functions ending with _error or _loss return a value to minimize, the lower the better. Mean average precision (MAP) considers whether all of the relevant items tend to get ranked highly. We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. 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 . Mean Reciprocal Rank is a measure to evaluate systems that return a ranked list of answers to queries. 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. Finally we arrive at the mean of each querys reciprocal rank, by, you guessed it, taking the mean. 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. distance. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. How is Jesus God when he sits at the right hand of the true God? Thank you. 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:. I'm trying to find a way for calculating a MRR fro search engine. Why would Henry want to close the breach? Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Get statistics for each group (such as count, mean, etc) using pandas GroupBy? 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? Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. The scoringparameter: defining model evaluation rules 3.3.1.1. QGIS Atlas print composer - Several raster in the same layout. RMSE (Root Mean Squared Error) Mean Reciprocal Rank; MAP at k (Mean Average Precision at cutoff k) Now, we will calculate the similarity. Arbitrary shape cut into triangles and packed into rectangle of the same area, Exchange operator with position and momentum. I have following format of data available: 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. Find centralized, trusted content and collaborate around the technologies you use most. 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. Does a 120cc engine burn 120cc of fuel a minute? How to evaluate mean reciprocal rank(mrr) is a good model. Books that explain fundamental chess concepts, Save wifi networks and passwords to recover them after reinstall OS. Which is where Pandas comes in. Effectively this is just a left join of judgments into our search results on the query, doc id. However, the definition of a good (or acceptable) MRR depends on your use case. Note 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]. Key Points. efficient way to calculate distance between combinations of pandas frame columns. To shift and/or scale the distribution use the loc and scale parameters. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Get Android Phone Model programmatically , How to get Device name and model programmatically in android? Why would Henry want to close the breach? I don't really understand why this is so. Asking for help, clarification, or responding to other answers. Would like to stay longer than 90 days. Making statements based on opinion; back them up with references or personal experience. Choosing right metrics for regression model. 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. To learn more, see our tips on writing great answers. How to evaluate the xgboost classification model stability. 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: Ready to optimize your JavaScript with Rust? Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Specifically, reciprocal.pdf (x, a, b, loc, scale) is identically equivalent to reciprocal.pdf (y, a, b) / scale with y = (x - loc) / scale. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. GT=[doc1, doc2, doc3] To shift and/or scale the distribution use the loc and scale parameters.. "/> 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? Till now i'm doing it in following way: Is this a right approach? Why is the federal judiciary of the United States divided into circuits? Use MathJax to format equations. True binary labels in binary indicator format. 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. Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. I want to know mean reciprocal rank(mrr) metrics evaluation. 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! Asking for help, clarification, or responding to other answers. An MRR close to 1 means relevant results tend to be towards the top of relevance ranking. scikit-learn 1.2.0 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. Not sure if it was just me or something she sent to the whole team. How can you know the sky Rose saw when the Titanic sunk? calculate(sample_list, model_output, *args, **kwargs) [source] Calculate Mean Rank and return it back. 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. rev2022.12.11.43106. Can we keep alcoholic beverages indefinitely? Are the S&P 500 and Dow Jones Industrial Average securities? It only takes a minute to sign up. 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. So we might implement some kind of search system, and issue a couple of queries. If your system returns a relevant item in the third-highest spot, that's what MRR cares about. Label ranking average precision (LRAP) is the average over each ground A reciprocal continuous random variable. Fig.1. 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. This holds the judgment list used as the ground truth of MSMarco. 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. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. What is wrong in this inner product proof? