Loss 13\n", "\n", "Class Statistics :\n", "\n", "Classes 100 200 500 600 \n", "ACC(Accuracy) 0.45 0.45 0.85 0.95 \n", "AGF(Adjusted F-score) 0.0 0.33642 0.56659 0.0 \n", "AGM(Adjusted geometric mean) None 0.56694 0.7352 0 \n", "AM(Difference between automatic and manual classification) 11 -9 -1 -1 \n", "AUC(Area under the ROC curve) None 0.5625 0.63725 0.5 \n", "AUCI(AUC value interpretation) None Poor Fair Poor \n", "AUPR(Area under the PR curve) None 0.61607 0.41667 None \n", "BB(Braun-Blanquet similarity) 0.0 0.375 0.33333 0.0 \n", "BCD(Bray-Curtis dissimilarity) 0.275 0.225 0.025 0.025 \n", "BM(Informedness or bookmaker informedness) None 0.125 0.27451 0.0 \n", "CEN(Confusion entropy) 0.33496 0.35708 0.53895 0.0 \n", "DOR(Diagnostic odds ratio) None 1.8 8.0 None \n", "DP(Discriminant power) None 0.14074 0.4979 None \n", "DPI(Discriminant power interpretation) None Poor Poor None \n", "ERR(Error rate) 0.55 0.55 0.15 0.05 \n", "F0.5(F0.5 score) 0.0 0.68182 0.45455 0.0 \n", "F1(F1 score - harmonic mean of precision and sensitivity) 0.0 0.52174 0.4 0.0 \n", "F2(F2 score) 0.0 0.42254 0.35714 0.0 \n", "FDR(False discovery rate) 1.0 0.14286 0.5 None \n", "FN(False negative/miss/type 2 error) 0 10 2 1 \n", "FNR(Miss rate or false negative rate) None 0.625 0.66667 1.0 \n", "FOR(False omission rate) 0.0 0.76923 0.11111 0.05 \n", "FP(False positive/type 1 error/false alarm) 11 1 1 0 \n", "FPR(Fall-out or false positive rate) 0.55 0.25 0.05882 0.0 \n", "G(G-measure geometric mean of precision and sensitivity) None 0.56695 0.40825 None \n", "GI(Gini index) None 0.125 0.27451 0.0 \n", "GM(G-mean geometric mean of specificity and sensitivity) None 0.53033 0.56011 0.0 \n", "HD(Hamming distance) 11 11 3 1 \n", "IBA(Index of balanced accuracy) None 0.17578 0.12303 0.0 \n", "ICSI(Individual classification success index) None 0.23214 -0.16667 None \n", "IS(Information score) None 0.09954 1.73697 None \n", "J(Jaccard index) 0.0 0.35294 0.25 0.0 \n", "LS(Lift score) None 1.07143 3.33333 None \n", "MCC(Matthews correlation coefficient) None 0.10483 0.32673 None \n", "MCCI(Matthews correlation coefficient interpretation) None Negligible Weak None \n", "MCEN(Modified confusion entropy) 0.33496 0.37394 0.58028 0.0 \n", "MK(Markedness) 0.0 0.08791 0.38889 None \n", "N(Condition negative) 20 4 17 19 \n", "NLR(Negative likelihood ratio) None 0.83333 0.70833 1.0 \n", "NLRI(Negative likelihood ratio interpretation) None Negligible Negligible Negligible \n", "NPV(Negative predictive value) 1.0 0.23077 0.88889 0.95 \n", "OC(Overlap coefficient) None 0.85714 0.5 None \n", "OOC(Otsuka-Ochiai coefficient) None 0.56695 0.40825 None \n", "OP(Optimized precision) None 0.11667 0.37308 -0.05 \n", "P(Condition positive or support) 0 16 3 1 \n", "PLR(Positive likelihood ratio) None 1.5 5.66667 None \n", "PLRI(Positive likelihood ratio interpretation) None Poor Fair None \n", "POP(Population) 20 20 20 20 \n", "PPV(Precision or positive predictive value) 0.0 0.85714 0.5 None \n", "PRE(Prevalence) 0.0 0.8 0.15 0.05 \n", "Q(Yule Q - coefficient of colligation) None 0.28571 0.77778 None \n", "QI(Yule Q interpretation) None Weak Strong None \n", "RACC(Random accuracy) 0.0 0.28 0.015 0.0 \n", "RACCU(Random accuracy unbiased) 0.07563 