Maybe the decision tree used a fraction of the features as a regularization technique. Read more in the User Guide. _splitter import Splitter 42 from . 24. In the case considered here, we simply what to make a fit, so we do not care about the notions too much, but we need to bring the first input to that function into the sklearn. This package is able to flexibly plot trees with various options. Jan 14, 2021 · I plotted my sklearn decision tree using the plot_tree function. We also provide code examples and best practices for creating clear and Aug 13, 2018 · grid_resolution=5) fig. User Guide. We provide Display classes that expose two methods for creating plots: from Apr 18, 2023 · Now, to plot the tree and get the underlying splits made by the model, we'll use Scikit-Learn's plot_tree() method and matplotlib to define a size for the plot. Warning. Open Anaconda prompt and write below command. To convert this to the absolute values, you can multiply these by the corresponding value of DecisionTreeClassifier. # plot decision tree from xgboost import XGBClassifier from xgboost import plot_tree import matplotlib. edited Apr 12 at 18:24. import seaborn as sns sns. Scikit-learn defines a simple API for creating visualizations for machine learning. As is shown in the result before discretization, linear model is fast to build and relatively straightforward to plot_confusion_matrix is deprecated in 1. could help but if it isn't you have to upgrade the whole python version. clf = tree. sklearn. After plotting a sklearn decision tree I check what it says in each box and there is one feature "value" that I am not sure what it refers. DecisionTreeClassifier(max_leaf_nodes=8) specifies (max) 8 leaves, so unless the tree builder has another reason to stop it will hit the max. The function to measure the quality of a split. Alternatively you can instead select the clusters at the leaves of the tree – this provides the most fine grained and homogeneous clusters. 13で1Google Colaboratory上で動かしています。. pip install --upgrade scikit-learn Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. fig, axes = plt. feature_namesarray-like of shape (n_features,), default=None. The nodes have the following structure: But I don't understand what does the value = [2417, 1059] mean. 012, which would suggest that none of the features are important. Raywho. plot_roc_curve — scikit-learn 0. e, plot1 + plot2; or to customize the style and elements in the plot. py in 38 from . Supervised learning. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical Feature importances are provided by the fitted attribute feature_importances_ and they are computed as the mean and standard deviation of accumulation of the impurity decrease within each tree. The Plot API supports both functional and object-oriented (OOP) interfaces. Read more about the export I work on OS X and the graphviz stuff seems to be no longer properly supported there. However if I put class_names in export function as . I have added plt. PCA. plot_tree) will not show anything if you don't have plt. The tree_. The i-th element of each # array holds information about the node `i`. 表示 Clustering — scikit-learn 1. 0 (roughly May 2019), Decision Trees can now be plotted with matplotlib using scikit-learn’s tree. 422, which means “this node is a leaf node, and the predicted Oct 20, 2016 · After you fit a random forest model in scikit-learn, you can visualize individual decision trees from a random forest. plot_tree(clf); plot_tree. subplots(figsize=(30, 30)) xgb. 117 2 13. . DecisionTreeClassifier(random_state=0). Please help me plot a tree of higher resolution as the image gets blurred when I increase the tree depth. Parameters: estimatorestimator instance. Apr 20, 2020 · I tried to plot confusion matrix with Jupyter notebook using sklearn. tree_ also stores the entire binary tree structure, represented as a Jul 12, 2018 · SVM-Decision-Boundary-Animator. For example, there are 29 nodes. 