From f1c5d7dfc51f601c06c29c6daf993168d15ceb8d Mon Sep 17 00:00:00 2001 From: WEgeophysics Date: Sat, 11 Nov 2023 23:06:03 +0800 Subject: [PATCH] fix changes in origin module --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index bfbe57e..5b91cc4 100644 --- a/README.md +++ b/README.md @@ -23,10 +23,10 @@ The system requires preferably Python 3.10+. ### Predict hydraulic conductivity ``K`` from logging dataset using MXS approach MXS stands for mixture learning strategy. It uses upstream unsupervised learning for -``k`` -aquifer similarity label prediction and the supervising learning for -final ``k``-value prediction. For our toy example, we use two boreholes data +``K`` -aquifer similarity label prediction and the supervising learning for +final ``K``-value prediction. For our toy example, we use two boreholes data stored in the software and merge them to compose a unique dataset. In addition, we dropped the -``remark`` observation which is subjective data not useful for ``k`` prediction as: +``remark`` observation which is subjective data not useful for ``K`` prediction as: ```python @@ -39,7 +39,7 @@ Out[3]: Index(['hole_id', 'depth_top', 'depth_bottom', 'strata_name', 'rock_name dtype='object') hdata = h.frame ``` -``k`` is collected as continue values (m/darcies) and should be categorized for the +``K`` is collected as continue values (m/darcies) and should be categorized for the naive group of aquifer prediction (NGA). The latter is used to predict upstream the MXS target ``ymxs``. Here, we used the default categorization provided by the software and we assume that in the area, there are at least ``2`` @@ -69,14 +69,14 @@ Xtrain, Xtest, ytrain, ytest = hlearn.sklearn.train_test_split (Xtransf, ymxs ) ypred_k_svc= hlearn.sklearn.SVC().fit(Xtrain, ytrain).predict(Xtest) ypred_k_rf = hlearn.sklearn.RandomForestClassifier ().fit(Xtrain, ytrain).predict(Xtest) ``` -We can now check the ``k`` prediction scores using ``accuracy_score`` function as: +We can now check the ``K`` prediction scores using ``accuracy_score`` function as: ```python hlearn.sklearn.accuracy_score (ytest, ypred_k_svc) Out[7]: 0.9272727272727272 hlearn.sklearn.accuracy_score (ytest, ypred_k_rf) Out[8]: 0.9636363636363636 ``` -As we can see, the results of ``k`` prediction are quite satisfactory for our +As we can see, the results of ``K`` prediction are quite satisfactory for our toy example using only two boreholes data. Note that things can become more interesting when using many boreholes data. @@ -85,6 +85,6 @@ interesting when using many boreholes data. 1. Department of Geophysics, School of Geosciences & Info-physics, [Central South University](https://en.csu.edu.cn/), China. 2. Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration Changsha, Hunan, China -3. Laboratoire de Geologie Ressources Minerales et Energetiques, UFR des Sciences de la Terre et des Ressources Mini�res, [Universit� F�lix Houphou�t-Boigny]( https://www.univ-fhb.edu.ci/index.php/ufr-strm/), Cote d'Ivoire. +3. Laboratoire de Geologie Ressources Minerales et Energetiques, UFR des Sciences de la Terre et des Ressources Mini�res, [Universite Felix Houphouet-Boigny]( https://www.univ-fhb.edu.ci/index.php/ufr-strm/), Cote d'Ivoire. Developer: [_L. Kouadio_](https://wegeophysics.github.io/) <> \ No newline at end of file