diff --git a/.github/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md index 2b962868..9610f145 100644 --- a/.github/CODE_OF_CONDUCT.md +++ b/.github/CODE_OF_CONDUCT.md @@ -2,75 +2,129 @@ ## Our Pledge -In the interest of fostering an open and welcoming environment, we as -contributors and maintainers pledge to making participation in our project and -our community a harassment-free experience for everyone, regardless of age, body -size, disability, ethnicity, sex characteristics, gender identity and expression, -level of experience, education, socio-economic status, nationality, personal -appearance, race, religion, or sexual identity and orientation. +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. ## Our Standards -Examples of behavior that contributes to creating a positive environment -include: +Examples of behavior that contributes to a positive environment for our +community include: -* Using welcoming and inclusive language -* Being respectful of differing viewpoints and experiences -* Gracefully accepting constructive criticism -* Focusing on what is best for the community -* Showing empathy towards other community members +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community -Examples of unacceptable behavior by participants include: +Examples of unacceptable behavior include: -* The use of sexualized language or imagery and unwelcome sexual attention or - advances -* Trolling, insulting/derogatory comments, and personal or political attacks +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks * Public or private harassment -* Publishing others' private information, such as a physical or electronic - address, without explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a professional setting -## Our Responsibilities +## Enforcement Responsibilities -Project maintainers are responsible for clarifying the standards of acceptable -behavior and are expected to take appropriate and fair corrective action in -response to any instances of unacceptable behavior. +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned to this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. ## Scope - This Code of Conduct applies both within project spaces and in public spaces -when an individual is representing the project or its community. Examples of -representing a project or community include using an official project e-mail -address, posting via an official social media account, or acting as an appointed -representative at an online or offline event. Representation of a project may be -further defined and clarified by project maintainers. +when an individual is representing the project or its community. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting the project team at info@pycm.ir. All -complaints will be reviewed and investigated and will result in a response that -is deemed necessary and appropriate to the circumstances. The project team is -obligated to maintain confidentiality with regard to the reporter of an incident. -Further details of specific enforcement policies may be posted separately. +reported to the community leaders responsible for enforcement at +info@pycm.ir. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. -Project maintainers who do not follow or enforce the Code of Conduct in good -faith may face temporary or permanent repercussions as determined by other -members of the project's leadership. +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. ## Attribution -This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, -available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. -[homepage]: https://www.contributor-covenant.org +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. -For answers to common questions about this code of conduct, see -https://www.contributor-covenant.org/faq +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index 93eb4f40..cec0e4d2 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -27,22 +27,21 @@ Please consider the following : ## Class statistic 1. Add new functions to `pycm_class_func.py` -2. Update `class_statistics` function in `pycm_class_func.py` - - Define a new variable as a dictionary - - Call statistic function and store result in this variable - - Add this variable to `result` dictionary -3. Update `__class_stat_init__` function in `pycm_handler.py` by a new attribute -4. Update `PARAMS_DESCRIPTION` dictionary in `pycm_param.py` by a short description +2. Update `CLASS_PARAMS` list in `pycm_param.py` +3. Update `class_statistics` function in `pycm_class_func.py` + - Call statistic function and store result in `result` dictionary +4. Update `__class_stat_init__` function in `pycm_handler.py` by a new attribute +5. Update `PARAMS_DESCRIPTION` dictionary in `pycm_param.py` by a short description - If you don't want capitalization, update `CAPITALIZE_FILTER` list in `pycm_param.py` (*Optional*) -5. Update `References` section in `Document.ipynb` and `README.md` (`IEEE` format) -6. Add description to `Class Statistics` section in `Document.ipynb` +6. Update `References` section in `Document.ipynb` and `README.md` (`IEEE` format) +7. Add description to `Class Statistics` section in `Document.ipynb` - Cite reference - Update table of contents - Use `LaTeX` for formula -7. Update `PARAMS_LINK` dictionary in `pycm_param.py` by document tag (without `#`) -8. Add tests to `overall_test.py` and `function_test.py` in `TEST` folder +8. Update `PARAMS_LINK` dictionary in `pycm_param.py` by document tag (without `#`) +9. Add tests to `overall_test.py` and `function_test.py` in `TEST` folder - If you have any verified test add them to `verified_test.py` -9. Run `autopep8.bat`/`autopep8.sh` (*Optional*, need to install latest version of `autopep8` package) +10. Run `autopep8.bat`/`autopep8.sh` (*Optional*, need to install latest version of `autopep8` package) diff --git a/.github/workflows/publish_conda.yaml b/.github/workflows/publish_conda.yaml new file mode 100644 index 00000000..17b7d640 --- /dev/null +++ b/.github/workflows/publish_conda.yaml @@ -0,0 +1,18 @@ +name: publish_conda + +on: + push: + # Sequence of patterns matched against refs/tags + tags: + - '*' # Push events to matching v*, i.e. v1.0, v20.15.10 + +jobs: + publish: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v1 + - name: publish-to-conda + uses: sepandhaghighi/conda-package-publish-action@v1.2 + with: + subDir: 'Otherfiles' + AnacondaToken: ${{ secrets.ANACONDA_TOKEN }} diff --git a/.github/workflows/publish.yml b/.github/workflows/publish_pypi.yml similarity index 100% rename from .github/workflows/publish.yml rename to .github/workflows/publish_pypi.yml diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index a62281c5..13b5ecab 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -46,6 +46,7 @@ jobs: if: matrix.python-version == 3.7 - name: Notebook check run: | + pip install notebook>=5.2.2 python Otherfiles/notebook_check.py if: matrix.python-version == 3.7 && matrix.os == 'ubuntu-latest' - name: Other tests diff --git a/CHANGELOG.md b/CHANGELOG.md index 9c1f1cef..0b947a40 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,19 @@ 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] +## [3.5] - 2022-04-27 +### Added +- Anaconda workflow +- Custom iterating setting +- Custom casting setting +### Changed +- `plot` method updated +- `class_statistics` function modified +- `overall_statistics` function modified +- `BCD_calc` function modified +- `CONTRIBUTING.md` updated +- `CODE_OF_CONDUCT.md` updated +- Document modified ## [3.4] - 2022-01-26 ### Added - Colab badge @@ -569,7 +582,8 @@ 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/v3.4...dev +[Unreleased]: https://github.com/sepandhaghighi/pycm/compare/v3.5...dev +[3.5]: https://github.com/sepandhaghighi/pycm/compare/v3.4...v3.5 [3.4]: https://github.com/sepandhaghighi/pycm/compare/v3.3...v3.4 [3.3]: https://github.com/sepandhaghighi/pycm/compare/v3.2...v3.3 [3.2]: https://github.com/sepandhaghighi/pycm/compare/v3.1...v3.2 diff --git a/Document/Document.ipynb b/Document/Document.ipynb index 1db5e982..b99a442b 100644 --- a/Document/Document.ipynb +++ b/Document/Document.ipynb @@ -18,7 +18,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Version : 3.4\n", + "### Version : 3.5\n", "-----" ] }, @@ -47,6 +47,7 @@ "
    \n", "
  1. From Vector
    2. \n", "
  2. Direct CM
    3. \n", + "
  3. Iterating And Casting
    4. \n", "
  4. Activation Threshold
    5. \n", "
  5. Load From File
    6. \n", "
  6. Sample Weights
    7. \n", @@ -58,7 +59,7 @@ "
  7. Plot
    8. \n", "
  8. Online Help
    9. \n", "
  9. Parameter Recommender
    10. \n", - "
  10. Comapre
    11. \n", + "
  11. Compare
    12. \n", "
  12. Acceptable Data Types
    13. \n", "
\n", "  \n", @@ -283,7 +284,7 @@ "metadata": {}, "source": [ "### Source code\n", - "- Download [Version 3.4](https://github.com/sepandhaghighi/pycm/archive/v3.4.zip) or [Latest Source ](https://github.com/sepandhaghighi/pycm/archive/dev.zip)\n", + "- Download [Version 3.5](https://github.com/sepandhaghighi/pycm/archive/v3.5.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)" ] @@ -296,7 +297,7 @@ "\n", "\n", "- Check [Python Packaging User Guide](https://packaging.python.org/installing/) \n", - "- Run `pip install pycm==3.4` or `pip3 install pycm==3.4` (Need root access)" + "- Run `pip install pycm==3.5` or `pip3 install pycm==3.5` (Need root access)" ] }, { @@ -927,7 +928,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.4-py3.5.egg\\pycm\\pycm_util.py:387: 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-3.5-py3.5.egg\\pycm\\pycm_util.py:387: RuntimeWarning: Used classes is not a subset of classes in actual and predict vectors.\n" ] } ], @@ -1228,6 +1229,112 @@ " " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Iterating and casting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From `version 3.5`, `ConfusionMatrix` is an **iterator** object." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 {0: 3, 1: 0, 4: 0}\n", + "1 {0: 0, 1: 1, 4: 0}\n", + "4 {0: 0, 1: 0, 4: 0}\n" + ] + } + ], + "source": [ + "for row, col in cm:\n", + " print(row, col)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, {0: 3, 1: 0, 4: 0})" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cm_iter = iter(cm)\n", + "next(cm_iter)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: {0: 3, 1: 0, 4: 0}, 1: {0: 0, 1: 1, 4: 0}, 4: {0: 0, 1: 0, 4: 0}}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cm_dict = dict(cm)\n", + "cm_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, {0: 3, 1: 0, 4: 0}), (1, {0: 0, 1: 1, 4: 0}), (4, {0: 0, 1: 0, 4: 0})]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cm_list = list(cm)\n", + "cm_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1321,7 +1428,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1330,7 +1437,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1379,7 +1486,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1388,7 +1495,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1397,7 +1504,7 @@ "pycm.ConfusionMatrix(classes: ['L1', 'L2', 'L3'])" ] }, - "execution_count": 35, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1445,7 +1552,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1456,7 +1563,7 @@ " 2: {'FN': [0, 3, 7], 'FP': [5, 10], 'TN': [1, 4, 6, 9], 'TP': [2, 8, 11]}}" ] }, - "execution_count": 36, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1500,7 +1607,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1511,7 +1618,7 @@ " [0, 2, 3]])" ] }, - "execution_count": 37, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1522,7 +1629,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1533,7 +1640,7 @@ " [0. , 0.4, 0.6]])" ] }, - "execution_count": 38, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1544,7 +1651,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1554,7 +1661,7 @@ " [0. , 1. ]])" ] }, - "execution_count": 39, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1618,7 +1725,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1695,7 +1802,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1707,16 +1814,16 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 42, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -1739,16 +1846,16 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 43, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, @@ -1771,16 +1878,16 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 44, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, @@ -1803,16 +1910,16 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 45, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, @@ -1835,16 +1942,16 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 46, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, @@ -1953,7 +2060,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -2166,7 +2273,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -2175,7 +2282,7 @@ "False" ] }, - "execution_count": 48, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -2186,7 +2293,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -2195,7 +2302,7 @@ "False" ] }, - "execution_count": 49, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -2206,7 +2313,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2228,7 +2335,7 @@ " 'Zero-one Loss']" ] }, - "execution_count": 50, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -2246,7 +2353,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -2255,7 +2362,7 @@ "True" ] }, - "execution_count": 51, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -2267,7 +2374,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2276,7 +2383,7 @@ "False" ] }, - "execution_count": 52, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -2317,7 +2424,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Comapre" + "### Compare" ] }, { @@ -2385,7 +2492,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -2395,7 +2502,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -2404,7 +2511,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -2426,7 +2533,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -2436,7 +2543,7 @@ " 'cm3': {'class': 0.33611, 'overall': 0.33056}}" ] }, - "execution_count": 56, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -2447,7 +2554,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -2456,7 +2563,7 @@ "['cm2', 'cm3']" ] }, - "execution_count": 57, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -2467,7 +2574,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -2476,7 +2583,7 @@ "pycm.ConfusionMatrix(classes: [0, 1, 2])" ] }, - "execution_count": 58, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -2487,7 +2594,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -2496,7 +2603,7 @@ "'cm2'" ] }, - "execution_count": 59, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -2507,7 +2614,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -2516,7 +2623,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -2538,7 +2645,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -2547,7 +2654,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -2569,7 +2676,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -2578,7 +2685,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -2607,7 +2714,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -2645,7 +2752,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### ConfusionMatrix" + "ConfusionMatrix" ] }, { @@ -2675,7 +2782,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Compare" + "Compare" ] }, { @@ -2739,7 +2846,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -2748,7 +2855,7 @@ "{'L1': 3, 'L2': 1, 'L3': 3}" ] }, - "execution_count": 67, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -2774,7 +2881,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -2783,7 +2890,7 @@ "{'L1': 7, 'L2': 8, 'L3': 4}" ] }, - "execution_count": 68, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -2809,7 +2916,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -2818,7 +2925,7 @@ "{'L1': 0, 'L2': 2, 'L3': 3}" ] }, - "execution_count": 69, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -2844,7 +2951,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -2853,7 +2960,7 @@ "{'L1': 2, 'L2': 1, 'L3': 2}" ] }, - "execution_count": 70, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -2886,7 +2993,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -2895,7 +3002,7 @@ "{'L1': 5, 'L2': 2, 'L3': 5}" ] }, - "execution_count": 71, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -2927,7 +3034,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -2936,7 +3043,7 @@ "{'L1': 7, 'L2': 10, 'L3': 7}" ] }, - "execution_count": 72, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -2968,7 +3075,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -2977,7 +3084,7 @@ "{'L1': 3, 'L2': 3, 'L3': 6}" ] }, - "execution_count": 73, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -3009,7 +3116,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -3018,7 +3125,7 @@ "{'L1': 9, 'L2': 9, 'L3': 6}" ] }, - "execution_count": 74, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -3050,7 +3157,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -3059,7 +3166,7 @@ "{'L1': 12, 'L2': 12, 'L3': 12}" ] }, - "execution_count": 75, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -3107,7 +3214,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -3116,7 +3223,7 @@ "{'L1': 0.6, 'L2': 0.5, 'L3': 0.6}" ] }, - "execution_count": 76, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -3150,7 +3257,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -3159,7 +3266,7 @@ "{'L1': 1.0, 'L2': 0.8, 'L3': 0.5714285714285714}" ] }, - "execution_count": 77, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -3194,7 +3301,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -3203,7 +3310,7 @@ "{'L1': 1.0, 'L2': 0.3333333333333333, 'L3': 0.5}" ] }, - "execution_count": 78, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -3238,7 +3345,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -3247,7 +3354,7 @@ "{'L1': 0.7777777777777778, 'L2': 0.8888888888888888, 'L3': 0.6666666666666666}" ] }, - "execution_count": 79, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -3281,7 +3388,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -3290,7 +3397,7 @@ "{'L1': 0.4, 'L2': 0.5, 'L3': 0.4}" ] }, - "execution_count": 80, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -3326,7 +3433,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -3335,7 +3442,7 @@ "{'L1': 0.0, 'L2': 0.19999999999999996, 'L3': 0.4285714285714286}" ] }, - "execution_count": 81, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -3369,7 +3476,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -3378,7 +3485,7 @@ "{'L1': 0.0, 'L2': 0.6666666666666667, 'L3': 0.5}" ] }, - "execution_count": 82, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -3412,7 +3519,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -3423,7 +3530,7 @@ " 'L3': 0.33333333333333337}" ] }, - "execution_count": 83, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -3457,7 +3564,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -3466,7 +3573,7 @@ "{'L1': 0.8333333333333334, 'L2': 0.75, 'L3': 0.5833333333333334}" ] }, - "execution_count": 84, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -3498,7 +3605,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -3507,7 +3614,7 @@ "{'L1': 0.16666666666666663, 'L2': 0.25, 'L3': 0.41666666666666663}" ] }, - "execution_count": 85, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -3551,7 +3658,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -3560,7 +3667,7 @@ "{'L1': 0.75, 'L2': 0.4, 'L3': 0.5454545454545454}" ] }, - "execution_count": 86, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -3571,7 +3678,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -3580,7 +3687,7 @@ "{'L1': 0.8823529411764706, 'L2': 0.35714285714285715, 'L3': 0.5172413793103449}" ] }, - "execution_count": 87, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -3591,7 +3698,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -3600,7 +3707,7 @@ "{'L1': 0.6521739130434783, 'L2': 0.45454545454545453, 'L3': 0.5769230769230769}" ] }, - "execution_count": 88, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -3611,7 +3718,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -3620,7 +3727,7 @@ "{'L1': 0.6144578313253012, 'L2': 0.4857142857142857, 'L3': 0.5930232558139535}" ] }, - "execution_count": 89, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -3693,7 +3800,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -3702,7 +3809,7 @@ "{'L1': 0.6831300510639732, 'L2': 0.25819888974716115, 'L3': 0.1690308509457033}" ] }, - "execution_count": 90, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -3736,7 +3843,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -3747,7 +3854,7 @@ " 'L3': 0.17142857142857126}" ] }, - "execution_count": 91, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -3779,7 +3886,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -3788,7 +3895,7 @@ "{'L1': 0.7777777777777777, 'L2': 0.2222222222222221, 'L3': 0.16666666666666652}" ] }, - "execution_count": 92, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -3824,7 +3931,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -3833,7 +3940,7 @@ "{'L1': 'None', 'L2': 2.5000000000000004, 'L3': 1.4}" ] }, - "execution_count": 93, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -3878,7 +3985,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -3887,7 +3994,7 @@ "{'L1': 0.4, 'L2': 0.625, 'L3': 0.7000000000000001}" ] }, - "execution_count": 94, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -3930,7 +4037,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -3939,7 +4046,7 @@ "{'L1': 'None', 'L2': 4.000000000000001, 'L3': 1.9999999999999998}" ] }, - "execution_count": 95, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -3973,7 +4080,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -3982,7 +4089,7 @@ "{'L1': 0.4166666666666667, 'L2': 0.16666666666666666, 'L3': 0.4166666666666667}" ] }, - "execution_count": 96, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -4016,7 +4123,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -4025,7 +4132,7 @@ "{'L1': 0.7745966692414834, 'L2': 0.408248290463863, 'L3': 0.5477225575051661}" ] }, - "execution_count": 97, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -4057,7 +4164,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -4068,7 +4175,7 @@ " 'L3': 0.20833333333333334}" ] }, - "execution_count": 98, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -4109,7 +4216,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -4120,7 +4227,7 @@ " 'L3': 0.21006944444444442}" ] }, - "execution_count": 99, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -4165,7 +4272,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -4174,7 +4281,7 @@ "{'L1': 0.6, 'L2': 0.25, 'L3': 0.375}" ] }, - "execution_count": 100, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -4216,7 +4323,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 105, "metadata": {}, "outputs": [ { @@ -4225,7 +4332,7 @@ "{'L1': 1.2630344058337937, 'L2': 0.9999999999999998, 'L3': 0.26303440583379367}" ] }, - "execution_count": 101, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -4282,7 +4389,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -4291,7 +4398,7 @@ "{'L1': 0.25, 'L2': 0.49657842846620864, 'L3': 0.6044162769630221}" ] }, - "execution_count": 102, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -4355,7 +4462,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -4364,7 +4471,7 @@ "{'L1': 0.2643856189774724, 'L2': 0.5, 'L3': 0.6875}" ] }, - "execution_count": 103, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -4408,7 +4515,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -4417,7 +4524,7 @@ "{'L1': 0.8, 'L2': 0.65, 'L3': 0.5857142857142856}" ] }, - "execution_count": 104, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -4459,7 +4566,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -4468,7 +4575,7 @@ "{'L1': 0.4, 'L2': 0.5385164807134504, 'L3': 0.5862367008195198}" ] }, - "execution_count": 105, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -4509,7 +4616,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -4518,7 +4625,7 @@ "{'L1': 0.717157287525381, 'L2': 0.6192113447068046, 'L3': 0.5854680534700882}" ] }, - "execution_count": 106, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } @@ -4576,7 +4683,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -4585,7 +4692,7 @@ "{'L1': 'None', 'L2': 0.33193306999649924, 'L3': 0.1659665349982495}" ] }, - "execution_count": 107, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -4635,7 +4742,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -4646,7 +4753,7 @@ " 'L3': 0.17142857142857126}" ] }, - "execution_count": 108, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -4710,7 +4817,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -4719,7 +4826,7 @@ "{'L1': 'None', 'L2': 'Poor', 'L3': 'Poor'}" ] }, - "execution_count": 109, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -4783,7 +4890,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 114, "metadata": {}, "outputs": [ { @@ -4792,7 +4899,7 @@ "{'L1': 'Poor', 'L2': 'Negligible', 'L3': 'Negligible'}" ] }, - "execution_count": 110, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -4856,7 +4963,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -4865,7 +4972,7 @@ "{'L1': 'None', 'L2': 'Poor', 'L3': 'Poor'}" ] }, - "execution_count": 111, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } @@ -4932,7 +5039,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -4941,7 +5048,7 @@ "{'L1': 'Very Good', 'L2': 'Fair', 'L3': 'Poor'}" ] }, - "execution_count": 112, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -5010,7 +5117,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -5019,7 +5126,7 @@ "{'L1': 'Moderate', 'L2': 'Negligible', 'L3': 'Negligible'}" ] }, - "execution_count": 113, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" } @@ -5092,7 +5199,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -5101,7 +5208,7 @@ "{'L1': 'None', 'L2': 'Moderate', 'L3': 'Weak'}" ] }, - "execution_count": 114, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -5146,7 +5253,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -5157,7 +5264,7 @@ " 'L3': 0.17142857142857126}" ] }, - "execution_count": 115, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" } @@ -5198,7 +5305,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -5207,7 +5314,7 @@ "{'L1': 2.4, 'L2': 2.0, 'L3': 1.2}" ] }, - "execution_count": 116, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -5248,7 +5355,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -5257,7 +5364,7 @@ "{'L1': -2, 'L2': 1, 'L3': 1}" ] }, - "execution_count": 117, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -5295,12 +5402,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "$$BCD=\\frac{|AM|}{\\sum_{i=1}^{|C|}\\Big(TOP_i+P_i\\Big)}$$" + "$$BCD=\\frac{|AM|}{\\sum_{i=1}^{|C|}\\Big(TOP_i+P_i\\Big)}=\\frac{|AM|}{2\\times POP}$$" ] }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 122, "metadata": {}, "outputs": [ { @@ -5311,7 +5418,7 @@ " 'L3': 0.041666666666666664}" ] }, - "execution_count": 118, + "execution_count": 122, "metadata": {}, "output_type": "execute_result" } @@ -5355,7 +5462,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -5364,7 +5471,7 @@ "{'L1': 0.5833333333333334, 'L2': 0.5192307692307692, 'L3': 0.5589430894308943}" ] }, - "execution_count": 119, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } @@ -5406,7 +5513,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -5415,7 +5522,7 @@ "{'L1': 0.36, 'L2': 0.27999999999999997, 'L3': 0.35265306122448975}" ] }, - "execution_count": 120, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -5426,7 +5533,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -5435,7 +5542,7 @@ "{'L1': 0.48, 'L2': 0.34, 'L3': 0.3477551020408163}" ] }, - "execution_count": 121, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } @@ -5446,7 +5553,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -5455,7 +5562,7 @@ "{'L1': 0.576, 'L2': 0.388, 'L3': 0.34383673469387754}" ] }, - "execution_count": 122, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -5524,7 +5631,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 127, "metadata": {}, "outputs": [ { @@ -5533,7 +5640,7 @@ "{'L1': 0.7745966692414834, 'L2': 0.6324555320336759, 'L3': 0.5855400437691198}" ] }, - "execution_count": 123, + "execution_count": 127, "metadata": {}, "output_type": "execute_result" } @@ -5585,7 +5692,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -5594,7 +5701,7 @@ "{'L1': 'None', 'L2': 0.6, 'L3': 0.3333333333333333}" ] }, - "execution_count": 124, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -5649,7 +5756,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 129, "metadata": {}, "outputs": [ { @@ -5658,7 +5765,7 @@ "{'L1': 0.8576400016262, 'L2': 0.708612108382005, 'L3': 0.5803410802752335}" ] }, - "execution_count": 125, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -5713,7 +5820,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -5722,7 +5829,7 @@ "{'L1': 0.7285871475307653, 'L2': 0.6286946134619315, 'L3': 0.610088876086563}" ] }, - "execution_count": 126, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -5765,7 +5872,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 131, "metadata": {}, "outputs": [ { @@ -5774,7 +5881,7 @@ "{'L1': 1.0, 'L2': 0.5, 'L3': 0.6}" ] }, - "execution_count": 127, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -5817,7 +5924,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 132, "metadata": {}, "outputs": [ { @@ -5826,7 +5933,7 @@ "{'L1': 0.7745966692414834, 'L2': 0.4082482904638631, 'L3': 0.5477225575051661}" ] }, - "execution_count": 128, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -5869,7 +5976,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 133, "metadata": {}, "outputs": [ { @@ -5878,7 +5985,7 @@ "{'L1': 0.42857142857142855, 'L2': 0.1111111111111111, 'L3': 0.1875}" ] }, - "execution_count": 129, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } @@ -5949,7 +6056,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -5958,7 +6065,7 @@ "{'L1': 0.8, 'L2': 0.41666666666666663, 'L3': 0.55}" ] }, - "execution_count": 130, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } @@ -6006,7 +6113,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 135, "metadata": {}, "outputs": [ { @@ -6017,7 +6124,7 @@ " 'L3': 0.10000000000000009}" ] }, - "execution_count": 131, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -6137,7 +6244,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -6148,7 +6255,7 @@ " 'L3': [0.21908902300206645, (0.17058551491594975, 1.0294144850840503)]}" ] }, - "execution_count": 132, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -6159,7 +6266,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 137, "metadata": {}, "outputs": [ { @@ -6170,7 +6277,7 @@ " 'L3': [0.21908902300206645, (-0.2769850810763853, 1.0769850810763852)]}" ] }, - "execution_count": 133, + "execution_count": 137, "metadata": {}, "output_type": "execute_result" } @@ -6181,7 +6288,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 138, "metadata": {}, "outputs": [ { @@ -6192,7 +6299,7 @@ " 'L3': [0.14231876063832774, (0.19325746190524654, 0.6804926643446272)]}" ] }, - "execution_count": 134, + "execution_count": 138, "metadata": {}, "output_type": "execute_result" } @@ -6203,7 +6310,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 139, "metadata": {}, "outputs": [ { @@ -6212,7 +6319,7 @@ "[0.14231876063832777, (0.2805568916340536, 0.8343177950165198)]" ] }, - "execution_count": 135, + "execution_count": 139, "metadata": {}, "output_type": "execute_result" } @@ -6223,7 +6330,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 140, "metadata": {}, "outputs": [ { @@ -6232,7 +6339,7 @@ "[0.14231876063832777, (0.30438856248221097, 0.8622781041844558)]" ] }, - "execution_count": 136, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" } @@ -6333,7 +6440,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -6342,7 +6449,7 @@ "{'L1': 0.25, 'L2': 0.0735, 'L3': 0.23525}" ] }, - "execution_count": 137, + "execution_count": 141, "metadata": {}, "output_type": "execute_result" } @@ -6406,7 +6513,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 142, "metadata": {}, "outputs": [ { @@ -6415,7 +6522,7 @@ "0.6111111111111112" ] }, - "execution_count": 138, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -6426,7 +6533,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -6435,7 +6542,7 @@ "0.5651515151515151" ] }, - "execution_count": 139, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -6446,7 +6553,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -6455,7 +6562,7 @@ "3.0000000000000004" ] }, - "execution_count": 140, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -6522,7 +6629,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -6531,7 +6638,7 @@ "0.6805555555555557" ] }, - "execution_count": 141, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -6542,7 +6649,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -6551,7 +6658,7 @@ "0.606439393939394" ] }, - "execution_count": 142, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -6562,7 +6669,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -6571,7 +6678,7 @@ "2.5714285714285716" ] }, - "execution_count": 143, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" } @@ -6582,7 +6689,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -6591,7 +6698,7 @@ "0.7152097902097903" ] }, - "execution_count": 144, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } @@ -6672,7 +6779,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -6681,7 +6788,7 @@ "{'L1': 'None', 'L2': 0.8416212335729143, 'L3': 0.4333594729285047}" ] }, - "execution_count": 145, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } @@ -6750,7 +6857,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -6759,7 +6866,7 @@ "0.35483870967741943" ] }, - "execution_count": 146, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -6802,7 +6909,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 151, "metadata": {}, "outputs": [ { @@ -6811,7 +6918,7 @@ "0.34426229508196726" ] }, - "execution_count": 147, + "execution_count": 151, "metadata": {}, "output_type": "execute_result" } @@ -6852,7 +6959,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -6861,7 +6968,7 @@ "0.16666666666666674" ] }, - "execution_count": 148, + "execution_count": 152, "metadata": {}, "output_type": "execute_result" } @@ -6923,7 +7030,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 153, "metadata": {}, "outputs": [ { @@ -6932,7 +7039,7 @@ "0.39130434782608675" ] }, - "execution_count": 149, + "execution_count": 153, "metadata": {}, "output_type": "execute_result" } @@ -6943,14 +7050,14 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 154, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.4-py3.5.egg\\pycm\\pycm_obj.py:791: 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-3.5-py3.5.egg\\pycm\\pycm_obj.py:800: RuntimeWarning: The weight format is wrong, the result is for unweighted kappa.\n" ] }, { @@ -6959,7 +7066,7 @@ "0.35483870967741943" ] }, - "execution_count": 150, + "execution_count": 154, "metadata": {}, "output_type": "execute_result" } @@ -7028,7 +7135,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 155, "metadata": {}, "outputs": [ { @@ -7037,7 +7144,7 @@ "0.2203645326012817" ] }, - "execution_count": 151, + "execution_count": 155, "metadata": {}, "output_type": "execute_result" } @@ -7078,7 +7185,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 156, "metadata": {}, "outputs": [ { @@ -7087,7 +7194,7 @@ "(-0.07707577422109269, 0.7867531935759315)" ] }, - "execution_count": 152, + "execution_count": 156, "metadata": {}, "output_type": "execute_result" } @@ -7137,7 +7244,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 157, "metadata": {}, "outputs": [ { @@ -7146,7 +7253,7 @@ "6.6000000000000005" ] }, - "execution_count": 153, + "execution_count": 157, "metadata": {}, "output_type": "execute_result" } @@ -7187,7 +7294,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 158, "metadata": {}, "outputs": [ { @@ -7196,7 +7303,7 @@ "4" ] }, - "execution_count": 154, + "execution_count": 158, "metadata": {}, "output_type": "execute_result" } @@ -7239,7 +7346,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 159, "metadata": {}, "outputs": [ { @@ -7248,7 +7355,7 @@ "0.55" ] }, - "execution_count": 155, + "execution_count": 159, "metadata": {}, "output_type": "execute_result" } @@ -7293,7 +7400,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 160, "metadata": {}, "outputs": [ { @@ -7302,7 +7409,7 @@ "0.5244044240850758" ] }, - "execution_count": 156, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" } @@ -7345,7 +7452,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 161, "metadata": {}, "outputs": [ { @@ -7354,7 +7461,7 @@ "0.14231876063832777" ] }, - "execution_count": 157, + "execution_count": 161, "metadata": {}, "output_type": "execute_result" } @@ -7397,7 +7504,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 162, "metadata": {}, "outputs": [ { @@ -7406,7 +7513,7 @@ "(0.30438856248221097, 0.8622781041844558)" ] }, - "execution_count": 158, + "execution_count": 162, "metadata": {}, "output_type": "execute_result" } @@ -7466,7 +7573,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 163, "metadata": {}, "outputs": [ { @@ -7475,7 +7582,7 @@ "0.37500000000000006" ] }, - "execution_count": 159, + "execution_count": 163, "metadata": {}, "output_type": "execute_result" } @@ -7528,7 +7635,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 164, "metadata": {}, "outputs": [ { @@ -7537,7 +7644,7 @@ "0.34426229508196726" ] }, - "execution_count": 160, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" } @@ -7592,7 +7699,7 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 165, "metadata": {}, "outputs": [ { @@ -7601,7 +7708,7 @@ "0.3893129770992367" ] }, - "execution_count": 161, + "execution_count": 165, "metadata": {}, "output_type": "execute_result" } @@ -7656,7 +7763,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 166, "metadata": {}, "outputs": [ { @@ -7665,7 +7772,7 @@ "1.4833557549816874" ] }, - "execution_count": 162, + "execution_count": 166, "metadata": {}, "output_type": "execute_result" } @@ -7720,7 +7827,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 167, "metadata": {}, "outputs": [ { @@ -7729,7 +7836,7 @@ "1.5" ] }, - "execution_count": 163, + "execution_count": 167, "metadata": {}, "output_type": "execute_result" } @@ -7793,7 +7900,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 168, "metadata": {}, "outputs": [ { @@ -7802,7 +7909,7 @@ "1.5833333333333335" ] }, - "execution_count": 164, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -7857,7 +7964,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 169, "metadata": {}, "outputs": [ { @@ -7866,7 +7973,7 @@ "2.4591479170272446" ] }, - "execution_count": 165, + "execution_count": 169, "metadata": {}, "output_type": "execute_result" } @@ -7923,7 +8030,7 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 170, "metadata": {}, "outputs": [ { @@ -7932,7 +8039,7 @@ "0.9757921620455572" ] }, - "execution_count": 166, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -7989,7 +8096,7 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 171, "metadata": {}, "outputs": [ { @@ -7998,7 +8105,7 @@ "0.09997757835164581" ] }, - "execution_count": 167, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } @@ -8070,7 +8177,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 172, "metadata": {}, "outputs": [ { @@ -8079,7 +8186,7 @@ "0.5242078379544428" ] }, - "execution_count": 168, + "execution_count": 172, "metadata": {}, "output_type": "execute_result" } @@ -8122,7 +8229,7 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 173, "metadata": {}, "outputs": [ { @@ -8131,7 +8238,7 @@ "0.42857142857142855" ] }, - "execution_count": 169, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -8174,7 +8281,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 174, "metadata": {}, "outputs": [ { @@ -8183,7 +8290,7 @@ "0.16666666666666666" ] }, - "execution_count": 170, + "execution_count": 174, "metadata": {}, "output_type": "execute_result" } @@ -8255,7 +8362,7 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 175, "metadata": {}, "outputs": [ { @@ -8264,7 +8371,7 @@ "'Fair'" ] }, - "execution_count": 171, + "execution_count": 175, "metadata": {}, "output_type": "execute_result" } @@ -8324,7 +8431,7 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 176, "metadata": {}, "outputs": [ { @@ -8333,7 +8440,7 @@ "'Poor'" ] }, - "execution_count": 172, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" } @@ -8401,7 +8508,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 177, "metadata": {}, "outputs": [ { @@ -8410,7 +8517,7 @@ "'Fair'" ] }, - "execution_count": 173, + "execution_count": 177, "metadata": {}, "output_type": "execute_result" } @@ -8474,7 +8581,7 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -8483,7 +8590,7 @@ "'Poor'" ] }, - "execution_count": 174, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } @@ -8555,7 +8662,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 179, "metadata": {}, "outputs": [ { @@ -8564,7 +8671,7 @@ "'Relatively Strong'" ] }, - "execution_count": 175, + "execution_count": 179, "metadata": {}, "output_type": "execute_result" } @@ -8633,7 +8740,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -8642,7 +8749,7 @@ "'Weak'" ] }, - "execution_count": 176, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } @@ -8699,7 +8806,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 181, "metadata": {}, "outputs": [ { @@ -8708,7 +8815,7 @@ "0.5833333333333334" ] }, - "execution_count": 177, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } @@ -8749,7 +8856,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 182, "metadata": {}, "outputs": [ { @@ -8758,7 +8865,7 @@ "0.3541666666666667" ] }, - "execution_count": 178, + "execution_count": 182, "metadata": {}, "output_type": "execute_result" } @@ -8799,7 +8906,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 183, "metadata": {}, "outputs": [ { @@ -8808,7 +8915,7 @@ "0.3645833333333333" ] }, - "execution_count": 179, + "execution_count": 183, "metadata": {}, "output_type": "execute_result" } @@ -8856,7 +8963,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 184, "metadata": {}, "outputs": [ { @@ -8865,7 +8972,7 @@ "0.5833333333333334" ] }, - "execution_count": 180, + "execution_count": 184, "metadata": {}, "output_type": "execute_result" } @@ -8913,7 +9020,7 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 185, "metadata": {}, "outputs": [ { @@ -8922,7 +9029,7 @@ "0.5833333333333334" ] }, - "execution_count": 181, + "execution_count": 185, "metadata": {}, "output_type": "execute_result" } @@ -8963,7 +9070,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 186, "metadata": {}, "outputs": [ { @@ -8972,7 +9079,7 @@ "0.7916666666666666" ] }, - "execution_count": 182, + "execution_count": 186, "metadata": {}, "output_type": "execute_result" } @@ -9013,7 +9120,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 187, "metadata": {}, "outputs": [ { @@ -9022,7 +9129,7 @@ "0.20833333333333337" ] }, - "execution_count": 183, + "execution_count": 187, "metadata": {}, "output_type": "execute_result" } @@ -9063,7 +9170,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 