02. class_namesarray-like of shape (n_classes The decision tree correctly identifies even and odd numbers and the predictions are working properly. value gives an array of the relative size of the classes. May 20, 2023 · The size of boxes representing tree nodes in decision tree plot in Scikit-learn can greatly affect the readability and interpretation of the model. plot_tree without relying on the dot library which is a hard-to-install dependency which we will cover later on in the blog post. 1. plot_tree(model, num_trees=4, ax=ax) plt. show() The standard approach for HDBSCAN* is to use an Excess of Mass ( "eom" ) algorithm to find the most persistent clusters. set_style() the plot output from tree. figsize'] = 80,50. cluster. Graph objects have a to_string() method which returns the DOT source code string of the tree, which can also be used with the graphviz. You pass the fit model into the plot_tree() method as the main argument. metrics import accuracy_score. Let's suppose the label I want to add is "lambda", and I already have a different value for each terminal node based on their node ID. As you can see, visualizing a decision tree has become a lot simpler with sklearn models. Update Mar/2018: Added alternate link to download the dataset as the original appears […] The permutation importance on the right plot shows that permuting a feature drops the accuracy by at most 0. Script File: Loads, normalises, and organises the Iris dataset from Sklearn package. This example plots the corresponding dendrogram of a hierarchical clustering using AgglomerativeClustering and the dendrogram method available in scipy. The SVM-Decision-Boundary-Animator GitHub repo animates the SVM Decision Boundary Hyperplane on the Iris data using matplotlib. Oct 31, 2016 · One thing may needs to be changed is from fig. 0. Iris plants dataset# Data Set Characteristics: Number of Instances: 150 (50 in each of three classes) Number of Attributes: Jul 15, 2018 · original_tree. Only np. There should be an option to specify image size or resolution. import pandas as pd. float32, np. The desired data-type for the output. Use the figsize or dpi arguments of plt. 2 Release Highlights for scikit-learn 0. plot_tree(decision_tree=clf, feature_names=feature_names, class_names=class_names, filled=True, rounded=True, fontsize=10, max_depth=4,dpi=300) #adjust the dpi to the parameter that fits best your output plt Jul 23, 2021 · base_dtr_score = cross_dtr_score. import numpy as np from matplotlib import pyplot as plt from scipy. _tree import DepthFirstTreeBuilder scikit-learnのDecisionTreeClassifierの基本的使い方を解説します。. I had the same issue on 3. 13. 9, which means “this node splits on the feature named “Column_10”, with threshold 875. Getting the data into shape. 显示的样本计数使用可能存在的任何样本权重进行加权。. 24). Apr 19, 2023 · Plot Decision Boundaries Using Python and Scikit-Learn. The code below first fits a random forest model. The key feature of this API is to allow for quick plotting and visual adjustments without recalculation. predict(iris. data) sklearn_evaluation. If None, then nodes are expanded until all At least on windows matplotlib (which is used to show the tree with tree. Linear dimensionality reduction using Singular Value Decomposition of the data to Nov 20, 2023 · Pruning is a process of removing or collapsing some nodes or branches of a decision tree, to reduce its size and complexity. import sklearn print (sklearn. You need to use the predict method. Removing features with low variance Aug 31, 2017 · type(graph) <type 'list'>. decomposition. The decision tree is basically like this (in pdf) is_even<=0. This is in contradiction with the high test accuracy computed as baseline: some feature must be important. 1 ), instead of absolute values, clf. 1. get_feature_names() #Shows feature names. class sklearn. The sample counts that are shown are weighted with any sample_weights that might be present. How can I fix this? Learning curves show the effect of adding more samples during the training process. A single estimator thus handles several joint classification tasks. 最近気づい The values of this array sum to 1, unless all trees are single node trees consisting of only the root node, in which case it will be an array of zeros. plot_tree(clf,filled=True,rounded=True) plt. This way, you can generate 6 models and see which parameters lead to the best score, which will be the best model to choose, given these parameters. criterion{“gini”, “entropy”, “log_loss”}, default=”gini”. target_names) Aug 12, 2014 · Then if you have matplotlib installed, you can plot with sklearn. Plot specified tree. Non-leaf nodes have labels like Column_10 <= 875. Using these two return values, extra options become available such as setting the width and height, independently. This is the first time I am facing this problem as usually any kind of tree based model does not take this long. tree import DecisionTreeClassifier from sklearn import tree classifier = DecisionTreeClassifier(max_depth = 3,random_state = 0) tree. It's kind of like a pixel-grid; I shrunk the size down to an array of only 2000 and noticed that the coordinates were just May 20, 2016 · from xgboost import plot_tree. It will give you much more information. answered May 4, 2022 at 8:27. datasets import load_iris Jun 5, 2021 · According to the documentation of plot_tree for its filled parameter:. The example decision tree will look like: Then if you have matplotlib installed, you can plot with sklearn. 