diff --git a/CHANGELOG.md b/CHANGELOG.md index 21748df0..ca3b0ae8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html). ## [Unreleased] +## [4.0] - 2023-06-07 ### Added - `pycmMultiLabelError` class - `MultiLabelCM` class @@ -708,7 +709,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0. - TPR - documents and `README.md` -[Unreleased]: https://github.com/sepandhaghighi/pycm/compare/v3.9...dev +[Unreleased]: https://github.com/sepandhaghighi/pycm/compare/v4.0...dev +[4.0]: https://github.com/sepandhaghighi/pycm/compare/v3.9...v4.0 [3.9]: https://github.com/sepandhaghighi/pycm/compare/v3.8...v3.9 [3.8]: https://github.com/sepandhaghighi/pycm/compare/v3.7...v3.8 [3.7]: https://github.com/sepandhaghighi/pycm/compare/v3.6...v3.7 diff --git a/Document/Document.ipynb b/Document/Document.ipynb index 1843c8cb..e615ca7e 100644 --- a/Document/Document.ipynb +++ b/Document/Document.ipynb @@ -18,7 +18,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Version : 3.9 " + "### Version : 4.0 " ] }, { @@ -319,7 +319,7 @@ "metadata": {}, "source": [ "### Source code\n", - "- Download [Version 3.9](https://github.com/sepandhaghighi/pycm/archive/v3.9.zip) or [Latest Source](https://github.com/sepandhaghighi/pycm/archive/dev.zip)\n", + "- Download [Version 4.0](https://github.com/sepandhaghighi/pycm/archive/v4.0.zip) or [Latest Source](https://github.com/sepandhaghighi/pycm/archive/dev.zip)\n", "- Run `pip install -r requirements.txt` or `pip3 install -r requirements.txt` (Need root access)\n", "- Run `python3 setup.py install` or `python setup.py install` (Need root access)" ] @@ -332,7 +332,7 @@ "\n", "\n", "- Check [Python Packaging User Guide](https://packaging.python.org/installing/) \n", - "- Run `pip install pycm==3.9` or `pip3 install pycm==3.9` (Need root access)" + "- Run `pip install pycm==4.0` or `pip3 install pycm==4.0` (Need root access)" ] }, { @@ -971,7 +971,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.9-py3.5.egg\\pycm\\pycm_util.py:400: RuntimeWarning: Used classes is not a subset of classes in actual and predict vectors.\n" + "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-4.0-py3.5.egg\\pycm\\pycm_util.py:400: RuntimeWarning: Used classes is not a subset of classes in actual and predict vectors.\n" ] } ], @@ -1624,9 +1624,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "L1 {'L1': 1, 'L3': 3, 'L2': 2}\n", - "L3 {'L1': 1, 'L3': 3, 'L2': 2}\n", - "L2 {'L1': 4, 'L3': 1, 'L2': 6}\n" + "L2 {'L2': 6, 'L3': 1, 'L1': 4}\n", + "L3 {'L2': 2, 'L3': 3, 'L1': 1}\n", + "L1 {'L2': 2, 'L3': 3, 'L1': 1}\n" ] } ], @@ -1643,7 +1643,7 @@ { "data": { "text/plain": [ - "('L1', {'L1': 1, 'L2': 2, 'L3': 3})" + "('L2', {'L1': 4, 'L2': 6, 'L3': 1})" ] }, "execution_count": 51, @@ -1687,9 +1687,9 @@ { "data": { "text/plain": [ - "[('L1', {'L1': 1, 'L2': 2, 'L3': 3}),\n", + "[('L2', {'L1': 4, 'L2': 6, 'L3': 1}),\n", " ('L3', {'L1': 1, 'L2': 2, 'L3': 3}),\n", - " ('L2', {'L1': 4, 'L2': 6, 'L3': 1})]" + " ('L1', {'L1': 1, 'L2': 2, 'L3': 3})]" ] }, "execution_count": 53, @@ -2407,7 +2407,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 67, @@ -2416,7 +2416,7 @@ }, { "data": { - "image/png": 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", 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\n", 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", 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\n", 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", 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\n", 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", 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\n", 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", 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\n", 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", 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\n", 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" ] @@ -3261,7 +3261,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.9-py3.5.egg\\pycm\\pycm_curve.py:382: RuntimeWarning: The curve axes contain non-numerical value(s).\n" + "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-4.0-py3.5.egg\\pycm\\pycm_curve.py:382: RuntimeWarning: The curve axes contain non-numerical value(s).\n" ] }, { @@ -3295,7 +3295,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 94, @@ -3304,7 +3304,7 @@ }, { "data": { - "image/png": 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", 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\n", "text/plain": [ "
" ] @@ -3344,9 +3344,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1, 0, 1], [0, 1, 0]]\n", + "[[1, 0, 0], [0, 1, 1]]\n", + "Predict 0 1 \n", + "Actual\n", + "0 1 0 \n", + "\n", + "1 0 1 \n", + "\n", + "\n", + "Predict 0 1 \n", + "Actual\n", + "0 1 0 \n", + "\n", + "1 1 1 \n", + "\n", + "\n" + ] + } + ], "source": [ "mlcm = MultiLabelCM(actual_vector=[{\"cat\", \"bird\"}, {\"dog\"}], \n", " predict_vector=[{\"cat\"}, {\"dog\", \"bird\"}], \n", @@ -3411,7 +3434,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -3774,7 +3797,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -3783,7 +3806,7 @@ "{'L1': 3, 'L2': 1, 'L3': 3}" ] }, - "execution_count": 96, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -3809,7 +3832,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -3818,7 +3841,7 @@ "{'L1': 7, 'L2': 8, 'L3': 4}" ] }, - "execution_count": 97, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -3844,7 +3867,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -3853,7 +3876,7 @@ "{'L1': 0, 'L2': 2, 'L3': 3}" ] }, - "execution_count": 98, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -3879,7 +3902,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -3888,7 +3911,7 @@ "{'L1': 2, 'L2': 1, 'L3': 2}" ] }, - "execution_count": 99, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -3921,7 +3944,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -3930,7 +3953,7 @@ "{'L1': 5, 'L2': 2, 'L3': 5}" ] }, - "execution_count": 100, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -3962,7 +3985,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -3971,7 +3994,7 @@ "{'L1': 7, 'L2': 10, 'L3': 7}" ] }, - "execution_count": 101, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -4003,7 +4026,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -4012,7 +4035,7 @@ "{'L1': 3, 'L2': 3, 'L3': 6}" ] }, - "execution_count": 102, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -4044,7 +4067,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -4053,7 +4076,7 @@ "{'L1': 9, 'L2': 9, 'L3': 6}" ] }, - "execution_count": 103, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -4085,7 +4108,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 105, "metadata": {}, "outputs": [ { @@ -4094,7 +4117,7 @@ "{'L1': 12, 'L2': 12, 'L3': 12}" ] }, - "execution_count": 104, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -4142,7 +4165,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -4151,7 +4174,7 @@ "{'L1': 0.6, 'L2': 0.5, 'L3': 0.6}" ] }, - "execution_count": 105, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -4185,7 +4208,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -4194,7 +4217,7 @@ "{'L1': 1.0, 'L2': 0.8, 'L3': 0.5714285714285714}" ] }, - "execution_count": 106, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -4229,7 +4252,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -4238,7 +4261,7 @@ "{'L1': 1.0, 'L2': 0.3333333333333333, 'L3': 0.5}" ] }, - "execution_count": 107, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -4273,7 +4296,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -4282,7 +4305,7 @@ "{'L1': 0.7777777777777778, 'L2': 0.8888888888888888, 'L3': 0.6666666666666666}" ] }, - "execution_count": 108, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -4316,7 +4339,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -4325,7 +4348,7 @@ "{'L1': 0.4, 'L2': 0.5, 'L3': 0.4}" ] }, - "execution_count": 109, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } @@ -4361,7 +4384,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -4370,7 +4393,7 @@ "{'L1': 0.0, 'L2': 0.19999999999999996, 'L3': 0.4285714285714286}" ] }, - "execution_count": 110, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -4404,7 +4427,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -4413,7 +4436,7 @@ "{'L1': 0.0, 'L2': 0.6666666666666667, 'L3': 0.5}" ] }, - "execution_count": 111, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -4447,7 +4470,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -4458,7 +4481,7 @@ " 'L3': 0.33333333333333337}" ] }, - "execution_count": 112, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -4492,7 +4515,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 114, "metadata": {}, "outputs": [ { @@ -4501,7 +4524,7 @@ "{'L1': 0.8333333333333334, 'L2': 0.75, 'L3': 0.5833333333333334}" ] }, - "execution_count": 113, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -4533,7 +4556,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -4542,7 +4565,7 @@ "{'L1': 0.16666666666666663, 'L2': 0.25, 'L3': 0.41666666666666663}" ] }, - "execution_count": 114, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } @@ -4586,7 +4609,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -4595,7 +4618,7 @@ "{'L1': 0.75, 'L2': 0.4, 'L3': 0.5454545454545454}" ] }, - "execution_count": 115, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -4606,7 +4629,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -4615,7 +4638,7 @@ "{'L1': 0.8823529411764706, 'L2': 0.35714285714285715, 'L3': 0.5172413793103449}" ] }, - "execution_count": 116, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" } @@ -4626,7 +4649,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -4635,7 +4658,7 @@ "{'L1': 0.6521739130434783, 'L2': 0.45454545454545453, 'L3': 0.5769230769230769}" ] }, - "execution_count": 117, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -4646,7 +4669,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -4655,7 +4678,7 @@ "{'L1': 0.6144578313253012, 'L2': 0.4857142857142857, 'L3': 0.5930232558139535}" ] }, - "execution_count": 118, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" } @@ -4728,7 +4751,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -4737,7 +4760,7 @@ "{'L1': 0.6831300510639732, 'L2': 0.25819888974716115, 'L3': 0.1690308509457033}" ] }, - "execution_count": 119, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -4771,7 +4794,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -4782,7 +4805,7 @@ " 'L3': 0.17142857142857126}" ] }, - "execution_count": 120, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -4814,7 +4837,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 122, "metadata": {}, "outputs": [ { @@ -4823,7 +4846,7 @@ "{'L1': 0.7777777777777777, 'L2': 0.2222222222222221, 'L3': 0.16666666666666652}" ] }, - "execution_count": 121, + "execution_count": 122, "metadata": {}, "output_type": "execute_result" } @@ -4859,7 +4882,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -4868,7 +4891,7 @@ "{'L1': 'None', 'L2': 2.5000000000000004, 'L3': 1.4}" ] }, - "execution_count": 122, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } @@ -4913,7 +4936,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -4922,7 +4945,7 @@ "{'L1': 0.4, 'L2': 0.625, 'L3': 0.7000000000000001}" ] }, - "execution_count": 123, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -4965,7 +4988,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -4974,7 +4997,7 @@ "{'L1': 'None', 'L2': 4.000000000000001, 'L3': 1.9999999999999998}" ] }, - "execution_count": 124, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } @@ -5008,7 +5031,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -5017,7 +5040,7 @@ "{'L1': 0.4166666666666667, 'L2': 0.16666666666666666, 'L3': 0.4166666666666667}" ] }, - "execution_count": 125, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -5051,7 +5074,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 127, "metadata": {}, "outputs": [ { @@ -5060,7 +5083,7 @@ "{'L1': 0.7745966692414834, 'L2': 0.408248290463863, 'L3': 0.5477225575051661}" ] }, - "execution_count": 126, + "execution_count": 127, "metadata": {}, "output_type": "execute_result" } @@ -5092,7 +5115,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -5103,7 +5126,7 @@ " 'L3': 0.20833333333333334}" ] }, - "execution_count": 127, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -5144,7 +5167,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 129, "metadata": {}, "outputs": [ { @@ -5155,7 +5178,7 @@ " 'L3': 0.21006944444444442}" ] }, - "execution_count": 128, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -5200,7 +5223,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -5209,7 +5232,7 @@ "{'L1': 