188, "metadata": {}, "outputs": [ { @@ -9072,7 +9179,7 @@ "0.41666666666666663" ] }, - "execution_count": 184, + "execution_count": 188, "metadata": {}, "output_type": "execute_result" } @@ -9120,7 +9227,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 189, "metadata": {}, "outputs": [ { @@ -9129,7 +9236,7 @@ "0.5833333333333334" ] }, - "execution_count": 185, + "execution_count": 189, "metadata": {}, "output_type": "execute_result" } @@ -9170,7 +9277,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 190, "metadata": {}, "outputs": [ { @@ -9179,7 +9286,7 @@ "0.611111111111111" ] }, - "execution_count": 186, + "execution_count": 190, "metadata": {}, "output_type": "execute_result" } @@ -9220,7 +9327,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 191, "metadata": {}, "outputs": [ { @@ -9229,7 +9336,7 @@ "0.5666666666666668" ] }, - "execution_count": 187, + "execution_count": 191, "metadata": {}, "output_type": "execute_result" } @@ -9270,7 +9377,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 192, "metadata": {}, "outputs": [ { @@ -9279,7 +9386,7 @@ "0.7904761904761904" ] }, - "execution_count": 188, + "execution_count": 192, "metadata": {}, "output_type": "execute_result" } @@ -9320,7 +9427,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 193, "metadata": {}, "outputs": [ { @@ -9329,7 +9436,7 @@ "0.20952380952380956" ] }, - "execution_count": 189, + "execution_count": 193, "metadata": {}, "output_type": "execute_result" } @@ -9370,7 +9477,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 194, "metadata": {}, "outputs": [ { @@ -9379,7 +9486,7 @@ "0.43333333333333324" ] }, - "execution_count": 190, + "execution_count": 194, "metadata": {}, "output_type": "execute_result" } @@ -9420,7 +9527,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 195, "metadata": {}, "outputs": [ { @@ -9429,7 +9536,7 @@ "0.5651515151515151" ] }, - "execution_count": 191, + "execution_count": 195, "metadata": {}, "output_type": "execute_result" } @@ -9470,7 +9577,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 196, "metadata": {}, "outputs": [ { @@ -9479,7 +9586,7 @@ "0.7222222222222223" ] }, - "execution_count": 192, + "execution_count": 196, "metadata": {}, "output_type": "execute_result" } @@ -9534,7 +9641,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 197, "metadata": {}, "outputs": [ { @@ -9543,7 +9650,7 @@ "(1.225, 0.4083333333333334)" ] }, - "execution_count": 193, + "execution_count": 197, "metadata": {}, "output_type": "execute_result" } @@ -9584,7 +9691,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 198, "metadata": {}, "outputs": [ { @@ -9593,7 +9700,7 @@ "0.41666666666666663" ] }, - "execution_count": 194, + "execution_count": 198, "metadata": {}, "output_type": "execute_result" } @@ -9634,7 +9741,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 199, "metadata": {}, "outputs": [ { @@ -9643,7 +9750,7 @@ "5" ] }, - "execution_count": 195, + "execution_count": 199, "metadata": {}, "output_type": "execute_result" } @@ -9684,7 +9791,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 200, "metadata": {}, "outputs": [ { @@ -9693,7 +9800,7 @@ "0.4166666666666667" ] }, - "execution_count": 196, + "execution_count": 200, "metadata": {}, "output_type": "execute_result" } @@ -9761,7 +9868,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 201, "metadata": {}, "outputs": [ { @@ -9770,7 +9877,7 @@ "0.18926430237560654" ] }, - "execution_count": 197, + "execution_count": 201, "metadata": {}, "output_type": "execute_result" } @@ -9818,7 +9925,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 202, "metadata": {}, "outputs": [ { @@ -9827,7 +9934,7 @@ "0.4638112995385119" ] }, - "execution_count": 198, + "execution_count": 202, "metadata": {}, "output_type": "execute_result" } @@ -9882,7 +9989,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 203, "metadata": {}, "outputs": [ { @@ -9891,7 +9998,7 @@ "0.5189369467580801" ] }, - "execution_count": 199, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } @@ -9955,7 +10062,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 204, "metadata": {}, "outputs": [ { @@ -9964,7 +10071,7 @@ "0.36666666666666664" ] }, - "execution_count": 200, + "execution_count": 204, "metadata": {}, "output_type": "execute_result" } @@ -10005,7 +10112,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 205, "metadata": {}, "outputs": [ { @@ -10014,7 +10121,7 @@ "4.0" ] }, - "execution_count": 201, + "execution_count": 205, "metadata": {}, "output_type": "execute_result" } @@ -10057,7 +10164,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 206, "metadata": {}, "outputs": [ { @@ -10066,7 +10173,7 @@ "0.4777777777777778" ] }, - "execution_count": 202, + "execution_count": 206, "metadata": {}, "output_type": "execute_result" } @@ -10107,7 +10214,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 207, "metadata": {}, "outputs": [ { @@ -10116,7 +10223,7 @@ "0.6785714285714285" ] }, - "execution_count": 203, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" } @@ -10157,7 +10264,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 208, "metadata": {}, "outputs": [ { @@ -10166,7 +10273,7 @@ "0.6857142857142857" ] }, - "execution_count": 204, + "execution_count": 208, "metadata": {}, "output_type": "execute_result" } @@ -10229,7 +10336,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 209, "metadata": {}, "outputs": [ { @@ -10238,7 +10345,7 @@ "0.3533932006492363" ] }, - "execution_count": 205, + "execution_count": 209, "metadata": {}, "output_type": "execute_result" } @@ -10279,7 +10386,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 210, "metadata": {}, "outputs": [ { @@ -10288,7 +10395,7 @@ "0.5956833971812706" ] }, - "execution_count": 206, + "execution_count": 210, "metadata": {}, "output_type": "execute_result" } @@ -10330,7 +10437,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 211, "metadata": {}, "outputs": [ { @@ -10339,7 +10446,7 @@ "0.1777777777777778" ] }, - "execution_count": 207, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" } @@ -10391,7 +10498,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 212, "metadata": {}, "outputs": [ { @@ -10400,7 +10507,7 @@ "0.09206349206349207" ] }, - "execution_count": 208, + "execution_count": 212, "metadata": {}, "output_type": "execute_result" } @@ -10452,7 +10559,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 213, "metadata": {}, "outputs": [ { @@ -10461,7 +10568,7 @@ "0.37254901960784315" ] }, - "execution_count": 209, + "execution_count": 213, "metadata": {}, "output_type": "execute_result" } @@ -10526,7 +10633,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 214, "metadata": {}, "outputs": [ { @@ -10535,7 +10642,7 @@ "0.3715846994535519" ] }, - "execution_count": 210, + "execution_count": 214, "metadata": {}, "output_type": "execute_result" } @@ -10611,7 +10718,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 215, "metadata": {}, "outputs": [ { @@ -10620,7 +10727,7 @@ "0.374757281553398" ] }, - "execution_count": 211, + "execution_count": 215, "metadata": {}, "output_type": "execute_result" } @@ -10631,14 +10738,14 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 216, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Sepkjaer\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm-3.4-py3.5.egg\\pycm\\pycm_obj.py:813: 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-3.5-py3.5.egg\\pycm\\pycm_obj.py:822: RuntimeWarning: The weight format is wrong, the result is for unweighted alpha.\n" ] }, { @@ -10647,7 +10754,7 @@ "0.3715846994535519" ] }, - "execution_count": 212, + "execution_count": 216, "metadata": {}, "output_type": "execute_result" } @@ -10751,7 +10858,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 217, "metadata": {}, "outputs": [ { @@ -10760,7 +10867,7 @@ "0.38540577344968524" ] }, - "execution_count": 213, + "execution_count": 217, "metadata": {}, "output_type": "execute_result" } @@ -10771,7 +10878,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 218, "metadata": {}, "outputs": [ { @@ -10780,7 +10887,7 @@ "0.38545857383594895" ] }, - "execution_count": 214, + "execution_count": 218, "metadata": {}, "output_type": "execute_result" } @@ -10859,7 +10966,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 219, "metadata": {}, "outputs": [ { @@ -10868,7 +10975,7 @@ "0.03749999999999999" ] }, - "execution_count": 215, + "execution_count": 219, "metadata": {}, "output_type": "execute_result" } @@ -10880,7 +10987,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 220, "metadata": {}, "outputs": [ { @@ -10889,7 +10996,7 @@ "0.6875" ] }, - "execution_count": 216, + "execution_count": 220, "metadata": {}, "output_type": "execute_result" } @@ -10969,7 +11076,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 221, "metadata": {}, "outputs": [ { @@ -11135,7 +11242,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 222, "metadata": {}, "outputs": [ { @@ -11160,7 +11267,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 223, "metadata": {}, "outputs": [ { @@ -11171,7 +11278,7 @@ " 'L3': {'L1': 0, 'L2': 2, 'L3': 3}}" ] }, - "execution_count": 219, + "execution_count": 223, "metadata": {}, "output_type": "execute_result" } @@ -11182,7 +11289,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 224, "metadata": {}, "outputs": [ { @@ -11205,7 +11312,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 225, "metadata": {}, "outputs": [], "source": [ @@ -11214,7 +11321,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 226, "metadata": {}, "outputs": [ { @@ -11287,7 +11394,7 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 227, "metadata": {}, "outputs": [ { @@ -11312,7 +11419,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 228, "metadata": {}, "outputs": [ { @@ -11323,7 +11430,7 @@ " 'L3': {'L1': 0.0, 'L2': 0.4, 'L3': 0.6}}" ] }, - "execution_count": 224, + "execution_count": 228, "metadata": {}, "output_type": "execute_result" } @@ -11334,7 +11441,7 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 229, "metadata": {}, "outputs": [ { @@ -11357,7 +11464,7 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 230, "metadata": {}, "outputs": [ { @@ -11430,7 +11537,7 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 231, "metadata": {}, "outputs": [ { @@ -11577,7 +11684,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 232, "metadata": {}, "outputs": [ { @@ -11604,7 +11711,7 @@ }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 233, "metadata": {}, "outputs": [ { @@ -11631,7 +11738,7 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 234, "metadata": {}, "outputs": [ { @@ -11739,7 +11846,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 235, "metadata": {}, "outputs": [ { @@ -11761,7 +11868,7 @@ }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 236, "metadata": {}, "outputs": [ { @@ -11790,7 +11897,7 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 237, "metadata": {}, "outputs": [], "source": [ @@ -11808,7 +11915,7 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 238, "metadata": {}, "outputs": [ { @@ -11818,7 +11925,7 @@ " 'Status': True}" ] }, - "execution_count": 234, + "execution_count": 238, "metadata": {}, "output_type": "execute_result" } @@ -11836,7 +11943,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 239, "metadata": {}, "outputs": [ { @@ -11846,7 +11953,7 @@ " 'Status': True}" ] }, - "execution_count": 235, + "execution_count": 239, "metadata": {}, "output_type": "execute_result" } @@ -11864,7 +11971,7 @@ }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 240, "metadata": {}, "outputs": [ { @@ -11874,7 +11981,7 @@ " 'Status': True}" ] }, - "execution_count": 236, + "execution_count": 240, "metadata": {}, "output_type": "execute_result" } @@ -11892,7 +11999,7 @@ }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 241, "metadata": {}, "outputs": [ { @@ -11902,7 +12009,7 @@ " 'Status': True}" ] }, - "execution_count": 237, + "execution_count": 241, "metadata": {}, "output_type": "execute_result" } @@ -11920,7 +12027,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 242, "metadata": {}, "outputs": [ { @@ -11930,7 +12037,7 @@ " 'Status': True}" ] }, - "execution_count": 238, + "execution_count": 242, "metadata": {}, "output_type": "execute_result" } @@ -11948,7 +12055,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 243, "metadata": {}, "outputs": [ { @@ -11958,7 +12065,7 @@ " 'Status': False}" ] }, - "execution_count": 239, + "execution_count": 243, "metadata": {}, "output_type": "execute_result" } @@ -12041,7 +12148,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 244, "metadata": {}, "outputs": [ { @@ -12051,7 +12158,7 @@ " 'Status': True}" ] }, - "execution_count": 240, + "execution_count": 244, "metadata": {}, "output_type": "execute_result" } @@ -12069,7 +12176,7 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 245, "metadata": {}, "outputs": [ { @@ -12079,7 +12186,7 @@ " 'Status': True}" ] }, - "execution_count": 241, + "execution_count": 245, "metadata": {}, "output_type": "execute_result" } @@ -12097,7 +12204,7 @@ }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 246, "metadata": {}, "outputs": [ { @@ -12107,7 +12214,7 @@ " 'Status': True}" ] }, - "execution_count": 242, + "execution_count": 246, "metadata": {}, "output_type": "execute_result" } @@ -12125,7 +12232,7 @@ }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 247, "metadata": {}, "outputs": [ { @@ -12135,7 +12242,7 @@ " 'Status': True}" ] }, - "execution_count": 243, + "execution_count": 247, "metadata": {}, "output_type": "execute_result" } @@ -12153,7 +12260,7 @@ }, { "cell_type": "code", - "execution_count": 244, + "execution_count": 248, "metadata": {}, "outputs": [ { @@ -12163,7 +12270,7 @@ " 'Status': True}" ] }, - "execution_count": 244, + "execution_count": 248, "metadata": {}, "output_type": "execute_result" } @@ -12181,7 +12288,7 @@ }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 249, "metadata": {}, "outputs": [ { @@ -12191,7 +12298,7 @@ " 'Status': True}" ] }, - "execution_count": 245, + "execution_count": 249, "metadata": {}, "output_type": "execute_result" } @@ -12209,7 +12316,7 @@ }, { "cell_type": "code", - "execution_count": 246, + "execution_count": 250, "metadata": {}, "outputs": [ { @@ -12219,7 +12326,7 @@ " 'Status': True}" ] }, - "execution_count": 246, + "execution_count": 250, "metadata": {}, "output_type": "execute_result" } @@ -12237,7 +12344,7 @@ }, { "cell_type": "code", - "execution_count": 247, + "execution_count": 251, "metadata": {}, "outputs": [ { @@ -12247,7 +12354,7 @@ " 'Status': False}" ] }, - "execution_count": 247, + "execution_count": 251, "metadata": {}, "output_type": "execute_result" } @@ -12360,7 +12467,7 @@ }, { "cell_type": "code", - "execution_count": 248, + "execution_count": 252, "metadata": {}, "outputs": [ { @@ -12370,7 +12477,7 @@ " 'Status': True}" ] }, - "execution_count": 248, + "execution_count": 252, "metadata": {}, "output_type": "execute_result" } @@ -12390,7 +12497,7 @@ }, { "cell_type": "code", - "execution_count": 249, + "execution_count": 253, "metadata": {}, "outputs": [ { @@ -12400,7 +12507,7 @@ " 'Status': True}" ] }, - "execution_count": 249, + "execution_count": 253, "metadata": {}, "output_type": "execute_result" } @@ -12420,7 +12527,7 @@ }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 254, "metadata": {}, "outputs": [ { @@ -12430,7 +12537,7 @@ " 'Status': True}" ] }, - "execution_count": 250, + "execution_count": 254, "metadata": {}, "output_type": "execute_result" } @@ -12450,7 +12557,7 @@ }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 255, "metadata": {}, "outputs": [ { @@ -12460,7 +12567,7 @@ " 'Status': True}" ] }, - "execution_count": 251, + "execution_count": 255, "metadata": {}, "output_type": "execute_result" } @@ -12480,7 +12587,7 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 256, "metadata": {}, "outputs": [ { @@ -12490,7 +12597,7 @@ " 'Status': True}" ] }, - "execution_count": 252, + "execution_count": 256, "metadata": {}, "output_type": "execute_result" } @@ -12510,7 +12617,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 257, "metadata": {}, "outputs": [ { @@ -12520,7 +12627,7 @@ " 'Status': True}" ] }, - "execution_count": 253, + "execution_count": 257, "metadata": {}, "output_type": "execute_result" } @@ -12538,7 +12645,7 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 258, "metadata": {}, "outputs": [ { @@ -12548,7 +12655,7 @@ " 'Status': False}" ] }, - "execution_count": 254, + "execution_count": 258, "metadata": {}, "output_type": "execute_result" } @@ -12641,7 +12748,7 @@ }, { "cell_type": "code", - "execution_count": 255, + "execution_count": 259, "metadata": {}, "outputs": [ { @@ -12651,7 +12758,7 @@ " 'Status': True}" ] }, - "execution_count": 255, + "execution_count": 259, "metadata": {}, "output_type": "execute_result" } @@ -12669,7 +12776,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 260, "metadata": {}, "outputs": [ { @@ -12679,7 +12786,7 @@ " 'Status': True}" ] }, - "execution_count": 256, + "execution_count": 260, "metadata": {}, "output_type": "execute_result" } @@ -12697,7 +12804,7 @@ }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 261, "metadata": {}, "outputs": [ { @@ -12707,7 +12814,7 @@ " 'Status': True}" ] }, - "execution_count": 257, + "execution_count": 261, "metadata": {}, "output_type": "execute_result" } @@ -12725,7 +12832,7 @@ }, { "cell_type": "code", - "execution_count": 258, + "execution_count": 262, "metadata": {}, "outputs": [ { @@ -12735,7 +12842,7 @@ " 'Status': False}" ] }, - "execution_count": 258, + "execution_count": 262, "metadata": {}, "output_type": "execute_result" } @@ -12797,7 +12904,7 @@ }, { "cell_type": "code", - "execution_count": 259, + "execution_count": 263, "metadata": {}, "outputs": [ { @@ -12807,7 +12914,7 @@ " 'Status': True}" ] }, - "execution_count": 259, + "execution_count": 263, "metadata": {}, "output_type": "execute_result" } @@ -12825,7 +12932,7 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 264, "metadata": {}, "outputs": [ { @@ -12835,7 +12942,7 @@ " 'Status': False}" ] }, - "execution_count": 260, + "execution_count": 264, "metadata": {}, "output_type": "execute_result" } @@ -12877,7 +12984,7 @@ }, { "cell_type": "code", - "execution_count": 261, + "execution_count": 265, "metadata": {}, "outputs": [ { @@ -12897,7 +13004,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": 266, "metadata": { "scrolled": true }, @@ -12919,7 +13026,7 @@ }, { "cell_type": "code", - "execution_count": 263, + "execution_count": 267, "metadata": {}, "outputs": [ { @@ -12939,7 +13046,7 @@ }, { "cell_type": "code", - "execution_count": 264, + "execution_count": 268, "metadata": {}, "outputs": [ { @@ -12959,7 +13066,7 @@ }, { "cell_type": "code", - "execution_count": 265, + "execution_count": 269, "metadata": {}, "outputs": [ { @@ -12979,7 +13086,7 @@ }, { "cell_type": "code", - "execution_count": 266, + "execution_count": 270, "metadata": {}, "outputs": [ { @@ -12999,7 +13106,7 @@ }, { "cell_type": "code", - "execution_count": 267, + "execution_count": 271, "metadata": {}, "outputs": [ { @@ -13019,7 +13126,7 @@ }, { "cell_type": "code", - "execution_count": 268, + "execution_count": 272, "metadata": {}, "outputs": [ { @@ -13039,7 +13146,7 @@ }, { "cell_type": "code", - "execution_count": 269, + "execution_count": 273, "metadata": {}, "outputs": [ { @@ -13059,7 +13166,7 @@ }, { "cell_type": "code", - "execution_count": 270, + "execution_count": 274, "metadata": {}, "outputs": [ { @@ -13079,7 +13186,7 @@ }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 275, "metadata": {}, "outputs": [ { @@ -13099,7 +13206,7 @@ }, { "cell_type": "code", - "execution_count": 272, + "execution_count": 276, "metadata": {}, "outputs": [ { @@ -13119,7 +13226,7 @@ }, { "cell_type": "code", - "execution_count": 273, + "execution_count": 277, "metadata": {}, "outputs": [ { @@ -13139,7 +13246,7 @@ }, { "cell_type": "code", - "execution_count": 274, + "execution_count": 278, "metadata": {}, "outputs": [ { @@ -13159,7 +13266,7 @@ }, { "cell_type": "code", - "execution_count": 275, + "execution_count": 279, "metadata": {}, "outputs": [ { @@ -13180,7 +13287,7 @@ }, { "cell_type": "code", - "execution_count": 276, + "execution_count": 280, "metadata": {}, "outputs": [ { @@ -13200,7 +13307,7 @@ }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 281, "metadata": {}, "outputs": [ { @@ -13220,7 +13327,7 @@ }, { "cell_type": "code", - "execution_count": 278, + "execution_count": 282, "metadata": {}, "outputs": [ { @@ -13240,7 +13347,7 @@ }, { "cell_type": "code", - "execution_count": 279, + "execution_count": 283, "metadata": {}, "outputs": [ { @@ -13260,7 +13367,7 @@ }, { "cell_type": "code", - "execution_count": 280, + "execution_count": 284, "metadata": {}, "outputs": [ { @@ -13280,7 +13387,7 @@ }, { "cell_type": "code", - "execution_count": 281, + "execution_count": 285, "metadata": {}, "outputs": [ { @@ -13300,7 +13407,7 @@ }, { "cell_type": "code", - "execution_count": 282, + "execution_count": 286, "metadata": {}, "outputs": [ { @@ -13320,7 +13427,7 @@ }, { "cell_type": "code", - "execution_count": 283, + "execution_count": 287, "metadata": {}, "outputs": [ { diff --git a/Document/Document_Files/cm1.html b/Document/Document_Files/cm1.html index daecd4ce..af8f9502 100644 --- a/Document/Document_Files/cm1.html +++ b/Document/Document_Files/cm1.html @@ -749,6 +749,6 @@

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Generated By PyCM Version 3.4

+

Generated By PyCM Version 3.5

diff --git a/Document/Example4.ipynb b/Document/Example4.ipynb index c19ee044..557341c9 100644 --- a/Document/Example4.ipynb +++ b/Document/Example4.ipynb @@ -542,7 +542,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{\"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"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]]]], \"Digit\": 5, \"Imbalanced\": true, \"Sample-Weight\": null, \"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}\n" + "{\"Sample-Weight\": null, \"Transpose\": false, \"Prob-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]]]], \"Imbalanced\": true, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Digit\": 5}\n" ] } ], @@ -559,7 +559,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{\"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"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]]]], \"Overall-Stat\": {\"Kappa Unbiased\": -0.12554112554112543, \"AUNU\": \"None\", \"NIR\": 0.8, \"Kappa\": 0.07801418439716304, \"Gwet AC1\": 0.19504643962848295, \"Overall MCC\": 0.1264200803632855, \"P-Value\": 0.9999981549942787, \"Phi-Squared\": \"None\", \"Overall RACCU\": 0.42249999999999993, \"Lambda B\": 0.0, \"FNR Macro\": \"None\", \"TNR Micro\": 0.7833333333333333, \"SOA5(Cramer)\": \"None\", \"SOA3(Altman)\": \"Poor\", \"ACC Macro\": 0.675, \"FNR Micro\": 0.65, \"Cross Entropy\": 1.709947752496911, \"Conditional Entropy\": 1.235789374242786, \"RR\": 5.0, \"Krippendorff Alpha\": -0.09740259740259723, \"PPV Macro\": \"None\", \"Response Entropy\": 1.3366664819166876, \"SOA4(Cicchetti)\": \"Poor\", \"AUNP\": \"None\", \"Joint Entropy\": 2.119973094021975, \"SOA2(Fleiss)\": \"Poor\", \"Overall MCEN\": 0.3746281299595305, \"Standard Error\": 0.1066536450385077, \"Chi-Squared DF\": 9, \"KL Divergence\": \"None\", \"Zero-one Loss\": 13, \"CBA\": 0.17708333333333331, \"Kappa 95% CI\": [-0.21849807698648957, 0.3745264457808156], \"F1 Micro\": 0.35, \"Kappa No Prevalence\": -0.30000000000000004, \"PPV Micro\": 0.35, \"Overall CEN\": 0.3648028121279775, \"95% CI\": [0.14095885572452488, 0.559041144275475], \"FPR Micro\": 0.21666666666666667, \"TNR Macro\": 0.7852941176470588, \"TPR Micro\": 0.35, \"Cramer V\": \"None\", \"RCI\": 0.11409066398451011, \"Kappa Standard Error\": 0.15128176601206766, \"Hamming Loss\": 0.65, \"SOA6(Matthews)\": \"Negligible\", \"SOA1(Landis & Koch)\": \"Slight\", \"CSI\": \"None\", \"Scott PI\": -0.12554112554112543, \"Overall ACC\": 0.35, \"Overall J\": [0.6029411764705883, 0.15073529411764708], \"Bangdiwala B\": 0.3135593220338983, \"Reference Entropy\": 0.8841837197791889, \"Overall RACC\": 0.29500000000000004, \"Lambda A\": 0.0, \"Chi-Squared\": \"None\", \"ARI\": 0.02298247455136956, \"TPR Macro\": \"None\", \"FPR Macro\": 0.2147058823529412, \"Bennett S\": 0.1333333333333333, \"F1 Macro\": 0.23043478260869565, \"Mutual Information\": 0.10087710767390168, \"Pearson C\": \"None\"}, \"Digit\": 5, \"Imbalanced\": true, \"Sample-Weight\": null, \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Transpose\": false, \"Class-Stat\": {\"FDR\": {\"200\": 0.1428571428571429, \"500\": 0.5, \"100\": 1.0, \"600\": \"None\"}, \"F1\": {\"200\": 0.5217391304347826, \"500\": 0.4, \"100\": 0.0, \"600\": 0.0}, \"RACC\": {\"200\": 0.28, \"500\": 0.015, \"100\": 0.0, \"600\": 0.0}, \"MCEN\": {\"200\": 0.3739448088748241, \"500\": 0.5802792108518123, \"100\": 0.3349590631259315, \"600\": 0.0}, \"sInd\": {\"200\": 0.5240141808835057, \"500\": 0.5267639848569737, \"100\": \"None\", \"600\": 0.29289321881345254}, \"MK\": {\"200\": 0.08791208791208782, \"500\": 0.38888888888888884, \"100\": 0.0, \"600\": \"None\"}, \"N\": {\"200\": 4, \"500\": 17, \"100\": 20, \"600\": 19}, \"dInd\": {\"200\": 0.673145600891813, \"500\": 0.6692567908186672, \"100\": \"None\", \"600\": 1.0}, \"AUC\": {\"200\": 0.5625, \"500\": 0.6372549019607843, \"100\": \"None\", \"600\": 0.5}, \"DOR\": {\"200\": 1.7999999999999998, \"500\": 7.999999999999997, \"100\": \"None\", \"600\": \"None\"}, \"TN\": {\"200\": 3, \"100\": 9, \"500\": 16, \"600\": 19}, \"AGF\": {\"200\": 0.33642097801219245, \"500\": 0.5665926996700735, \"100\": 0.0, \"600\": 0.0}, \"FPR\": {\"200\": 0.25, \"500\": 0.05882352941176472, \"100\": 0.55, \"600\": 0.0}, \"F0.5\": {\"200\": 0.6818181818181818, \"500\": 0.45454545454545453, \"100\": 0.0, \"600\": 0.0}, \"Y\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"F2\": {\"200\": 0.4225352112676056, \"500\": 0.35714285714285715, \"100\": 0.0, \"600\": 0.0}, \"GM\": {\"200\": 0.5303300858899106, \"500\": 0.5601120336112039, \"100\": \"None\", \"600\": 0.0}, \"OOC\": {\"200\": 0.5669467095138409, \"500\": 0.4082482904638631, \"100\": \"None\", \"600\": \"None\"}, \"GI\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"BM\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"TON\": {\"200\": 13, \"500\": 18, \"100\": 9, \"600\": 20}, \"LS\": {\"200\": 1.0714285714285714, \"500\": 3.3333333333333335, \"100\": \"None\", \"600\": \"None\"}, \"TOP\": {\"200\": 7, \"500\": 2, \"100\": 11, \"600\": 0}, \"G\": {\"200\": 0.5669467095138409, \"500\": 0.408248290463863, \"100\": \"None\", \"600\": \"None\"}, \"ICSI\": {\"200\": 0.2321428571428572, \"500\": -0.16666666666666674, \"100\": \"None\", \"600\": \"None\"}, \"FOR\": {\"200\": 0.7692307692307692, \"500\": 0.11111111111111116, \"100\": 0.0, \"600\": 0.050000000000000044}, \"MCCI\": {\"200\": \"Negligible\", \"500\": \"Weak\", \"100\": \"None\", \"600\": \"None\"}, \"POP\": {\"200\": 20, \"500\": 20, \"100\": 20, \"600\": 20}, \"FP\": {\"200\": 1, \"100\": 11, \"500\": 1, \"600\": 0}, \"PRE\": {\"200\": 0.8, \"500\": 0.15, \"100\": 0.0, \"600\": 0.05}, \"NLRI\": {\"200\": \"Negligible\", \"500\": \"Negligible\", \"100\": \"None\", \"600\": \"Negligible\"}, \"AUPR\": {\"200\": 0.6160714285714286, \"500\": 0.41666666666666663, \"100\": \"None\", \"600\": \"None\"}, \"Q\": {\"200\": 0.28571428571428575, \"500\": 0.7777777777777778, \"100\": \"None\", \"600\": \"None\"}, \"PLR\": {\"200\": 1.5, \"500\": 5.666666666666665, \"100\": \"None\", \"600\": \"None\"}, \"BCD\": {\"200\": 0.225, \"500\": 0.025, \"100\": 0.275, \"600\": 0.025}, \"AGM\": {\"200\": 0.5669417382415922, \"500\": 0.7351956938438939, \"100\": \"None\", \"600\": 0}, \"DP\": {\"200\": 0.1407391082701595, \"500\": 0.49789960499474867, \"100\": \"None\", \"600\": \"None\"}, \"PLRI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"None\"}, \"J\": {\"200\": 0.35294117647058826, \"500\": 0.25, \"100\": 0.0, \"600\": 0.0}, \"DPI\": {\"200\": \"Poor\", \"500\": \"Poor\", \"100\": \"None\", \"600\": \"None\"}, \"P\": {\"200\": 16, \"500\": 3, \"100\": 0, \"600\": 1}, \"AM\": {\"200\": -9, \"500\": -1, \"100\": 11, \"600\": -1}, \"OP\": {\"200\": 0.1166666666666667, \"500\": 0.373076923076923, \"100\": \"None\", \"600\": -0.050000000000000044}, \"PPV\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": 0.0, \"600\": \"None\"}, \"OC\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": \"None\", \"600\": \"None\"}, \"NLR\": {\"200\": 0.8333333333333334, \"500\": 0.7083333333333334, \"100\": \"None\", \"600\": 1.0}, \"CEN\": {\"200\": 0.3570795472009597, \"500\": 0.5389466410223563, \"100\": 0.3349590631259315, \"600\": 0.0}, \"IS\": {\"200\": 0.09953567355091428, \"500\": 1.736965594166206, \"100\": \"None\", \"600\": \"None\"}, \"FN\": {\"200\": 10, \"100\": 0, \"500\": 2, \"600\": 1}, \"IBA\": {\"200\": 0.17578125, \"500\": 0.1230296039984621, \"100\": \"None\", \"600\": 0.0}, \"MCC\": {\"200\": 0.10482848367219183, \"500\": 