6,172 7 7 gold badges 49 49 silver badges 100 100 Aug 27, 2020 · Plotting individual decision trees can provide insight into the gradient boosting process for a given dataset. IsolationForest example. The classes in the sklearn. plot #. 0 and will be removed in 1. Fit the gradient boosting model. But there is an errror appeared in the console. (graph, ) = pydot. 6 20120305 (Red Hat 4. Aug 19, 2020 · Rでは決定木の可視化は非常に楽だが、Pythonでは他のツールを入れながらでないと、、、と昔は大変だったのですが、現在ではsklearnのplot_treeだけで簡単に表示できるようになっています。. I find this incredibly useful for interpretation especially of the nodes on a tree plot are very small and hard to see. The from Jun 8, 2019 · make use of feature_names and class_names parameters:. But value? machine-learning. 22 Plot classification probability K-means Clustering Plot H Dec 4, 2019 · Below are my code: from sklearn import tree. In the case considered here, we simply what to make a fit, so we do not care about the notions too much, but we need to bring the first input to that function into the May 15, 2023 · Sklearn plot_tree plot is too small. graph_from_dot_data(dot_data. Let’s get started. PCA(n_components=None, *, copy=True, whiten=False, svd_solver='auto', tol=0. dt = DecisionTreeClassifier() dt. graphviz also helps to create appealing tree visualizations for the Decision Trees. A Bagging classifier. 視覚化は軸のサイズに自動的に適合します。. export_graphviz method (graphviz needed) plot with dtreeviz package (dtreeviz and graphviz needed) These datasets are useful to quickly illustrate the behavior of the various algorithms implemented in scikit-learn. utils. 1 documentation. Maybe you set a maximum depth of 2, or some other parameter that prevents additional splitting. plot_tree too small，大家都在找解答。sklearn. set_figwidth(8) fig. compute_node_depths() method computes the depth of each node in the tree. さらにplot_treeはmatplotlibと同様に操作できるため、pandasなどに慣れて In jupyter notebook the following plots the decision tree: from sklearn. tree_. Pruning can be done either before or after the tree is fully grown. Changed in version 0. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. to_string()) gvz_graph Cost complexity pruning provides another option to control the size of a tree. MosQuan. 決定木をプロットします。. Improve this answer. pylab import rcParams. Both the number of properties and the number of classes per property is greater than 2. The hint to look at is the return value of the method (which is "fig" and "ax"). It does not produce the nodes or arrows to actually visualize the tree. mean() The problem is it is taking too long to run, even for the baseline model. fit(X, y) dot_data = tree. show() somewhere. 7. pyplot as plt. 7. Greater values of ccp_alpha increase the number of nodes pruned. fit_transform(data) vec. graphviz. so instead of it displaying X [0], I would want it to See decision tree for more information on the estimator. Jul 18, 2018 · 1. from sklearn import tree. png: resized_tree. lightgbm. My workflow to output the tree is roughly as follows. #. 要绘制的决策树。. tight_layout() This provides really good layout with customisable height and width. In the past, it would take me about 10 to 15 minutes to write a code with two different packages that can be done with two lines of code. In the example shown, 5 of the 8 leaves have a very small amount of samples (<=3) compared to the others 3 leaves (>50), a possible sign of over-fitting. 21 版本中的新增内容。. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both Gallery examples: Release Highlights for scikit-learn 1. DecisionTreeClassifier() Jun 20, 2022 · This new-ish function is much easier to use than the older Graphviz visualization. Parameters: criterion{“gini”, “entropy”, “log_loss”}, default=”gini”. While the functional API allows you to quickly generate out-of-the-box plots and is the easiest to get started with, the OOP API offers more flexibility to compare models using a simple synatx, i. pdf") Sep 19, 2016 · svr = SVR(kernel=kernel, C=c, degree=4) svr. save () to fig. __version__) If the version shows less than 0. Using KBinsDiscretizer to discretize continuous features. I am definitely looking forward to future updates that support random forest and ensemble models. plot_tree(finalmodel, num_trees=X) hope this will help, I think you should set up the matplotlib parameters first. so no need to use sklearn. answered Apr 14, 2020 at 1:38. answered Mar 14, 2017 at 12:36. n_node_samples for the same node index. feature_names, class_names=iris. tree import DecisionTreeClassifier from sklearn import tree model = DecisionTreeClassifier() model. float32 and if a sparse matrix is provided to a sparse csr_matrix. 