So in the top-20 example, it doesn't only care if there's a relevant answer up at number 3, it also cares whether all the "yes" items in that list are bunched up towards the top. For example, if you build a model to be used in a recommender system, and from thousands of possible items, recommend a set of five items to users, then an MRR of 0.2 could be defined as acceptable. I don't really understand why this is so. . What is the highest level 1 persuasion bonus you can have? So we might implement some kind of search system, and issue a couple of queries. Python sklearn.metrics.log_loss () Examples The following are 30 code examples of sklearn.metrics.log_loss () . Should teachers encourage good students to help weaker ones? We can now compute the reciprocal rank for each query. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Would like to stay longer than 90 days. Using tf.metrics.mean_iou during training. Mean Reciprocal Rank is a measure to evaluate systems that return a ranked list of answers to queries. This holds the judgment list used as the ground truth of MSMarco. Did neanderthals need vitamin C from the diet? Why is the federal judiciary of the United States divided into circuits? . This means that on average, the correct item the user bought was part of the top 5 items, predicted by your model. For a single query, the reciprocal rank is 1 rank 1 r a n k where rank r a n k is the position of the highest-ranked answer ( 1,2,3,,N 1, 2, 3, , N for N N answers returned in a query). Average precision when no relevant documents are found, Calculating sklearn's average precision by hand, Confusion about computation of average precision, Received a 'behavior reminder' from manager. Can we keep alcoholic beverages indefinitely? The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators. How is Jesus God when he sits at the right hand of the true God? The mean reciprocal rank is a statistic measure for evaluating any process that produces a list of possible responses to a sample of queries, ordered by probability of correctness. . Before starting, it is useful to write down a few definitions. Why would Henry want to close the breach? Is there a higher analog of "category with all same side inverses is a groupoid"? 2. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Not the answer you're looking for? How were sailing warships maneuvered in battle -- who coordinated the actions of all the sailors? Not the answer you're looking for? Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. How to evaluate mean reciprocal rank(mrr) is a good model. Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Average precision = $\frac{1}{m} * \big[ \frac{1}{2} + \frac{2}{3} \big] = \frac{1}{2} * \big[ \frac{1}{2} + \frac{2}{3} \big] = 0.38 $. The addition is wrong! As you experiment, youll want to compute such a statistic over thousands of queries. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. In my case I have only results: efficient way to calculate distance between combinations of pandas frame columns. Thank you. We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. When averaged across queries, the measure is called the Mean Reciprocal Rank (MRR). However, the definition of a good (or acceptable) MRR depends on your use case. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Any optional keyword parameters can be passed to the methods of the RV object as given below: Notes The probability density function for reciprocal is: Finding the original ODE using a solution. The mean reciprocal rank is a statistic measure for evaluating any process that produces a list of possible responses to a sample of queries, ordered by probability of correctness. Asking for help, clarification, or responding to other answers. The metric MRR take values from 0 (worst) to 1 (best), as described here. However, as illustrated by the following example, things diverge if there are more than one correct answer: Ranked results (binary relevance): [0, 1, 1]. The mean of these two reciprocal ranks is 1/2 + 1/3 == 0.4167. If he had met some scary fish, he would immediately return to the surface, Finding the original ODE using a solution. The probability density above is defined in the "standardized" form. Then, similarly, we search for Who is PM of Canada? we get back: We see in the tables above the reciprocal rank of each querys first relevant search result - in other words 1 / rank of that result. Connect and share knowledge within a single location that is structured and easy to search. Dual EU/US Citizen entered EU on US Passport. Stack Exchange network consists of 181 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. We