0.32673201960653564, \"100\": \"None\", \"600\": \"None\"}, \"NPV\": {\"200\": 0.23076923076923078, \"500\": 0.8888888888888888, \"100\": 1.0, \"600\": 0.95}, \"TPR\": {\"200\": 0.375, \"500\": 0.3333333333333333, \"100\": \"None\", \"600\": 0.0}, \"QI\": {\"200\": \"Weak\", \"500\": \"Strong\", \"100\": \"None\", \"600\": \"None\"}, \"RACCU\": {\"200\": 0.33062499999999995, \"500\": 0.015625, \"100\": 0.07562500000000001, \"600\": 0.0006250000000000001}, \"FNR\": {\"200\": 0.625, \"500\": 0.6666666666666667, \"100\": \"None\", \"600\": 1.0}, \"AUCI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"Poor\"}, \"TNR\": {\"200\": 0.75, \"500\": 0.9411764705882353, \"100\": 0.45, \"600\": 1.0}, \"ERR\": {\"200\": 0.55, \"500\": 0.15000000000000002, \"100\": 0.55, \"600\": 0.050000000000000044}, \"ACC\": {\"200\": 0.45, \"500\": 0.85, \"100\": 0.45, \"600\": 0.95}, \"TP\": {\"200\": 6, \"100\": 0, \"500\": 1, \"600\": 0}}, \"Prob-Vector\": null}\n" + "{\"Overall-Stat\": {\"Overall RACC\": 0.29500000000000004, \"Overall MCC\": 0.1264200803632855, \"SOA3(Altman)\": \"Poor\", \"SOA2(Fleiss)\": \"Poor\", \"Phi-Squared\": \"None\", \"Kappa\": 0.07801418439716304, \"Overall MCEN\": 0.3746281299595305, \"TNR Micro\": 0.7833333333333333, \"RCI\": 0.11409066398451011, \"95% CI\": [0.14095885572452488, 0.559041144275475], \"Chi-Squared\": \"None\", \"Cross Entropy\": 1.709947752496911, \"FPR Macro\": 0.2147058823529412, \"Reference Entropy\": 0.8841837197791889, \"Kappa 95% CI\": [-0.21849807698648957, 0.3745264457808156], \"FNR Micro\": 0.65, \"Pearson C\": \"None\", \"CSI\": \"None\", \"Kappa Unbiased\": -0.12554112554112543, \"F1 Macro\": 0.23043478260869565, \"TPR Micro\": 0.35, \"SOA1(Landis & Koch)\": \"Slight\", \"ACC Macro\": 0.675, \"TPR Macro\": \"None\", \"Lambda A\": 0.0, \"FNR Macro\": \"None\", \"Joint Entropy\": 2.119973094021975, \"SOA4(Cicchetti)\": \"Poor\", \"FPR Micro\": 0.21666666666666667, \"P-Value\": 0.9999981549942787, \"TNR Macro\": 0.7852941176470588, \"Cramer V\": \"None\", \"Conditional Entropy\": 1.235789374242786, \"AUNP\": \"None\", \"Response Entropy\": 1.3366664819166876, \"ARI\": 0.02298247455136956, \"Hamming Loss\": 0.65, \"Bennett S\": 0.1333333333333333, \"Standard Error\": 0.1066536450385077, \"SOA6(Matthews)\": \"Negligible\", \"F1 Micro\": 0.35, \"PPV Micro\": 0.35, \"Kappa No Prevalence\": -0.30000000000000004, \"Zero-one Loss\": 13, \"Overall RACCU\": 0.42249999999999993, \"AUNU\": \"None\", \"Scott PI\": -0.12554112554112543, \"CBA\": 0.17708333333333331, \"PPV Macro\": \"None\", \"Overall ACC\": 0.35, \"SOA5(Cramer)\": \"None\", \"Bangdiwala B\": 0.3135593220338983, \"Lambda B\": 0.0, \"Chi-Squared DF\": 9, \"NIR\": 0.8, \"Kappa Standard Error\": 0.15128176601206766, \"Mutual Information\": 0.10087710767390168, \"Overall J\": [0.6029411764705883, 0.15073529411764708], \"Krippendorff Alpha\": -0.09740259740259723, \"KL Divergence\": \"None\", \"Overall CEN\": 0.3648028121279775, \"RR\": 5.0, \"Gwet AC1\": 0.19504643962848295}, \"Sample-Weight\": null, \"Transpose\": false, \"Class-Stat\": {\"DP\": {\"200\": 0.1407391082701595, \"500\": 0.49789960499474867, \"100\": \"None\", \"600\": \"None\"}, \"Y\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"F0.5\": {\"200\": 0.6818181818181818, \"500\": 0.45454545454545453, \"100\": 0.0, \"600\": 0.0}, \"MCEN\": {\"200\": 0.3739448088748241, \"500\": 0.5802792108518123, \"100\": 0.3349590631259315, \"600\": 0.0}, \"MCC\": {\"200\": 0.10482848367219183, \"500\": 0.32673201960653564, \"100\": \"None\", \"600\": \"None\"}, \"QI\": {\"200\": \"Weak\", \"500\": \"Strong\", \"100\": \"None\", \"600\": \"None\"}, \"dInd\": {\"200\": 0.673145600891813, \"500\": 0.6692567908186672, \"100\": \"None\", \"600\": 1.0}, \"OOC\": {\"200\": 0.5669467095138409, \"500\": 0.4082482904638631, \"100\": \"None\", \"600\": \"None\"}, \"P\": {\"200\": 16, \"500\": 3, \"100\": 0, \"600\": 1}, \"GM\": {\"200\": 0.5303300858899106, \"500\": 0.5601120336112039, \"100\": \"None\", \"600\": 0.0}, \"IS\": {\"200\": 0.09953567355091428, \"500\": 1.736965594166206, \"100\": \"None\", \"600\": \"None\"}, \"ICSI\": {\"200\": 0.2321428571428572, \"500\": -0.16666666666666674, \"100\": \"None\", \"600\": \"None\"}, \"AGM\": {\"200\": 0.5669417382415922, \"500\": 0.7351956938438939, \"100\": \"None\", \"600\": 0}, \"NPV\": {\"200\": 0.23076923076923078, \"500\": 0.8888888888888888, \"100\": 1.0, \"600\": 0.95}, \"F1\": {\"200\": 0.5217391304347826, \"500\": 0.4, \"100\": 0.0, \"600\": 0.0}, \"RACC\": {\"200\": 0.28, \"500\": 0.015, \"100\": 0.0, \"600\": 0.0}, \"RACCU\": {\"200\": 0.33062499999999995, \"500\": 0.015625, \"100\": 0.07562500000000001, \"600\": 0.0006250000000000001}, \"PLR\": {\"200\": 1.5, \"500\": 5.666666666666665, \"100\": \"None\", \"600\": \"None\"}, \"AUCI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"Poor\"}, \"G\": {\"200\": 0.5669467095138409, \"500\": 0.408248290463863, \"100\": \"None\", \"600\": \"None\"}, \"Q\": {\"200\": 0.28571428571428575, \"500\": 0.7777777777777778, \"100\": \"None\", \"600\": \"None\"}, \"BCD\": {\"200\": 0.225, \"500\": 0.025, \"100\": 0.275, \"600\": 0.025}, \"ACC\": {\"200\": 0.45, \"500\": 0.85, \"100\": 0.45, \"600\": 0.95}, \"AM\": {\"200\": -9, \"500\": -1, \"100\": 11, \"600\": -1}, \"FN\": {\"200\": 10, \"100\": 0, \"500\": 2, \"600\": 1}, \"IBA\": {\"200\": 0.17578125, \"500\": 0.1230296039984621, \"100\": \"None\", \"600\": 0.0}, \"PRE\": {\"200\": 0.8, \"500\": 0.15, \"100\": 0.0, \"600\": 0.05}, \"TNR\": {\"200\": 0.75, \"500\": 0.9411764705882353, \"100\": 0.45, \"600\": 1.0}, \"TOP\": {\"200\": 7, \"500\": 2, \"100\": 11, \"600\": 0}, \"F2\": {\"200\": 0.4225352112676056, \"500\": 0.35714285714285715, \"100\": 0.0, \"600\": 0.0}, \"AUPR\": {\"200\": 0.6160714285714286, \"500\": 0.41666666666666663, \"100\": \"None\", \"600\": \"None\"}, \"AUC\": {\"200\": 0.5625, \"500\": 0.6372549019607843, \"100\": \"None\", \"600\": 0.5}, \"NLR\": {\"200\": 0.8333333333333334, \"500\": 0.7083333333333334, \"100\": \"None\", \"600\": 1.0}, \"TON\": {\"200\": 13, \"500\": 18, \"100\": 9, \"600\": 20}, \"TP\": {\"200\": 6, \"100\": 0, \"500\": 1, \"600\": 0}, \"MCCI\": {\"200\": \"Negligible\", \"500\": \"Weak\", \"100\": \"None\", \"600\": \"None\"}, \"FPR\": {\"200\": 0.25, \"500\": 0.05882352941176472, \"100\": 0.55, \"600\": 0.0}, \"DPI\": {\"200\": \"Poor\", \"500\": \"Poor\", \"100\": \"None\", \"600\": \"None\"}, \"AGF\": {\"200\": 0.33642097801219245, \"500\": 0.5665926996700735, \"100\": 0.0, \"600\": 0.0}, \"FNR\": {\"200\": 0.625, \"500\": 0.6666666666666667, \"100\": \"None\", \"600\": 1.0}, \"GI\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"POP\": {\"200\": 20, \"500\": 20, \"100\": 20, \"600\": 20}, \"TPR\": {\"200\": 0.375, \"500\": 0.3333333333333333, \"100\": \"None\", \"600\": 0.0}, \"PLRI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"None\"}, \"N\": {\"200\": 4, \"500\": 17, \"100\": 20, \"600\": 19}, \"sInd\": {\"200\": 0.5240141808835057, \"500\": 0.5267639848569737, \"100\": \"None\", \"600\": 0.29289321881345254}, \"TN\": {\"200\": 3, \"100\": 9, \"500\": 16, \"600\": 19}, \"FP\": {\"200\": 1, \"100\": 11, \"500\": 1, \"600\": 0}, \"PPV\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": 0.0, \"600\": \"None\"}, \"DOR\": {\"200\": 1.7999999999999998, \"500\": 7.999999999999997, \"100\": \"None\", \"600\": \"None\"}, \"MK\": {\"200\": 0.08791208791208782, \"500\": 0.38888888888888884, \"100\": 0.0, \"600\": \"None\"}, \"OP\": {\"200\": 0.1166666666666667, \"500\": 0.373076923076923, \"100\": \"None\", \"600\": -0.050000000000000044}, \"OC\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": \"None\", \"600\": \"None\"}, \"FDR\": {\"200\": 0.1428571428571429, \"500\": 0.5, \"100\": 1.0, \"600\": \"None\"}, \"J\": {\"200\": 0.35294117647058826, \"500\": 0.25, \"100\": 0.0, \"600\": 0.0}, \"CEN\": {\"200\": 0.3570795472009597, \"500\": 0.5389466410223563, \"100\": 0.3349590631259315, \"600\": 0.0}, \"FOR\": {\"200\": 0.7692307692307692, \"500\": 0.11111111111111116, \"100\": 0.0, \"600\": 0.050000000000000044}, \"LS\": {\"200\": 1.0714285714285714, \"500\": 3.3333333333333335, \"100\": \"None\", \"600\": \"None\"}, \"ERR\": {\"200\": 0.55, \"500\": 0.15000000000000002, \"100\": 0.55, \"600\": 0.050000000000000044}, \"NLRI\": {\"200\": \"Negligible\", \"500\": \"Negligible\", \"100\": \"None\", \"600\": \"Negligible\"}, \"BM\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}}, \"Prob-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]]]], \"Imbalanced\": true, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Digit\": 5}\n" ] } ], @@ -576,7 +576,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{\"Predict-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]]]], \"Digit\": 5, \"Imbalanced\": true, \"Sample-Weight\": null, \"Actual-Vector\": null, \"Transpose\": false, \"Prob-Vector\": null}\n" + "{\"Sample-Weight\": null, \"Transpose\": false, \"Prob-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]]]], \"Imbalanced\": true, \"Predict-Vector\": null, \"Actual-Vector\": null, \"Digit\": 5}\n" ] } ], diff --git a/Document/Example4_Files/cm.obj b/Document/Example4_Files/cm.obj index 44ffd52c..30f64541 100644 --- a/Document/Example4_Files/cm.obj +++ b/Document/Example4_Files/cm.obj @@ -1 +1 @@ -{"Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 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]]]], "Digit": 5, "Imbalanced": true, "Sample-Weight": null, "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} \ No newline at end of file +{"Sample-Weight": null, "Transpose": false, "Prob-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]]]], "Imbalanced": true, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Digit": 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3.3333333333333335, "100": "None", "600": "None"}, "ERR": {"200": 0.55, "500": 0.15000000000000002, "100": 0.55, "600": 0.050000000000000044}, "NLRI": {"200": "Negligible", "500": "Negligible", "100": "None", "600": "Negligible"}, "BM": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}}, "Prob-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]]]], "Imbalanced": true, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Digit": 5} \ No newline at end of file diff --git a/Document/Example6.ipynb b/Document/Example6.ipynb index 19900815..f614493d 100644 --- a/Document/Example6.ipynb +++ b/Document/Example6.ipynb @@ -261,13 +261,13 @@ "Class4 0.0 0.0 2e-05 0.99998 \n", "\n", "\n", - "ACC: {'Class3': 0.9999250299880048, 'Class2': 0.9999500199920032, 'Class1': 0.9999750099960016, 'Class4': 0.9999500199920032}\n", - "MCC: {'Class3': 0.7302602381427055, 'Class2': 0.7999750068731099, 'Class1': 0.8944160139432883, 'Class4': 0.9333083339583177}\n", - "CEN: {'Class3': 0.3649884090288471, 'Class2': 0.25701944178769376, 'Class1': 0.13625493172565745, 'Class4': 0.0001575200922489127}\n", - "MCEN: {'Class3': 0.4654427710721536, 'Class2': 0.3333333333333333, 'Class1': 0.17964888034078544, 'Class4': 0.00029569133318617423}\n", - "DP: {'Class3': 2.7032690544190636, 'Class2': 2.869241573973406, 'Class1': 'None', 'Class4': 3.1691421556058055}\n", + "ACC: {'Class4': 0.9999500199920032, 'Class3': 0.9999250299880048, 'Class2': 0.9999500199920032, 'Class1': 0.9999750099960016}\n", + "MCC: {'Class4': 0.9333083339583177, 'Class3': 0.7302602381427055, 'Class2': 0.7999750068731099, 'Class1': 0.8944160139432883}\n", + "CEN: {'Class4': 0.0001575200922489127, 'Class3': 0.3649884090288471, 'Class2': 0.25701944178769376, 'Class1': 0.13625493172565745}\n", + "MCEN: {'Class4': 0.00029569133318617423, 'Class3': 0.4654427710721536, 'Class2': 0.3333333333333333, 'Class1': 0.17964888034078544}\n", + "DP: {'Class4': 3.1691421556058055, 'Class3': 2.7032690544190636, 'Class2': 2.869241573973406, 'Class1': 'None'}\n", "Kappa: 0.8666333383326446\n", - "RCI: 0.8711441699127425\n", + "RCI: 0.8711441699127427\n", "SOA1: Almost Perfect\n" ] } @@ -320,11 +320,11 @@ "Class4 0.25 0.25 0.25 0.25 \n", "\n", "\n", - "ACC: {'Class3': 0.625, 'Class2': 0.625, 'Class1': 0.625, 'Class4': 0.625}\n", - "MCC: {'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n", - "CEN: {'Class3': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.8704188162777186, 'Class4': 0.8704188162777186}\n", - "MCEN: {'Class3': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.9308855421443073, 'Class4': 0.9308855421443073}\n", - "DP: {'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n", + "ACC: {'Class4': 0.625, 'Class3': 0.625, 'Class2': 0.625, 'Class1': 0.625}\n", + "MCC: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "CEN: {'Class4': 0.8704188162777186, 'Class3': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.8704188162777186}\n", + "MCEN: {'Class4': 0.9308855421443073, 'Class3': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.9308855421443073}\n", + "DP: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", "Kappa: 0.0\n", "RCI: 0.0\n", "SOA1: Slight\n" @@ -379,13 +379,13 @@ "Class4 0.76923 0.07692 0.07692 0.07692 \n", "\n", "\n", - "ACC: {'Class3': 0.76, 'Class2': 0.76, 'Class1': 0.4, 'Class4': 0.4}\n", - "MCC: {'Class3': 0.10714285714285714, 'Class2': 0.10714285714285714, 'Class1': -0.2358640882624316, 'Class4': -0.2358640882624316}\n", - "CEN: {'Class3': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.6392779429225794, 'Class4': 0.6392779429225796}\n", - "MCEN: {'Class3': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.647512271542988, 'Class4': 0.647512271542988}\n", - "DP: {'Class3': 0.16596653499824943, 'Class2': 0.16596653499824943, 'Class1': -0.33193306999649924, 'Class4': -0.3319330699964992}\n", + "ACC: {'Class4': 0.4, 'Class3': 0.76, 'Class2': 0.76, 'Class1': 0.4}\n", + "MCC: {'Class4': -0.2358640882624316, 'Class3': 0.10714285714285714, 'Class2': 0.10714285714285714, 'Class1': -0.2358640882624316}\n", + "CEN: {'Class4': 0.6392779429225796, 'Class3': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.6392779429225794}\n", + "MCEN: {'Class4': 0.647512271542988, 'Class3': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.647512271542988}\n", + "DP: {'Class4': -0.3319330699964992, 'Class3': 0.16596653499824943, 'Class2': 0.16596653499824943, 'Class1': -0.33193306999649924}\n", "Kappa: -0.07361963190184047\n", - "RCI: 0.11603030564493641\n", + "RCI: 0.11603030564493627\n", "SOA1: Poor\n" ] } @@ -438,13 +438,13 @@ "Class4 0.76923 0.07692 0.07692 0.07692 \n", "\n", "\n", - "ACC: {'Class3': 0.999400898652022, 'Class2': 0.999400898652022, 'Class1': 0.000998502246630055, 'Class4': 