2 documentation. plot_tree method (matplotlib needed) plot with sklearn. png') However, the saved image is totally blank. Internally, it will be converted to dtype=np. Visualizations #. The label1 is marked "o" and not "e". 24 Release Highlights for scikit-learn 0. fit(X_train, y_train) # plot tree. pyplot as plt # fit model no training data model = XGBClassifier() model. Python3. decision-trees. Each node in the graph represents a node in the tree. plt. subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300) tree. fit takes two arguments. figure 的 figsize 或 dpi 参数来控制渲染的大小。. So why is it taking such a long time to run even for something as simple sklearn. Principal component analysis (PCA). For checking Version Open any python idle Running below program. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. fit(train_features, train_target) score = svr. We also show the tree structure of a model built on all of the features. ensemble import RandomForestClassifier. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0. dtype{np. 表示されるサンプル数は、存在する可能性のあるsample_weightsで重み付けされます。. Tree-based models have become a popular choice for Machine Learning, not only due to their results, and the need for fewer transformations when working with data (due to robustness to input and scale invariance), but also because there is a way to take a peek inside of If using scikit-learn and seaborn together, when using sns. 17: parameter drop_intermediate. tree' has no attribute 'plot_tree' Although I install Jul 30, 2022 · Save the Tree Representation of the plot_tree method… fig. Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that, given train data, returns an array of integer 0. show() # mandatory on Windows. 環境. I want to show decision tree figure for my data visualization. We are only interested in first element of the list. vec = DictVectorizer() data_vectorized = vec. 0, iterated_power='auto', n_oversamples=10, power_iteration_normalizer='auto', random_state=None) [source] #. ##set up the parameters. sometree = . plot_tree(classifier); Sep 10, 2015 · 17. cluster import AgglomerativeClustering from sklearn. The first line will be the column and the value where it splits, the gini the "disorder" of the data and sample the number of samples in the node. plot_tree: tree. It can be an instance of DecisionTreeClassifier or DecisionTreeRegressor. The effect is depicted by checking the statistical performance of the model in terms of training score and testing score. rcParams['figure. The way I managed to plot the damn dendogram was using the software package ete3. 2, random_state=55) # Use the random grid to search for best hyperparameters. figure(figsize=(50,30)) artists = sklearn. 9”. Cássia Sampaio. We will also pass the features and classes names, and customize the plot so that each tree node is displayed Gallery examples: Release Highlights for scikit-learn 1. Similarly, the change in accuracy score computed on the test set Sep 12, 2015 · 4. filled: bool, default=False When set to True, paint nodes to indicate majority class for classification, extremity of values for regression, or purity of node for multi-output. show() To save it, you can do. Added in version 0. The example: You can find a comparison of different Dec 3, 2016 · 2. Aug 24, 2023 · I want to add a custom label above "value" in a decision tree made with the DecisionTreeRegressor class from sklearn. # create tree object. plot_tree(dt,fontsize=10) Im looking to replace these X [featureNumber] with the actual feature name. A decision tree classifier. plot_tree. 5. In this article, we explore different methods to optimize the size of these boxes, including adjusting the figure size, changing the font size, and using custom node sizes. # First create the base model to tune. Repository consists of a script file, hyperplane generator function and the gif file. pip install --upgrade sklearn. getvalue()) 2) Or collect entire list in graph but just use first element to be sent to pdf. plot_tree (decision_tree, *, max_depth=None, feature_names=None, class_names=None, label=&#39;all&#39 Pixel importances with a parallel forest of trees; Plot class probabilities calculated by the VotingClassifier; Plot individual and voting regression predictions; Plot the decision boundaries of a VotingClassifier; Plot the decision surfaces of ensembles of trees on the iris dataset; Prediction Intervals for Gradient Boosting Regression May 31, 2020 · I want to plot the tree corresponding to best fit parameter that gridsearch has found out. First the "training data", which should be a 2D array, and second the "target values". Impurity-based feature importances can be misleading for high cardinality features (many unique values). So you can do this one of following of two ways, 1) Change line where you collect dot_data value in graph to. The decision tree estimator to be exported. figure(figsize=(20, 20)) before plotting, but the figure size did not change with output text 'Figure size 1440x1440 with 0 Axes'. tree import plot_tree. validation import check_is_fitted 39 ---> 40 from . Visualizations — scikit-learn 1. import matplotlib. 