├── .gitignore ├── nona ├── __init__.py └── nona.py ├── requirements.txt ├── setup.py ├── README.md └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /nona/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pip==22.2.2 2 | wheel==0.38.4 3 | pandas==1.5.2 4 | numpy==1.24.1 5 | scikit-learn==1.2.0 6 | tqdm==4.64.1 7 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | with open("README.md", "r") as readme_file: 4 | readme = readme_file.read() 5 | 6 | requirements = ["pandas>=1", "numpy>=1", "scikit-learn>=1", "tqdm>=4"] 7 | 8 | setup( 9 | name="nona", 10 | version="0.0.1", 11 | author="Timur Abdualimov", 12 | author_email="timur.atp@yandex.ru", 13 | description="Python gap filling toolkit", 14 | long_description=readme, 15 | long_description_content_type="text/markdown", 16 | url="https://github.com/AbdualimovTP/nona", 17 | packages=find_packages(), 18 | install_requires=requirements, 19 | classifiers=[ 20 | "Programming Language :: Python :: 3.10", 21 | "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", 22 | ], 23 | ) 24 | -------------------------------------------------------------------------------- /nona/nona.py: -------------------------------------------------------------------------------- 1 | """ 2 | Author: Timur Pulatovich Abdualimov 3 | Date code: 08.12.2023 4 | """ 5 | 6 | 7 | import pandas as pd 8 | import numpy as np 9 | from tqdm import tqdm 10 | from sklearn.linear_model import Ridge 11 | from sklearn.ensemble import RandomForestClassifier 12 | from sklearn.pipeline import make_pipeline 13 | from sklearn.preprocessing import StandardScaler 14 | 15 | 16 | def nona(data, algreg = make_pipeline(StandardScaler(with_mean=False), Ridge(alpha=0.1)), algclass = RandomForestClassifier(max_depth=2, random_state=0)): 17 | 18 | """ 19 | Missing value prediction function. We go through all the columns, identifying the column with missing values. We divide the sample into train and test. We predict missing values ​​using machine learning. 20 | Parameters: 21 | Data: prepared dataset 22 | algreg: Regression algorithm to predict missing values ​​in columns 23 | algclss: Classification algorithm to predict missing values ​​in columns 24 | """ 25 | 26 | for i in tqdm(data.columns): # loop through all columns from first to last 27 | # if there are gaps in the first column, fill in the missing values with the median 28 | if i == data.columns[0] and data[i].isna().sum() != 0: 29 | data[i] = data[i].fillna(data[i].median()) 30 | continue 31 | 32 | # check if there are gaps in the column, if there are - the algorithm works 33 | if data[i].isna().sum() != 0: 34 | indexna = data[data[i].isna()].index # display row indices in a column with gaps 35 | datanona = data.loc[:, data.isna().any()==False] # output columns without gaps 36 | X_train_nona = datanona.loc[datanona.index.isin(indexna) == False] # create a train sample for training, it includes only columns without gaps, perform fibration on the index of the test sample, leave only the columns in which we know the predicted value 37 | X_test_nona = datanona.loc[indexna] # create a test sample, leave the columns in which we do not know the predicted value 38 | y_train_nona = data[i].loc[datanona.index.isin(indexna) == False] # leave the values in the predicted column without gaps 39 | 40 | # if the predicted number is an integer and the number of predicted values is less than 20, we solve the classification 41 | if data[i].nunique() < 20 and float(data[i].unique()[~np.isnan (data[i].unique())][0]).is_integer(): 42 | class_nona = algclass 43 | class_nona.fit(X_train_nona, y_train_nona) 44 | data.loc[data[i].isna(), i] = class_nona.predict(X_test_nona) # predict the values and insert them into the missing values in the column 45 | 46 | else: 47 | reg_nona = algreg 48 | reg_nona.fit(X_train_nona, y_train_nona) 49 | data.loc[data[i].isna(), i] = reg_nona.predict(X_test_nona) # predict the values and insert them into the missing values in the column 50 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |
2 |
3 |