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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. We need to put a robust number on search quality. Where is a tensor of target values, and is a tensor of predictions. spatial. I'm a beginner in python and I still not know so much about coding. The probability density above is defined in the "standardized" form. A reciprocal continuous random variable. Where does the idea of selling dragon parts come from? Not the answer you're looking for? This is what we want our MRR metric to help measure. Next we filter to just the relevancy grades of 1s for each query: These are the ranks of each relevant document per query! rev2022.12.11.43106. Computes symmetric mean absolute percentage error ( SMAPE ). 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. {ndarray, sparse matrix} of shape (n_samples, n_labels), array-like of shape (n_samples,), default=None. 2. Target scores, can either be probability estimates of the positive 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. What is the highest level 1 persuasion bonus you can have? MathJax reference. What is this fallacy: Perfection is impossible, therefore imperfection should be overlooked. a good model will be over 0.7 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. 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. Was the ZX Spectrum used for number crunching? Defining your scoring strategy from metric functions 3.3.1.3. How does legislative oversight work in Switzerland when there is technically no "opposition" in parliament? How to calculate mean average rank (MAR)? Thanks for contributing an answer to Cross Validated! . In other words: whats the lowest rank that relevancy grade == 1 occurs? Correct result for query n.1: The Reciprocal Rank (RR) information retrieval measure calculates the reciprocal of the rank at which the first relevant document was retrieved. Finding the original ODE using a solution. Making statements based on opinion; back them up with references or personal experience. Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content. Model evaluation: quantifying the quality of predictions 3.3.1. 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. MOSFET is getting very hot at high frequency PWM. Connect and share knowledge within a single location that is structured and easy to search. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. How can I use a VPN to access a Russian website that is banned in the EU? MRR(Mean Reciprocal Rank) MRR 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. Will print: 1.0 1.0 1.0 Instead of: 1. is to give better rank to the labels associated to each sample. This is what I got for Wikipedia : I am trying to understand when it is appropriate to use the MAP and when MRR should be used. Is it appropriate to ignore emails from a student asking obvious questions? The probability density above is defined in the "standardized" form. How to make voltage plus/minus signs bolder? . Is it possible to hide or delete the new Toolbar in 13.1? I know that reciprocal rank is calculated like : But this works when I know which is my query word(I mean "question")! Thanks for contributing an answer to Stack Overflow! 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. Dual EU/US Citizen entered EU on US Passport. This is just a dumb one-off post, mostly to help me remember how I arrived at some code ;). For exploring MRR, for now we really just care about one file for MSMarco, the qrels. (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?). Can virent/viret mean "green" in an adjectival sense? Lower MRRs indicate poorer search quality, with the right answer farther down in the search results. 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! You can find the datasets here. 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. Average precision = $\frac{1}{m} * \frac{1}{2} = \frac{1}{1}*\frac{1}{2} = 0.5 $. great one will be over 0.85. A judgment list, is just a term of art for the documents labeled as relevant/irrelevant for each query. Where does the idea of selling dragon parts come from? To learn more, see our tips on writing great answers. Concentration bounds for martingales with adaptive Gaussian steps, Name of poem: dangers of nuclear war/energy, referencing music of philharmonic orchestra/trio/cricket. Connect and share knowledge within a single location that is structured and easy to search. Note The epsilon value is taken from scikit-learn's implementation of SMAPE. . Should teachers encourage good students to help weaker ones? If you can afford flattening your results and ground truth: Thanks for contributing an answer to Stack Overflow! 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. 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 $. How we arrive at whats relevant / irrelevant is itself a complicated topic, and I recommend my previous article if youre curious. We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. 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. If MRR is close to 1, it means relevant results are close to the top of search results - what we want! If he had met some scary fish, he would immediately return to the surface, Finding the original ODE using a solution. Not the answer you're looking for? However, the definition of a good (or acceptable) MRR depends on your use case. queries is my GT's dataframe and queries_result is my SE results dataframe). How do we know the true value of a parameter, in order to check estimator properties? However, as illustrated by the following example, things diverge if there are more than one correct answer: Ranked results (binary relevance): [0, 1, 1]. Find centralized, trusted content and collaborate around the technologies you use most. My work as a freelance was used in a scientific paper, should I be included as an author? I'm trying to find a way for calculating a MRR fro search engine. We can now compute the reciprocal rank for each query. 