Xgboost gridsearchcv. I'm using GridSearchCV to find the best parameters.

However, I also tried to fit the model on the entire training dataset, and I have noticed that the 'roc_auc' performance metric is higher than when I used the Grid Search. evals_result() Instead of. Both classes require two arguments. best_parameters and pass them to a new model by unpacking like:. KFold. 935 (this is what I read from GS output). keyboard_arrow_up. import numpy as np. get the best_iteration directly from the fitted object instead of relying on the parameter grid values because we might Jun 17, 2020 · Final Model. I myself am hoping to find an alternative to GridSearchCV, but I don't think there is one. In this blog, we discuss how to perform hyperparameter tuning for XGBoost. When subsets of rows of the training data are also taken when . When creating gradient boosting models with XGBoost using the scikit-learn wrapper, the learning_rate parameter can be set to control the weighting of new trees added to the model. This is just a demonstration of it, but you could also set it up to track each CV fold, and log the time taken etc. Drop the dimensions booster from your hyperparameter search space. However, when I use the same code for other classifiers like random forest, it works and it returns complete results. The best classificator scored ~0. Aug 7, 2023 · Aug 7, 2023 4 min. How to have a multi-class prediction output for RandomForrest as XGBoost (i. GridSearchCV, please control the number of threads it can use. estimator, param_grid, cv, and scoring. Before running XGBoost, we must set three types of parameters: general parameters, booster parameters and task parameters. Load 7 more related questions Show Como el ajuste de hiperparametros o hyperparameter tunning mejora el desempeño de nuestros modelos?Quieres aprender las etapas del machine learning paso a p May 14, 2020 · Scikit-learn AIPによるXGBoostとクロスバリデーション. Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic - Machine Learning Aug 27, 2020 · Tuning Learning Rate in XGBoost. May 15, 2020 · 前回はクロスバリデーション(CV)までやりました。今回はグリッドサーチ(GS)と組み合わせて最適なパラメーターを探していきます。 GridSearchCVでGSCV forで書いてもいいんですが、sklearnにGridSearchCVというとても便利な関数があります。 GSをループさせながらCVでmean_best_scoreを探してそのパラメーター Jun 5, 2018 · Thus, in order to pass those in the GridSearchCV optimisation one has to provide it as an argument of the GridSearchCV. content_copy. You asked for suggestions for your specific scenario, so here are some of mine. Feb 16, 2023 · Step 2: Use GridSearchCV for improving the baseline. xgb_Gridcv will be the object which contains your best XGB model which can be accessed via xgb_Gridcv. XGBClassifier () # Create a new pipeline with preprocessing steps and model Jan 15, 2019 · Defining a list of parameters. Suppose the following code fits your model without monotonicity constraints. e predict_0, predict_1, predict_2)? The sample output are given in the MWEs above. For every pair of parameters in the Cartesian product of param_grid, we fit cv models and average their performance. May 15, 2020 · To recap then, the original code needs to be modified in 3 ways: upgrade xgboost library to 1. 1 . It involves specifying a set of possible values for each hyperparameter, and then training and evaluating the Dec 7, 2021 · GridSearchCV does not give the same results as expected when compared to xgboost. datasets import load_digits digits How does one convert the MWE for XGBoost using the Pipeline and GridSearchCV technique in MWE for RandomForest? Have to use 'num_class' where XGBRegressor() does not support. 1 Using GridSearchCV with xgbranker. May 7, 2017 · I have some classification problem in which I want to use xgboost. 👍 1. Modified 3 months ago. Here we’ll look at just a few of the most common and influential parameters that we’ll need to pay most attention to. import sys !{sys. where step_name is the corresponding name in your pipeline. But if i start then get "multiclass format is not supported". With EarlyStopping I would try to find the optimal number of epochs, but I don't know how I can combine EarlyStopping Jul 13, 2017 · I just want to point out that using the grid. I am trying to run GradientBoostingClassifier() with the help of gridsearchcv. XGBClassifier(n_estimators=200, tree_method='gpu_hist', predictor='gpu_predictor') xgb. IF this was the test set, this doesn't seem to be appropriate because, if I have understood Feb 15, 2017 · fold_auc = metrics. Why is xgb_cv using both when the tree_method defined is 'gpu_hist'. Jun 28, 2020 · The problem is that both GPU (NVIDIA 1050) and CPU cores are being used at the same time. NVIDIA system monitor shows a utilization of 85 to 90% and linux system monitor shows all cores working. XGBoost has many parameters that can be adjusted to achieve greater accuracy or generalisation for our models. It is very simple to enforce monotonicity constraints in XGBoost. 