4 | 5 | ----------------- 6 | 7 | # nona: Python gap filling toolkit 8 | 9 | ## What is it? 10 | 11 | **nona** a simple toolkit for filling gaps in a dataset. Filling in the gap using artificial intelligence methods. 12 | 13 | We go through all the columns. We find a column with gaps and split the dataset into a train, this is the part in which we know all the values ​​​​and the test, where there is no missing value in the column and predict using any machine learning method that supports a simple implementation of fit and predict. 14 | 15 | ## Main Features 16 | 17 | Here are just a few of the things that **nona** does well: 18 | 19 | - Easy and fast filling of missing values. 20 | - Using Machine Learning Methods 21 | - Support for machine learning methods with the base implementation of fit and predict 22 | - High Prediction Accuracy of Missing Values 23 | 24 | ## Where to get it 25 | 26 | The source code is currently hosted on GitHub at: 27 | [GitHub - AbdualimovTP/nona: library for filling in missing values ​​using artificial intelligence methods](https://github.com/AbdualimovTP/nona) 28 | Binary installers for the latest released version are available at the [Python 29 | Package Index (PyPI)](https://pypi.org/project/nona) 30 | 31 | ```sh 32 | # PyPI 33 | pip install nona 34 | ``` 35 | 36 | ## Dependencies 37 | 38 | - [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org) 39 | - [Pandas - Python data analysis toolkit]([pandas documentation — pandas 1.5.2 documentation](http://pandas.pydata.org/pandas-docs/stable/)) 40 | - [Scikit-Learn - machine learning in Python](https://scikit-learn.org/stable/) 41 | - [GitHub - tqdm/tqdm: A Fast, Extensible Progress Bar for Python and CLI](https://github.com/tqdm/tqdm) 42 | 43 | ## Quick start 44 | 45 | Out of the box, use ridge regression to fill in the gaps with the regression problem, and RandomForestClassifier for the classification problem in columns with missing values. 46 | 47 | ```python 48 | # load library 49 | from nona.nona import nona 50 | 51 | 52 | # prepare your data with na to ML 53 | # only numerical values ​​in the dataset 54 | 55 | 56 | # fill the missing values 57 | nona(YOUR_DATA) 58 | ``` 59 | 60 | ## Accuracy improvement 61 | 62 | You can insert other machine learning methods into the function. They should support a simple implementation of fit and predict. 63 | 64 | Parameters: 65 | 66 | - data: prepared dataset 67 | 68 | - algreg: Regression algorithm to predict missing values ​​in columns 69 | 70 | - algclss: Classification algorithm to predict missing values ​​in columns 71 | 72 | ```python 73 | # load library 74 | from nona.nona import nona 75 | 76 | 77 | # prepare your data with na to ML 78 | # only numerical values ​​in the dataset 79 | 80 | 81 | # fill the missing values 82 | nona(data=YOUR_DATA, algreg=make_pipeline(StandardScaler(with_mean=False), Ridge(alpha=0.1)), algclass=RandomForestClassifier(max_depth=2, random_state=0)) 83 | ``` 84 | 85 | ## Comparison of accuracy with other gap filling methods 86 | 87 | [Framingham heart study dataset | Kaggle](https://www.kaggle.com/datasets/aasheesh200/framingham-heart-study-dataset) 88 | 89 | ![](https://ltdfoto.ru/images/2023/01/08/test_nona_fr.png) 90 | 91 | 92 | 93 | Results of RMSE techniques for filling gaps depending on the percentage of missing values ​​in the dataset. 94 | 95 | | | 10% | 20% | 30% | 40% | 50% | 70% | 90% | 96 | | ----------------- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | 97 | | **Baseline_MEAN** | 2.67 | 3.8 | 4.7 | 5.66 | 6.4 | 7.4 | 8.43 | 98 | | **KNN** | 2.48 | 3.7 | 4.57 | 5.55 | 6.35 | 7.47 | 8.49 | 99 | | **MICE** | 2.12 | 3.17 | 4.59 | 5.41 | 5.94 | 7.33 | 8.61 | 100 | | **MISSFOREST** | 2.26 | 3.36 | 4.31 | 5.33 | 6.15 | 8.06 | 9.85 | 101 | | **NONA** | 2.24 | 3.35 | 4.28 | 5.16 | 5.83 | 7.12 | 8.43 | 102 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. For the purposes of this definition, 18 | "control" means (i) the power, direct or indirect, to cause the 19 | direction or management of such entity, whether by contract or 20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 21 | outstanding shares, or (iii) beneficial ownership of such entity. 22 | 23 | "You" (or "Your") shall mean an individual or Legal Entity 24 | exercising permissions granted by this License. 25 | 26 | "Source" form shall mean the preferred form for making modifications, 27 | including but not limited to software source code, documentation 28 | source, and configuration files. 29 | 30 | "Object" form shall mean any form resulting from mechanical 31 | transformation or translation of a Source form, including but 32 | not limited to compiled object code, generated documentation, 33 | and conversions to other media types. 