0.000998502246630055}\n", - "MCC: {'Class3': 0.24970032963739885, 'Class2': 0.24970032963739885, 'Class1': -0.43266656861311537, 'Class4': -0.43266656861311537}\n", - "CEN: {'Class3': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.0029588592520426657, 'Class4': 0.0029588592520426657}\n", - "MCEN: {'Class3': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.002903385725603509, 'Class4': 0.002903385725603509}\n", - "DP: {'Class3': 1.6794055876913858, 'Class2': 1.6794055876913858, 'Class1': -1.9423127303715728, 'Class4': -1.9423127303715728}\n", + "ACC: {'Class4': 0.000998502246630055, 'Class3': 0.999400898652022, 'Class2': 0.999400898652022, 'Class1': 0.000998502246630055}\n", + "MCC: {'Class4': -0.43266656861311537, 'Class3': 0.24970032963739885, 'Class2': 0.24970032963739885, 'Class1': -0.43266656861311537}\n", + "CEN: {'Class4': 0.0029588592520426657, 'Class3': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.0029588592520426657}\n", + "MCEN: {'Class4': 0.002903385725603509, 'Class3': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.002903385725603509}\n", + "DP: {'Class4': -1.9423127303715728, 'Class3': 1.6794055876913858, 'Class2': 1.6794055876913858, 'Class1': -1.9423127303715728}\n", "Kappa: -0.0003990813465900262\n", - "RCI: 0.5536610475678805\n", + "RCI: 0.5536610475678804\n", "SOA1: Poor\n" ] } @@ -497,11 +497,11 @@ "Class4 0.25 0.25 0.25 0.25 \n", "\n", "\n", - "ACC: {'Class3': 0.7115384615384616, 'Class2': 0.7115384615384616, 'Class1': 0.7115384615384616, 'Class4': 0.36538461538461536}\n", - "MCC: {'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n", - "CEN: {'Class3': 0.6392779429225794, 'Class2': 0.6392779429225794, 'Class1': 0.6392779429225794, 'Class4': 0.6522742127953861}\n", - "MCEN: {'Class3': 0.647512271542988, 'Class2': 0.647512271542988, 'Class1': 0.647512271542988, 'Class4': 0.7144082229288313}\n", - "DP: {'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n", + "ACC: {'Class4': 0.36538461538461536, 'Class3': 0.7115384615384616, 'Class2': 0.7115384615384616, 'Class1': 0.7115384615384616}\n", + "MCC: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "CEN: {'Class4': 0.6522742127953861, 'Class3': 0.6392779429225794, 'Class2': 0.6392779429225794, 'Class1': 0.6392779429225794}\n", + "MCEN: {'Class4': 0.7144082229288313, 'Class3': 0.647512271542988, 'Class2': 0.647512271542988, 'Class1': 0.647512271542988}\n", + "DP: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", "Kappa: 0.0\n", "RCI: 0.0\n", "SOA1: Slight\n" @@ -556,11 +556,11 @@ "Class4 0.25 0.25 0.25 0.25 \n", "\n", "\n", - "ACC: {'Class3': 0.7499500149955014, 'Class2': 0.7499500149955014, 'Class1': 0.7499500149955014, 'Class4': 0.25014995501349596}\n", - "MCC: {'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n", - "CEN: {'Class3': 0.0029588592520426657, 'Class2': 0.0029588592520426657, 'Class1': 0.0029588592520426657, 'Class4': 0.539296694603886}\n", - "MCEN: {'Class3': 0.002903385725603509, 'Class2': 0.002903385725603509, 'Class1': 0.002903385725603509, 'Class4': 0.580710610324597}\n", - "DP: {'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0, 'Class4': 0.0}\n", + "ACC: {'Class4': 0.25014995501349596, 'Class3': 0.7499500149955014, 'Class2': 0.7499500149955014, 'Class1': 0.7499500149955014}\n", + "MCC: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "CEN: {'Class4': 0.539296694603886, 'Class3': 0.0029588592520426657, 'Class2': 0.0029588592520426657, 'Class1': 0.0029588592520426657}\n", + "MCEN: {'Class4': 0.580710610324597, 'Class3': 0.002903385725603509, 'Class2': 0.002903385725603509, 'Class1': 0.002903385725603509}\n", + "DP: {'Class4': 0.0, 'Class3': 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 new file mode 100644 index 00000000..24f0ee28 --- /dev/null +++ b/Otherfiles/meta.yaml @@ -0,0 +1,36 @@ +{% set name = "pycm" %} +{% set version = "3.5" %} + +package: + name: {{ name|lower }} + version: {{ version }} +source: + git_url: https://github.com/sepandhaghighi/pycm + git_rev: v{{ version }} +build: + noarch: python + number: 0 + script: {{ PYTHON }} -m pip install . -vv +requirements: + host: + - pip + - setuptools + - python >=3.5 + run: + - art >=1.8 + - numpy >=1.9.0 + - python >=3.5 +about: + home: https://github.com/sepandhaghighi/pycm + license: MIT + license_family: MIT + summary: Multi-class confusion matrix library in Python + description: | + PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and accurate evaluation of a large variety of classifiers. + + Website: https://www.pycm.ir + + Repo: https://github.com/sepandhaghighi/pycm +extra: + recipe-maintainers: + - sepandhaghighi diff --git a/Otherfiles/test.html b/Otherfiles/test.html index daecd4ce..af8f9502 100644 --- a/Otherfiles/test.html +++ b/Otherfiles/test.html @@ -749,6 +749,6 @@

Class Statistics :

Similarity index -

Generated By PyCM Version 3.4

+

Generated By PyCM Version 3.5

diff --git a/Otherfiles/test.obj b/Otherfiles/test.obj index 3e8f5ac1..7913b36b 100644 --- a/Otherfiles/test.obj +++ b/Otherfiles/test.obj @@ -1 +1 @@ -{"Transpose": true, "Sample-Weight": null, "Digit": 5, "Actual-Vector": null, "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]]]], "Predict-Vector": null, "Imbalanced": false} \ No newline at end of file +{"Predict-Vector": null, "Prob-Vector": null, "Transpose": true, "Digit": 5, "Sample-Weight": null, "Imbalanced": false, "Actual-Vector": null, "Matrix": [["L1", [["L2", 0], ["L1", 3], ["L3", 2]]], ["L2", [["L2", 1], ["L1", 0], ["L3", 1]]], ["L3", [["L2", 2], ["L1", 0], ["L3", 3]]]]} \ No newline at end of file diff --git a/Otherfiles/version_check.py b/Otherfiles/version_check.py index 0b78a993..33db0aa4 100644 --- a/Otherfiles/version_check.py +++ b/Otherfiles/version_check.py @@ -4,7 +4,7 @@ import sys import codecs Failed = 0 -PYCM_VERSION = "3.4" +PYCM_VERSION = "3.5" SETUP_ITEMS = [ @@ -25,7 +25,9 @@ "pip3 install pycm=={0}"] HTML_ITEMS = ["Version {0}"] PARAMS_ITEMS = ['PYCM_VERSION = "{0}"'] +META_ITEMS = ['% set version = "{0}" %'] FILES = { + os.path.join("Otherfiles", "meta.yaml"): META_ITEMS, "setup.py": SETUP_ITEMS, "README.md": README_ITEMS, "CHANGELOG.md": CHANGELOG_ITEMS, os.path.join( "Document", "Document.ipynb"): DOCUMENT_ITEMS, os.path.join( "Document", "Example1_Files", "cm1.html"): HTML_ITEMS, os.path.join( diff --git a/README.md b/README.md index e0bb598e..d8cba6a5 100644 --- a/README.md +++ b/README.md @@ -100,7 +100,7 @@ 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 3.4](https://github.com/sepandhaghighi/pycm/archive/v3.4.zip) or [Latest Source ](https://github.com/sepandhaghighi/pycm/archive/dev.zip) +- Download [Version 3.5](https://github.com/sepandhaghighi/pycm/archive/v3.5.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) @@ -108,7 +108,7 @@ PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scie - Check [Python Packaging User Guide](https://packaging.python.org/installing/) -- Run `pip install pycm==3.4` or `pip3 install pycm==3.4` (Need root access) +- Run `pip install pycm==3.5` or `pip3 install pycm==3.5` (Need root access) ### Conda @@ -691,7 +691,7 @@ pycm.ConfusionMatrix(classes: [0, 1, 2]) ### Acceptable data types -#### ConfusionMatrix +**ConfusionMatrix** 1. `actual_vector` : python `list` or numpy `array` of any stringable objects @@ -707,7 +707,7 @@ pycm.ConfusionMatrix(classes: [0, 1, 2]) * Run `help(ConfusionMatrix)` for `ConfusionMatrix` object details -#### Compare +**Compare** 1. `cm_dict` : python `dict` of `ConfusionMatrix` object (`str` : `ConfusionMatrix`) 2. `by_class` : `bool` diff --git a/Test/function_test.py b/Test/function_test.py index 87858009..3084231c 100644 --- a/Test/function_test.py +++ b/Test/function_test.py @@ -213,7 +213,7 @@ 'None' >>> ARI_calc([1,2],{1:{1:0,2:0},2:{1:0,2:0}},{1:0,2:0},{1:0,2:0},0) 'None' ->>> BCD_calc(2, 2, "None") +>>> BCD_calc(2, "None") 'None' >>> AM_calc(3, "None") 'None' diff --git a/Test/overall_test.py b/Test/overall_test.py index fcb1d8fc..b57ee323 100644 --- a/Test/overall_test.py +++ b/Test/overall_test.py @@ -157,6 +157,8 @@ dInd(Distance index) 0.22222 0.67586 0.60093 sInd(Similarity index) 0.84287 0.52209 0.57508 +>>> cm.matrix == dict(cm) +True >>> cm.relabel({0:"L1",1:"L2",2:"L3"}) >>> y_actu == y_actu_copy True diff --git a/dev-requirements.txt b/dev-requirements.txt index 7f8edefd..6592e294 100644 --- a/dev-requirements.txt +++ b/dev-requirements.txt @@ -1,5 +1,5 @@ -art==5.4 -numpy==1.22.1 +art==5.6 +numpy==1.22.3 codecov>=2.0.15 pytest>=4.3.1 pytest-cov>=2.6.1 @@ -7,7 +7,6 @@ setuptools>=40.8.0 vulture>=1.0 bandit>=1.5.1 pydocstyle>=3.0.0 -notebook>=5.2.2 matplotlib>=3.0.0 seaborn>=0.9.1 diff --git a/pycm/pycm_class_func.py b/pycm/pycm_class_func.py index 0c1b2c45..0e4f6490 100644 --- a/pycm/pycm_class_func.py +++ b/pycm/pycm_class_func.py @@ -4,6 +4,7 @@ import math from .pycm_util import normal_quantile from .pycm_interpret import * +from .pycm_param import CLASS_PARAMS def sensitivity_index_calc(TPR, FPR): @@ -657,26 +658,46 @@ def IBA_calc(TPR, TNR, alpha=1): return "None" -def BCD_calc(TOP, P, AM): +def BCD_calc(AM, POP): """ Calculate Bray-Curtis dissimilarity (BCD). - :param TOP: number of positives in predict vector - :type TOP: dict - :param P: number of actual positives - :type P: dict :param AM: Automatic/Manual :type AM: int + :param POP: population or total number of samples + :type POP: int :return: BCD as float """ try: - TOP_sum = sum(TOP.values()) - P_sum = sum(P.values()) - return abs(AM) / (P_sum + TOP_sum) + return abs(AM) / (2 * POP) except (ZeroDivisionError, TypeError, AttributeError): return "None" +def basic_statistics(TP, TN, FP, FN): + """ + Init classes' statistics. + + :param TP: true positive + :type TP: dict + :param TN: true negative + :type TN: dict + :param FP: false positive + :type FP: dict + :param FN: false negative + :type FN: dict + :return: basic statistics as dict + """ + result = {} + for i in CLASS_PARAMS: + result[i] = {} + result["TP"] = TP + result["TN"] = TN + result["FP"] = FP + result["FN"] = FN + return result + + def class_statistics(TP, TN, FP, FN, classes, table): """ Return All statistics of classes. @@ -695,182 +716,81 @@ def class_statistics(TP, TN, FP, FN, classes, table): :type table: dict :return: classes' statistics as dict """ - TPR = {} - TNR = {} - PPV = {} - NPV = {} - FNR = {} - FPR = {} - FDR = {} - FOR = {} - ACC = {} - F1_SCORE = {} - MCC = {} - BM = {} - MK = {} - PLR = {} - NLR = {} - DOR = {} - POP = {} - P = {} - N = {} - TOP = {} - TON = {} - PRE = {} - G = {} - RACC = {} - F05_Score = {} - F2_Score = {} - ERR = {} - RACCU = {} - Jaccrd_Index = {} - IS = {} - CEN = {} - MCEN = {} - AUC = {} - dInd = {} - sInd = {} - DP = {} - Y = {} - PLRI = {} - NLRI = {} - DPI = {} - AUCI = {} - GI = {} - LS = {} - AM = {} - BCD = {} - OP = {} - IBA = {} - GM = {} - Q = {} - QI = {} - AGM = {} - MCCI = {} - AGF = {} - OC = {} - OOC = {} - AUPR = {} - ICSI = {} - for i in TP.keys(): - POP[i] = TP[i] + TN[i] + FP[i] + FN[i] - P[i] = TP[i] + FN[i] - N[i] = TN[i] + FP[i] - TOP[i] = TP[i] + FP[i] - TON[i] = TN[i] + FN[i] - TPR[i] = TTPN_calc(TP[i], FN[i]) - TNR[i] = TTPN_calc(TN[i], FP[i]) - PPV[i] = TTPN_calc(TP[i], FP[i]) - NPV[i] = TTPN_calc(TN[i], FN[i]) - FNR[i] = FXR_calc(TPR[i]) - FPR[i] = FXR_calc(TNR[i]) - FDR[i] = FXR_calc(PPV[i]) - FOR[i] = FXR_calc(NPV[i]) - ACC[i] = ACC_calc(TP[i], TN[i], FP[i], FN[i]) - F1_SCORE[i] = F_calc(TP[i], FP[i], FN[i], 1) - F05_Score[i] = F_calc(TP[i], FP[i], FN[i], 0.5) - F2_Score[i] = F_calc(TP[i], FP[i], FN[i], 2) - MCC[i] = MCC_calc(TP[i], TN[i], FP[i], FN[i]) - BM[i] = MK_BM_calc(TPR[i], TNR[i]) - MK[i] = MK_BM_calc(PPV[i], NPV[i]) - PLR[i] = LR_calc(TPR[i], FPR[i]) - NLR[i] = LR_calc(FNR[i], TNR[i]) - DOR[i] = LR_calc(PLR[i], NLR[i]) - PRE[i] = PRE_calc(P[i], POP[i]) - G[i] = G_calc(PPV[i], TPR[i]) - RACC[i] = RACC_calc(TOP[i], P[i], POP[i]) - ERR[i] = ERR_calc(ACC[i]) - RACCU[i] = RACCU_calc(TOP[i], P[i], POP[i]) - Jaccrd_Index[i] = jaccard_index_calc(TP[i], TOP[i], P[i]) - IS[i] = IS_calc(TP[i], FP[i], FN[i], POP[i]) - CEN[i] = CEN_calc(classes, table, TOP[i], P[i], i) - MCEN[i] = CEN_calc(classes, table, TOP[i], P[i], i, True) - AUC[i] = AUC_calc(TNR[i], TPR[i]) - dInd[i] = dInd_calc(TNR[i], TPR[i]) - sInd[i] = sInd_calc(dInd[i]) - DP[i] = DP_calc(TPR[i], TNR[i]) - Y[i] = BM[i] - PLRI[i] = PLR_analysis(PLR[i]) - NLRI[i] = NLR_analysis(NLR[i]) - DPI[i] = DP_analysis(DP[i]) - AUCI[i] = AUC_analysis(AUC[i]) - GI[i] = GI_calc(AUC[i]) - LS[i] = lift_calc(PPV[i], PRE[i]) - AM[i] = AM_calc(TOP[i], P[i]) - OP[i] = OP_calc(ACC[i], TPR[i], TNR[i]) - IBA[i] = IBA_calc(TPR[i], TNR[i]) - GM[i] = G_calc(TNR[i], TPR[i]) - Q[i] = Q_calc(TP[i], TN[i], FP[i], FN[i]) - QI[i] = Q_analysis(Q[i]) - AGM[i] = AGM_calc(TPR[i], TNR[i], GM[i], N[i], POP[i]) - MCCI[i] = MCC_analysis(MCC[i]) - AGF[i] = AGF_calc(TP[i], FP[i], FN[i], TN[i]) - OC[i] = OC_calc(TP[i], TOP[i], P[i]) - OOC[i] = OOC_calc(TP[i], TOP[i], P[i]) - AUPR[i] = AUC_calc(PPV[i], TPR[i]) - ICSI[i] = MK_BM_calc(PPV[i], TPR[i]) + result = basic_statistics(TP, TN, FP, FN) for i in TP.keys(): - BCD[i] = BCD_calc(TOP, P, AM[i]) - result = { - "TPR": TPR, - "TNR": TNR, - "PPV": PPV, - "NPV": NPV, - "FNR": FNR, - "FPR": FPR, - "FDR": FDR, - "FOR": FOR, - "ACC": ACC, - "F1": F1_SCORE, - "MCC": MCC, - "BM": BM, - "MK": MK, - "PLR": PLR, - "NLR": NLR, - "DOR": DOR, - "TP": TP, - "TN": TN, - "FP": FP, - "FN": FN, - "POP": POP, - "P": P, - "N": N, - "TOP": TOP, - "TON": TON, - "PRE": PRE, - "G": G, - "RACC": RACC, - "F0.5": F05_Score, - "F2": F2_Score, - "ERR": ERR, - "RACCU": RACCU, - "J": Jaccrd_Index, - "IS": IS, - "CEN": CEN, - "MCEN": MCEN, - "AUC": AUC, - "sInd": sInd, - "dInd": dInd, - "DP": DP, - "Y": Y, - "PLRI": PLRI, - "DPI": DPI, - "AUCI": AUCI, - "GI": GI, - "LS": LS, - "AM": AM, - "BCD": BCD, - "OP": OP, - "IBA": IBA, - "GM": GM, - "Q": Q, - "AGM": AGM, - "NLRI": NLRI, - "MCCI": MCCI, - "AGF": AGF, - "OC": OC, - "OOC": OOC, - "AUPR": AUPR, - "ICSI": ICSI, - "QI": QI} + result["POP"][i] = TP[i] + TN[i] + FP[i] + FN[i] + result["P"][i] = TP[i] + FN[i] + result["N"][i] = TN[i] + FP[i] + result["TOP"][i] = TP[i] + FP[i] + result["TON"][i] = TN[i] + FN[i] + result["TPR"][i] = TTPN_calc(TP[i], FN[i]) + result["TNR"][i] = TTPN_calc(TN[i], FP[i]) + result["PPV"][i] = TTPN_calc(TP[i], FP[i]) + result["NPV"][i] = TTPN_calc(TN[i], FN[i]) + result["FNR"][i] = FXR_calc(result["TPR"][i]) + result["FPR"][i] = FXR_calc(result["TNR"][i]) + result["FDR"][i] = FXR_calc(result["PPV"][i]) + result["FOR"][i] = FXR_calc(result["NPV"][i]) + result["ACC"][i] = ACC_calc(TP[i], TN[i], FP[i], FN[i]) + result["F1"][i] = F_calc(TP[i], FP[i], FN[i], 