0.6, 'L2': 0.25, 'L3': 0.375}" ] }, - "execution_count": 129, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -5251,7 +5274,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 131, "metadata": {}, "outputs": [ { @@ -5260,7 +5283,7 @@ "{'L1': 1.2630344058337937, 'L2': 0.9999999999999998, 'L3': 0.26303440583379367}" ] }, - "execution_count": 130, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -5317,7 +5340,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 132, "metadata": {}, "outputs": [ { @@ -5326,7 +5349,7 @@ "{'L1': 0.25, 'L2': 0.49657842846620864, 'L3': 0.6044162769630221}" ] }, - "execution_count": 131, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -5390,7 +5413,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 133, "metadata": {}, "outputs": [ { @@ -5399,7 +5422,7 @@ "{'L1': 0.2643856189774724, 'L2': 0.5, 'L3': 0.6875}" ] }, - "execution_count": 132, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } @@ -5443,7 +5466,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -5452,7 +5475,7 @@ "{'L1': 0.8, 'L2': 0.65, 'L3': 0.5857142857142856}" ] }, - "execution_count": 133, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } @@ -5494,7 +5517,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 135, "metadata": {}, "outputs": [ { @@ -5503,7 +5526,7 @@ "{'L1': 0.4, 'L2': 0.5385164807134504, 'L3': 0.5862367008195198}" ] }, - "execution_count": 134, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -5544,7 +5567,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -5553,7 +5576,7 @@ "{'L1': 0.717157287525381, 'L2': 0.6192113447068046, 'L3': 0.5854680534700882}" ] }, - "execution_count": 135, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -5611,7 +5634,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 137, "metadata": {}, "outputs": [ { @@ -5620,7 +5643,7 @@ "{'L1': 'None', 'L2': 0.33193306999649924, 'L3': 0.1659665349982495}" ] }, - "execution_count": 136, + "execution_count": 137, "metadata": {}, "output_type": "execute_result" } @@ -5670,7 +5693,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 138, "metadata": {}, "outputs": [ { @@ -5681,7 +5704,7 @@ " 'L3': 0.17142857142857126}" ] }, - "execution_count": 137, + "execution_count": 138, "metadata": {}, "output_type": "execute_result" } @@ -5745,7 +5768,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 139, "metadata": {}, "outputs": [ { @@ -5754,7 +5777,7 @@ "{'L1': 'None', 'L2': 'Poor', 'L3': 'Poor'}" ] }, - "execution_count": 138, + "execution_count": 139, "metadata": {}, "output_type": "execute_result" } @@ -5818,7 +5841,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 140, "metadata": {}, "outputs": [ { @@ -5827,7 +5850,7 @@ "{'L1': 'Poor', 'L2': 'Negligible', 'L3': 'Negligible'}" ] }, - "execution_count": 139, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" } @@ -5891,7 +5914,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -5900,7 +5923,7 @@ "{'L1': 'None', 'L2': 'Poor', 'L3': 'Poor'}" ] }, - "execution_count": 140, + "execution_count": 141, "metadata": {}, "output_type": "execute_result" } @@ -5967,7 +5990,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 142, "metadata": {}, "outputs": [ { @@ -5976,7 +5999,7 @@ "{'L1': 'Very Good', 'L2': 'Fair', 'L3': 'Poor'}" ] }, - "execution_count": 141, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -6045,7 +6068,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -6054,7 +6077,7 @@ "{'L1': 'Moderate', 'L2': 'Negligible', 'L3': 'Negligible'}" ] }, - "execution_count": 142, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -6127,7 +6150,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -6136,7 +6159,7 @@ "{'L1': 'None', 'L2': 'Moderate', 'L3': 'Weak'}" ] }, - "execution_count": 143, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -6181,7 +6204,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -6192,7 +6215,7 @@ " 'L3': 0.17142857142857126}" ] }, - "execution_count": 144, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -6233,7 +6256,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -6242,7 +6265,7 @@ "{'L1': 2.4, 'L2': 2.0, 'L3': 1.2}" ] }, - "execution_count": 145, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -6283,7 +6306,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -6292,7 +6315,7 @@ "{'L1': -2, 'L2': 1, 'L3': 1}" ] }, - "execution_count": 146, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" } @@ -6335,7 +6358,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -6346,7 +6369,7 @@ " 'L3': 0.041666666666666664}" ] }, - "execution_count": 147, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } @@ -6390,7 +6413,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -6399,7 +6422,7 @@ "{'L1': 0.5833333333333334, 'L2': 0.5192307692307692, 'L3': 0.5589430894308943}" ] }, - "execution_count": 148, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } @@ -6441,7 +6464,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -6450,7 +6473,7 @@ "{'L1': 0.36, 'L2': 0.27999999999999997, 'L3': 0.35265306122448975}" ] }, - "execution_count": 149, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -6461,7 +6484,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 151, "metadata": {}, "outputs": [ { @@ -6470,7 +6493,7 @@ "{'L1': 0.48, 'L2': 0.34, 'L3': 0.3477551020408163}" ] }, - "execution_count": 150, + "execution_count": 151, "metadata": {}, "output_type": "execute_result" } @@ -6481,7 +6504,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -6490,7 +6513,7 @@ "{'L1': 0.576, 'L2': 0.388, 'L3': 0.34383673469387754}" ] }, - "execution_count": 151, + "execution_count": 152, "metadata": {}, "output_type": "execute_result" } @@ -6559,7 +6582,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 153, "metadata": {}, "outputs": [ { @@ -6568,7 +6591,7 @@ "{'L1': 0.7745966692414834, 'L2': 0.6324555320336759, 'L3': 0.5855400437691198}" ] }, - "execution_count": 152, + "execution_count": 153, "metadata": {}, "output_type": "execute_result" } @@ -6620,7 +6643,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 154, "metadata": {}, "outputs": [ { @@ -6629,7 +6652,7 @@ "{'L1': 'None', 'L2': 0.6, 'L3': 0.3333333333333333}" ] }, - "execution_count": 153, + "execution_count": 154, "metadata": {}, "output_type": "execute_result" } @@ -6684,7 +6707,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 155, "metadata": {}, "outputs": [ { @@ -6693,7 +6716,7 @@ "{'L1': 0.8576400016262, 'L2': 0.708612108382005, 'L3': 0.5803410802752335}" ] }, - "execution_count": 154, + "execution_count": 155, "metadata": {}, "output_type": "execute_result" } @@ -6748,7 +6771,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 156, "metadata": {}, "outputs": [ { @@ -6757,7 +6780,7 @@ "{'L1': 0.7285871475307653, 'L2': 0.6286946134619315, 'L3': 0.610088876086563}" ] }, - "execution_count": 155, + "execution_count": 156, "metadata": {}, "output_type": "execute_result" } @@ -6800,7 +6823,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 157, "metadata": {}, "outputs": [ { @@ -6809,7 +6832,7 @@ "{'L1': 1.0, 'L2': 0.5, 'L3': 0.6}" ] }, - "execution_count": 156, + "execution_count": 157, "metadata": {}, "output_type": "execute_result" } @@ -6850,7 +6873,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 158, "metadata": {}, "outputs": [ { @@ -6859,7 +6882,7 @@ "{'L1': 0.6, 'L2': 0.3333333333333333, 'L3': 0.5}" ] }, - "execution_count": 157, + "execution_count": 158, "metadata": {}, "output_type": "execute_result" } @@ -6902,7 +6925,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 159, "metadata": {}, "outputs": [ { @@ -6911,7 +6934,7 @@ "{'L1': 0.7745966692414834, 'L2': 0.4082482904638631, 'L3': 0.5477225575051661}" ] }, - "execution_count": 158, + "execution_count": 159, "metadata": {}, "output_type": "execute_result" } @@ -6954,7 +6977,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 160, "metadata": {}, "outputs": [ { @@ -6963,7 +6986,7 @@ "{'L1': 0.42857142857142855, 'L2': 0.1111111111111111, 'L3': 0.1875}" ] }, - "execution_count": 159, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" } @@ -7034,7 +7057,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 161, "metadata": {}, "outputs": [ { @@ -7043,7 +7066,7 @@ "{'L1': 0.8, 'L2': 0.41666666666666663, 'L3': 0.55}" ] }, - "execution_count": 160, + "execution_count": 161, "metadata": {}, "output_type": "execute_result" } @@ -7091,7 +7114,7 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 162, "metadata": {}, "outputs": [ { @@ -7102,7 +7125,7 @@ " 'L3': 0.10000000000000009}" ] }, - "execution_count": 161, + "execution_count": 162, "metadata": {}, "output_type": "execute_result" } @@ -7222,7 +7245,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 163, "metadata": {}, "outputs": [ { @@ -7233,7 +7256,7 @@ " 'L3': [0.21908902300206645, (0.17058551491594975, 1.0294144850840503)]}" ] }, - "execution_count": 162, + "execution_count": 163, "metadata": {}, "output_type": "execute_result" } @@ -7244,7 +7267,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 164, "metadata": {}, "outputs": [ { @@ -7255,7 +7278,7 @@ " 'L3': [0.21908902300206645, (-0.2769850810763853, 1.0769850810763852)]}" ] }, - "execution_count": 163, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" } @@ -7266,7 +7289,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 165, "metadata": {}, "outputs": [ { @@ -7277,7 +7300,7 @@ " 'L3': [0.14231876063832774, (0.19325746190524654, 0.6804926643446272)]}" ] }, - "execution_count": 164, + "execution_count": 165, "metadata": {}, "output_type": "execute_result" } @@ -7288,7 +7311,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 166, "metadata": {}, "outputs": [ { @@ -7297,7 +7320,7 @@ "[0.14231876063832777, (0.2805568916340536, 0.8343177950165198)]" ] }, - "execution_count": 165, + "execution_count": 166, "metadata": {}, "output_type": "execute_result" } @@ -7308,7 +7331,7 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 167, "metadata": {}, "outputs": [ { @@ -7317,7 +7340,7 @@ "[0.14231876063832777, (0.30438856248221097, 0.8622781041844558)]" ] }, - "execution_count": 166, + "execution_count": 167, "metadata": {}, "output_type": "execute_result" } @@ -7418,7 +7441,7 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 168, "metadata": {}, "outputs": [ { @@ -7427,7 +7450,7 @@ "{'L1': 0.25, 'L2': 0.0735, 'L3': 0.23525}" ] }, - "execution_count": 167, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -7491,7 +7514,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 169, "metadata": {}, "outputs": [ { @@ -7500,7 +7523,7 @@ "0.6111111111111112" ] }, - "execution_count": 168, + "execution_count": 169, "metadata": {}, "output_type": "execute_result" } @@ -7511,7 +7534,7 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 170, "metadata": {}, "outputs": [ { @@ -7520,7 +7543,7 @@ "0.5651515151515151" ] }, - "execution_count": 169, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -7531,7 +7554,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 171, "metadata": {}, "outputs": [ { @@ -7540,7 +7563,7 @@ "3.0000000000000004" ] }, - "execution_count": 170, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } @@ -7607,7 +7630,7 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 172, "metadata": {}, "outputs": [ { @@ -7616,7 +7639,7 @@ "0.6805555555555555" ] }, - "execution_count": 171, + "execution_count": 172, "metadata": {}, "output_type": "execute_result" } @@ -7627,16 +7650,16 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.6064393939393938" + "0.606439393939394" ] }, - "execution_count": 172, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -7647,7 +7670,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 174, "metadata": {}, "outputs": [ { @@ -7656,7 +7679,7 @@ "2.5714285714285716" ] }, - "execution_count": 173, + "execution_count": 174, "metadata": {}, "output_type": "execute_result" } @@ -7667,16 +7690,16 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 175, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.7152097902097903" + "0.7152097902097901" ] }, - "execution_count": 174, + "execution_count": 175, "metadata": {}, "output_type": "execute_result" } @@ -7757,7 +7780,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 176, "metadata": {}, "outputs": [ { @@ -7766,7 +7789,7 @@ "{'L1': 'None', 'L2': 0.8416212335729143, 'L3': 0.4333594729285047}" ] }, - "execution_count": 175, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" } @@ -7830,7 +7853,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 177, "metadata": {}, "outputs": [ { @@ -7839,7 +7862,7 @@ "{'L1': 2, 'L2': 3, 'L3': 5}" ] }, - "execution_count": 176, + "execution_count": 177, "metadata": {}, "output_type": "execute_result" } @@ -7894,7 +7917,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -7903,7 +7926,7 @@ "0.35483870967741943" ] }, - "execution_count": 177, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } @@ -7946,7 +7969,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 179, "metadata": {}, "outputs": [ { @@ -7955,7 +7978,7 @@ "0.34426229508196726" ] }, - "execution_count": 178, + "execution_count": 179, "metadata": {}, "output_type": "execute_result" } @@ -7996,7 +8019,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -8005,7 +8028,7 @@ "0.16666666666666674" ] }, - "execution_count": 179, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } @@ -8067,7 +8090,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 181, "metadata": {}, "outputs": [ { @@ -8076,7 +8099,7 @@ "0.39130434782608675" ] }, - "execution_count": 180, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } @@ -8091,14 +8114,14 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 182, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.9-py3.5.egg\\pycm\\pycm_obj.py:850: RuntimeWarning: The weight format is wrong, the result is for unweighted kappa.