22. png: Note also that pydotplus. Last remark: don't get deceived by the superficial differences in the tree layouts, which reflect only design choices of the respective visualization packages; the regression tree you have plotted (which, admittedly, does not look much like a tree) is structurally similar to the classification one taken from the docs - simply imagine a top-down Dec 6, 2019 · Plot tree is available after sklearn version > 0. In this tutorial you will discover how you can plot individual decision trees from a trained gradient boosting model using XGBoost in Python. Increasing false positive rates such that element i is the false positive rate of predictions with score >= thresholds[i]. savefig("temp. 可视化会自动适应轴的大小。. figure の figsize または dpi 引数を使用して、レンダリングのサイズを制御します The number of trees in the forest. For exemple, to plot the 4th tree, use: fig, ax = plt. 訓練、枝刈り、評価、決定木描画をしていきます。. scikit-learn. 2. 2. datasets import load_iris. DecisionTreeClassifier(criterion='gini') Dec 5, 2019 · A lot of the information coming from these arrays can be seen on the tree plot. Visualize the Decision Tree with Graphviz. Plot a decision tree. An example using IsolationForest for anomaly detection. The decision classifier has an attribute called tree_ which allows access to low level attributes such as node_count, the total number of nodes, and max_depth, the maximal depth of the tree. plot_tree(clf, feature_names=iris. export_text method; plot with sklearn. Leaf nodes have labels like leaf 2: 0. model_selection import train_test_split. 3 Recognizing hand-written digits A demo of K-Means clustering on the handwritten digits data Feature agglomeration Various Agglomerative Clu Jun 1, 2022 · if you use xgboost, there is already a plot_tree function. metrics. Source object in your question: import graphviz gvz_graph = graphviz. Source(pydot_graph. May 22, 2020 · For those coming in with more recent versions of sklearn (mine is 1. The visualization is fit automatically to the size of the axis. Pre Aug 19, 2018 · There are 4 methods which I'm aware of for plotting the scikit-learn decision tree: The simplest is to export to the text representation. plot_tree. The code below plots a decision tree using scikit-learn. 5. – David sklearn. I am building a decision tree in scikit-learn then want to produce a pdf of the tree. _criterion import Criterion 41 from . Oct 27, 2021 · from sklearn. AttributeError: module 'sklearn. LinearRegression. 4. data, iris. float64}, default=None. 5 /\ / \ label1 label2 The problem is this. Steps/Code to Reproduce. If None, output dtype is consistent with input dtype. 7 python and solve it by installing 3. png") 3. plot_tree(model) Bottom line: there will probably be more broken things in that material. The only difficulty was to convert sklearn's children_ output to the Newick Tree format that can be read and understood by ete3. figure(figsize=(20,16))# set plot size (denoted in inches) tree. To plot or save the tree first we need to export it to DOT format with export_graphviz method. plot_confusion_matrix package, but the default figure size is a little bit small. export_graphviz(model, feature_names=feature_names, class_names=class_names, filled=True, rounded=True, special_characters=True, out_file=None, ) graph = graphviz. model_selection import cross_val_score. The sklearn. tree import plot_tree plot_tree(t) (where t is an instance of DecisionTreeClassifier) This is the output: Sklearn plot_tree plot is too small. from matplotlib. savefig("decistion_tree. fit(X, y) # plot single tree plot_tree(model) plt. The input samples. plot_tree: Oct 6, 2021 · Regression tree. This is useful in order to create lighter ROC curves. plot_tree(clf) # the clf is your decision tree model The example output is similar to what you will get with export_graphviz: You can also try dtreeviz package. This saved image should look better. 