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17,700 | determining the word types determining the word types is what part of speech (postagging is all about pos tagger parses full sentence with the goal to arrange it into dependence treewhere each node corresponds to word and the parent-child relationship determines which word it depends on with this treeit can then make m... |
17,701 | pos tag description example nn nounsingular or mass book nns noun plural books nnp proper nounsingular sean nnps proper nounplural vikings pdt predeterminer both the boys pos possessive ending friend' prp personal pronoun iheit prppossessive pronoun myhis rb adverb howeverusuallynaturallyheregood rbr adverbcomparative ... |
17,702 | successfully cheating using sentiwordnet while the linguistic information that we discussed earlier will most likely help usthere is something better we can do to harvest itsentiwordnet (sentiwordnet isti cnr itsimply putit is mb file that assigns most of the english words positive and negative value in more complicate... |
17,703 | to find out which of the synsets to takewe would have to really understand the meaning of the tweetswhich is beyond the scope of this the field of research that focuses on this challenge is called word sense disambiguation for our taskwe take the easy route and simply average the scores over all the synsets in which te... |
17,704 | our first estimator now we have everything in place to create our first vectorizer the most convenient way to do it is to inherit it from baseestimator it requires us to implement the following three methodsget_feature_names()this returns list of strings of the features that we will return in transform(fit(documenty=no... |
17,705 | for , in taggedpn , sent_pos_type none if startswith("nn")sent_pos_type "nnouns + elif startswith("jj")sent_pos_type "aadjectives + elif startswith("vb")sent_pos_type "vverbs + elif startswith("rb")sent_pos_type "radverbs + if sent_pos_type is not nonesent_word "% /% "%(sent_pos_typewif sent_word in sent_word_netp, sen... |
17,706 | for in documentsallcaps append(np sum([ isupper(for in split(if len( )> ])exclamation append( count("!")question append( count("?")hashtag append( count("#")mentioning append( count("@")result np array([obj_valpos_valneg_valnounsadjectivesverbsadverbsallcapsexclamationquestionhashtagmentioning] return result putting ev... |
17,707 | pipeline pipeline([('all'all_features)('clf'clf)]if paramspipeline set_params(**paramsreturn pipeline training and testing on the combined featurizers gives another percent improvement on positive versus negative=pos vs neg = =pos/neg vs irrelevant/neutral = =pos vs rest = =neg vs rest = with these resultswe probably d... |
17,708 | recommendations you have probably learned about regression already in high school mathematics classthis was probably called ordinary least squares (olsregression then this centuries old technique is fast to run and can be effectively used for many real-world problems in this we will start by reviewing ols regression an... |
17,709 | we start by using scikit-learn' methods to load the dataset this is one of the built-in datasets that scikit-learn comes withso it is very easyfrom sklearn datasets import load_boston boston load_boston(the boston object is composite object with several attributesin particularboston data and boston target will be of in... |
17,710 | the preceding graph shows all the points (as dotsand our fit (the solid linethis does not look very good in factusing this one-dimensional modelwe understand that house price is multiple of the rm variable (the number of roomsthis would mean thaton averagea house with two rooms would be double the price of single room ... |
17,711 | in the following screenshotwe can see that visually it looks better (even though few outliers may be having disproportionate impact on the result)ideallythoughwe would like to measure how good of fit this is quantitatively in order to do sowe can ask how close our prediction is for thiswe now look at one of those other... |
17,712 | root mean squared error and prediction the root mean squared error corresponds approximately to an estimate of the standard deviation since most of the data is at the most two standard deviations from the meanwe can double our rmse to obtain rough confident interval this is only completely valid if the errors are norma... |
17,713 | the linearregression class implements ols regression as followslr linearregression(fit_intercept=truewe set the fit_intercept parameter to true in order to add bias term this is exactly what we had done beforebut in more convenient interfacelr fit( ,yp map(lr predictxlearning and prediction are performed for classifica... |
17,714 | penalized regression the important variations of ols regression fall under the theme of penalized regression in ordinary regressionthe returned fit is the best fit on the training datawhich can lead to overfitting penalizing means that we add penalty for overconfidence in the parameter values penalized regression is ab... |
17,715 | ridgelassoand elastic nets these penalized models often go by rather interesting names the penalized model is often called the lassowhile an penalized model is known as ridge regression of coursewe can combine the two and we obtain an elastic net model both the lasso and the ridge result in smaller coefficients than un... |
17,716 | greater than scenarios the title of this section is bit of inside jargonwhich you will now learn starting in the sfirst in the biomedical domain and then on the webproblems started to appear when was greater than what this means is that the number of featurespwas greater than the number of examplesn (these letters were... |
17,717 | howeverand this is major problemzero training error does not mean that your solution will generalize well in factit may generalize very poorly whereas before regularization could give you little extra boostit is now completely required for meaningful result an example based on text we will now turn to an example which ... |
17,718 | sowe can see that the data lies between - and - now that we have an estimate datawe can check what happens when we use ols to predict note that we can use exactly the same classes and methods as beforefrom sklearn linear_model import linearregression lr linearregression(fit_intercept=truelr fit(data,targetp np array(ma... |