\n" + "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-4.0-py3.5.egg\\pycm\\pycm_obj.py:850: RuntimeWarning: The weight format is wrong, the result is for unweighted kappa.\n" ] }, { @@ -8107,7 +8130,7 @@ "0.35483870967741943" ] }, - "execution_count": 181, + "execution_count": 182, "metadata": {}, "output_type": "execute_result" } @@ -8176,7 +8199,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 183, "metadata": {}, "outputs": [ { @@ -8185,7 +8208,7 @@ "0.2203645326012817" ] }, - "execution_count": 182, + "execution_count": 183, "metadata": {}, "output_type": "execute_result" } @@ -8226,7 +8249,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 184, "metadata": {}, "outputs": [ { @@ -8235,7 +8258,7 @@ "(-0.07707577422109269, 0.7867531935759315)" ] }, - "execution_count": 183, + "execution_count": 184, "metadata": {}, "output_type": "execute_result" } @@ -8285,7 +8308,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 185, "metadata": {}, "outputs": [ { @@ -8294,7 +8317,7 @@ "6.6000000000000005" ] }, - "execution_count": 184, + "execution_count": 185, "metadata": {}, "output_type": "execute_result" } @@ -8335,7 +8358,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 186, "metadata": {}, "outputs": [ { @@ -8344,7 +8367,7 @@ "4" ] }, - "execution_count": 185, + "execution_count": 186, "metadata": {}, "output_type": "execute_result" } @@ -8387,7 +8410,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 187, "metadata": {}, "outputs": [ { @@ -8396,7 +8419,7 @@ "0.55" ] }, - "execution_count": 186, + "execution_count": 187, "metadata": {}, "output_type": "execute_result" } @@ -8441,7 +8464,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 188, "metadata": {}, "outputs": [ { @@ -8450,7 +8473,7 @@ "0.5244044240850758" ] }, - "execution_count": 187, + "execution_count": 188, "metadata": {}, "output_type": "execute_result" } @@ -8493,7 +8516,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 189, "metadata": {}, "outputs": [ { @@ -8502,7 +8525,7 @@ "0.14231876063832777" ] }, - "execution_count": 188, + "execution_count": 189, "metadata": {}, "output_type": "execute_result" } @@ -8545,7 +8568,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 190, "metadata": {}, "outputs": [ { @@ -8554,7 +8577,7 @@ "(0.30438856248221097, 0.8622781041844558)" ] }, - "execution_count": 189, + "execution_count": 190, "metadata": {}, "output_type": "execute_result" } @@ -8614,7 +8637,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 191, "metadata": {}, "outputs": [ { @@ -8623,7 +8646,7 @@ "0.37500000000000006" ] }, - "execution_count": 190, + "execution_count": 191, "metadata": {}, "output_type": "execute_result" } @@ -8676,7 +8699,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 192, "metadata": {}, "outputs": [ { @@ -8685,7 +8708,7 @@ "0.34426229508196726" ] }, - "execution_count": 191, + "execution_count": 192, "metadata": {}, "output_type": "execute_result" } @@ -8740,7 +8763,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 193, "metadata": {}, "outputs": [ { @@ -8749,7 +8772,7 @@ "0.3893129770992367" ] }, - "execution_count": 192, + "execution_count": 193, "metadata": {}, "output_type": "execute_result" } @@ -8804,7 +8827,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 194, "metadata": {}, "outputs": [ { @@ -8813,7 +8836,7 @@ "1.4833557549816874" ] }, - "execution_count": 193, + "execution_count": 194, "metadata": {}, "output_type": "execute_result" } @@ -8868,7 +8891,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 195, "metadata": {}, "outputs": [ { @@ -8877,7 +8900,7 @@ "1.5" ] }, - "execution_count": 194, + "execution_count": 195, "metadata": {}, "output_type": "execute_result" } @@ -8941,7 +8964,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 196, "metadata": {}, "outputs": [ { @@ -8950,7 +8973,7 @@ "1.5833333333333335" ] }, - "execution_count": 195, + "execution_count": 196, "metadata": {}, "output_type": "execute_result" } @@ -9005,7 +9028,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 197, "metadata": {}, "outputs": [ { @@ -9014,7 +9037,7 @@ "2.4591479170272446" ] }, - "execution_count": 196, + "execution_count": 197, "metadata": {}, "output_type": "execute_result" } @@ -9071,7 +9094,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 198, "metadata": {}, "outputs": [ { @@ -9080,7 +9103,7 @@ "0.9757921620455572" ] }, - "execution_count": 197, + "execution_count": 198, "metadata": {}, "output_type": "execute_result" } @@ -9137,7 +9160,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 199, "metadata": {}, "outputs": [ { @@ -9146,7 +9169,7 @@ "0.09997757835164581" ] }, - "execution_count": 198, + "execution_count": 199, "metadata": {}, "output_type": "execute_result" } @@ -9218,7 +9241,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 200, "metadata": {}, "outputs": [ { @@ -9227,7 +9250,7 @@ "0.5242078379544428" ] }, - "execution_count": 199, + "execution_count": 200, "metadata": {}, "output_type": "execute_result" } @@ -9270,7 +9293,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 201, "metadata": {}, "outputs": [ { @@ -9279,7 +9302,7 @@ "0.42857142857142855" ] }, - "execution_count": 200, + "execution_count": 201, "metadata": {}, "output_type": "execute_result" } @@ -9322,7 +9345,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 202, "metadata": {}, "outputs": [ { @@ -9331,7 +9354,7 @@ "0.16666666666666666" ] }, - "execution_count": 201, + "execution_count": 202, "metadata": {}, "output_type": "execute_result" } @@ -9403,7 +9426,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 203, "metadata": {}, "outputs": [ { @@ -9412,7 +9435,7 @@ "'Fair'" ] }, - "execution_count": 202, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } @@ -9472,7 +9495,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 204, "metadata": {}, "outputs": [ { @@ -9481,7 +9504,7 @@ "'Poor'" ] }, - "execution_count": 203, + "execution_count": 204, "metadata": {}, "output_type": "execute_result" } @@ -9549,7 +9572,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 205, "metadata": {}, "outputs": [ { @@ -9558,7 +9581,7 @@ "'Fair'" ] }, - "execution_count": 204, + "execution_count": 205, "metadata": {}, "output_type": "execute_result" } @@ -9622,7 +9645,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 206, "metadata": {}, "outputs": [ { @@ -9631,7 +9654,7 @@ "'Poor'" ] }, - "execution_count": 205, + "execution_count": 206, "metadata": {}, "output_type": "execute_result" } @@ -9703,7 +9726,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 207, "metadata": {}, "outputs": [ { @@ -9712,7 +9735,7 @@ "'Relatively Strong'" ] }, - "execution_count": 206, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" } @@ -9781,7 +9804,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 208, "metadata": {}, "outputs": [ { @@ -9790,7 +9813,7 @@ "'Weak'" ] }, - "execution_count": 207, + "execution_count": 208, "metadata": {}, "output_type": "execute_result" } @@ -9870,7 +9893,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 209, "metadata": {}, "outputs": [ { @@ -9879,7 +9902,7 @@ "'Moderate'" ] }, - "execution_count": 208, + "execution_count": 209, "metadata": {}, "output_type": "execute_result" } @@ -9950,7 +9973,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 210, "metadata": {}, "outputs": [ { @@ -9959,7 +9982,7 @@ "'Very Weak'" ] }, - "execution_count": 209, + "execution_count": 210, "metadata": {}, "output_type": "execute_result" } @@ -10017,7 +10040,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 211, "metadata": {}, "outputs": [ { @@ -10026,7 +10049,7 @@ "'Low'" ] }, - "execution_count": 210, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" } @@ -10088,7 +10111,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 212, "metadata": {}, "outputs": [ { @@ -10097,7 +10120,7 @@ "'Strong'" ] }, - "execution_count": 211, + "execution_count": 212, "metadata": {}, "output_type": "execute_result" } @@ -10145,7 +10168,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 213, "metadata": {}, "outputs": [ { @@ -10154,7 +10177,7 @@ "0.5833333333333334" ] }, - "execution_count": 212, + "execution_count": 213, "metadata": {}, "output_type": "execute_result" } @@ -10195,7 +10218,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 214, "metadata": {}, "outputs": [ { @@ -10204,7 +10227,7 @@ "0.3541666666666667" ] }, - "execution_count": 213, + "execution_count": 214, "metadata": {}, "output_type": "execute_result" } @@ -10245,7 +10268,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 215, "metadata": {}, "outputs": [ { @@ -10254,7 +10277,7 @@ "0.3645833333333333" ] }, - "execution_count": 214, + "execution_count": 215, "metadata": {}, "output_type": "execute_result" } @@ -10302,7 +10325,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 216, "metadata": {}, "outputs": [ { @@ -10311,7 +10334,7 @@ "0.5833333333333334" ] }, - "execution_count": 215, + "execution_count": 216, "metadata": {}, "output_type": "execute_result" } @@ -10352,7 +10375,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 217, "metadata": {}, "outputs": [ { @@ -10361,7 +10384,7 @@ "0.7916666666666666" ] }, - "execution_count": 216, + "execution_count": 217, "metadata": {}, "output_type": "execute_result" } @@ -10409,7 +10432,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 218, "metadata": {}, "outputs": [ { @@ -10418,7 +10441,7 @@ "0.5833333333333334" ] }, - "execution_count": 217, + "execution_count": 218, "metadata": {}, "output_type": "execute_result" } @@ -10459,7 +10482,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 219, "metadata": {}, "outputs": [ { @@ -10468,7 +10491,7 @@ "0.7916666666666666" ] }, - "execution_count": 218, + "execution_count": 219, "metadata": {}, "output_type": "execute_result" } @@ -10509,7 +10532,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 220, "metadata": {}, "outputs": [ { @@ -10518,7 +10541,7 @@ "0.20833333333333337" ] }, - "execution_count": 219, + "execution_count": 220, "metadata": {}, "output_type": "execute_result" } @@ -10559,7 +10582,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 221, "metadata": {}, "outputs": [ { @@ -10568,7 +10591,7 @@ "0.41666666666666663" ] }, - "execution_count": 220, + "execution_count": 221, "metadata": {}, "output_type": "execute_result" } @@ -10616,7 +10639,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 222, "metadata": {}, "outputs": [ { @@ -10625,7 +10648,7 @@ "0.5833333333333334" ] }, - "execution_count": 221, + "execution_count": 222, "metadata": {}, "output_type": "execute_result" } @@ -10666,7 +10689,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 223, "metadata": {}, "outputs": [ { @@ -10675,7 +10698,7 @@ "0.611111111111111" ] }, - "execution_count": 222, + "execution_count": 223, "metadata": {}, "output_type": "execute_result" } @@ -10716,7 +10739,7 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 224, "metadata": {}, "outputs": [ { @@ -10725,7 +10748,7 @@ "0.7777777777777777" ] }, - "execution_count": 223, + "execution_count": 224, "metadata": {}, "output_type": "execute_result" } @@ -10766,7 +10789,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 225, "metadata": {}, "outputs": [ { @@ -10775,7 +10798,7 @@ "0.5666666666666668" ] }, - "execution_count": 224, + "execution_count": 225, "metadata": {}, "output_type": "execute_result" } @@ -10816,7 +10839,7 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 226, "metadata": {}, "outputs": [ { @@ -10825,7 +10848,7 @@ "0.7904761904761904" ] }, - "execution_count": 225, + "execution_count": 226, "metadata": {}, "output_type": "execute_result" } @@ -10866,7 +10889,7 