[ Fixes: #78 ] Covariate Data #80
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Updating master
Updating KaairaGupta/master
1. Added SVM classifier with outlier removal and hyperparameter tuning 2. Notebook is reused from issue#2 3. Added code to run multiple test/train split and test the accuracy of the model"
1. Increased the number of loops for test-train cycle
1. Increased the number of loops for test-train cycle
1. Updated test_train_split function 2. Updated function call in python notebook
1. Fixed Transformation Code 2. Added warnings
* visual for eeg * code restructured * mozilla#3 data-split space mapped * fixes issue3 * studied data splits for all classifiers * added graph in the loop * docstrings added * validation sets added * formatting * evaluated all classifiers * compared models * result added * indexed * removed plot-recall-curve * learning-curve * added models * env refresh * final estimate added * black formats * conclusion added
* visual for eeg * code restructured * mozilla#3 data-split space mapped * tabulated relation btw k and evaluation metrics * gain-lift charts of models * auc-roc implemented * fixes issue3 * studied data splits for all classifiers * added graph in the loop * docstrings added * validation sets added * formatting * evaluated all classifiers * compared models * result added * interprtation added * docstring, interpretation added * indexed * removed plot-recall-curve * shorten PR * conflict resolve Co-authored-by: mlopatka <[email protected]>
* Outreachy startup task 1. Added module with all functions toimport from jupyter notebook. 2. Added Jupyter Notebook with outputs. * made the dataset path a variable agrument for the function * Details about confusion matrix * Added functions to see scatterplot and explained confusion matrix and kernel method. * Removed redundant imports and cells. * Used standard scalar to standardize data * minor changes * minor changes * Added relative paths * Added the dataset in the repo and python black formatting * Changed path to relative path in repo
* Data Loaded from vehicles.csv * Data visulaization and training model with ifferent algorithms * Evaluation of model is done. * Changed model from Logistic Regression to Support Vector Machine At first attempt i used three differnet models but Logistic Regression , Support Vector Machine and Decision Tree, and the overall accuracy with LR was better than any other but with changing validation parameters in SVM classification , model accuacy increased from 82% to 88%. * Delete train and test model-checkpoint.ipynb * Changed file named. * all python modules were added * docstrings were added * labels added in confusion matrix * Histogram colors were changed into single color * solved histogram issue * Update modules.py * changes made in histogram * Update modules.py * sorted histogram * Update modules.py * labels were added for confusion matrix * Python Custom Modules were added * Update Vehicle_Classifier.ipynb * Update modules.py * Update modules.py * Update modules.py * added labels in confusion matrix * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Requested Changes were made * Update modules.py * change categorical data into numerical data * change areguments in LR model * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Requested changes were made * Update modules.py * Update modules.py * Update modules.py * Update Vehicle_Classifier.ipynb * updates file * Delete Untitled.ipynb * Update modules.py * code formatted using python Black * Requested changes were made * shifted classifier's code from modules.py to ModelEvaluation.py * removed learning curves from file * added function for model evaluation in Model Evaluation file * updated svm and lr * added comments * added doc strings * Update ModelEvaluation.py * Update ModelEvaluation.py * Update Vehicle_Classifier.ipynb * Update modules.py * added descriptions * added interpretations of visualization * creating another branch from master * solving branching issues * main visulaization file is added * Added module for visualization of missclassification * added docstring and reformatted to python black * added interpretation of misclassification * removed unnecessary comments * changed name of file from VehicleClassifier to TrainTestSplit_Traversal * custom module file of Train_Test_plit_Traversal is added * version 2 of train_test_split_traversal is added with some changes plus code is reformatted with python black * changed plot labels * removed error * Update TrainTest_Split_Traversal.py * minor changes were done * chnaged file names and custom module file of CrossValidationFold_Traversal is added * added docstrings and resolved rrors * all files are moved to folder * added docstring and file reformatted to python black * added comments
…ozilla#32) * 1. Simple scatter plot 2. Violin and Box plots * Added plots for visualising misclassifications for Logistic regression and SVM classification. * Revert "1. Simple scatter plot" This reverts commit cb26342. * Removed redundant commits and updated notebook according to start up task * Changed to csv file from the repo and updated notebook acc to PR mozilla#22 * Refactored code in module and added python black formatting * 1. Added ROC curves with AUC 2. Formatting and refactoring. * Minor changes
* Create Readme.md * Create files for exploring issue mozilla#2 * Format using black * Remove notebook from master * Increase modularization * create file for issue 6 * remove file added by mistake * Create notebook for issue 6 * Re-upload to the right folder * Delete file from the incorrect folder
* initial commit * updated notebook template to get started * added comment in notebook * Added notebook for issue#5 - calibration plots 1. Added module to plot calibration curve 2. Notebook to read data and display the calibration plot * fixed formatting
mlopatka
suggested changes
Mar 27, 2020
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I have reviewed the notebook's rendered version in github, the progress looks good.