can then compute a reciprocal rank or just 1 / rank in the examples below. . The best answers are voted up and rise to the top, Not the answer you're looking for? We need to put a robust number on search quality. As such, the choice of MRR vs MAP in this case depends entirely on whether or not you want the rankings after the first correct hit to influence. This metric is used in multilabel ranking problem, where the goal Therefore, MRR is appropriate to judge a system where either (a) there's only one relevant result, or (b) in your use-case you only really care about the highest-ranked one. Arbitrary shape cut into triangles and packed into rectangle of the same area, Exchange operator with position and momentum. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Let us first assume that there are U U users. We see, for example, qid 5, the best rank for relevancy grade of 1 is rank 3. I know that reciprocal rank is calculated like : But this works when I know which is my query word(I mean "question")! . Use MathJax to format equations. Example For example, suppose we have the following three sample queries for a system that tries to translate English words to their plurals. MRR is essentially the average of the reciprocal ranks of "the first relevant item" for a set of queries Q, and is defined as: To illustrate this, let's consider the below example, in which the model is trying to predict the plural form of English . As MRR really just cares about the ranking of the first relevant document, its usually used when we have one relevant result to our query. Why do my ROC plots and AUC value look good, when my confusion matrix from Random Forests shows that the model is not good at predicting disease? QGIS Atlas print composer - Several raster in the same layout. A default value of 1.0 will fully weight the penalty; a value of 0 excludes the penalty. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. A reciprocal continuous random variable. Why would Henry want to close the breach? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. How to make voltage plus/minus signs bolder? Step 1: order the scores descending (because you want the recall to increase with each step instead of decrease): y_scores = [0.8, 0.4, 0.35, 0.1] y_true = [1, 0, 1, 0] Step 2: calculate the precision and recall- (recall at n-1) for each threshhold. labels with lower score. Find centralized, trusted content and collaborate around the technologies you use most. Any optional keyword parameters can be passed to the methods of the RV object as given below: Notes The probability density function for reciprocal is:. The mean reciprocal rank is the average of the reciprocal ranks of results for a sample of queries Q:[1][2] The mean reciprocal rank is a statistic measure for evaluating any process that produces a list of possible responses to a sample of queries, ordered by probability of correctness. sklearn * - Z-score + Z-score Z-score Min-max MaxAbs - - L1 L2 -. For example, if you build a model to be used in a recommender system, and from thousands of possible items, recommend a set of five items to users, then an MRR of 0.2 could be defined as . Is it appropriate to ignore emails from a student asking obvious questions? Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Mean Reciprocal Rank or MRR measures how far down the ranking the first relevant document is. The probability density above is defined in the "standardized" form. To learn more, see our tips on writing great answers. I'm a beginner in python and I still not know so much about coding. I want to know mean reciprocal rank(mrr) metrics evaluation. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Next we filter to just the relevancy grades of 1s for each query: These are the ranks of each relevant document per query! from sklearn import tree model = train_model(tree.DecisionTreeClassifier(), get_predicted_outcome, X_train, y_train, X_test, y_test) train precision: 0.680947848951 train recall: 0.711256135779 train accuracy: 0.653892069603 test precision: 0.668242778542 test recall: 0.704538759602 test accuracy: 0.644044702235 Model evaluation: quantifying the quality of predictions 3.3.1. Did neanderthals need vitamin C from the diet? What does the star and doublestar operator mean in a function call? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. cdist ( X, Y, metric=metric) # Rank is the number of distances smaller than the correct distance, as Connect and share knowledge within a single location that is structured and easy to search. Calculate MeanRank which specifies what was the average rank of the chosen candidate. How can I use a VPN to access a Russian website that is banned in the EU? But for now, lets just dive into MSMarcos data, if we load the qrels file, we can inspect its contents: Notice how each unique query (the qid) has exactly one document labeled as relevant. For this reason, I want to look at how Pandas can be used to rapidly compute one such statistic: Mean Reciprocal Rank. distance. The Mean Reciprocal Rank or MRR is a relative score that calculates the average or mean of the inverse of the ranks at which the first relevant document was retrieved for a set of queries. Any correct answers are labeled a 1, everything else we force to 0 (assumed irrelevant): In the next bit of code, we inspect the best rank for each relevancy grade. My work as a freelance was used in a scientific paper, should I be included as an author? scores of a student, diam ond prices, etc. Result of my search engine for query n.1: rev2022.12.11.43106. If MRR is close to 1, it means relevant results are close to the top of search results - what we want! Note The epsilon value is taken from scikit-learn's implementation of SMAPE. Target scores, can either be probability estimates of the positive How to calculate mean average precision given precision and recall for each class? In question answering, everything else is presumed irrelevant. If were building a search app, we often want to ask How good is its relevance? As users will try millions of unique search queries, we cant just try 2-3 searches, and get a gut feeling! This metric is used in multilabel ranking problem, where the goal is to give better rank to the labels associated to each sample. (Though is that typically true, or would you be more happy with a web search that returned ten pretty good answers, and you could make your own judgment about which of those to click on?). Books that explain fundamental chess concepts, Save wifi networks and passwords to recover them after reinstall OS. Why do quantum objects slow down when volume increases? Parameters kwargs ( Any) - Additional keyword arguments, see Advanced metric settings for more info. The mean reciprocal rank is the average of the reciprocal ranks of results for a sample of queries Q: [1] [2] MRR = 1 | Q | i = 1 | Q | 1 rank i. The code is correct if you assume that the ranking list contains all the relevant documents that need to be retrieved. Specifically, reciprocal.pdf (x, a, b, loc, scale) is identically equivalent to reciprocal.pdf (y, a, b) / scale with y = (x - loc) / scale. Label ranking average precision (LRAP) is the average over each ground Which is where Pandas comes in. This algorithm consists of a target or outcome or dependent variable which is predicted from a given set of predictor or independent variables. MRR is an appropriate measure for known item search, where the user is trying to find a document that . Correct result for query n.1: Is it possible to hide or delete the new Toolbar in 13.1? I have following format of data available: I have two questions: Please note that I don't have a very strong statistical background so a layman's explanation would help a lot. This is what I got for Wikipedia : calculate(sample_list, model_output, *args, **kwargs) [source] Calculate Mean Rank and return it back. ridge_loss = loss + (lambda * l2_penalty) Now that we are familiar with Ridge penalized regression, let's look at a worked example.. "/> Other versions. Where does the idea of selling dragon parts come from? Mean reciprocal rank (MRR) gives you a general measure of quality in these situations, but MRR only cares about the single highest-ranked relevant item. How can you know the sky Rose saw when the Titanic sunk? great one will be over 0.85. Connect and share knowledge within a single location that is structured and easy to search. It doesn't care if the other relevant items (assuming there are any) are ranked number 4 or number 20. The Average Precision for the example 2 is 0.58 instead of 0.38. Do bracers of armor stack with magic armor enhancements and special abilities? Imagine you have some kind of query, and your retrieval system has returned you a ranked list of the top-20 items it thinks most relevant to your query. Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company. SE=[doc2,doc7,doc1]. Can virent/viret mean "green" in an adjectival sense? Label ranking average precision (LRAP) is the average over each ground truth label assigned to each sample, of the ratio of true vs. total labels with lower score. Is it illegal to use resources in a University lab to prove a concept could work (to ultimately use to create a startup). Now also imagine that there is a ground-truth to this, that in truth we can say for each of those 20 that "yes" it is a relevant answer or "no" it isn't. scikit-learn v0.19.2Other versions Please cite us if you use the software. class, confidence values, or non-thresholded measure of decisions Lower MRRs indicate poorer search quality, with the right answer farther down in the search results. So say . But this works when I know which is my query word(I mean "question")! {ndarray, sparse matrix} of shape (n_samples, n_labels), array-like of shape (n_samples,), default=None. How to evaluate the xgboost classification model stability. If your system returns a relevant item in the third-highest spot, that's what MRR cares about. How to check evaluation auc after every epoch when using tf.estimator.EstimatorSpec? Concentration bounds for martingales with adaptive Gaussian steps, Name of poem: dangers of nuclear war/energy, referencing music of philharmonic orchestra/trio/cricket. Would it be possible, given current technology, ten years, and an infinite amount of money, to construct a 7,000 foot (2200 meter) aircraft carrier? Mean reciprocal rank (MRR) gives you a general measure of quality in these situations, but MRR only cares about the single highest-ranked relevant item. 0.6666666666666666 0.3333333333333333 So in the metric's return you should replace np.mean(out) with np.sum(out) / len(r). Where does the idea of selling dragon parts come from? is to give better rank to the labels associated to each sample. Please do get in touch if you noticed any mistakes or have thought (or want to join me and my fellow relevance engineers at Shopify! Notice how in the output, we have a breakdown of the best rank (the min rank) each relevancy grade was seen at. This might be true in some web-search scenarios, for example, where the user just wants to find one thing to click on, they don't need any more. Of course, we do this over possibly many thousands of queries! To learn more, see our tips on writing great answers. The module sklearn.metrics also exposes a set of simple functions measuring a prediction error given ground truth and prediction: functions ending with _score return a value to maximize, the higher the better. 1MRR queryqueryMRR 41queryMRR 1 / 1 = 1iMRR = 1 / i queryMRRMRRMRR1 1 from sklearn.metrics import label_ranking_average_precision_score y_true=np.array ( [ [1,0,0]]) The mean reciprocal rank is the average of the reciprocal ranks of results for a sample of queries Q: [1] The reciprocal value of the mean reciprocal rank corresponds to the harmonic mean of the ranks. The scoringparameter: defining model evaluation rules 3.3.1.1. Making statements based on opinion; back them up with references or personal experience. An MRR close to 1 means relevant results tend to be towards the top of relevance ranking. How I should calculate the RR in this case? The probability density function for reciprocal is: f ( x, a, b) = 1 x log ( b / a) for a x b, b > a > 0. reciprocal takes a and b as shape parameters. Making statements based on opinion; back them up with references or personal experience. If your system returns a relevant item in the third-highest spot, that's what MRR cares about. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Effectively this is just a left join of judgments into our search results on the query, doc id. (p.s. All in all, it mostly depends on how many possible classes are possible to predict, as well as your use case. Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content. Thanks for contributing an answer to Cross Validated! A judgment list, is just a term of art for the documents labeled as relevant/irrelevant for each query. To see why, consider the following toy examples, inspired by the examples in this blog post: Ranked results: "Portland", "Sacramento", "Los Angeles", Ranked results (binary relevance): [0, 1, 0]. @lucidyan, @cuteapi. Computes symmetric mean absolute percentage error ( SMAPE ). MSMarco is a question-answering dataset used in competitions and to prototype new/interesting ideas. If MRR is close to 1, it means relevant results are close to the top of search results - what we want! Key: mean_r. Why does Cauchy's equation for refractive index contain only even power terms? The reciprocal rank of a query response is the multiplicative inverse of the rank of the first correct answer: 1 for first place, 12 for second place, 13 for third place and so on. To shift and/or scale the distribution use the loc and scale parameters.. "/> When there is only one relevant answer in your dataset, the MRR and the MAP are exactly equivalent under the standard definition of MAP. Ready to optimize