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 227, "metadata": {}, "outputs": [ { @@ -10875,7 +10898,7 @@ "0.20952380952380956" ] }, - "execution_count": 226, + "execution_count": 227, "metadata": {}, "output_type": "execute_result" } @@ -10916,7 +10939,7 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 228, "metadata": {}, "outputs": [ { @@ -10925,7 +10948,7 @@ "0.43333333333333324" ] }, - "execution_count": 227, + "execution_count": 228, "metadata": {}, "output_type": "execute_result" } @@ -10966,7 +10989,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 229, "metadata": {}, "outputs": [ { @@ -10975,7 +10998,7 @@ "0.5651515151515151" ] }, - "execution_count": 228, + "execution_count": 229, "metadata": {}, "output_type": "execute_result" } @@ -11016,7 +11039,7 @@ }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 230, "metadata": {}, "outputs": [ { @@ -11025,7 +11048,7 @@ "0.7222222222222223" ] }, - "execution_count": 229, + "execution_count": 230, "metadata": {}, "output_type": "execute_result" } @@ -11080,7 +11103,7 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 231, "metadata": {}, "outputs": [ { @@ -11089,7 +11112,7 @@ "(1.225, 0.4083333333333334)" ] }, - "execution_count": 230, + "execution_count": 231, "metadata": {}, "output_type": "execute_result" } @@ -11130,7 +11153,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 232, "metadata": {}, "outputs": [ { @@ -11139,7 +11162,7 @@ "0.41666666666666663" ] }, - "execution_count": 231, + "execution_count": 232, "metadata": {}, "output_type": "execute_result" } @@ -11180,7 +11203,7 @@ }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 233, "metadata": {}, "outputs": [ { @@ -11189,7 +11212,7 @@ "5" ] }, - "execution_count": 232, + "execution_count": 233, "metadata": {}, "output_type": "execute_result" } @@ -11230,7 +11253,7 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 234, "metadata": {}, "outputs": [ { @@ -11239,7 +11262,7 @@ "0.4166666666666667" ] }, - "execution_count": 233, + "execution_count": 234, "metadata": {}, "output_type": "execute_result" } @@ -11307,7 +11330,7 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 235, "metadata": {}, "outputs": [ { @@ -11316,7 +11339,7 @@ "0.18926430237560654" ] }, - "execution_count": 234, + "execution_count": 235, "metadata": {}, "output_type": "execute_result" } @@ -11364,7 +11387,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 236, "metadata": {}, "outputs": [ { @@ -11373,7 +11396,7 @@ "0.4638112995385119" ] }, - "execution_count": 235, + "execution_count": 236, "metadata": {}, "output_type": "execute_result" } @@ -11428,7 +11451,7 @@ }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 237, "metadata": {}, "outputs": [ { @@ -11437,7 +11460,7 @@ "0.5189369467580801" ] }, - "execution_count": 236, + "execution_count": 237, "metadata": {}, "output_type": "execute_result" } @@ -11501,7 +11524,7 @@ }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 238, "metadata": {}, "outputs": [ { @@ -11510,7 +11533,7 @@ "0.36666666666666664" ] }, - "execution_count": 237, + "execution_count": 238, "metadata": {}, "output_type": "execute_result" } @@ -11551,7 +11574,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 239, "metadata": {}, "outputs": [ { @@ -11560,7 +11583,7 @@ "4.0" ] }, - "execution_count": 238, + "execution_count": 239, "metadata": {}, "output_type": "execute_result" } @@ -11603,7 +11626,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 240, "metadata": {}, "outputs": [ { @@ -11612,7 +11635,7 @@ "0.4777777777777778" ] }, - "execution_count": 239, + "execution_count": 240, "metadata": {}, "output_type": "execute_result" } @@ -11653,7 +11676,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 241, "metadata": {}, "outputs": [ { @@ -11662,7 +11685,7 @@ "0.6785714285714285" ] }, - "execution_count": 240, + "execution_count": 241, "metadata": {}, "output_type": "execute_result" } @@ -11703,7 +11726,7 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 242, "metadata": {}, "outputs": [ { @@ -11712,7 +11735,7 @@ "0.6857142857142857" ] }, - "execution_count": 241, + "execution_count": 242, "metadata": {}, "output_type": "execute_result" } @@ -11775,7 +11798,7 @@ }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 243, "metadata": {}, "outputs": [ { @@ -11784,7 +11807,7 @@ "0.3533932006492363" ] }, - "execution_count": 242, + "execution_count": 243, "metadata": {}, "output_type": "execute_result" } @@ -11825,7 +11848,7 @@ }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 244, "metadata": {}, "outputs": [ { @@ -11834,7 +11857,7 @@ "0.5956833971812706" ] }, - "execution_count": 243, + "execution_count": 244, "metadata": {}, "output_type": "execute_result" } @@ -11876,7 +11899,7 @@ }, { "cell_type": "code", - "execution_count": 244, + "execution_count": 245, "metadata": {}, "outputs": [ { @@ -11885,7 +11908,7 @@ "0.1777777777777778" ] }, - "execution_count": 244, + "execution_count": 245, "metadata": {}, "output_type": "execute_result" } @@ -11937,7 +11960,7 @@ }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 246, "metadata": {}, "outputs": [ { @@ -11946,7 +11969,7 @@ "0.09206349206349207" ] }, - "execution_count": 245, + "execution_count": 246, "metadata": {}, "output_type": "execute_result" } @@ -11998,7 +12021,7 @@ }, { "cell_type": "code", - "execution_count": 246, + "execution_count": 247, "metadata": {}, "outputs": [ { @@ -12007,7 +12030,7 @@ "0.37254901960784315" ] }, - "execution_count": 246, + "execution_count": 247, "metadata": {}, "output_type": "execute_result" } @@ -12072,7 +12095,7 @@ }, { "cell_type": "code", - "execution_count": 247, + "execution_count": 248, "metadata": {}, "outputs": [ { @@ -12081,7 +12104,7 @@ "0.3715846994535519" ] }, - "execution_count": 247, + "execution_count": 248, "metadata": {}, "output_type": "execute_result" } @@ -12157,7 +12180,7 @@ }, { "cell_type": "code", - "execution_count": 248, + "execution_count": 249, "metadata": {}, "outputs": [ { @@ -12166,7 +12189,7 @@ "0.374757281553398" ] }, - "execution_count": 248, + "execution_count": 249, "metadata": {}, "output_type": "execute_result" } @@ -12181,14 +12204,14 @@ }, { "cell_type": "code", - "execution_count": 249, + "execution_count": 250, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.9-py3.5.egg\\pycm\\pycm_obj.py:873: RuntimeWarning: The weight format is wrong, the result is for unweighted alpha.\n" + "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-4.0-py3.5.egg\\pycm\\pycm_obj.py:873: RuntimeWarning: The weight format is wrong, the result is for unweighted alpha.\n" ] }, { @@ -12197,7 +12220,7 @@ "0.3715846994535519" ] }, - "execution_count": 249, + "execution_count": 250, "metadata": {}, "output_type": "execute_result" } @@ -12301,7 +12324,7 @@ }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 251, "metadata": {}, "outputs": [ { @@ -12310,7 +12333,7 @@ "0.38540577344968524" ] }, - "execution_count": 250, + "execution_count": 251, "metadata": {}, "output_type": "execute_result" } @@ -12321,7 +12344,7 @@ }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 252, "metadata": {}, "outputs": [ { @@ -12330,7 +12353,7 @@ "0.38545857383594895" ] }, - "execution_count": 251, + "execution_count": 252, "metadata": {}, "output_type": "execute_result" } @@ -12409,7 +12432,7 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 253, "metadata": {}, "outputs": [ { @@ -12418,7 +12441,7 @@ "0.03749999999999999" ] }, - "execution_count": 252, + "execution_count": 253, "metadata": {}, "output_type": "execute_result" } @@ -12430,7 +12453,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 254, "metadata": {}, "outputs": [ { @@ -12439,7 +12462,7 @@ "0.6875" ] }, - "execution_count": 253, + "execution_count": 254, "metadata": {}, "output_type": "execute_result" } @@ -12530,7 +12553,7 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 255, "metadata": {}, "outputs": [ { @@ -12539,7 +12562,7 @@ "0.19763488164214868" ] }, - "execution_count": 254, + "execution_count": 255, "metadata": {}, "output_type": "execute_result" } @@ -12550,7 +12573,7 @@ }, { "cell_type": "code", - "execution_count": 255, + "execution_count": 256, "metadata": {}, "outputs": [ { @@ -12559,7 +12582,7 @@ "1.854645225687032" ] }, - "execution_count": 255, + "execution_count": 256, "metadata": {}, "output_type": "execute_result" } @@ -12640,7 +12663,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 257, "metadata": {}, "outputs": [ { @@ -12814,7 +12837,7 @@ }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 258, "metadata": {}, "outputs": [ { @@ -12839,7 +12862,7 @@ }, { "cell_type": "code", - "execution_count": 258, + "execution_count": 259, "metadata": {}, "outputs": [ { @@ -12850,7 +12873,7 @@ " 'L3': {'L1': 0, 'L2': 2, 'L3': 3}}" ] }, - "execution_count": 258, + "execution_count": 259, "metadata": {}, "output_type": "execute_result" } @@ -12861,7 +12884,7 @@ }, { "cell_type": "code", - "execution_count": 259, + "execution_count": 260, "metadata": {}, "outputs": [ { @@ -12884,7 +12907,7 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 261, "metadata": {}, "outputs": [], "source": [ @@ -12893,7 +12916,7 @@ }, { "cell_type": "code", - "execution_count": 261, + "execution_count": 262, "metadata": {}, "outputs": [ { @@ -12966,7 +12989,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": 263, "metadata": {}, "outputs": [ { @@ -12991,7 +13014,7 @@ }, { "cell_type": "code", - "execution_count": 263, + "execution_count": 264, "metadata": {}, "outputs": [ { @@ -13002,7 +13025,7 @@ " 'L3': {'L1': 0.0, 'L2': 0.4, 'L3': 0.6}}" ] }, - "execution_count": 263, + "execution_count": 264, "metadata": {}, "output_type": "execute_result" } @@ -13013,7 +13036,7 @@ }, { "cell_type": "code", - "execution_count": 264, + "execution_count": 265, "metadata": {}, "outputs": [ { @@ -13036,7 +13059,7 @@ }, { "cell_type": "code", - "execution_count": 265, + "execution_count": 266, "metadata": {}, "outputs": [ { @@ -13109,7 +13132,7 @@ }, { "cell_type": "code", - "execution_count": 266, + "execution_count": 267, "metadata": {}, "outputs": [ { @@ -13264,7 +13287,7 @@ }, { "cell_type": "code", - "execution_count": 267, + "execution_count": 268, "metadata": {}, "outputs": [ { @@ -13291,7 +13314,7 @@ }, { "cell_type": "code", - "execution_count": 268, + "execution_count": 269, "metadata": {}, "outputs": [ { @@ -13318,7 +13341,7 @@ }, { "cell_type": "code", - "execution_count": 269, + "execution_count": 270, "metadata": {}, "outputs": [ { @@ -13427,7 +13450,7 @@ }, { "cell_type": "code", - "execution_count": 270, + "execution_count": 271, "metadata": {}, "outputs": [ { @@ -13449,7 +13472,7 @@ }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 272, "metadata": {}, "outputs": [ { @@ -13478,7 +13501,7 @@ }, { "cell_type": "code", - "execution_count": 272, + "execution_count": 273, "metadata": {}, "outputs": [], "source": [ @@ -13496,7 +13519,7 @@ }, { "cell_type": "code", - "execution_count": 273, + "execution_count": 274, "metadata": {}, "outputs": [ { @@ -13506,7 +13529,7 @@ " 'Status': True}" ] }, - "execution_count": 273, + "execution_count": 274, "metadata": {}, "output_type": "execute_result" } @@ -13524,7 +13547,7 @@ }, { "cell_type": "code", - "execution_count": 274, + "execution_count": 275, "metadata": {}, "outputs": [ { @@ -13534,7 +13557,7 @@ " 'Status': True}" ] }, - "execution_count": 274, + "execution_count": 275, "metadata": {}, "output_type": "execute_result" } @@ -13555,7 +13578,7 @@ }, { "cell_type": "code", - "execution_count": 275, + "execution_count": 276, "metadata": {}, "outputs": [ { @@ -13565,7 +13588,7 @@ " 'Status': True}" ] }, - "execution_count": 275, + "execution_count": 276, "metadata": {}, "output_type": "execute_result" } @@ -13587,7 +13610,7 @@ }, { "cell_type": "code", - "execution_count": 276, + "execution_count": 277, "metadata": {}, "outputs": [ { @@ -13597,7 +13620,7 @@ " 'Status': True}" ] }, - "execution_count": 276, + "execution_count": 277, "metadata": {}, "output_type": "execute_result" } @@ -13617,7 +13640,7 @@ }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 278, "metadata": {}, "outputs": [ { @@ -13627,7 +13650,7 @@ " 'Status': True}" ] }, - "execution_count": 277, + "execution_count": 278, "metadata": {}, "output_type": "execute_result" } @@ -13648,7 +13671,7 @@ }, { "cell_type": "code", - "execution_count": 278, + "execution_count": 279, "metadata": {}, "outputs": [ { @@ -13658,7 +13681,7 @@ " 'Status': False}" ] }, - "execution_count": 278, + "execution_count": 279, "metadata": {}, "output_type": "execute_result" } @@ -13741,7 +13764,7 @@ }, { "cell_type": "code", - "execution_count": 279, + "execution_count": 280, "metadata": {}, "outputs": [ { @@ -13751,7 +13774,7 @@ " 