19. I have the following: alg = xgb. Examples. The Python implementation gives access to a vast number of inner parameters to tweak for better precision and accuracy. show() The built-in plot_importance function in the xgboost package seems like what you're looking for. 21. XGBoost can also be used for time series […] Jan 16, 2023 · Grid search is one of the most widely used techniques for hyperparameter tuning. feature_importance() if you happen ran this through a Pipeline and receive object has no attribute 'feature_importance' try optimized_GBM. However, the docs for GridSearchCV state I can use a int, cross-validation generator or an iterable, optional Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. 43759658 0. But now when I run best classificator on the same data: roc_auc_score(Y, clf_best_xgb. Carlos Carlos. model_no_constraints = xgb. The eval_set argument in XGboost seems to be evaluating the model on the passed data. 在Xgboost调参过程中，可以使用GridSearchCV ()进行网格调参，不用很麻烦的进行手动调参。. Using randomized search for the code example below took 3. On the first try, we will use parameter values that are close to those used by XGBoost by default: GridSearchCV implements a “fit” and a “score” method. The first is the model that you are optimizing. 50224188 2. Now I want to check the feature importance. In the example we tune subsample, colsample_bytree, max_depth, min_child_weight and learning_rate. executable} -m pip install xgboost Results: Feb 26, 2016 · Your code uses GridSearchCV which is an exhaustive search over specified parameter values for an estimator. I am aware that you in GridSearchCV can specify scoring=make_scorer(), using a metric from sklearn. DavidS. ModuleNotFoundError: No module named 'xgboost' Finally I solved Try this in the Jupyter Notebook cell. Check the docs. 0001), max_depth = c(2, 4, 6, 8, 10), gamma = 1 ) # pack the training control parameters xgb_trcontrol_1 = trainControl( method = "cv Jun 22, 2021 · As per xgboost documentation if I would save xgboost model using save_model it would be compatible with later versions but in my case the saved object is a pipeline object so I can not save it as xgboost object. Explore and run machine learning code with Kaggle Notebooks | Using data from House prices data. 04951167 0. Refresh. Some important features of XGBoost are: Parallelization: The model is implemented to train with multiple CPU cores. The AUC values returned by GridSearchCV are always higher than the one manually calculated (e. A simple technique for ensembling decision trees involves training trees on subsamples of the training dataset. grid_search import GridSearchCV from sklearn import Jun 6, 2021 · XGBoost can be tricky to navigate the different options when incorporating CV or parameter tuning. You will use these to find the best model exhaustively from a Oct 22, 2019 · 3. best_estimator_) plt. grid( nrounds = 1000, eta = c(0. DMatrix is the basic data storage for XGBoost used by all XGBoost algorithms including both training, prediction and explanation. Another advantage is that sometimes a split of negative loss, say -2, may be followed by a split of positive loss +10. It is both fast and efficient, performing well, if not the best, on a wide range of predictive modeling tasks and is a favorite among data science competition winners, such as those on Kaggle. For instance: GridSearchCV(clf, param_grid, cv=cv, scoring='accuracy', verbose=10) answered Jun 10, 2014 at 15:15. Banyak yang menganggapnya sebagai salah satu algoritme terbaik dan, karena kinerjanya yang hebat untuk masalah regresi dan klasifikasi, akan merekomendasikannya sebagai pilihan pertama dalam I'm using xgboost to perform binary classification. e. SyntaxError: Unexpected token < in JSON at position 4. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and Oct 15, 2019 · Since the XGBClassifier is being used, a sklearn’s adaptation of the XGBoost, we are going to use we will use GridSearchCV method with 5 folds in the cross-validation. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used. there are two problems here. 2. Print the best parameter values and lowest RMSE, using the . We will be using the GridSearchCV class from Scikit-learn which accepts possible values for desired hyperparameters and fits separate models on the given data for each combination of hyperparameters. It implements machine learning algorithms under the Gradient Boosting framework. Mar 28, 2017 · An update to @glao's answer and a response to @Vasim's comment/question, as of sklearn 0. It took 30 mins to train model with no parameter tuning. I have values Xtrn and Ytrn. Jun 5, 2023 · 1. For every combination of parameter, I also need "Precison", "recall" and accuracy in tabular format. 1 or as an additional fit_params argument in GridSearchCV instantiation in older sklearn versions Mar 27, 2021 · 4. 또한 snp_info 데이터를 활용하기 위해 데이터를 유전체 그룹 별로 묶어서 성능을 확인해보고 Feb 4, 2022 · As mentioned earlier, cross validation & grid tuning lead to longer training times given the repeated number of iterations a model must train through. Here we will give an example using Python, but the same general idea generalizes to other platforms. 데이터가 많지 않아서 최적의 모델 정확도를 내기 위해 Gradient Boosting 알고리즘의 일종인 xgboost와 머신러닝 모델의 성능 향상을 위해 GridSearchCv를 사용하였습니다. OK, we can give it a static eval set held out from GridSearchCV. 35 seconds. Both techniques evaluate models for a given hyperparameter vector using cross-validation, hence the “ CV ” suffix of each class name. Could you tell me how the score is evaluated in both cases? Aug 10, 2020 · Sklearn GridSearchCV with XGBoost - parameters cv might not be used. I ran GridSearchCV with score='roc_auc' on xgboost. com Feb 4, 2020 · I am trying to use 'AUCPR' as evaluation criteria for early-stopping using Sklearn's RandomSearchCV & Xgboost but I am unable to specify maximize=True for early stopping fit params. You can use any metric to perform cv and testing. I am using GridSearchCV to find the best params. Explore and run machine learning code with Kaggle Notebooks | Using data from Breast Cancer Wisconsin (Diagnostic) Data Set Jul 9, 2024 · clf = GridSearchCv(estimator, param_grid, cv, scoring) Primarily, it takes 4 arguments i. Also specify verbose=1 so you can better understand the output. I'm using GridSearchCV to find the best parameters. Scikit-Learn also has RandomizedSearchCV which samples a given number of candidates from a parameter space with a specified distribution. It appears very slow. g. Got it. Unexpected token < in JSON at position 4. In this code snippet we train an XGBoost classifier model, using GridSearchCV to tune five hyperparamters. See Custom refit strategy of a grid search with cross-validation for an example of Grid Search computation on the digits dataset. For instance, creating a fold of data for cross validation can consume a significant amount of memory: If the issue persists, it's likely a problem on our side. Now, we will use GridSearchCV [18] to search for a good combination of parameters in a parameter grid. model_selection. append(fold_auc) performance = np. As it is my first time to use XGBoost, I GridSearchCV实例：对Xgboost回归任务进行网格调参. kf = StratifiedKFold(n_splits=10, shuffle=False Oct 13, 2017 · I get the problem: GridSearchCV is trying to call len(cv) but my_cv is an iterator without length. best_estimator_ and now you can call evals_result method on it so in order to get the evals_result you need to use: xgb_Gridcv. This example of values: Xtrn Ytrn. Booster parameters depend on which booster you have chosen. If XGBoost uses eval_metric=‘mae’ I hope to avoid redefining MAE in GridSearchCV scoring=make_scorer(). -1. I won't go into detail about how Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. pip3 install xgboost But it doesn't work. fit(X_train, y_train) XGBoost Documentation. GridSearchCV from sklearn. Explore and run machine learning code with Kaggle Notebooks | Using data from Sberbank Russian Housing May 9, 2017 · Assuming GridSearchCV has the functionality to do the early stopping n_rounds for each fold, then we will have N(number of fold) n_rounds for each set of hyperparameter. Member. xgboost: treeの勾配ブースティングによる高性能な分類・予測モデル。 