17,719 | setting hyperparameters in smart way in the preceding examplewe set the penalty parameter to we could just as well have set it to (or halfor or millionnaturallythe results vary each time if we pick an overly large valuewe get underfitting in extreme casethe learning system will just return every coefficient equal to ze... |
17,720 | fortunatelyscikit-learn makes it very easy to do the right thingit has classes named lassocvridgecvand elasticnetcvall of which encapsulate cross-validation check for the inner parameter the code is percent like the previous oneexcept that we do not need to specify any value for alpha from sklearn linear_model import e... |
17,721 | unfortunatelyfor legal reasonsthis dataset is no longer available (although the data was anonymousthere were concerns that it might be possible to discover who the clients were and reveal the private details of movie rentalshoweverwe can use an academic dataset with similar characteristics this data comes from grouplen... |
17,722 | the loading of the dataset is just basic pythonso let us jump ahead to the learning we have sparse matrixwhere there are entries from to whenever we have rating (most of the entries are zero to denote that this user has not rated these moviesthis timeas regression methodfor varietywe are going to be using the lassocv c... |
17,723 | reg fit(xcy[train]- we need to perform the same normalization while testing xc, movie_norm( [test] np array(map(reg predictxc)ravel( ( + )- [testerr +np sum( *ewe did not explain the movie_norm function this function performs per-movie normalizationsome movies are just generally better and get higher average marksdef m... |
17,724 | summary in this we started with the oldest trick in the bookordinary least squares it is still sometimes good enough howeverwe also saw that more modern approaches that avoid overfitting can give us better results we used ridgelassoand elastic netsthese are the state-of-the-art methods for regression we once again saw ... |
17,725 | recommendations improved at the end of the last we used very simple method to build recommendation enginewe used regression to guess ratings value in the first part of this we will continue this work and build more advanced (and betterrating estimator we start with few ideas that are helpful and then combine all of the... |
17,726 | using the binary matrix of recommendations one of the interesting conclusions from the netflix challenge was one of those obvious-in-hindsight ideaswe can learn lot about you just from knowing which movies you ratedeven without looking at which rating was giveneven with binary matrix where we have rating of where user ... |
17,727 | we are now going to use this binary matrix to make predictions of movie ratings the general algorithm will be (in pseudocodeas follows for each userrank every other user in terms of closeness for this stepwe will use the binary matrix and use correlation as the measure of closeness (interpreting the binary matrix as ze... |
17,728 | now we can use this in several ways simple one is to select the nearest neighbors of each user these are the users that most resemble it we will use the measured correlation discussed earlierdef estimate(userrest)''estimate movie ratings for 'userbased on the 'restof the universe ''binary version of user ratings bu use... |
17,729 | nowwe iterate over all the moviesfor in range(nmovies)movie_likeness[iall_correlations(reviews[:, ]reviews tmovie_likeness[ , - we set the diagonal to - otherwisethe most similar movie to any movie is itselfwhich is truebut very unhelpful this is the same trick we used in learning how to classify with real-world exampl... |
17,730 | we can try weighted averagemultiplying each prediction by given weight before summing it all up how do we find the best weights thoughwe learn them from the data of courseensemble learning we are using general technique in machine learning called ensemble learningthis is not only applicable in regression we learn an en... |
17,731 | coefficients [we are now going to run leave- -out cross-validation loop for in xrange(reviews shape[ ])for all user ids es np delete(es, , all but user np delete(reviewsu , np where( we only care about actual predictions es[:, , [ reg fit( ,ycoefficients append(reg coef_prediction reg predict(es[:, ,reviews[ tmeasure e... |
17,732 | howeverwe do have to be careful to not overfit our dataset in factif we randomly try too many thingssome of them will work well on this dataset but will not generalize even though we are using cross-validationwe are not cross-validating our design decisions in order to have good estimateand if data is plentifulyou shou... |
17,733 | the beer and diapers story one of the stories that is often mentioned in the context of basket analysis is the "diapers and beerstory it states that when supermarkets first started to look at their datathey found that diapers were often bought together with beer supposedlyit was the father who would go out to the super... |
17,734 | we begin by loading the dataset and looking at some statisticsfrom collections import defaultdict from itertools import chain file format is line per transaction of the form ' dataset [[int(tokfor tok in ,line strip(split()for line in open('retail dat')count how often each product was purchasedcounts defaultdict(intfor... |
17,735 | formallyapriori takes collection of sets (that isyour shopping basketsand returns sets that are very frequent as subsets (that isitems that together are part of many shopping basketsthe algorithm works according to the bottom-up approachstarting with the smallest candidates (those composed of one single element)it buil... |
17,736 | this works correctlybut is very slow better implementation has more infrastructure so you can avoid having to loop over all the datasets to get the count (c_newsetin particularwe keep track of which shopping baskets have which frequent itemsets this accelerates the loop but makes the code harder to follow thereforewe w... |