'Status': True}" ] }, - "execution_count": 279, + "execution_count": 280, "metadata": {}, "output_type": "execute_result" } @@ -13769,7 +13792,7 @@ }, { "cell_type": "code", - "execution_count": 280, + "execution_count": 281, "metadata": {}, "outputs": [ { @@ -13779,7 +13802,7 @@ " 'Status': True}" ] }, - "execution_count": 280, + "execution_count": 281, "metadata": {}, "output_type": "execute_result" } @@ -13800,7 +13823,7 @@ }, { "cell_type": "code", - "execution_count": 281, + "execution_count": 282, "metadata": {}, "outputs": [ { @@ -13810,7 +13833,7 @@ " 'Status': True}" ] }, - "execution_count": 281, + "execution_count": 282, "metadata": {}, "output_type": "execute_result" } @@ -13832,7 +13855,7 @@ }, { "cell_type": "code", - "execution_count": 282, + "execution_count": 283, "metadata": {}, "outputs": [ { @@ -13842,7 +13865,7 @@ " 'Status': True}" ] }, - "execution_count": 282, + "execution_count": 283, "metadata": {}, "output_type": "execute_result" } @@ -13862,7 +13885,7 @@ }, { "cell_type": "code", - "execution_count": 283, + "execution_count": 284, "metadata": {}, "outputs": [ { @@ -13872,7 +13895,7 @@ " 'Status': True}" ] }, - "execution_count": 283, + "execution_count": 284, "metadata": {}, "output_type": "execute_result" } @@ -13892,7 +13915,7 @@ }, { "cell_type": "code", - "execution_count": 284, + "execution_count": 285, "metadata": {}, "outputs": [ { @@ -13902,7 +13925,7 @@ " 'Status': True}" ] }, - "execution_count": 284, + "execution_count": 285, "metadata": {}, "output_type": "execute_result" } @@ -13923,7 +13946,7 @@ }, { "cell_type": "code", - "execution_count": 285, + "execution_count": 286, "metadata": {}, "outputs": [ { @@ -13933,7 +13956,7 @@ " 'Status': True}" ] }, - "execution_count": 285, + "execution_count": 286, "metadata": {}, "output_type": "execute_result" } @@ -13954,7 +13977,7 @@ }, { "cell_type": "code", - "execution_count": 286, + "execution_count": 287, "metadata": {}, "outputs": [ { @@ -13964,7 +13987,7 @@ " 'Status': False}" ] }, - "execution_count": 286, + "execution_count": 287, "metadata": {}, "output_type": "execute_result" } @@ -14077,7 +14100,7 @@ }, { "cell_type": "code", - "execution_count": 287, + "execution_count": 288, "metadata": {}, "outputs": [ { @@ -14087,7 +14110,7 @@ " 'Status': True}" ] }, - "execution_count": 287, + "execution_count": 288, "metadata": {}, "output_type": "execute_result" } @@ -14107,7 +14130,7 @@ }, { "cell_type": "code", - "execution_count": 288, + "execution_count": 289, "metadata": {}, "outputs": [ { @@ -14117,7 +14140,7 @@ " 'Status': True}" ] }, - "execution_count": 288, + "execution_count": 289, "metadata": {}, "output_type": "execute_result" } @@ -14139,7 +14162,7 @@ }, { "cell_type": "code", - "execution_count": 289, + "execution_count": 290, "metadata": {}, "outputs": [ { @@ -14149,7 +14172,7 @@ " 'Status': True}" ] }, - "execution_count": 289, + "execution_count": 290, "metadata": {}, "output_type": "execute_result" } @@ -14172,7 +14195,7 @@ }, { "cell_type": "code", - "execution_count": 290, + "execution_count": 291, "metadata": {}, "outputs": [ { @@ -14182,7 +14205,7 @@ " 'Status': True}" ] }, - "execution_count": 290, + "execution_count": 291, "metadata": {}, "output_type": "execute_result" } @@ -14205,7 +14228,7 @@ }, { "cell_type": "code", - "execution_count": 291, + "execution_count": 292, "metadata": {}, "outputs": [ { @@ -14215,7 +14238,7 @@ " 'Status': True}" ] }, - "execution_count": 291, + "execution_count": 292, "metadata": {}, "output_type": "execute_result" } @@ -14237,7 +14260,7 @@ }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 293, "metadata": {}, "outputs": [ { @@ -14247,7 +14270,7 @@ " 'Status': True}" ] }, - "execution_count": 292, + "execution_count": 293, "metadata": {}, "output_type": "execute_result" } @@ -14268,7 +14291,7 @@ }, { "cell_type": "code", - "execution_count": 293, + "execution_count": 294, "metadata": {}, "outputs": [ { @@ -14278,7 +14301,7 @@ " 'Status': False}" ] }, - "execution_count": 293, + "execution_count": 294, "metadata": {}, "output_type": "execute_result" } @@ -14371,7 +14394,7 @@ }, { "cell_type": "code", - "execution_count": 294, + "execution_count": 295, "metadata": {}, "outputs": [ { @@ -14381,7 +14404,7 @@ " 'Status': True}" ] }, - "execution_count": 294, + "execution_count": 295, "metadata": {}, "output_type": "execute_result" } @@ -14399,7 +14422,7 @@ }, { "cell_type": "code", - "execution_count": 295, + "execution_count": 296, "metadata": {}, "outputs": [ { @@ -14409,7 +14432,7 @@ " 'Status': True}" ] }, - "execution_count": 295, + "execution_count": 296, "metadata": {}, "output_type": "execute_result" } @@ -14429,7 +14452,7 @@ }, { "cell_type": "code", - "execution_count": 296, + "execution_count": 297, "metadata": {}, "outputs": [ { @@ -14439,7 +14462,7 @@ " 'Status': True}" ] }, - "execution_count": 296, + "execution_count": 297, "metadata": {}, "output_type": "execute_result" } @@ -14459,7 +14482,7 @@ }, { "cell_type": "code", - "execution_count": 297, + "execution_count": 298, "metadata": {}, "outputs": [ { @@ -14469,7 +14492,7 @@ " 'Status': False}" ] }, - "execution_count": 297, + "execution_count": 298, "metadata": {}, "output_type": "execute_result" } @@ -14531,7 +14554,7 @@ }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 299, "metadata": {}, "outputs": [ { @@ -14541,7 +14564,7 @@ " 'Status': True}" ] }, - "execution_count": 298, + "execution_count": 299, "metadata": {}, "output_type": "execute_result" } @@ -14559,7 +14582,7 @@ }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 300, "metadata": {}, "outputs": [ { @@ -14569,7 +14592,7 @@ " 'Status': False}" ] }, - "execution_count": 299, + "execution_count": 300, "metadata": {}, "output_type": "execute_result" } @@ -14611,7 +14634,7 @@ }, { "cell_type": "code", - "execution_count": 300, + "execution_count": 301, "metadata": {}, "outputs": [ { @@ -14631,7 +14654,7 @@ }, { "cell_type": "code", - "execution_count": 301, + "execution_count": 302, "metadata": { "scrolled": true }, @@ -14653,7 +14676,7 @@ }, { "cell_type": "code", - "execution_count": 302, + "execution_count": 303, "metadata": {}, "outputs": [ { @@ -14673,7 +14696,7 @@ }, { "cell_type": "code", - "execution_count": 303, + "execution_count": 304, "metadata": {}, "outputs": [ { @@ -14693,7 +14716,7 @@ }, { "cell_type": "code", - "execution_count": 304, + "execution_count": 305, "metadata": {}, "outputs": [ { @@ -14713,7 +14736,7 @@ }, { "cell_type": "code", - "execution_count": 305, + "execution_count": 306, "metadata": {}, "outputs": [ { @@ -14733,7 +14756,7 @@ }, { "cell_type": "code", - "execution_count": 306, + "execution_count": 307, "metadata": {}, "outputs": [ { @@ -14753,7 +14776,7 @@ }, { "cell_type": "code", - "execution_count": 307, + "execution_count": 308, "metadata": {}, "outputs": [ { @@ -14773,7 +14796,7 @@ }, { "cell_type": "code", - "execution_count": 308, + "execution_count": 309, "metadata": {}, "outputs": [ { @@ -14793,7 +14816,7 @@ }, { "cell_type": "code", - "execution_count": 309, + "execution_count": 310, "metadata": {}, "outputs": [ { @@ -14813,7 +14836,7 @@ }, { "cell_type": "code", - "execution_count": 310, + "execution_count": 311, "metadata": {}, "outputs": [ { @@ -14833,7 +14856,7 @@ }, { "cell_type": "code", - "execution_count": 311, + "execution_count": 312, "metadata": {}, "outputs": [ { @@ -14856,7 +14879,7 @@ }, { "cell_type": "code", - "execution_count": 312, + "execution_count": 313, "metadata": {}, "outputs": [ { @@ -14878,7 +14901,7 @@ }, { "cell_type": "code", - "execution_count": 313, + "execution_count": 314, "metadata": {}, "outputs": [ { @@ -14900,7 +14923,7 @@ }, { "cell_type": "code", - "execution_count": 314, + "execution_count": 315, "metadata": {}, "outputs": [ { @@ -14921,7 +14944,7 @@ }, { "cell_type": "code", - "execution_count": 315, + "execution_count": 316, "metadata": {}, "outputs": [ { @@ -14941,7 +14964,7 @@ }, { "cell_type": "code", - "execution_count": 316, + "execution_count": 317, "metadata": {}, "outputs": [ { @@ -14961,7 +14984,7 @@ }, { "cell_type": "code", - "execution_count": 317, + "execution_count": 318, "metadata": {}, "outputs": [ { @@ -14981,7 +15004,7 @@ }, { "cell_type": "code", - "execution_count": 318, + "execution_count": 319, "metadata": {}, "outputs": [ { @@ -15001,7 +15024,7 @@ }, { "cell_type": "code", - "execution_count": 319, + "execution_count": 320, "metadata": {}, "outputs": [ { @@ -15021,7 +15044,7 @@ }, { "cell_type": "code", - "execution_count": 320, + "execution_count": 321, "metadata": {}, "outputs": [ { @@ -15041,7 +15064,7 @@ }, { "cell_type": "code", - "execution_count": 321, + "execution_count": 322, "metadata": {}, "outputs": [ { @@ -15061,7 +15084,7 @@ }, { "cell_type": "code", - "execution_count": 322, + "execution_count": 323, "metadata": {}, "outputs": [ { @@ -15081,7 +15104,7 @@ }, { "cell_type": "code", - "execution_count": 323, + "execution_count": 324, "metadata": {}, "outputs": [ { @@ -15101,7 +15124,7 @@ }, { "cell_type": "code", - "execution_count": 324, + "execution_count": 325, "metadata": {}, "outputs": [ { @@ -15121,7 +15144,7 @@ }, { "cell_type": "code", - "execution_count": 325, + "execution_count": 326, "metadata": {}, "outputs": [ { @@ -15144,7 +15167,7 @@ }, { "cell_type": "code", - "execution_count": 326, + "execution_count": 327, "metadata": {}, "outputs": [ { @@ -15167,7 +15190,7 @@ }, { "cell_type": "code", - "execution_count": 327, + "execution_count": 328, "metadata": {}, "outputs": [ { @@ -15190,7 +15213,7 @@ }, { "cell_type": "code", - "execution_count": 328, + "execution_count": 329, "metadata": {}, "outputs": [ { @@ -15213,7 +15236,7 @@ }, { "cell_type": "code", - "execution_count": 329, + "execution_count": 330, "metadata": {}, "outputs": [ { @@ -15236,7 +15259,7 @@ }, { "cell_type": "code", - "execution_count": 330, + "execution_count": 331, "metadata": {}, "outputs": [ { @@ -15260,7 +15283,7 @@ }, { "cell_type": "code", - "execution_count": 331, + "execution_count": 332, "metadata": {}, "outputs": [ { @@ -15284,7 +15307,7 @@ }, { "cell_type": "code", - "execution_count": 332, + "execution_count": 333, "metadata": {}, "outputs": [ { @@ -15308,7 +15331,7 @@ }, { "cell_type": "code", - "execution_count": 333, + "execution_count": 334, "metadata": {}, "outputs": [ { @@ -15331,7 +15354,7 @@ }, { "cell_type": "code", - "execution_count": 334, + "execution_count": 335, "metadata": {}, "outputs": [ { @@ -15354,7 +15377,7 @@ }, { "cell_type": "code", - "execution_count": 335, + "execution_count": 336, "metadata": {}, "outputs": [ { @@ -15378,9 +15401,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 337, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class extraction from input failed. Input vectors should be a list of sets with unified types.\n" + ] + } + ], "source": [ "try:\n", " mlcm = MultiLabelCM([[0, 1], [1, 1]], [[1, 0], [1, 0]])\n", @@ -15390,9 +15421,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 338, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Given class name is not among problem's classes.\n" + ] + } + ], "source": [ "try:\n", " mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], [{'cat'}, {'cat'}])\n", @@ -15403,9 +15442,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 339, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Given index is out of vector's range.\n" + ] + } + ], "source": [ "try:\n", " mlcm.get_cm_by_sample(2)\n", @@ -15415,9 +15462,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 340, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The type of input vectors is assumed to be a list or a NumPy array\n" + ] + } + ], "source": [ "try:\n", " mlcm = MultiLabelCM(2, [{1, 0}, {1, 0}])\n", @@ -15427,9 +15482,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 341, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input vectors must have same length\n" + ] + } + ], "source": [ "try:\n", " mlcm = MultiLabelCM([{1, 0}, {1, 0}, {1,1}], [{1, 0}, {1, 0}])\n", @@ -15439,9 +15502,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 342, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input vectors are empty\n" + ] + } + ], "source": [ "try:\n", " mlcm = MultiLabelCM([], [])\n", @@ -15451,9 +15522,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 343, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The classes list isn't unique. It contains duplicated labels.\n" + ] + } + ], "source": [ "try:\n", " mlcm = MultiLabelCM([{1, 0}, {1, 0}], [{1, 0}, {1, 0}], classes=[1,0,1])\n", diff --git a/Document/Document_files/cm1.html b/Document/Document_files/cm1.html index f8e480c0..29c91105 100644 --- a/Document/Document_files/cm1.html +++ b/Document/Document_files/cm1.html @@ -787,6 +787,6 @@