import xgboost as xgb from sklearn. Additionally, XGB has xgb. pyplot as plt from xgboost import plot_importance plot_importance(model. Subsets of the the rows in the training data can be taken to train individual trees called bagging. You probably want to go with the default booster 'gbtree'. When the model is trained with 'hist' and not 'gpu Oct 24, 2020 · <xgboostでグリッドサーチ(GridSearchCV)>*1 ※ 2020/04/09にQrunchで書いた記事を移行しました。 scikit-learnのGridSearchCVを利用して、グリッドサーチを行いました。 xgboostにはscikit-learnのWrapperが用意されているため、scikit-learnを使ったことがある人であれば、違和感なく使うことが出来ます。 使い方 データ Jan 9, 2021 · xgboost; gridsearchcv; Share. my_model = KNeighborsClassifier(**grid. 001, 0. We can use the grid search capability in scikit-learn to evaluate the effect on logarithmic loss of training a gradient boosting Aug 11, 2023 · For many cases, XGBoost is better than usual gradient boosting algorithms. XGBClassifier(objective='binary:logistic') And I am testing it log loss with: cross_validation. Finally, it is time to super-charge our XGBoost classifier. I have often read that GridSearchCV can be used in combination with early stopping, but I can not find a sample code in which this is demonstrated. 24381777 2. Oct 31, 2021 · Parameter tuning is a dark art in machine learning, the optimal parameters of a model can depend on many scenarios. feature_importances_. When I run the model to tune the parameter of XGBoost, it returns nan. So dask only enters this code then as the GridSearchCV. Jan 11, 2019 · In a multi-class setup we need to pass sample_weight parameter with a list of values (weights) matching the count of data-points (for example number of rows in X_train), to fit() of XGBoostClassifier. Mengapa XGBoost begitu populer? Awalnya dimulai sebagai proyek penelitian pada tahun 2014, XGBoost dengan cepat menjadi salah satu algoritma Pembelajaran Mesin paling populer dalam beberapa tahun terakhir. GridSearchCV allows you to choose your scorer with the 'scoring' parameter, and r2 is a valid option. Apr 7, 2021 · Hyperparameter Tuning of XGBoost with GridSearchCV. Explore and run machine learning code with Kaggle Notebooks | Using data from Flavours of Physics: Finding τ → μμμ. I searched around and I found this: Feb 18, 2022 · import matplotlib. cv 2 GridSearchCV and XGBClassifier with eval_metric = 'mlogloss' Tuning XGBoost Hyperparameters with Grid Search. 下面这个例子是使用Xgboost进行回归任务时使用GridSearchCV (). 174. The description of the arguments is as follows: 1. fit() method in the case of sklearn v0. Here is code that you can reproduce: GridSearch: Oct 30, 2020 · It should be possible to use GridSearchCV with XGBoost. We need to be a bit careful to pull the relevant parameters from our classifier object (i. Oct 20, 2017 · I know its a bit late, but still, If the installation of cuda is done correctly, the following code should work: Without GridSearch: import xgboost xgb = xgboost. Compared to our first iteration of the XGBoost model, we managed to improve slightly in terms of accuracy and micro F1-score. named_steps ["step_name"]. There are a few variants of DMatrix including normal DMatrix, which is a CSR matrix, QuantileDMatrix, which is used by histogram-based tree methods for saving memory, and lastly the experimental external-memory-based DMatrix, which reads data in batches during training. , the parameters and performance of each of the tested models, and loops through them, logging the results with MLFlow. param_grid=param_grid takes our pre-defined search space for the grid search. The overall GridSearchCV model took about four minutes to run, which may not seem like much, but take into consideration that we only had around 1k observations in this dataset. Jan 27, 2020 · I created a GridSearchCV for a Random Forest Regressor. best_params_) is good and all and I personally used it a lot. Since refit=True by default, the best fit is then validated on the eval set provided (a true test score). 337 1 1 gold badge 4 4 silver badges 18 18 bronze badges. (꽤 많은 파라미터와 어떤 값을 넣어야 할지 모를때! 그렇다면 여러 값을 넣어보고 최적의 파라미터를 찾아주는) 그래도 궁금하신 점은 최대한 아는대로 대답해드리겠습니다 3. Scikit-learnっぽくXGBoostやるよ、みたいな関数があります。web検索してもこっちのコードばっかりヒットするが、GridSearchCVが使えるからみんなこっち使ってるんだと思う。 