34 | 35 | "Work" shall mean the work of authorship, whether in Source or 36 | Object form, made available under the License, as indicated by a 37 | copyright notice that is included in or attached to the work 38 | (an example is provided in the Appendix below). 39 | 40 | "Derivative Works" shall mean any work, whether in Source or Object 41 | form, that is based on (or derived from) the Work and for which the 42 | editorial revisions, annotations, elaborations, or other modifications 43 | represent, as a whole, an original work of authorship. For the purposes 44 | of this License, Derivative Works shall not include works that remain 45 | separable from, or merely link (or bind by name) to the interfaces of, 46 | the Work and Derivative Works thereof. 47 | 48 | "Contribution" shall mean any work of authorship, including 49 | the original version of the Work and any modifications or additions 50 | to that Work or Derivative Works thereof, that is intentionally 51 | submitted to Licensor for inclusion in the Work by the copyright owner 52 | or by an individual or Legal Entity authorized to submit on behalf of 53 | the copyright owner. For the purposes of this definition, "submitted" 54 | means any form of electronic, verbal, or written communication sent 55 | to the Licensor or its representatives, including but not limited to 56 | communication on electronic mailing lists, source code control systems, 57 | and issue tracking systems that are managed by, or on behalf of, the 58 | Licensor for the purpose of discussing and improving the Work, but 59 | excluding communication that is conspicuously marked or otherwise 60 | designated in writing by the copyright owner as "Not a Contribution." 61 | 62 | "Contributor" shall mean Licensor and any individual or Legal Entity 63 | on behalf of whom a Contribution has been received by Licensor and 64 | subsequently incorporated within the Work. 65 | 66 | 2. Grant of Copyright License. Subject to the terms and conditions of 67 | this License, each Contributor hereby grants to You a perpetual, 68 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 69 | copyright license to reproduce, prepare Derivative Works of, 70 | publicly display, publicly perform, sublicense, and distribute the 71 | Work and such Derivative Works in Source or Object form. 72 | 73 | 3. Grant of Patent License. Subject to the terms and conditions of 74 | this License, each Contributor hereby grants to You a perpetual, 75 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 76 | (except as stated in this section) patent license to make, have made, 77 | use, offer to sell, sell, import, and otherwise transfer the Work, 78 | where such license applies only to those patent claims licensable 79 | by such Contributor that are necessarily infringed by their 80 | Contribution(s) alone or by combination of their Contribution(s) 81 | with the Work to which such Contribution(s) was submitted. If You 82 | institute patent litigation against any entity (including a 83 | cross-claim or counterclaim in a lawsuit) alleging that the Work 84 | or a Contribution incorporated within the Work constitutes direct 85 | or contributory patent infringement, then any patent licenses 86 | granted to You under this License for that Work shall terminate 87 | as of the date such litigation is filed. 88 | 89 | 4. Redistribution. You may reproduce and distribute copies of the 90 | Work or Derivative Works thereof in any medium, with or without 91 | modifications, and in Source or Object form, provided that You 92 | meet the following conditions: 93 | 94 | (a) You must give any other recipients of the Work or 95 | Derivative Works a copy of this License; and 96 | 97 | (b) You must cause any modified files to carry prominent notices 98 | stating that You changed the files; and 99 | 100 | (c) You must retain, in the Source form of any Derivative Works 101 | that You distribute, all copyright, patent, trademark, and 102 | attribution notices from the Source form of the Work, 103 | excluding those notices that do not pertain to any part of 104 | the Derivative Works; and 105 | 106 | (d) If the Work includes a "NOTICE" text file as part of its 107 | distribution, then any