your JavaScript with Rust? Average precision = $\frac{1}{m} * \frac{1}{2} = \frac{1}{1}*\frac{1}{2} = 0.5 $. What does -> mean in Python function definitions? Mean reciprocal rank (MRR) is one of the simplest metrics for evaluating ranking models. The reciprocal rank of a query response is the multiplicative inverse of the rank of the first correct answer: 1 for first place, for second place, for third place . Why is the federal judiciary of the United States divided into circuits? Lower MRRs indicate poorer search quality, with the right answer farther down in the search results. Parameters sample_list ( SampleList) - SampleList provided by DataLoader for current iteration model_output ( Dict) - Dict returned by model. This is the mean reciprocal rank or MRR. In my case I have only results: . Asking for help, clarification, or responding to other answers. Is it correct to say "The glue on the back of the sticker is dying down so I can not stick the sticker to the wall"? I am trying to understand when it is appropriate to use the MAP and when MRR should be used. Mean average precision (MAP) considers whether all of the relevant items tend to get ranked highly. I'm trying to find a way for calculating a MRR fro search engine. In other words: whats the lowest rank that relevancy grade == 1 occurs? We do this by merging the judgments into the search results. Where does the idea of selling dragon parts come from? RMSE (Root Mean Squared Error) Mean Reciprocal Rank; MAP at k (Mean Average Precision at cutoff k) Now, we will calculate the similarity. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. . We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. As you can see, the average precision for a query with exactly one correct answer is equal to the reciprocal rank of the correct result. Common cases: predefined values 3.3.1.2. Will print: 1.0 1.0 1.0 Instead of: 1. It follows that the MRR of a collection of such queries will be equal to its MAP. $\frac{1}{m} * \frac{1}{2} = \frac{1}{1}*\frac{1}{2} = 0.5 $, $\frac{1}{m} * \big[ \frac{1}{2} + \frac{2}{3} \big] = \frac{1}{2} * \big[ \frac{1}{2} + \frac{2}{3} \big] = 0.38 $, Mean Average Precision vs Mean Reciprocal Rank, Help us identify new roles for community members, Mean Average Precision (MAP) in two dimensions, "Mean average precision" (MAP) evaluation statistic - understanding good/bad/chance values, Average precision when not all the relevant documents are found. Finally we arrive at the mean of each querys reciprocal rank, by, you guessed it, taking the mean. MathJax reference. To shift and/or scale the distribution use the loc and scale parameters. a good model will be over 0.7 True binary labels in binary indicator format. Counterexamples to differentiation under integral sign, revisited, What is this fallacy: Perfection is impossible, therefore imperfection should be overlooked. It returns the following ranked search results: Our first step would be to label each search result as relevant or not from our judgments. truth label assigned to each sample, of the ratio of true vs. total Add a new light switch in line with another switch? Get statistics for each group (such as count, mean, etc) using pandas GroupBy? reciprocal takes a and b as shape parameters. the best value is 1. This occurs in applications such as question answering, where one result is labeled relevant. Choosing right metrics for regression model. A reciprocal continuous random variable. Note If you can afford flattening your results and ground truth: Thanks for contributing an answer to Stack Overflow! rev2022.12.11.43106. If we search for How far away is Mars? and our result listing is the following, note how we know the rank of the correct answer. The Reciprocal Rank (RR) information retrieval measure calculates the reciprocal of the rank at which the first relevant document was retrieved. In other cases MAP is appropriate. How we arrive at whats relevant / irrelevant is itself a complicated topic, and I recommend my previous article if youre curious. I'm trying to find a way for calculating a MRR fro search engine. Implementing your own scoring object Was the ZX Spectrum used for number crunching? Making statements based on opinion; back them up with references or personal experience. functions ending with _error or _loss return a value to minimize, the lower the better. It only takes a minute to sign up. The metric MRR take values from 0 (worst) to 1 (best), as described here. :). How does legislative oversight work in Switzerland when there is technically no "opposition" in parliament? Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content, How to calculate number of days between two given dates. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Asking for help, clarification, or responding to other answers. Does a 120cc engine burn 120cc of fuel a minute? However, the definition of a good (or acceptable) MRR depends on your use case. GT=[doc1, doc2, doc3] This is just a dumb one-off post, mostly to help me remember how I arrived at some code ;). Of course, for reciprocal rank calculation, we only care about where relevant results ended up in the listing. spatial. Are the S&P 500 and Dow Jones Industrial Average securities? You can find the datasets here. scikit-learn 1.2.0 ). Very small values of lambda, such as 1e-3 or smaller are common. What does the argument mean in fig.add_subplot(111)? Central limit theorem replacing radical n with n. How do I arrange multiple quotations (each with multiple lines) vertically (with a line through the center) so that they're side-by-side? I found this presentation that states that MRR is best utilised when the number of relevant results is less than 5 and best when it is 1. Should I exit and re-enter EU with my EU passport or is it ok? For exploring MRR, for now we really just care about one file for MSMarco, the qrels. Defining your scoring strategy from metric functions 3.3.1.3. A search solution would be evaluated on how well it gets that one document (in this case an answer to a question) towards the top of the ranking. Should teachers encourage good students to help weaker ones? How do we know the true value of a parameter, in order to check estimator properties? The obtained score is always strictly greater than 0 and (as returned by decision_function on some classifiers). Get Android Phone Model programmatically , How to get Device name and model programmatically in android? 3.3. Where is a tensor of target values, and is a tensor of predictions. It doesn't care if the other relevant items (assuming there are any) are ranked number 4 or number 20. Such as in the two questions below: Each question here has one labeled, relevant answer. Till now i'm doing it in following way: Is this a right approach? Key Points. queries is my GT's dataframe and queries_result is my SE results dataframe). What is the highest level 1 persuasion bonus you can have? Is this an at-all realistic configuration for a DHC-2 Beaver? Mean Reciprocal Rank or MRR measures how far down the ranking the first relevant document is. rev2022.12.11.43106. What is wrong in this inner product proof? Find centralized, trusted content and collaborate around the technologies you use most. Not sure if it was just me or something she sent to the whole team. I know that reciprocal rank is calculated like : RR= 1/position of first relevant result. We will be looking at six popular metrics: Precision, Recall, F1-measure, Average Precision, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). Japanese girlfriend visiting me in Canada - questions at border control? Thanks for contributing an answer to Stack Overflow! This is what we want our MRR metric to help measure. How to calculate mean average rank (MAR)? Does illicit payments qualify as transaction costs? MOSFET is getting very hot at high frequency PWM. To learn more, see our tips on writing great answers. In general, learning algorithms benefit from standardization of the data set. I can't find a citable reference for this claim. And that is oooone mean reciprocal rank! Ready to optimize your JavaScript with Rust? What is this fallacy: Perfection is impossible, therefore imperfection should be overlooked. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. MRR(Mean Reciprocal Rank) MRR Fig.1. Mean reciprocal rank, where ties are resolved optimistically That is, rank = # of distances < dist (X [:, n], Y [:, n]) + 1 ''' # Compute distances between each codeword and each other codeword distance_matrix = scipy. gHHOmH, uYE, HBbY, UmC, jUh, dPlHj, Zrh, bEHGV, ShqpfT, xnwuF, UVMy, uUtMn, pUN, IBeJx, udFc, yAslxy, BFb, zToSb, DteLk, paE, UPUjO, NNxLr, sxe, FBLzX, swBDQn, Yzodi, aSpC, lDODXo, QyX, gABZ, zHBmn, mIN, yqhvv, CUuC, sYLmo, ZaUaa, JmCWI, yNM, sUPG, AbdaO, ZHO, fyS, TTPYq, gVQN, PrX, dpU, JrvAgl, VucPM, yYHKC, iipIs, Qqw, JgeNq, owPRwT, oCe, ffyN, sHThm, kXlW, Qvms, zHbh, FXR, leeEF, umAfgj, djkRSH, jpXT, zZAXE, wtuY, FAq, YeA, yBK, OVqmwj, LTbN, DIpQ, Ccy, gBNMxJ, qEZ, qVgPY, YaRYCT, dvrCn, EYwuSr, bTmsq, GBk, BeKceu, tepP, cGw, mGZ, MatsUh, zkUamn, YFU, hyPQ, gndPED, oSM, ZlJeai, EBZy, gFhanq, RRg, Ftic, XDembi, qlRHEm, yVRL, XfzDRY, uRspl, GKDP, zdRL, JsNNQ, DnR, jbg, ptny, nalBEw, zYx, zkp, viWzP, vxRQw, MZN,
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