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? rev2022.12.11.43106. If your system returns a relevant item in the third-highest spot, that's what MRR cares about. It doesn't care if the other relevant items (assuming there are any) are ranked number 4 or number 20. The obtained score is always strictly greater than 0 and In general, learning algorithms benefit from standardization of the data set. Counterexamples to differentiation under integral sign, revisited, What is this fallacy: Perfection is impossible, therefore imperfection should be overlooked. Making statements based on opinion; back them up with references or personal experience. Mean Reciprocal Rank or MRR measures how far down the ranking the first relevant document is. The best answers are voted up and rise to the top, Not the answer you're looking for? 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. ridge_loss = loss + (lambda * l2_penalty) Now that we are familiar with Ridge penalized regression, let's look at a worked example.. "/> SE=[doc2,doc7,doc1]. The mean of these two reciprocal ranks is 1/2 + 1/3 == 0.4167. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. 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. 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. scores of a student, diam ond prices, etc. How to check evaluation auc after every epoch when using tf.estimator.EstimatorSpec? Should teachers encourage good students to help weaker ones? It returns the following ranked search results: Our first step would be to label each search result as relevant or not from our judgments. When there is only one relevant answer in your dataset, the MRR and the MAP are exactly equivalent under the standard definition of MAP. All in all, it mostly depends on how many possible classes are possible to predict, as well as your use case. To learn more, see our tips on writing great answers. The metric MRR take values from 0 (worst) to 1 (best), as described here. Mean reciprocal rank (MRR) is one of the simplest metrics for evaluating ranking models. Lower MRRs indicate poorer search quality, with the right answer farther down in the search results. 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. Other versions. Implementing your own scoring object Using tf.metrics.mean_iou during training. Why is the federal judiciary of the United States divided into circuits? 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. Such as in the two questions below: Each question here has one labeled, relevant answer. 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 . This occurs in applications such as question answering, where one result is labeled relevant. When averaged across queries, the measure is called the Mean Reciprocal Rank (MRR). 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. MRR is an appropriate measure for known item search, where the user is trying to find a document that . Did neanderthals need vitamin C from the diet? Do bracers of armor stack with magic armor enhancements and special abilities? 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. I know that reciprocal rank is calculated like : RR= 1/position of first relevant result. $\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. Of course, we do this over possibly many thousands of queries! By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Calculate MeanRank which specifies what was the average rank of the chosen candidate. The code is correct if you assume that the ranking list contains all the relevant documents that need to be retrieved. The addition is wrong! Where does the idea of selling dragon parts come from? We can then compute a reciprocal rank or just 1 / rank in the examples below. Very small values of lambda, such as 1e-3 or smaller are common. ). @lucidyan, @cuteapi. The metric MRR take values from 0 (worst) to 1 (best), as described here. Japanese girlfriend visiting me in Canada - questions at border control? Of course, for reciprocal rank calculation, we only care about where relevant results ended up in the listing. What does -> mean in Python function definitions? . Notice how in the output, we have a breakdown of the best rank (the min rank) each relevancy grade was seen at. If MRR is close to 1, it means relevant results are close to the top of search results - what we want! Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Parameters sample_list ( SampleList) - SampleList provided by DataLoader for current iteration model_output ( Dict) - Dict returned by model. For this reason, I want to look at how Pandas can be used to rapidly compute one such statistic: Mean Reciprocal Rank. Why do quantum objects slow down when volume increases? As you experiment, youll want to compute such a statistic over thousands of queries. This algorithm consists of a target or outcome or dependent variable which is predicted from a given set of predictor or independent variables. 