3. max_depthint, default=None The maximum depth of the tree. datasets import load_iris from sklearn import tree iris = load_iris() clf = tree. So it is essentially taking baby steps across the domain of the data's min and max and plotting/filling as it goes, according to the model's predictions. Google Colabプリインストールされているパッケージはそのまま使っています。. Here is the code. 22: The default value of n_estimators changed from 10 to 100 in 0. 5) or development (unstable) versions. savefig('foo. Mar 20, 2021 · Just increase figsize=(50,30), adjust dpi=300 and apply the code to save the image in png. PV8 PV8. An array containing the feature names. score(test_features, test_target) print kernel, c, score. from sklearn. The example compares prediction result of linear regression (linear model) and decision tree (tree based model) with and without discretization of real-valued features. Clustering #. Feature selection #. My current tree is: I'm plotting it with: Apr 1, 2020 · As of scikit-learn version 21. 请阅读 User Guide 了解更多信息。. Here, we compute the learning curve of a naive Bayes classifier and a SVM classifier with a RBF kernel using the digits dataset. class_names=['e','o'] May 15, 2020 · Am using the following code to extract rules. One can count them on the tree plot as well. clf = DecisionTreeClassifier(random_state=0) iris = load_iris() tree = clf. After training the tree, you feed the X values to predict their output. First load the copy of the Iris dataset shipped with Maybe the data was perfectly separated using that variable. Fitted classifier or a fitted Pipeline in which the last estimator is a classifier. Try the latest stable release (version 1. float64 are supported. Multiclass-multioutput classification (also known as multitask classification) is a classification task which labels each sample with a set of non-binary properties. feature_selection module can be used for feature selection/dimensionality reduction on sample sets, either to improve estimators’ accuracy scores or to boost their performance on very high-dimensional datasets. For an example of the different strategies see: Demonstrating the different strategies of KBinsDiscretizer. datasets import load_breast_cancer. Apr 10, 2019 · ~\Anaconda3\lib\site-packages\sklearn\tree\tree. float32 and np. tree import DecisionTreeClassifier. 绘制决策树。. Jan 31, 2021 · The font is too small to be visualized so I wish to save the image and view it locally instead of on Jupyter. tree. Operating System: linux Compiler: GCC 4. hierarchy import dendrogram from sklearn. 6. plot_tree(sometree) plt. 21. Mar 18, 2015 · I came across the exact same problem some time ago. fit(iris. target) tree. Use one of the following class methods: from_predictions or from_estimator. figure to control the size of the rendering. allow_single_clusterbool, default=False. model_gini_class = tree. 6-4)] on linux Package used (python/R/jvm/ BaggingClassifier. They are however often too small to be representative of real world machine learning tasks. For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples. Also the train and test dataset is not huge. Share. set_figheight(15) fig. The number of splittings required to isolate a sample is lower for outliers and higher for Mar 9, 2021 · from sklearn. So I wrote a simple ASCII based decision tree visualizer for the sklearn DecisionTreeClassifier: tree _print (see attached). Follow answered May 15, 2023 at 12:46. This is documentation for an old release of Scikit-learn (version 0. See Permutation feature importance as I had the same problem recently and the only way I found is by trying diffent figure size (it can still be bluery with big figure. plot_tree() only produces the labels of each split. set_style('whitegrid') #Note: this can be any option for set_style Mar 15, 2020 · Because plot_tree is defined after sklearn version 0. Jan 26, 2019 · There are 4 methods which I'm aware of for plotting the scikit-learn decision tree: print the text representation of the tree with sklearn. If None generic names will be used (“feature_0”, “feature_1”, …). Clustering of unlabeled data can be performed with the module sklearn. Source(dot_data) graph May 12, 2017 · This is due to the fact the step size/plot step is very small . 21 then you need to upgrade the sklearn library. tree. The Isolation Forest is an ensemble of “Isolation Trees” that “isolate” observations by recursive random partitioning, which can be represented by a tree structure. savefig () saving the tree results in an image of unreadably low resolution. fig = plt. A simpler way is to let sklearn to do most of Dec 3, 2016 · 2. 使用 plt. So unless you really need the DOT file for some reasons, you should be able to do this: from sklearn. . aj hi sn rm mi rr kb dl jp dm