Class Statistics :

Similarity index -

Generated By PyCM Version 3.9

+

Generated By PyCM Version 4.0

diff --git a/Document/Document_files/cm1.obj b/Document/Document_files/cm1.obj index e9160239..4fc44c8c 100644 --- a/Document/Document_files/cm1.obj +++ b/Document/Document_files/cm1.obj @@ -1 +1 @@ -{"Predict-Vector": null, "Sample-Weight": null, "Digit": 5, "Actual-Vector": null, "Imbalanced": false, "Prob-Vector": null, "Matrix": [["L1", [["L1", 3], ["L2", 0], ["L3", 2]]], ["L2", [["L1", 0], ["L2", 1], ["L3", 1]]], ["L3", [["L1", 0], ["L2", 2], ["L3", 3]]]], "Transpose": true} \ No newline at end of file +{"Predict-Vector": null, "Prob-Vector": null, "Imbalanced": false, "Matrix": [["L1", [["L2", 0], ["L3", 2], ["L1", 3]]], ["L2", [["L2", 1], ["L3", 1], ["L1", 0]]], ["L3", [["L2", 2], ["L3", 3], ["L1", 0]]]], "Digit": 5, "Transpose": true, "Actual-Vector": null, "Sample-Weight": null} \ No newline at end of file diff --git a/Document/Document_files/cm1_colored.html b/Document/Document_files/cm1_colored.html index 537d5c5b..cddc2615 100644 --- a/Document/Document_files/cm1_colored.html +++ b/Document/Document_files/cm1_colored.html @@ -787,6 +787,6 @@

Class Statistics :

Similarity index -

Generated By PyCM Version 3.9

+

Generated By PyCM Version 4.0

diff --git a/Document/Document_files/cm1_colored2.html b/Document/Document_files/cm1_colored2.html index a91a7155..fd243078 100644 --- a/Document/Document_files/cm1_colored2.html +++ b/Document/Document_files/cm1_colored2.html @@ -787,6 +787,6 @@

Class Statistics :

Similarity index -

Generated By PyCM Version 3.9

+

Generated By PyCM Version 4.0

diff --git a/Document/Document_files/cm1_filtered.html b/Document/Document_files/cm1_filtered.html index bf27d2d9..60fa95ef 100644 --- a/Document/Document_files/cm1_filtered.html +++ b/Document/Document_files/cm1_filtered.html @@ -95,6 +95,6 @@

Class Statistics :

Sensitivity, recall, hit rate, or true positive rate -

Generated By PyCM Version 3.9

+

Generated By PyCM Version 4.0

diff --git a/Document/Document_files/cm1_filtered2.html b/Document/Document_files/cm1_filtered2.html index 8f3e9708..171dc2a5 100644 --- a/Document/Document_files/cm1_filtered2.html +++ b/Document/Document_files/cm1_filtered2.html @@ -87,6 +87,6 @@

Class Statistics :

Sensitivity, recall, hit rate, or true positive rate -

Generated By PyCM Version 3.9

+

Generated By PyCM Version 4.0

diff --git a/Document/Document_files/cm1_no_vectors.obj b/Document/Document_files/cm1_no_vectors.obj index e9160239..4fc44c8c 100644 --- a/Document/Document_files/cm1_no_vectors.obj +++ b/Document/Document_files/cm1_no_vectors.obj @@ -1 +1 @@ -{"Predict-Vector": null, "Sample-Weight": null, "Digit": 5, "Actual-Vector": null, "Imbalanced": false, "Prob-Vector": null, "Matrix": [["L1", [["L1", 3], ["L2", 0], ["L3", 2]]], ["L2", [["L1", 0], ["L2", 1], ["L3", 1]]], ["L3", [["L1", 0], ["L2", 2], ["L3", 3]]]], "Transpose": true} \ No newline at end of file +{"Predict-Vector": null, "Prob-Vector": null, "Imbalanced": false, "Matrix": [["L1", [["L2", 0], ["L3", 2], ["L1", 3]]], ["L2", [["L2", 1], ["L3", 1], ["L1", 0]]], ["L3", [["L2", 2], ["L3", 3], ["L1", 0]]]], "Digit": 5, "Transpose": true, "Actual-Vector": null, "Sample-Weight": null} \ No newline at end of file diff --git a/Document/Document_files/cm1_normalized.html b/Document/Document_files/cm1_normalized.html index a1b6ed7f..b8ef8dc4 100644 --- a/Document/Document_files/cm1_normalized.html +++ b/Document/Document_files/cm1_normalized.html @@ -787,6 +787,6 @@

Class Statistics :