3 days ago · XGBoost parameters, on the other hand, makes splits up to the max_depth specified and then starts pruning the tree backward and removing splits beyond which there is no positive gain. Jan 22, 2018 · 22. print("%f with: %r" % (mean,param)) 版权声明：本文为weixin Aug 19, 2022 · GridSearchCV performs cv for hyperparameter tuning using only training data. 0. cv () for performing a cross validation. fit () you can use xgb. We’ll get an intuition for these parameters by discussing how different values can impact the performance of pip install xgboost and. 3 (note that fit_params has been moved out of the instantiation of GridSearchCV and been moved into the fit() method; also, the import specifically pulls in the sklearn wrapper module from xgboost): Jan 7, 2016 · I find this code super useful because R’s implementation of xgboost (and to my knowledge Python’s) otherwise lacks support for a grid search: # set up the cross-validated hyper-parameter search xgb_grid_1 = expand. estimator=xgboost means we are using XGBoost as the model. We achieved lower multi class logistic loss and classification error! We see that a high feature importance score is assigned to ‘unknown’ marital status. 1. In order to do this we create a pipeline with multiple steps. estimator – A scikit-learn model. Here is the code: scoring= ['accuracy', 'precision','recall'] parameters = {#'nthread':[3,4], #when use hyperthread, xgboost may become slower. 62 vs. Finally, the search grid Nov 16, 2019 · The optimal hyperparameter I try to find via GridSearchCV from Scikit-learn. While using XGBoostClassifier with scikit-learn GridSearchCV, you can pass sample_weight directly to the fit() of GridSearchCV. Feb 28, 2020 · Thank you for responding! I have probably simplified my question too much. 다음에는 XGBoost+KFold+GridSearchCV 결합해서 하는 방법을 올리도록 하겠습니다. evals_result() Create a GridSearchCV object called grid_mse, passing in: the parameter grid to param_grid, the XGBRegressor to estimator, "neg_mean_squared_error" to scoring, and 4 to cv. However, xgboost has also a parameter called 'eval_metric' and I am a bit confused between the two. 878. metrics. To send the data to the model it must be cleaned and prepared. model_selection import GridSearchCV May 20, 2017 · My first multiclass classication. Jul 7, 2020 · Now that you've learned how to tune parameters individually with XGBoost, let's take your parameter tuning to the next level by using scikit-learn's GridSearch and RandomizedSearch capabilities with internal cross-validation using the GridSearchCV and RandomizedSearchCV functions. However, it would be odd to use a different metric for cv hyperparameter optimization and testing phases. It goes something like this : optimized_GBM. param_grid – A dictionary with parameter names as keys and lists of parameter values. Jan 16, 2019 · My situation is the following, I noticed GridSearchCV has a parameter called 'scoring' to which I can pass even more than one sklearn. 0. I have defined an XGBoost model and would like to tune some of its hyperparameters. However, like most machine learning algorithms, getting the most out of XGBoost requires optimizing its hyperparameters. use the xgboost library for the classifier instead of dask_xgboost. Save the best model (parameters) Load the best model paramerts so that we can apply a range of other classifiers on this defined model. Ytrn have 5 values [0,1,2,3,4]. XGBoost is a powerful and popular gradient-boosting library that is widely used for building regression and classification models. Dec 31, 2022 · GridSearchCV是XGBoost模型最常用的调参方法。 本文主要介绍了如何使用GridSearchCV寻找XGBoost的最优参数，有完整的代码和数据文件。 文中详细介绍了GridSearchCV的工作原理，param_grid等常用参数；常见的learning_rate和max_depth等可调参数及调参顺序；最后总结了GridSearchCV的 Aug 27, 2020 · Stochastic Gradient Boosting with XGBoost and scikit-learn in Python. May 15, 2022 · We specified a few options for GridSearchCV. train(params, dtrain, num_boost_round = 1000, evals = evallist, early Feb 27, 2022 · A XGBoost model is optimized with GridSearchCV by tuning hyperparameters: learning rate, number of estimators, max depth, min child weight, subsample, colsample bytree, gamma (min split loss), If the issue persists, it's likely a problem on our side. Fit the GridSearchCV object to X and y. Instead of using xgb. It’s best to let XGBoost to run in parallel instead of asking GridSearchCV to run multiple experiments at the same time. I have already referred to this question: GridSearchCV - XGBoost - Early Stopping 本篇知乎专栏博客详细介绍了如何在Scikit中进行特征选择和XGBoost回归预测的调参优化。 