And while I appreciate you taking the initiative to use another dataset, this PR needs to be refactored to merge in to master.
The *.zip file is still included in the PR despite it's addition to the .gitignore. If you want to work with this data set please submit a PR that adds the extracted CSV into the repo's main dataset directory and then load it from that source as with other datasets.
* adds .ipynb and .py * updates code by formatting
* Data Loaded from vehicles.csv * Data visulaization and training model with ifferent algorithms * Evaluation of model is done. * Changed model from Logistic Regression to Support Vector Machine At first attempt i used three differnet models but Logistic Regression , Support Vector Machine and Decision Tree, and the overall accuracy with LR was better than any other but with changing validation parameters in SVM classification , model accuacy increased from 82% to 88%. * Delete train and test model-checkpoint.ipynb * Changed file named. * all python modules were added * docstrings were added * labels added in confusion matrix * Histogram colors were changed into single color * solved histogram issue * Update modules.py * changes made in histogram * Update modules.py * sorted histogram * Update modules.py * labels were added for confusion matrix * Python Custom Modules were added * Update Vehicle_Classifier.ipynb * Update modules.py * Update modules.py * Update modules.py * added labels in confusion matrix * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Requested Changes were made * Update modules.py * change categorical data into numerical data * change areguments in LR model * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Requested changes were made * Update modules.py * Update modules.py * Update modules.py * Update Vehicle_Classifier.ipynb * updates file * Delete Untitled.ipynb * Update modules.py * code formatted using python Black * Requested changes were made * shifted classifier's code from modules.py to ModelEvaluation.py * removed learning curves from file * added function for model evaluation in Model Evaluation file * updated svm and lr * added comments * added doc strings * Update ModelEvaluation.py * Update ModelEvaluation.py * Update Vehicle_Classifier.ipynb * Update modules.py * added descriptions * added interpretations of visualization * creating another branch from master * solving branching issues * main visulaization file is added * Added module for visualization of missclassification * added docstring and reformatted to python black * added interpretation of misclassification * removed unnecessary comments * changed name of file from VehicleClassifier to TrainTestSplit_Traversal * custom module file of Train_Test_plit_Traversal is added * version 2 of train_test_split_traversal is added with some changes plus code is reformatted with python black * changed plot labels * removed error * Update TrainTest_Split_Traversal.py * minor changes were done * chnaged file names and custom module file of CrossValidationFold_Traversal is added * added docstrings and resolved rrors * all files are moved to folder * added docstring and file reformatted to python black * added comments * addedd calibrationplot .py * added docstrings * Update Calibration plot.ipynb
* Update .gitignore * Preliminary Analysis * Helper modules (Bar and Hist graph) * Rough KNN algorithm implemented * Delete libraries.py * KNN classifier refactored and polished Returns only variable of intests for use the metrics calculations. * refactored for performance just the required functions imported * draft mlp classifier implemented to be reviewed * ... * Threshold conversion logic implemented Since knn.predict calculates a probability, we implement a logic for binary classification * Prelimary cleaning and knn model classification implemented! * Adjusted plor error with title placement * ... * Files