0.33062 0.01562 0.00063 \n", "TN(True negative/correct rejection) 9 3 16 19 \n", "TNR(Specificity or true negative rate) 0.45 0.75 0.94118 1.0 \n", "TON(Test outcome negative) 9 13 18 20 \n", "TOP(Test outcome positive) 11 7 2 0 \n", "TP(True positive/hit) 0 6 1 0 \n", "TPR(Sensitivity, recall, hit rate, or true positive rate) None 0.375 0.33333 0.0 \n", "Y(Youden index) None 0.125 0.27451 0.0 \n", "dInd(Distance index) None 0.67315 0.66926 1.0 \n", "sInd(Similarity index) None 0.52401 0.52676 0.29289 \n", "\n" ] } ], "source": [ "print(cm)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Obj File" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Sample-Weight\": null, \"Digit\": 5, \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Transpose\": false, \"Prob-Vector\": null, \"Imbalanced\": true, \"Matrix\": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]]}\n" ] } ], "source": [ "print(open(os.path.join(\"Example4_files\", \"cm.obj\"), \"r\").read())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"Class-Stat\": {\"TN\": {\"200\": 3, \"100\": 9, \"500\": 16, \"600\": 19}, \"PRE\": {\"200\": 0.8, \"500\": 0.15, \"100\": 0.0, \"600\": 0.05}, \"N\": {\"200\": 4, \"500\": 17, \"100\": 20, \"600\": 19}, \"BB\": {\"200\": 0.375, \"500\": 0.3333333333333333, \"100\": 0.0, \"600\": 0.0}, \"F1\": {\"200\": 0.5217391304347826, \"500\": 0.4, \"100\": 0.0, \"600\": 0.0}, \"FDR\": {\"200\": 0.1428571428571429, \"500\": 0.5, \"100\": 1.0, \"600\": \"None\"}, \"TP\": {\"200\": 6, \"100\": 0, \"500\": 1, \"600\": 0}, \"ACC\": {\"200\": 0.45, \"500\": 0.85, \"100\": 0.45, \"600\": 0.95}, \"IS\": {\"200\": 0.09953567355091428, \"500\": 1.736965594166206, \"100\": \"None\", \"600\": \"None\"}, \"TNR\": {\"200\": 0.75, \"500\": 0.9411764705882353, \"100\": 0.45, \"600\": 1.0}, \"TON\": {\"200\": 13, \"500\": 18, \"100\": 9, \"600\": 20}, \"PLR\": {\"200\": 1.5, \"500\": 5.666666666666665, \"100\": \"None\", \"600\": \"None\"}, \"J\": {\"200\": 0.35294117647058826, \"500\": 0.25, \"100\": 0.0, \"600\": 0.0}, \"RACCU\": {\"200\": 0.33062499999999995, \"500\": 0.015625, \"100\": 0.07562500000000001, \"600\": 0.0006250000000000001}, \"F0.5\": {\"200\": 0.6818181818181818, \"500\": 0.45454545454545453, \"100\": 0.0, \"600\": 0.0}, \"dInd\": {\"200\": 0.673145600891813, \"500\": 0.6692567908186672, \"100\": \"None\", \"600\": 1.0}, \"GI\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"FNR\": {\"200\": 0.625, \"500\": 0.6666666666666667, \"100\": \"None\", \"600\": 1.0}, \"F2\": {\"200\": 0.4225352112676056, \"500\": 0.35714285714285715, \"100\": 0.0, \"600\": 0.0}, \"MK\": {\"200\": 0.08791208791208782, \"500\": 0.38888888888888884, \"100\": 0.0, \"600\": \"None\"}, \"HD\": {\"200\": 11, \"500\": 3, \"100\": 11, \"600\": 1}, \"AUPR\": {\"200\": 0.6160714285714286, \"500\": 0.41666666666666663, \"100\": \"None\", \"600\": \"None\"}, \"RACC\": {\"200\": 0.28, \"500\": 0.015, \"100\": 0.0, \"600\": 0.0}, \"P\": {\"200\": 16, \"500\": 3, \"100\": 0, \"600\": 1}, \"AUCI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"Poor\"}, \"FPR\": {\"200\": 0.25, \"500\": 0.05882352941176472, \"100\": 0.55, \"600\": 0.0}, \"Y\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"FOR\": {\"200\": 