Aug 22, 2018 · I am using Python to train an XGBoost Regressor on a 25 feature column dataset and SKlearn's GridSearchCV for parameter tuning. Follow asked Jan 9, 2021 at 18:15. The GridSearchCV instance implements the usual estimator API: when “fitting” it on a dataset all the possible combinations of parameter values are evaluated and the best combination is retained. Applying a pipeline with GridSearchCV on the parameters, using LogisticRegression () as a baseline to find the best model parameters. 01, 0. Explore and run machine learning code with Kaggle Notebooks | Using data from Santander Customer Transaction Prediction Apr 21, 2022 · I would like to use GridSearchCV to tune a XGBoost classifier. If I run GridSearchCV to train model with 3 folds and 6 learning rate values, it will take more than 10 hours to return. best Grid search with XGBoost# Now that you’ve learned how to tune parameters individually with XGBoost, let’s take your parameter tuning to the next level by using scikit-learn’s GridSearch and RandomizedSearch capabilities with internal cross-validation using the GridSearchCV and RandomizedSearchCV functions. So far I have created the following code: # Create a new instance of the classifier xgbr = xgb. auc(fpr, tpr) aucs. predict(X)) it gives me score ~0. Set the verbose parameter in GridSearchCV to a positive number (the greater the number the more detail you will get). Oct 15, 2019 · Building the Pipeline. Viewed 9k times 0 I'm currently trying to analyze data for the See full list on towardsdatascience. If you are using a HPO library like sklearn. Instead the eval_metric minimizes for AUCPR. I am using gridsearchcv to tune the parameters of my model and I also use pipeline and cross-validation. You probably need to provide to GridSearchCV a score function that return the logloss (negative, the grid select the higher score models, and we want the lesser loss models) , and uses the model of the best iteration, as in: from xgboost import XGBClassifier from sklearn. One of the checks that I would like to do is the graphical analysis of the loss from train and test. However, I don't know how to save the best model once the model with the best parameters has Aug 19, 2022 · GridSearchCV is used to find optimal parameters. The first step will call our preprocessing Aug 11, 2020 · Python-Classifier-Xgboost - show cv with params, duration time, score in GridSearchCV Hot Network Questions Can I convert 50 amp electric oven circuit to subpanel, and power oven plus water heater, plus maybe a car charger? May 11, 2018 · Scoring in GridSearchCV for XGBoost. KFold instead of dask_ml. mean(aucs) where I manually pre-split the data into training and test set (same 5 CV approach). Mar 18, 2021 · XGBoost is an efficient implementation of gradient boosting for classification and regression problems. Sep 18, 2020 · Specifically, it provides the RandomizedSearchCV for random search and GridSearchCV for grid search. Improve this question. gbm. I am using XGBoost to train 1 million rows and ~15 features from Kaggle project Rossmann Store Sales. Now, GridSearchCV does k-fold cross-validation in the training set but XGBoost uses a separate dedicated eval set for early stopping. But when we also try to use early stopping, XGBoost wants an eval set. train () to utilize the DMatrix object. XGBoost Parameters. I am experimenting with xgboost. You will use these to find the Apr 19, 2017 · Yes, it's possible. Is there any way to load this in new version without retraining model? Jul 25, 2020 · Using early stopping when performing hyper-parameter tuning saves us time and allows us to explore a more diverse set of parameters. For instance, creating a fold of data for cross validation can consume a significant amount of memory: 7. 70) when using the same parameter for RandomForest . May 11, 2016 · I used grid search on xgboost with different learning rates, max depths and number of estimators. 35173485 1. 7. Maybe average of n_rounds can be used for final best hyperparameter set, but it might not be a good choice when the n_rounds different too much from each other. General parameters relate to which booster we are using to do boosting, commonly tree or linear model. use sklearn. Ask Question Asked 6 years, 2 months ago. metrics as shown here. best_estimator_. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a Apr 2, 2020 · This code takes the results of the cross-validation (i. XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable . ml li kf ay if jh el ex ga zw