1) + result["F0.5"][i] = F_calc(TP[i], FP[i], FN[i], 0.5) + result["F2"][i] = F_calc(TP[i], FP[i], FN[i], 2) + result["MCC"][i] = MCC_calc(TP[i], TN[i], FP[i], FN[i]) + result["BM"][i] = MK_BM_calc(result["TPR"][i], result["TNR"][i]) + result["MK"][i] = MK_BM_calc(result["PPV"][i], result["NPV"][i]) + result["PLR"][i] = LR_calc(result["TPR"][i], result["FPR"][i]) + result["NLR"][i] = LR_calc(result["FNR"][i], result["TNR"][i]) + result["DOR"][i] = LR_calc(result["PLR"][i], result["NLR"][i]) + result["PRE"][i] = PRE_calc(result["P"][i], result["POP"][i]) + result["G"][i] = G_calc(result["PPV"][i], result["TPR"][i]) + result["RACC"][i] = RACC_calc( + result["TOP"][i], result["P"][i], result["POP"][i]) + result["ERR"][i] = ERR_calc(result["ACC"][i]) + result["RACCU"][i] = RACCU_calc( + result["TOP"][i], result["P"][i], result["POP"][i]) + result["J"][i] = jaccard_index_calc( + TP[i], result["TOP"][i], result["P"][i]) + result["IS"][i] = IS_calc(TP[i], FP[i], FN[i], result["POP"][i]) + result["CEN"][i] = CEN_calc( + classes, table, result["TOP"][i], result["P"][i], i) + result["MCEN"][i] = CEN_calc( + classes, + table, + result["TOP"][i], + result["P"][i], + i, + True) + result["AUC"][i] = AUC_calc(result["TNR"][i], result["TPR"][i]) + result["dInd"][i] = dInd_calc(result["TNR"][i], result["TPR"][i]) + result["sInd"][i] = sInd_calc(result["dInd"][i]) + result["DP"][i] = DP_calc(result["TPR"][i], result["TNR"][i]) + result["Y"][i] = result["BM"][i] + result["PLRI"][i] = PLR_analysis(result["PLR"][i]) + result["NLRI"][i] = NLR_analysis(result["NLR"][i]) + result["DPI"][i] = DP_analysis(result["DP"][i]) + result["AUCI"][i] = AUC_analysis(result["AUC"][i]) + result["GI"][i] = GI_calc(result["AUC"][i]) + result["LS"][i] = lift_calc(result["PPV"][i], result["PRE"][i]) + result["AM"][i] = AM_calc(result["TOP"][i], result["P"][i]) + result["OP"][i] = OP_calc( + result["ACC"][i], + result["TPR"][i], + result["TNR"][i]) + result["IBA"][i] = IBA_calc(result["TPR"][i], result["TNR"][i]) + result["GM"][i] = G_calc(result["TNR"][i], result["TPR"][i]) + result["Q"][i] = Q_calc(TP[i], TN[i], FP[i], FN[i]) + result["QI"][i] = Q_analysis(result["Q"][i]) + result["AGM"][i] = AGM_calc( + result["TPR"][i], + result["TNR"][i], + result["GM"][i], + result["N"][i], + result["POP"][i]) + result["MCCI"][i] = MCC_analysis(result["MCC"][i]) + result["AGF"][i] = AGF_calc(TP[i], FP[i], FN[i], TN[i]) + result["OC"][i] = OC_calc(TP[i], result["TOP"][i], result["P"][i]) + result["OOC"][i] = OOC_calc(TP[i], result["TOP"][i], result["P"][i]) + result["AUPR"][i] = AUC_calc(result["PPV"][i], result["TPR"][i]) + result["ICSI"][i] = MK_BM_calc(result["PPV"][i], result["TPR"][i]) + result["BCD"][i] = BCD_calc(result["AM"][i], result["POP"][i]) return result diff --git a/pycm/pycm_obj.py b/pycm/pycm_obj.py index 742c6523..fe9282d7 100644 --- a/pycm/pycm_obj.py +++ b/pycm/pycm_obj.py @@ -206,6 +206,15 @@ def __str__(self): warn(CLASS_NUMBER_WARNING, RuntimeWarning) return result + def __iter__(self): + """ + Iterate through confusion matrix. + + :return: None + """ + for key in self.matrix.keys(): + yield key, self.matrix[key] + def save_stat( self, name, @@ -982,7 +991,7 @@ def plot( if cmap is None: cmap = plt.cm.gray_r fig, ax = plt.subplots() - fig.canvas.set_window_title(title) + fig.canvas.manager.set_window_title(title) if plot_lib == 'seaborn': try: import seaborn as sns diff --git a/pycm/pycm_overall_func.py b/pycm/pycm_overall_func.py index 6d6fadcb..1a6d2b2f 100644 --- a/pycm/pycm_overall_func.py +++ b/pycm/pycm_overall_func.py @@ -952,6 +952,7 @@ def overall_statistics(**kwargs): :type kwargs: dict :return: overall statistics as dict """ + result = {} POP = kwargs["POP"] population = list(POP.values())[0] TP = kwargs["TP"] @@ -959,128 +960,87 @@ def overall_statistics(**kwargs): TOP = kwargs["TOP"] table = kwargs["table"] classes = kwargs["classes"] - overall_accuracy = overall_accuracy_calc(TP, population) - overall_random_accuracy_unbiased = overall_random_accuracy_calc( + result["Overall ACC"] = overall_accuracy_calc(TP, population) + result["Overall RACCU"] = overall_random_accuracy_calc( kwargs["RACCU"]) - overall_random_accuracy = overall_random_accuracy_calc(kwargs["RACC"]) - overall_kappa = reliability_calc(overall_random_accuracy, overall_accuracy) + result["Overall RACC"] = overall_random_accuracy_calc(kwargs["RACC"]) + result["Kappa"] = reliability_calc( + result["Overall RACC"], result["Overall ACC"]) PC_AC1 = PC_AC1_calc(P, TOP, POP) PC_S = PC_S_calc(classes) - AC1 = reliability_calc(PC_AC1, overall_accuracy) - S = reliability_calc(PC_S, overall_accuracy) - kappa_SE = kappa_SE_calc( - overall_accuracy, - overall_random_accuracy, population) - kappa_unbiased = reliability_calc( - overall_random_accuracy_unbiased, - overall_accuracy) - PI = kappa_unbiased - kappa_no_prevalence = kappa_no_prevalence_calc(overall_accuracy) - kappa_CI = CI_calc(overall_kappa, kappa_SE) - overall_accuracy_se = SE_calc(overall_accuracy, population) - overall_accuracy_CI = CI_calc(overall_accuracy, overall_accuracy_se) - chi_squared = chi_square_calc(classes, table, TOP, P, POP) - phi_squared = phi_square_calc(chi_squared, population) - cramer_V = cramers_V_calc(phi_squared, classes) - response_entropy = entropy_calc(TOP, POP) - reference_entropy = entropy_calc(P, POP) - cross_entropy = cross_entropy_calc(TOP, P, POP) - join_entropy = joint_entropy_calc(classes, table, POP) - conditional_entropy = conditional_entropy_calc(classes, table, P, POP) - mutual_information = mutual_information_calc( - response_entropy, conditional_entropy) - kl_divergence = kl_divergence_calc(P, TOP, POP) - lambda_B = lambda_B_calc(classes, table, TOP, population) - lambda_A = lambda_A_calc(classes, table, P, population) - DF = DF_calc(classes) - overall_jaccard_index = overall_jaccard_index_calc(list( + result["Gwet AC1"] = reliability_calc(PC_AC1, result["Overall ACC"]) + result["Bennett S"] = reliability_calc(PC_S, result["Overall ACC"]) + result["Kappa Standard Error"] = kappa_SE_calc( + result["Overall ACC"], + result["Overall RACC"], population) + result["Kappa Unbiased"] = reliability_calc( + result["Overall RACCU"], + result["Overall ACC"]) + result["Scott PI"] = result["Kappa Unbiased"] + result["Kappa No Prevalence"] = kappa_no_prevalence_calc( + result["Overall ACC"]) + result["Kappa 95% CI"] = CI_calc( + result["Kappa"], result["Kappa Standard Error"]) + result["Standard Error"] = SE_calc(result["Overall ACC"], population) + result["95% CI"] = CI_calc(result["Overall ACC"], result["Standard Error"]) + result["Chi-Squared"] = chi_square_calc(classes, table, TOP, P, POP) + result["Phi-Squared"] = phi_square_calc(result["Chi-Squared"], population) + result["Cramer V"] = cramers_V_calc(result["Phi-Squared"], classes) + result["Response Entropy"] = entropy_calc(TOP, POP) + result["Reference Entropy"] = entropy_calc(P, POP) + result["Cross Entropy"] = cross_entropy_calc(TOP, P, POP) + result["Joint Entropy"] = joint_entropy_calc(classes, table, POP) + result["Conditional Entropy"] = conditional_entropy_calc( + classes, table, P, POP) + result["Mutual Information"] = mutual_information_calc( + result["Response Entropy"], result["Conditional Entropy"]) + result["KL Divergence"] = kl_divergence_calc(P, TOP, POP) + result["Lambda B"] = lambda_B_calc(classes, table, TOP, population) + result["Lambda A"] = lambda_A_calc(classes, table, P, population) + result["Chi-Squared DF"] = DF_calc(classes) + result["Overall J"] = overall_jaccard_index_calc(list( kwargs["jaccard_list"].values())) - hamming_loss = hamming_calc(TP, population) - zero_one_loss = zero_one_loss_calc(TP, population) - NIR = NIR_calc(P, population) - p_value = p_value_calc(TP, population, NIR) - overall_CEN = overall_CEN_calc(classes, TP, TOP, P, kwargs["CEN_dict"]) - overall_MCEN = overall_CEN_calc( + result["Hamming Loss"] = hamming_calc(TP, population) + result["Zero-one Loss"] = zero_one_loss_calc(TP, population) + result["NIR"] = NIR_calc(P, population) + result["P-Value"] = p_value_calc(TP, population, result["NIR"]) + result["Overall CEN"] = overall_CEN_calc( + classes, TP, TOP, P, kwargs["CEN_dict"]) + result["Overall MCEN"] = overall_CEN_calc( classes, TP, TOP, P, kwargs["MCEN_dict"], True) - overall_MCC = overall_MCC_calc(classes, table, TOP, P) - RR = RR_calc(classes, TOP) - CBA = CBA_calc(classes, table, TOP, P) - AUNU = macro_calc(kwargs["AUC_dict"]) - AUNP = AUNP_calc(classes, P, POP, kwargs["AUC_dict"]) - RCI = RCI_calc(mutual_information, reference_entropy) - C = pearson_C_calc(chi_squared, population) - TPR_PPV_F1_micro = overall_accuracy - TPR_macro = macro_calc(kwargs["TPR"]) - CSI = macro_calc(kwargs["ICSI_dict"]) - ARI = ARI_calc(classes, table, TOP, P, population) - TNR_micro = micro_calc(item1=kwargs["TN"], item2=kwargs["FP"]) - TNR_macro = macro_calc(kwargs["TNR"]) - B = B_calc(classes, TP, TOP, P) - alpha = alpha_calc( - overall_random_accuracy_unbiased, - overall_accuracy, + result["Overall MCC"] = overall_MCC_calc(classes, table, TOP, P) + result["RR"] = RR_calc(classes, TOP) + result["CBA"] = CBA_calc(classes, table, TOP, P) + result["AUNU"] = macro_calc(kwargs["AUC_dict"]) + result["AUNP"] = AUNP_calc(classes, P, POP, kwargs["AUC_dict"]) + result["RCI"] = RCI_calc( + result["Mutual Information"], + result["Reference Entropy"]) + result["Pearson C"] = pearson_C_calc(result["Chi-Squared"], population) + result["TPR Micro"] = result["Overall ACC"] + result["TPR Macro"] = macro_calc(kwargs["TPR"]) + result["CSI"] = macro_calc(kwargs["ICSI_dict"]) + result["ARI"] = ARI_calc(classes, table, TOP, P, population) + result["TNR Micro"] = micro_calc(item1=kwargs["TN"], item2=kwargs["FP"]) + result["TNR Macro"] = macro_calc(kwargs["TNR"]) + result["Bangdiwala B"] = B_calc(classes, TP, TOP, P) + result["Krippendorff Alpha"] = alpha_calc( + result["Overall RACCU"], + result["Overall ACC"], population) - return { - "Overall ACC": overall_accuracy, - "Kappa": overall_kappa, - "Overall RACC": overall_random_accuracy, - "SOA1(Landis & Koch)": kappa_analysis_koch(overall_kappa), - "SOA2(Fleiss)": kappa_analysis_fleiss(overall_kappa), - "SOA3(Altman)": kappa_analysis_altman(overall_kappa), - "SOA4(Cicchetti)": kappa_analysis_cicchetti(overall_kappa), - "SOA5(Cramer)": V_analysis(cramer_V), - "SOA6(Matthews)": MCC_analysis(overall_MCC), - "TNR Macro": TNR_macro, - "TPR Macro": TPR_macro, - "FPR Macro": complement(TNR_macro), - "FNR Macro": complement(TPR_macro), - "PPV Macro": macro_calc(kwargs["PPV"]), - "ACC Macro": macro_calc(kwargs["ACC"]), - "F1 Macro": macro_calc(kwargs["F1"]), - "TNR Micro": TNR_micro, - "FPR Micro": complement(TNR_micro), - "TPR Micro": TPR_PPV_F1_micro, - "FNR Micro": complement(TPR_PPV_F1_micro), - "PPV Micro": TPR_PPV_F1_micro, - "F1 Micro": TPR_PPV_F1_micro, - "Scott PI": PI, - "Gwet AC1": AC1, - "Bennett S": S, - "Kappa Standard Error": kappa_SE, - "Kappa 95% CI": kappa_CI, - "Chi-Squared": chi_squared, - "Phi-Squared": phi_squared, - "Cramer V": cramer_V, - "Chi-Squared DF": DF, - "95% CI": overall_accuracy_CI, - "Standard Error": overall_accuracy_se, - "Response Entropy": response_entropy, - "Reference Entropy": reference_entropy, - "Cross Entropy": cross_entropy, - "Joint Entropy": join_entropy, - "Conditional Entropy": conditional_entropy, - "KL Divergence": kl_divergence, - "Lambda B": lambda_B, - "Lambda A": lambda_A, - "Kappa Unbiased": kappa_unbiased, - "Overall RACCU": overall_random_accuracy_unbiased, - "Kappa No Prevalence": kappa_no_prevalence, - "Mutual Information": mutual_information, - "Overall J": overall_jaccard_index, - "Hamming Loss": hamming_loss, - "Zero-one Loss": zero_one_loss, - "NIR": NIR, - "P-Value": p_value, - "Overall CEN": overall_CEN, - "Overall MCEN": overall_MCEN, - "Overall MCC": overall_MCC, - "RR": RR, - "CBA": CBA, - "AUNU": AUNU, - "AUNP": AUNP, - "RCI": RCI, - "Pearson C": C, - "CSI": CSI, - "ARI": ARI, - "Bangdiwala B": B, - "Krippendorff Alpha": alpha} + result["SOA1(Landis & Koch)"] = kappa_analysis_koch(result["Kappa"]) + result["SOA2(Fleiss)"] = kappa_analysis_fleiss(result["Kappa"]) + result["SOA3(Altman)"] = kappa_analysis_altman(result["Kappa"]) + result["SOA4(Cicchetti)"] = kappa_analysis_cicchetti(result["Kappa"]) + result["SOA5(Cramer)"] = V_analysis(result["Cramer V"]) + result["SOA6(Matthews)"] = MCC_analysis(result["Overall MCC"]) + result["FPR Macro"] = complement(result["TNR Macro"]) + result["FNR Macro"] = complement(result["TPR Macro"]) + result["PPV Macro"] = macro_calc(kwargs["PPV"]) + result["ACC Macro"] = macro_calc(kwargs["ACC"]) + result["F1 Macro"] = macro_calc(kwargs["F1"]) + result["FPR Micro"] = complement(result["TNR Micro"]) + result["FNR Micro"] = complement(result["TPR Micro"]) + result["PPV Micro"] = result["TPR Micro"] + result["F1 Micro"] = result["TPR Micro"] + return result diff --git a/pycm/pycm_param.py b/pycm/pycm_param.py index 29d57606..f867e323 100644 --- a/pycm/pycm_param.py +++ b/pycm/pycm_param.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """Parameters and constants.""" -PYCM_VERSION = "3.4" +PYCM_VERSION = "3.5" OVERVIEW = ''' @@ -107,6 +107,69 @@ BALANCE_RATIO_THRESHOLD = 3 +CLASS_PARAMS = [ + "TPR", + "TNR", + "PPV", + "NPV", + "FNR", + "FPR", + "FDR", + "FOR", + "ACC", + "F1", + "MCC", + "BM", + "MK", + "PLR", + "NLR", + "DOR", + "TP", + "TN", + "FP", + "FN", + "POP", + "P", + "N", + "TOP", + "TON", + "PRE", + "G", + "RACC", + "F0.5", + "F2", + "ERR", + "RACCU", + "J", + "IS", + "CEN", + "MCEN", + "AUC", + "sInd", + "dInd", + "DP", + "Y", + "PLRI", + "DPI", + "AUCI", + "GI", + "LS", + "AM", + "BCD", + "OP", + "IBA", + "GM", + "Q", + "AGM", + "NLRI", + "MCCI", + "AGF", + "OC", + "OOC", + "AUPR", + "ICSI", + "QI"] + SUMMARY_OVERALL = [ "ACC Macro", "Kappa", diff --git a/setup.py b/setup.py index 44ad5ca4..a9b07041 100644 --- a/setup.py +++ b/setup.py @@ -36,14 +36,14 @@ def read_description(): setup( name='pycm', packages=['pycm'], - version='3.4', + version='3.5', description='Multi-class confusion matrix library in Python', long_description=read_description(), long_description_content_type='text/markdown', author='Sepand Haghighi', author_email='info@pycm.ir', url='https://github.com/sepandhaghighi/pycm', - download_url='https://github.com/sepandhaghighi/pycm/tarball/v3.4', + download_url='https://github.com/sepandhaghighi/pycm/tarball/v3.5', keywords="confusion-matrix python3 python machine_learning ML", project_urls={ 'Webpage': 'https://www.pycm.ir',