reformated with 'Black' * Logistic Regression classifier * Refactores modules to improve modularity * Implemented Log Reg * Deleted mpl module to focus on knn and log reg * Refactors gotignore to my personal folder * refactored for readability * Implementation to add counts and relative percentages on bars graph * Refactored name mozilla#2, Completed Prelimary Analysis and Interpreted Results * Update Issue mozilla#2 - Train and test a classification model (PRESC).ipynb * Files reformated with 'Black' * Display Error corrected * Interpreted choice of hyper-parameters * Refactored and Added Modules used for Issue 3 * Prelimanry Analysis - Traversal of the space of train_test splits * Issue#3 complete * Removed Issues mozilla#2 and mozilla#3 ipynb * Issue mozilla#4 - completed Issue mozilla#4 - Traversal of the space of cross-validation folds * Delete defaults_data.csv Removing duplication of the existing data set which can be loaded from the repos root directory. Co-authored-by: mlopatka <[email protected]>
…aset (mozilla#92) * Classification model wine.csv * Classification model wine.csv * Merging modifications
mozilla#2 Dropped quality, shifted the logic to python file, shifted imports to the top, added confusion_matrix and classification_report
Adding logistic regression for winequality.csv
) * fixes mozilla#8 * fixes mozilla#4, attempt 1 * updated missclassification graph and brokedown functions * first attempt to fix # 3 * implemeneted all change requests * formatted code for all helper files * minor fix * fixed code formatting issues and removed extra file * fixed code formatting, added docstring to func * fixed relative path * fixed all changes requested * fixed relative path in notebook * fixing conflict with some file changes * fixing attempt last for conflicts
* fixes mozilla#8 * fixes mozilla#4, attempt 1 * first attempt to fix # 3 * implemeneted all change requests * formatted code for all helper files * minor fix * fixed code formatting, added docstring to func * first attempt on 63 * fixing conflicts Co-authored-by: mlopatka <[email protected]>
…ear Model (Stochastic Gradient Descent) on winequality.csv (mozilla#58) * adds incomplete files * adds .ipynb, .py and updates environment.yml * Delete winequality.ipynb removing duplicate files * Delete winequality_modules.py removing duplicate files * Delete winequality.ipynb removing incomplete files * Delete winequality_modules.py removing incomplete files * adds .ipynb, .py and updates environment.yml * adds description and deatiled reasoning for the methods, models and parameters used * drops quality column * updates .py file * adds files in a new folder * updates .yml
…mozilla#111) * WIP: mozilla#2 on the dataset 'eeg.csv' WIP: mozilla#2 on the dataset 'eeg.csv' * Add files via upload * Delete WIP: mozilla#2 on the dataset 'eeg.csv' * Delete mozilla#2 Train and test a classification model, eeg.csv-checkpoint.ipynb * WIP: mozilla#2 on the dataset 'eeg.csv' * Delete mozilla#2 Train and test a classification model, eeg.csv-checkpoint.ipynb * WIP: mozilla#2 Train and test a classification model, eeg.csv dataset * Delete mozilla#2 Train and test a classification model, eeg.csv.ipynb * Create README * WIP: mozilla#2 Train and test a classification model, eeg.csv dataset * Delete README
issue#3:traversal-of-the-space-of-train-test-splits
For mozilla#2: on the dataset 'winequality.csv'
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I was trying to push lfs files in the branch and in the process I made a few mistakes. I tried but could not find a way to revert the changes made. Is it okay if I close this PR and make a new one? Or if there is something else that could help solve the problem? |
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@archisha-chandel can you link to the new PR in a comment? |
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[ Fixes: #78, #3 ]