Similarity index -

Generated By PyCM Version 3.9

+

Generated By PyCM Version 4.0

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diff --git a/Document/Example4.ipynb b/Document/Example4.ipynb index 65c776ad..cee37f6b 100644 --- a/Document/Example4.ipynb +++ b/Document/Example4.ipynb @@ -569,7 +569,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{\"Prob-Vector\": null, \"Digit\": 5, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Imbalanced\": true, \"Transpose\": false, \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"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]]]], \"Sample-Weight\": null}\n" + "{\"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" ] } ], @@ -586,7 +586,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{\"Prob-Vector\": null, \"Digit\": 5, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Imbalanced\": true, \"Transpose\": false, \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"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]]]], \"Sample-Weight\": 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\"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" ] } ], @@ -603,7 +603,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{\"Prob-Vector\": null, \"Digit\": 5, \"Predict-Vector\": null, \"Imbalanced\": true, \"Transpose\": false, \"Actual-Vector\": null, \"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]]]], \"Sample-Weight\": null}\n" + "{\"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" ] } ], @@ -676,7 +676,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/Document/Example4_files/cm.obj b/Document/Example4_files/cm.obj index 719026eb..c2b707b2 100644 --- a/Document/Example4_files/cm.obj +++ b/Document/Example4_files/cm.obj @@ -1 +1 @@ -{"Prob-Vector": null, "Digit": 5, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Imbalanced": true, "Transpose": false, "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 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a/Document/Example4_files/cm_no_vectors.obj +++ b/Document/Example4_files/cm_no_vectors.obj @@ -1 +1 @@ -{"Prob-Vector": null, "Digit": 5, "Predict-Vector": null, "Imbalanced": true, "Transpose": false, "Actual-Vector": null, "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]]]], "Sample-Weight": null} \ No newline at end of file +{"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]]]]} \ No newline at end of file diff --git a/Document/Example4_files/cm_stat.obj b/Document/Example4_files/cm_stat.obj index be4df80b..e2fc2f31 100644 --- a/Document/Example4_files/cm_stat.obj +++ b/Document/Example4_files/cm_stat.obj @@ -1 +1 @@ -{"Prob-Vector": null, "Digit": 5, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Imbalanced": true, "Transpose": false, "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "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]]]], "Sample-Weight": null, "Overall-Stat": {"SOA7(Lambda A)": "None", "Pearson C": "None", "Scott PI": -0.12554112554112543, "ACC Macro": 0.675, "Overall ACC": 0.35, "RR": 5.0, "TNR Micro": 0.7833333333333333, "FPR Micro": 0.21666666666666667, "SOA2(Fleiss)": "Poor", "SOA1(Landis & Koch)": "Slight", "NIR": 0.8, "Zero-one Loss": 13, "Bennett S": 0.1333333333333333, "Hamming Loss": 0.65, 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"500": 0.5, "100": "None", "600": "None"}, "DOR": {"200": 1.7999999999999998, "500": 7.999999999999997, "100": "None", "600": "None"}, "Y": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "MK": {"200": 0.08791208791208782, "500": 0.38888888888888884, "100": 0.0, "600": "None"}, "IS": {"200": 0.09953567355091428, "500": 1.736965594166206, "100": "None", "600": "None"}, "TON": {"200": 13, "500": 18, "100": 9, "600": 20}, "RACCU": {"200": 0.33062499999999995, "500": 0.015625, "100": 0.07562500000000001, "600": 0.0006250000000000001}, "MCC": {"200": 0.10482848367219183, "500": 0.32673201960653564, "100": "None", "600": "None"}, "TPR": {"200": 0.375, "500": 0.3333333333333333, "100": "None", "600": 0.0}, "PLR": {"200": 1.5, "500": 5.666666666666665, "100": "None", "600": "None"}, "TNR": {"200": 0.75, "500": 0.9411764705882353, "100": 0.45, "600": 1.0}, "IBA": {"200": 0.17578125, "500": 0.1230296039984621, "100": "None", "600": 0.0}, "AM": {"200": -9, "500": -1, "100": 11, "600": -1}, "FOR": {"200": 0.7692307692307692, "500": 0.11111111111111116, "100": 0.0, "600": 0.050000000000000044}, "PPV": {"200": 0.8571428571428571, "500": 0.5, "100": 0.0, "600": "None"}, "AUCI": {"200": "Poor", "500": "Fair", "100": "None", "600": "Poor"}, "DP": {"200": 0.1407391082701595, "500": 0.49789960499474867, "100": "None", "600": "None"}, "ERR": {"200": 0.55, "500": 0.15000000000000002, "100": 0.55, "600": 0.050000000000000044}, "QI": {"200": "Weak", "500": "Strong", "100": "None", "600": "None"}, "FNR": {"200": 0.625, "500": 0.6666666666666667, "100": "None", "600": 1.0}, "N": {"200": 4, "500": 17, "100": 20, "600": 19}, "NLRI": {"200": "Negligible", "500": "Negligible", "100": "None", "600": "Negligible"}, "Q": {"200": 0.28571428571428575, "500": 0.7777777777777778, "100": "None", "600": "None"}, "DPI": {"200": "Poor", "500": "Poor", "100": "None", "600": "None"}, "OP": {"200": 0.1166666666666667, "500": 0.373076923076923, "100": "None", "600": -0.050000000000000044}, "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}, "BM": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "GI": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "BCD": {"200": 0.225, "500": 0.025, "100": 0.275, "600": 0.025}, "AUPR": {"200": 0.6160714285714286, "500": 0.41666666666666663, "100": "None", "600": "None"}, "AGF": {"200": 0.33642097801219245, "500": 0.5665926996700735, "100": 0.0, "600": 0.0}, "MCCI": {"200": "Negligible", "500": "Weak", "100": "None", "600": "None"}, "ICSI": {"200": 0.2321428571428572, "500": -0.16666666666666674, "100": "None", "600": "None"}, "G": {"200": 0.5669467095138409, "500": 0.408248290463863, "100": "None", "600": "None"}, "P": {"200": 16, "500": 3, "100": 0, "600": 1}, "FDR": {"200": 0.1428571428571429, "500": 0.5, "100": 1.0, "600": "None"}, "CEN": {"200": 0.3570795472009597, "500": 0.5389466410223563, "100": 0.3349590631259315, "600": 0.0}, "RACC": {"200": 0.28, "500": 0.015, "100": 0.0, "600": 0.0}, "NPV": {"200": 0.23076923076923078, "500": 0.8888888888888888, "100": 1.0, "600": 0.95}, "sInd": {"200": 0.5240141808835057, "500": 0.5267639848569737, "100": "None", "600": 0.29289321881345254}, "OOC": {"200": 0.5669467095138409, "500": 0.4082482904638631, "100": "None", "600": "None"}, "TP": {"200": 6, "100": 0, "500": 1, "600": 0}, "ACC": {"200": 0.45, "500": 0.85, "100": 0.45, "600": 0.95}, "TOP": {"200": 7, "500": 2, "100": 11, "600": 0}, "FP": {"200": 1, "100": 11, "500": 1, "600": 0}, "TN": {"200": 3, "100": 9, "500": 16, "600": 19}}} \ No newline at end of file +{"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]]]]} \ No newline at end of file diff --git a/Document/Example6.ipynb b/Document/Example6.ipynb index 1c860078..ec39a34a 100644 --- a/Document/Example6.ipynb +++ b/Document/Example6.ipynb @@ -88,11 +88,11 @@ "Class2 0.04762 0.95238 \n", "\n", "\n", - "ACC: {'Class1': 0.9976333515383216, 'Class2': 0.9976333515383216}\n", - "MCC: {'Class1': 0.9378574017402594, 'Class2': 0.9378574017402594}\n", - "CEN: {'Class1': 0.012858728415908176, 'Class2': 0.30489006849060607}\n", - "MCEN: {'Class1': 0.023280122318969122, 'Class2': 0.46949279678726225}\n", - "DP: {'Class1': 2.276283896527635, 'Class2': 2.276283896527635}\n", + "ACC: {'Class2': 0.9976333515383216, 'Class1': 0.9976333515383216}\n", + "MCC: {'Class2': 0.9378574017402594, 'Class1': 0.9378574017402594}\n", + "CEN: {'Class2': 0.30489006849060607, 'Class1': 0.012858728415908176}\n", + "MCEN: {'Class2': 0.46949279678726225, 'Class1': 0.023280122318969122}\n", + "DP: {'Class2': 2.276283896527635, 'Class1': 2.276283896527635}\n", "Kappa: 0.9377606597584491\n", "RCI: 0.8682877002417864\n", "SOA1: Almost Perfect\n" @@ -142,11 +142,11 @@ "Class2 0.95238 0.04762 \n", "\n", "\n", - "ACC: {'Class1': 0.982098458478369, 'Class2': 0.982098458478369}\n", - "MCC: {'Class1': 0.13048897476798949, 'Class2': 0.13048897476798949}\n", - "CEN: {'Class1': 0.06481573363174531, 'Class2': 0.4655917826576813}\n", - "MCEN: {'Class1': 0.11078640690031397, 'Class2': 0.4264929996758212}\n", - "DP: {'Class1': 0.864594924328404, 'Class2': 0.864594924328404}\n", + "ACC: {'Class2': 0.982098458478369, 'Class1': 0.982098458478369}\n", + "MCC: {'Class2': 0.13048897476798949, 'Class1': 0.13048897476798949}\n", + "CEN: {'Class2': 0.4655917826576813, 'Class1': 0.06481573363174531}\n", + "MCEN: {'Class2': 0.4264929996758212, 'Class1': 0.11078640690031397}\n", + "DP: {'Class2': 0.864594924328404, 'Class1': 0.864594924328404}\n", "Kappa: 0.08122239707598865\n", "RCI: 0.022375346499017443\n", "SOA1: Slight\n" @@ -196,11 +196,11 @@ "Class2 0.04762 0.95238 \n", "\n", "\n", - "ACC: {'Class1': 0.019661387220098307, 'Class2': 0.019661387220098307}\n", - "MCC: {'Class1': -0.13000800945464058, 'Class2': -0.13000800945464058}\n", - "CEN: {'Class1': 0.014927427128936136, 'Class2': 0.06103563616795208}\n", - "MCEN: {'Class1': 0.01281422838054554, 'Class2': 0.03655796690365652}\n", - "DP: {'Class1': -0.8416930356875597, 'Class2': -0.8416930356875597}\n", + "ACC: {'Class2': 0.019661387220098307, 'Class1': 0.019661387220098307}\n", + "MCC: {'Class2': -0.13000800945464058, 'Class1': -0.13000800945464058}\n", + "CEN: {'Class2': 0.06103563616795208, 'Class1': 0.014927427128936136}\n", + "MCEN: {'Class2': 0.03655796690365652, 'Class1': 0.01281422838054554}\n", + "DP: {'Class2': -0.8416930356875597, 'Class1': -0.8416930356875597}\n", "Kappa: -0.0017678372492452412\n", "RCI: 0.02192606003351106\n", "SOA1: Poor\n" @@ -261,13 +261,13 @@ "Class4 0.0 0.0 2e-05 0.99998 \n", "\n", "\n", - "ACC: {'Class3': 0.9999250299880048, 'Class4': 0.9999500199920032, 'Class1': 0.9999750099960016, 'Class2': 0.9999500199920032}\n", - "MCC: {'Class3': 0.7302602381427055, 'Class4': 0.9333083339583177, 'Class1': 0.8944160139432883, 'Class2': 0.7999750068731099}\n", - "CEN: {'Class3': 0.3649884090288471, 'Class4': 0.0001575200922489127, 'Class1': 0.13625493172565745, 'Class2': 0.25701944178769376}\n", - "MCEN: {'Class3': 0.4654427710721536, 'Class4': 0.00029569133318617423, 'Class1': 0.17964888034078544, 'Class2': 0.3333333333333333}\n", - "DP: {'Class3': 2.7032690544190636, 'Class4': 3.1691421556058055, 'Class1': 'None', 'Class2': 2.869241573973406}\n", + "ACC: {'Class3': 0.9999250299880048, 'Class4': 0.9999500199920032, 'Class2': 0.9999500199920032, 'Class1': 0.9999750099960016}\n", + "MCC: {'Class3': 0.7302602381427055, 'Class4': 0.9333083339583177, 'Class2': 0.7999750068731099, 'Class1': 0.8944160139432883}\n", + "CEN: {'Class3': 0.3649884090288471, 'Class4': 0.0001575200922489127, 'Class2': 0.25701944178769376, 'Class1': 0.13625493172565745}\n", + "MCEN: {'Class3': 0.4654427710721536, 'Class4': 0.00029569133318617423, 'Class2': 0.3333333333333333, 'Class1': 0.17964888034078544}\n", + "DP: {'Class3': 2.7032690544190636, 'Class4': 3.1691421556058055, 'Class2': 2.869241573973406, 'Class1': 'None'}\n", "Kappa: 0.8666333383326446\n", - "RCI: 0.8711441699127425\n", + "RCI: 0.8711441699127427\n", "SOA1: Almost Perfect\n" ] } @@ -324,11 +324,11 @@ "Class4 0.25 0.25 0.25 0.25 \n", "\n", "\n", - "ACC: {'Class3': 0.625, 'Class4': 0.625, 'Class1': 0.625, 'Class2': 0.625}\n", - "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n", - "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.8704188162777186, 'Class1': 0.8704188162777186, 'Class2': 0.8704188162777186}\n", - "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.9308855421443073, 'Class1': 0.9308855421443073, 'Class2': 0.9308855421443073}\n", - "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n", + "ACC: {'Class3': 0.625, 'Class4': 0.625, 'Class2': 0.625, 'Class1': 0.625}\n", + "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.8704188162777186}\n", + "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.9308855421443073}\n", + "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", "Kappa: 0.0\n", "RCI: 0.0\n", "SOA1: Slight\n" @@ -387,13 +387,13 @@ "Class4 0.76923 0.07692 0.07692 0.07692 \n", "\n", "\n", - "ACC: {'Class3': 0.76, 'Class4': 0.4, 'Class1': 0.4, 'Class2': 0.76}\n", - "MCC: {'Class3': 0.10714285714285714, 'Class4': -0.2358640882624316, 'Class1': -0.2358640882624316, 'Class2': 0.10714285714285714}\n", - "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.6392779429225796, 'Class1': 0.6392779429225794, 'Class2': 0.8704188162777186}\n", - "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.647512271542988, 'Class1': 0.647512271542988, 'Class2': 0.9308855421443073}\n", - "DP: {'Class3': 0.16596653499824943, 'Class4': -0.3319330699964992, 'Class1': -0.33193306999649924, 'Class2': 0.16596653499824943}\n", - "Kappa: -0.07361963190184051\n", - "RCI: 0.1160303056449364\n", + "ACC: {'Class3': 0.76, 'Class4': 0.4, 'Class2': 0.76, 'Class1': 0.4}\n", + "MCC: {'Class3': 0.10714285714285714, 'Class4': -0.2358640882624316, 'Class2': 0.10714285714285714, 'Class1': -0.2358640882624316}\n", + "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.6392779429225796, 'Class2': 0.8704188162777186, 'Class1': 0.6392779429225794}\n", + "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.647512271542988, 'Class2': 0.9308855421443073, 'Class1': 0.647512271542988}\n", + "DP: {'Class3': 0.16596653499824943, 'Class4': -0.3319330699964992, 'Class2': 0.16596653499824943, 'Class1': -0.33193306999649924}\n", + "Kappa: -0.07361963190184047\n", + "RCI: 0.11603030564493627\n", "SOA1: Poor\n" ] } @@ -450,13 +450,13 @@ "Class4 0.76923 0.07692 0.07692 0.07692 \n", "\n", "\n", - "ACC: {'Class3': 0.999400898652022, 'Class4': 0.000998502246630055, 'Class1': 0.000998502246630055, 'Class2': 0.999400898652022}\n", - "MCC: {'Class3': 0.24970032963739885, 'Class4': -0.43266656861311537, 'Class1': -0.43266656861311537, 'Class2': 0.24970032963739885}\n", - "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.0029588592520426657, 'Class1': 0.0029588592520426657, 'Class2': 0.8704188162777186}\n", - "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.002903385725603509, 'Class1': 0.002903385725603509, 'Class2': 0.9308855421443073}\n", - "DP: {'Class3': 1.6794055876913858, 'Class4': -1.9423127303715728, 'Class1': -1.9423127303715728, 'Class2': 1.6794055876913858}\n", + "ACC: {'Class3': 0.999400898652022, 'Class4': 0.000998502246630055, 'Class2': 0.999400898652022, 'Class1': 0.000998502246630055}\n", + "MCC: {'Class3': 0.24970032963739885, 'Class4': -0.43266656861311537, 'Class2': 0.24970032963739885, 'Class1': -0.43266656861311537}\n", + "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.0029588592520426657, 'Class2': 0.8704188162777186, 'Class1': 0.0029588592520426657}\n", + "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.002903385725603509, 'Class2': 0.9308855421443073, 'Class1': 0.002903385725603509}\n", + "DP: {'Class3': 1.6794055876913858, 'Class4': -1.9423127303715728, 'Class2': 1.6794055876913858, 'Class1': -1.9423127303715728}\n", "Kappa: -0.0003990813465900262\n", - "RCI: 0.5536610475678805\n", + "RCI: 0.5536610475678804\n", "SOA1: Poor\n" ] } @@ -513,11 +513,11 @@ "Class4 0.25 0.25 0.25 0.25 \n", "\n", "\n", - "ACC: {'Class3': 0.7115384615384616, 'Class4': 0.36538461538461536, 'Class1': 0.7115384615384616, 'Class2': 0.7115384615384616}\n", - "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n", - "CEN: {'Class3': 0.6392779429225794, 'Class4': 0.6522742127953861, 'Class1': 0.6392779429225794, 'Class2': 0.6392779429225794}\n", - "MCEN: {'Class3': 0.647512271542988, 'Class4': 0.7144082229288313, 'Class1': 0.647512271542988, 'Class2': 0.647512271542988}\n", - "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n", + "ACC: {'Class3': 0.7115384615384616, 'Class4': 0.36538461538461536, 'Class2': 0.7115384615384616, 'Class1': 0.7115384615384616}\n", + "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "CEN: {'Class3': 0.6392779429225794, 'Class4': 0.6522742127953861, 'Class2': 0.6392779429225794, 'Class1': 0.6392779429225794}\n", + "MCEN: {'Class3': 0.647512271542988, 'Class4': 0.7144082229288313, 'Class2': 0.647512271542988, 'Class1': 0.647512271542988}\n", + "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", "Kappa: 0.0\n", "RCI: 0.0\n", "SOA1: Slight\n" @@ -576,11 +576,11 @@ "Class4 0.25 0.25 0.25 0.25 \n", "\n", "\n", - "ACC: {'Class3': 0.7499500149955014, 'Class4': 0.25014995501349596, 'Class1': 0.7499500149955014, 'Class2': 0.7499500149955014}\n", - "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n", - "CEN: {'Class3': 0.0029588592520426657, 'Class4': 0.539296694603886, 'Class1': 0.0029588592520426657, 'Class2': 0.0029588592520426657}\n", - "MCEN: {'Class3': 0.002903385725603509, 'Class4': 0.580710610324597, 'Class1': 0.002903385725603509, 'Class2': 0.002903385725603509}\n", - "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class1': 0.0, 'Class2': 0.0}\n", + "ACC: {'Class3': 0.7499500149955014, 'Class4': 0.25014995501349596, 'Class2': 0.7499500149955014, 'Class1': 0.7499500149955014}\n", + "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "CEN: {'Class3': 0.0029588592520426657, 'Class4': 0.539296694603886, 'Class2': 0.0029588592520426657, 'Class1': 0.0029588592520426657}\n", + "MCEN: {'Class3': 0.002903385725603509, 'Class4': 0.580710610324597, 'Class2': 0.002903385725603509, 'Class1': 0.002903385725603509}\n", + "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", "Kappa: 0.0\n", "RCI: 0.0\n", "SOA1: Slight\n" diff --git a/Otherfiles/meta.yaml b/Otherfiles/meta.yaml index 4a491d35..9e153c08 100644 --- a/Otherfiles/meta.yaml +++ b/Otherfiles/meta.yaml @@ -1,5 +1,5 @@ {% set name = "pycm" %} -{% set version = "3.9" %} +{% set version = "4.0" %} package: name: {{ name|lower }} diff --git a/Otherfiles/test.html b/Otherfiles/test.html index f8e480c0..29c91105 100644 --- a/Otherfiles/test.html +++ b/Otherfiles/test.html @@ -787,6 +787,6 @@