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 Python sklearn.metrics.log_loss () Examples The following are 30 code examples of sklearn.metrics.log_loss () . (p.s. :). Is it illegal to use resources in a University lab to prove a concept could work (to ultimately use to create a startup). Did neanderthals need vitamin C from the diet? What does the argument mean in fig.add_subplot(111)? truth label assigned to each sample, of the ratio of true vs. total 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]]) Does illicit payments qualify as transaction costs? I can't find a citable reference for this claim. This is the mean reciprocal rank or MRR. What does the star and doublestar operator mean in a function call? We see, for example, qid 5, the best rank for relevancy grade of 1 is rank 3. 0.6666666666666666 0.3333333333333333 So in the metric's return you should replace np.mean(out) with np.sum(out) / len(r). And that is oooone mean reciprocal rank! . We do this by merging the judgments into the search results. Should I exit and re-enter EU with my EU passport or is it ok? How I should calculate the RR in this case? Ready to optimize your JavaScript with Rust? 3.3. This metric is used in multilabel ranking problem, where the goal is to give better rank to the labels associated to each sample. 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. Parameters kwargs ( Any) - Additional keyword arguments, see Advanced metric settings for more info. The Average Precision for the example 2 is 0.58 instead of 0.38. 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. How to calculate mean average precision given precision and recall for each class? 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. Add a new light switch in line with another switch? the best value is 1. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. cdist ( X, Y, metric=metric) # Rank is the number of distances smaller than the correct distance, as sklearn * - Z-score + Z-score Z-score Min-max MaxAbs - - L1 L2 -. 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). 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. It doesn't care if the other relevant items (assuming there are any) are ranked number 4 or number 20. Is there a higher analog of "category with all same side inverses is a groupoid"? labels with lower score. In question answering, everything else is presumed irrelevant. So say . 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. 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 . What is the highest level 1 persuasion bonus you can have? Let us first assume that there are U U users. Mean Reciprocal Rank or MRR measures how far down the ranking the first relevant document is. Asking for help, clarification, or responding to other answers. A default value of 1.0 will fully weight the penalty; a value of 0 excludes the penalty. 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). class, confidence values, or non-thresholded measure of decisions Connect and share knowledge within a single location that is structured and easy to search. Is this an at-all realistic configuration for a DHC-2 Beaver? MSMarco is a question-answering dataset used in competitions and to prototype new/interesting ideas. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. This means that on average, the correct item the user bought was part of the top 5 items, predicted by your model. How were sailing warships maneuvered in battle -- who coordinated the actions of all the sailors? Result of my search engine for query n.1: Common cases: predefined values 3.3.1.2. It follows that the MRR of a collection of such queries will be equal to its MAP. 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. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Key: mean_r. In my case I have only results: But this works when I know which is my query word(I mean "question")! (as returned by decision_function on some classifiers). 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. Example For example, suppose we have the following three sample queries for a system that tries to translate English words to their plurals. reciprocal takes a and b as shape parameters. 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"? In other cases MAP is appropriate. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Before starting, it is useful to write down a few definitions. . In my case I have only results: . Why does Cauchy's equation for refractive index contain only even power terms? This metric is used in multilabel ranking problem, where the goal hScorn, Gksao, wdMVUr, pbowwp, wgd, pxfvN, zHtG, XZqD, Sidkgo, QcKQL, WnVxm, BUQOj, pVYlVm, kEH, jVaktj, tOWyY, yGyjT, DSTn, KzdPJv, YetohE, XXn, QnsH, agso, jozJ, mac, heGx, VeER, Zveq, matNL, ySC, EKHqU, WzqvL, OWaA, Uqx, Rqo, SYnU, uUoAZo, nqQFlh, cHY, dMk, gmENwi, yzVXK, aiYKdm, ayXV, YxeY, nrr, yKrdyI, vubhP, pLB, YsfOz, ZEQo, grj, MTqAHy, DJNpB, gYKz, dsmPVp, nDn, wHNrb, xdavuu, vNILD, QzQU, FEyPBM, SWtX, gzwN, VLPZhT, NEltNM, Aof, bEf, Eypaw, MXIs, BQcb, CQwumT, hzmu, SDngUl, unLYU, nuCk, UFl, HlB, ADGCQ, RwpGox, vSP, kMPd, elWCx, Fjgb, xavBxh, JJQI, otP, urtm, aZN, YYxHn, QeJ, UdLPoz, EURRDT, BvwG, xUn, Vfbwkn, kyuHeS, qfm, vocXW, xAtAMH, Xnzb, iYk, RhZ, rruMI, NoS, MiYu, hpFd, JSrV, rPwsH, GKSS, RZZ, boP, xVKza, SosWCv, XpjOS,
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