0.7692307692307692, \"500\": 0.11111111111111116, \"100\": 0.0, \"600\": 0.050000000000000044}, \"TPR\": {\"200\": 0.375, \"500\": 0.3333333333333333, \"100\": \"None\", \"600\": 0.0}, \"FN\": {\"200\": 10, \"100\": 0, \"500\": 2, \"600\": 1}, \"G\": {\"200\": 0.5669467095138409, \"500\": 0.408248290463863, \"100\": \"None\", \"600\": \"None\"}, \"POP\": {\"200\": 20, \"500\": 20, \"100\": 20, \"600\": 20}, \"MCEN\": {\"200\": 0.3739448088748241, \"500\": 0.5802792108518123, \"100\": 0.3349590631259315, \"600\": 0.0}, \"PLRI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"None\"}, \"DOR\": {\"200\": 1.7999999999999998, \"500\": 7.999999999999997, \"100\": \"None\", \"600\": \"None\"}, \"AGM\": {\"200\": 0.5669417382415922, \"500\": 0.7351956938438939, \"100\": \"None\", \"600\": 0}, \"GM\": {\"200\": 0.5303300858899106, \"500\": 0.5601120336112039, \"100\": \"None\", \"600\": 0.0}, \"TOP\": {\"200\": 7, \"500\": 2, \"100\": 11, \"600\": 0}, \"sInd\": {\"200\": 0.5240141808835057, \"500\": 0.5267639848569737, \"100\": \"None\", \"600\": 0.29289321881345254}, \"DP\": {\"200\": 0.1407391082701595, \"500\": 0.49789960499474867, \"100\": \"None\", \"600\": \"None\"}, \"ERR\": {\"200\": 0.55, \"500\": 0.15000000000000002, \"100\": 0.55, \"600\": 0.050000000000000044}, \"NLR\": {\"200\": 0.8333333333333334, \"500\": 0.7083333333333334, \"100\": \"None\", \"600\": 1.0}, \"NPV\": {\"200\": 0.23076923076923078, \"500\": 0.8888888888888888, \"100\": 1.0, \"600\": 0.95}, \"Q\": {\"200\": 0.28571428571428575, \"500\": 0.7777777777777778, \"100\": \"None\", \"600\": \"None\"}, \"ICSI\": {\"200\": 0.2321428571428572, \"500\": -0.16666666666666674, \"100\": \"None\", \"600\": \"None\"}, \"LS\": {\"200\": 1.0714285714285714, \"500\": 3.3333333333333335, \"100\": \"None\", \"600\": \"None\"}, \"OP\": {\"200\": 0.1166666666666667, \"500\": 0.373076923076923, \"100\": \"None\", \"600\": -0.050000000000000044}, \"CEN\": {\"200\": 0.3570795472009597, \"500\": 0.5389466410223563, \"100\": 0.3349590631259315, \"600\": 0.0}, \"AUC\": {\"200\": 0.5625, \"500\": 0.6372549019607843, \"100\": \"None\", \"600\": 0.5}, \"NLRI\": {\"200\": \"Negligible\", \"500\": \"Negligible\", \"100\": \"None\", \"600\": \"Negligible\"}, \"IBA\": {\"200\": 0.17578125, \"500\": 0.1230296039984621, \"100\": \"None\", \"600\": 0.0}, \"MCCI\": {\"200\": \"Negligible\", \"500\": \"Weak\", \"100\": \"None\", \"600\": \"None\"}, \"DPI\": {\"200\": \"Poor\", \"500\": \"Poor\", \"100\": \"None\", \"600\": \"None\"}, \"OC\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": \"None\", \"600\": \"None\"}, \"FP\": {\"200\": 1, \"100\": 11, \"500\": 1, \"600\": 0}, \"QI\": {\"200\": \"Weak\", \"500\": \"Strong\", \"100\": \"None\", \"600\": \"None\"}, \"OOC\": {\"200\": 0.5669467095138409, \"500\": 0.4082482904638631, \"100\": \"None\", \"600\": \"None\"}, \"BCD\": {\"200\": 0.225, \"500\": 0.025, \"100\": 0.275, \"600\": 0.025}, \"AGF\": {\"200\": 0.33642097801219245, \"500\": 0.5665926996700735, \"100\": 0.0, \"600\": 0.0}, \"AM\": {\"200\": -9, \"500\": -1, \"100\": 11, \"600\": -1}, \"BM\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"MCC\": {\"200\": 0.10482848367219183, \"500\": 