Class Statistics :

Similarity index -

Generated By PyCM Version 3.9

+

Generated By PyCM Version 4.0

diff --git a/Otherfiles/test.obj b/Otherfiles/test.obj index e9160239..4fc44c8c 100644 --- a/Otherfiles/test.obj +++ b/Otherfiles/test.obj @@ -1 +1 @@ -{"Predict-Vector": null, "Sample-Weight": null, "Digit": 5, "Actual-Vector": null, "Imbalanced": false, "Prob-Vector": null, "Matrix": [["L1", [["L1", 3], ["L2", 0], ["L3", 2]]], ["L2", [["L1", 0], ["L2", 1], ["L3", 1]]], ["L3", [["L1", 0], ["L2", 2], ["L3", 3]]]], "Transpose": true} \ No newline at end of file +{"Predict-Vector": null, "Prob-Vector": null, "Imbalanced": false, "Matrix": [["L1", [["L2", 0], ["L3", 2], ["L1", 3]]], ["L2", [["L2", 1], ["L3", 1], ["L1", 0]]], ["L3", [["L2", 2], ["L3", 3], ["L1", 0]]]], "Digit": 5, "Transpose": true, "Actual-Vector": null, "Sample-Weight": null} \ No newline at end of file diff --git a/Otherfiles/version_check.py b/Otherfiles/version_check.py index 2e53e140..7f1d68bf 100644 --- a/Otherfiles/version_check.py +++ b/Otherfiles/version_check.py @@ -4,7 +4,7 @@ import sys import codecs Failed = 0 -PYCM_VERSION = "3.9" +PYCM_VERSION = "4.0" SETUP_ITEMS = [ diff --git a/README.md b/README.md index 6db5a1d8..3f59296b 100644 --- a/README.md +++ b/README.md @@ -96,14 +96,14 @@ PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scie ⚠️ Plotting capability requires **Matplotlib (>= 3.0.0)** or **Seaborn (>= 0.9.1)** ### Source code -- Download [Version 3.9](https://github.com/sepandhaghighi/pycm/archive/v3.9.zip) or [Latest Source ](https://github.com/sepandhaghighi/pycm/archive/dev.zip) +- Download [Version 4.0](https://github.com/sepandhaghighi/pycm/archive/v4.0.zip) or [Latest Source ](https://github.com/sepandhaghighi/pycm/archive/dev.zip) - Run `pip install -r requirements.txt` or `pip3 install -r requirements.txt` (Need root access) - Run `python3 setup.py install` or `python setup.py install` (Need root access) ### PyPI - Check [Python Packaging User Guide](https://packaging.python.org/installing/) -- Run `pip install pycm==3.9` or `pip3 install pycm==3.9` (Need root access) +- Run `pip install pycm==4.0` or `pip3 install pycm==4.0` (Need root access) ### Conda diff --git a/pycm/pycm_param.py b/pycm/pycm_param.py index 2f3856d0..17cfff97 100644 --- a/pycm/pycm_param.py +++ b/pycm/pycm_param.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """Parameters and constants.""" -PYCM_VERSION = "3.9" +PYCM_VERSION = "4.0" OVERVIEW = ''' diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 7c2b2874..00000000 --- a/setup.cfg +++ /dev/null @@ -1,2 +0,0 @@ -[bdist_wheel] -universal = 1 \ No newline at end of file diff --git a/setup.py b/setup.py index d78dc4c7..3e5729e0 100644 --- a/setup.py +++ b/setup.py @@ -36,14 +36,14 @@ def read_description(): setup( name='pycm', packages=['pycm'], - version='3.9', + version='4.0', description='Multi-class confusion matrix library in Python', long_description=read_description(), long_description_content_type='text/markdown', author='PyCM Development Team', author_email='info@pycm.io', url='https://github.com/sepandhaghighi/pycm', - download_url='https://github.com/sepandhaghighi/pycm/tarball/v3.9', + download_url='https://github.com/sepandhaghighi/pycm/tarball/v4.0', keywords="confusion-matrix python3 python machine_learning ML", project_urls={ 'Webpage': 'https://www.pycm.io',