Derivative Works that You distribute must 108 | include a readable copy of the attribution notices contained 109 | within such NOTICE file, excluding those notices that do not 110 | pertain to any part of the Derivative Works, in at least one 111 | of the following places: within a NOTICE text file distributed 112 | as part of the Derivative Works; within the Source form or 113 | documentation, if provided along with the Derivative Works; or, 114 | within a display generated by the Derivative Works, if and 115 | wherever such third-party notices normally appear. The contents 116 | of the NOTICE file are for informational purposes only and 117 | do not modify the License. You may add Your own attribution 118 | notices within Derivative Works that You distribute, alongside 119 | or as an addendum to the NOTICE text from the Work, provided 120 | that such additional attribution notices cannot be construed 121 | as modifying the License. 122 | 123 | You may add Your own copyright statement to Your modifications and 124 | may provide additional or different license terms and conditions 125 | for use, reproduction, or distribution of Your modifications, or 126 | for any such Derivative Works as a whole, provided Your use, 127 | reproduction, and distribution of the Work otherwise complies with 128 | the conditions stated in this License. 129 | 130 | 5. Submission of Contributions. Unless You explicitly state otherwise, 131 | any Contribution intentionally submitted for inclusion in the Work 132 | by You to the Licensor shall be under the terms and conditions of 133 | this License, without any additional terms or conditions. 134 | Notwithstanding the above, nothing herein shall supersede or modify 135 | the terms of any separate license agreement you may have executed 136 | with Licensor regarding such Contributions. 137 | 138 | 6. Trademarks. This License does not grant permission to use the trade 139 | names, trademarks, service marks, or product names of the Licensor, 140 | except as required for reasonable and customary use in describing the 141 | origin of the Work and reproducing the content of the NOTICE file. 142 | 143 | 7. Disclaimer of Warranty. Unless required by applicable law or 144 | agreed to in writing, Licensor provides the Work (and each 145 | Contributor provides its Contributions) on an "AS IS" BASIS, 146 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 147 | implied, including, without limitation, any warranties or conditions 148 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 149 | PARTICULAR PURPOSE. You are solely responsible for determining the 150 | appropriateness of using or redistributing the Work and assume any 151 | risks associated with Your exercise of permissions under this License. 152 | 153 | 8. Limitation of Liability. In no event and under no legal theory, 154 | whether in tort (including negligence), contract, or otherwise, 155 | unless required by applicable law (such as deliberate and grossly 156 | negligent acts) or agreed to in writing, shall any Contributor be 157 | liable to You for damages, including any direct, indirect, special, 158 | incidental, or consequential damages of any character arising as a 159 | result of this License or out of the use or inability to use the 160 | Work (including but not limited to damages for loss of goodwill, 161 | work stoppage, computer failure or malfunction, or any and all 162 | other commercial damages or losses), even if such Contributor 163 | has been advised of the possibility of such damages. 164 | 165 | 9. Accepting Warranty or Additional Liability. While redistributing 166 | the Work or Derivative Works thereof, You may choose to offer, 167 | and charge a fee for, acceptance of support, warranty, indemnity, 168 | or other liability obligations and/or rights consistent with this 169 | License. However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | APPENDIX: How to apply the Apache License to your work. 179 | 180 | To apply the Apache License to your work, attach the following 181 | boilerplate notice, with the fields enclosed by brackets "[]" 182 | replaced with your own identifying information. (Don't include 183 | the brackets!) The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | --------------------------------------------------------------------------------