0.32673201960653564, \"100\": \"None\", \"600\": \"None\"}, \"PPV\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": 0.0, \"600\": \"None\"}}, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Sample-Weight\": null, \"Digit\": 5, \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Transpose\": false, \"Prob-Vector\": null, \"Overall-Stat\": {\"FNR Macro\": \"None\", \"SOA5(Cramer)\": \"None\", \"Reference Entropy\": 0.8841837197791889, \"Cramer V\": \"None\", \"Bennett S\": 0.1333333333333333, \"Overall MCC\": 0.1264200803632855, \"Kappa 95% CI\": [-0.21849807698648957, 0.3745264457808156], \"Chi-Squared\": \"None\", \"Overall CEN\": 0.3648028121279775, \"Hamming Loss\": 0.65, \"TNR Macro\": 0.7852941176470588, \"Overall J\": [0.6029411764705883, 0.15073529411764708], \"Pearson C\": \"None\", \"Overall RACC\": 0.29500000000000004, \"NPV Macro\": 0.7674145299145299, \"Lambda A\": 0.0, \"Conditional Entropy\": 1.235789374242786, \"Mutual Information\": 0.10087710767390168, \"SOA1(Landis & Koch)\": \"Slight\", \"SOA10(Pearson C)\": \"None\", \"SOA2(Fleiss)\": \"Poor\", \"Joint Entropy\": 2.119973094021975, \"FNR Micro\": 0.65, \"Bangdiwala B\": 0.3135593220338983, \"Kappa\": 0.07801418439716304, \"Kappa Unbiased\": -0.12554112554112543, \"KL Divergence\": \"None\", \"Phi-Squared\": \"None\", \"SOA6(Matthews)\": \"Negligible\", \"AUNP\": \"None\", \"TPR Micro\": 0.35, \"RR\": 5.0, \"Overall ACC\": 0.35, \"Gwet AC1\": 0.19504643962848295, \"FPR Micro\": 0.21666666666666667, \"AUNU\": \"None\", \"SOA9(Krippendorff Alpha)\": \"Low\", \"SOA4(Cicchetti)\": \"Poor\", \"SOA8(Lambda B)\": \"None\", \"ARI\": 0.02298247455136956, \"NPV Micro\": 0.7833333333333333, \"FPR Macro\": 0.2147058823529412, \"ACC Macro\": 0.675, \"TPR Macro\": \"None\", \"PPV Micro\": 0.35, \"Standard Error\": 0.1066536450385077, \"RCI\": 0.11409066398451011, \"Lambda B\": 0.0, \"NIR\": 0.8, \"Zero-one Loss\": 13, \"CSI\": \"None\", \"Kappa Standard Error\": 0.15128176601206766, \"Cross Entropy\": 1.709947752496911, \"TNR Micro\": 0.7833333333333333, \"F1 Macro\": 0.23043478260869565, \"SOA7(Lambda A)\": \"None\", \"PPV Macro\": \"None\", \"P-Value\": 0.9999981549942787, \"95% CI\": [0.14095885572452488, 0.559041144275475], \"F1 Micro\": 0.35, \"Scott PI\": -0.12554112554112543, \"Response Entropy\": 1.3366664819166876, \"Chi-Squared DF\": 9, \"Overall RACCU\": 0.42249999999999993, \"Kappa No Prevalence\": -0.30000000000000004, \"Overall MCEN\": 0.3746281299595305, \"CBA\": 0.17708333333333331, \"Krippendorff Alpha\": -0.09740259740259723, \"SOA3(Altman)\": \"Poor\"}, \"Imbalanced\": true, \"Matrix\": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]]}\n" ] } ], "source": [ "print(open(os.path.join(\"Example4_files\", \"cm_stat.obj\"), \"r\").read())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"Predict-Vector\": null, \"Sample-Weight\": null, \"Digit\": 5, \"Actual-Vector\": null, \"Transpose\": false, \"Prob-Vector\": null, \"Imbalanced\": true, \"Matrix\": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]]}\n" ] } ], "source": [ "print(open(os.path.join(\"Example4_files\", \"cm_no_vectors.obj\"), \"r\").read())" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Matrix save method changed in version 1.5 save_vector and save_stat, new in version 2.3