17,737 | refer to the following codedef rules_from_itemset(itemsetdataset)itemset frozenset(itemsetnr_transactions float(len(dataset)for item in itemsetantecendent itemset-consequent base acountantecedent count acount ccount consequent count ccount for in datasetif item in dbase + if issuperset(itemset)ccount + if issuperset(an... |
17,738 | we can seefor examplethat there were transactions of which land were bought together of these also included so the estimated conditional probability is / compared to the fact that only of all transactions included this gives us lift of the need to have decent number of transactions in these counts in order to be able t... |
17,739 | summary in this we started by improving our rating predictions from the previous we saw couple of different ways in which to do so and then combined them all in single prediction by learning how to use set of weights these techniquesensemble or stacked learningare general techniques that can be used in many situations ... |
17,740 | genre classification so farwe have had the luxury that every training data instance could easily be described by vector of feature values in the iris datasetfor examplethe flowers are represented by vectors containing values for the length and width of certain aspects of flower in the text-based exampleswe could transf... |
17,741 | fetching the music data we will use the gtzan datasetwhich is frequently used to benchmark music genre classification tasks it is organized into distinct genresof which we will use only six for the sake of simplicityclassicaljazzcountrypoprockand metal the dataset contains the first seconds of songs per genre we can do... |
17,742 | matplotlib provides the convenient function specgram(that performs most of the under-the-hood calculation and plotting for usimport scipy from matplotlib pyplot import specgram sample_ratex scipy io wavfile read(wave_filenameprint sample_ratex shape ( ,specgram(xfs=sample_ratexextent=( , )the wave file we just read was... |
17,743 | just glancing at itwe immediately see the difference in the spectrum betweenfor examplemetal and classical songs while metal songs have high intensity over most of the frequency spectrum all the time (energize!)classical songs show more diverse pattern over time it should be possible to train classifier that discrimina... |
17,744 | |
17,745 | for real musicwe can quickly see that the fft looks not as beautiful as in the preceding toy exampleusing fft to build our first classifier neverthelesswe can now create some kind of musical fingerprint of song using fft if we do this for couple of songsand manually assign their corresponding genres as labelswe have th... |
17,746 | def create_fft(fn)sample_ratex scipy io wavfile read(fnfft_features abs(scipy fft( )[: ]base_fnext os path splitext(fndata_fn base_fn fftnp save(data_fnfft_featureswe save the data using numpy' save(functionwhich always appends npy to the filename we only have to do this once for every wave file needed for training or ... |
17,747 | using the confusion matrix to measure accuracy in multiclass problems with multiclass problemswe should also not limit our interest to how well we manage to correctly classify the genres in additionwe should also look at which genres we actually confuse with each other this can be done with the so-called confusion matr... |
17,748 | ax set_yticks(range(len(genre_list))ax set_yticklabels(genre_listpylab title(titlepylab colorbar(pylab grid(falsepylab xlabel('predicted class'pylab ylabel('true class'pylab grid(falsepylab show(when you create confusion matrixbe sure to choose color map (the cmap parameter of matshow()with an appropriate color orderin... |
17,749 | obviouslyusing fft points to the right direction (the classical genre was not that bad)but it is not enough to get decent classifier surelywe can play with the number of fft components (fixed to , but before we dive into parameter tuningwe should do our research there we find that fft is indeed not bad feature for genr... |
17,750 | on the left-hand side graphwe see the / curve for an ideal classifierwe would have the curve going from the top-left corner directly to the top-right corner and then to the bottom-right cornerresulting in an area under curve (aucof the right-hand side graph depicts the corresponding roc curve it plots the true positive... |
17,751 | y_label_test np asarray(y_test==labeldtype=intproba clf predict_proba(x_testproba_label proba[:,labelfprtprroc_thresholds roc_curve(y_label_testproba_ labelplot tpr over fpr the outcome will be the six roc plots shown in the following screenshot as we have already found outour first version of classifier only performs ... |
17,752 | improving classification performance with mel frequency cepstral coefficients we have already learned that fft is pointing us in the right directionbut in itself it will not be enough to finally arrive at classifier that successfully manages to organize our scrambled directory containing songs of diverse music genres i... |
17,753 | sure enoughthe benchmark dataset that we will be using contains only the first seconds of each songso that we would not need to cut off the last percent we do it neverthelessso that our code works on other datasets as wellwhich are most likely not truncated similar to our work with fftwe certainly would also want to ca... |
17,754 | we get the following promising resultsas shown in the next screenshotwith classifier that uses only features per song |
17,755 | the classification performance for all genres has improved jazz and metal are even at almost auc and indeedthe confusion matrix in the following plot also looks much better now we can clearly see the diagonal showing that the classifier manages to classify the genres correctly in most of the cases this classifier is ac... |
17,756 | summary in this we stepped out of our comfort zone when we built music genre classifier not having deep understanding of music theoryat first we failed to train classifier that predicts the music genre of songs with reasonable accuracy using fft but then we created classifier that showed really usable performance using... |
17,757 | recognition image analysis and computer vision has always been important in industrial applications with the popularization of cell phones with powerful cameras and internet connectionsthey are also increasingly being generated by the users thereforethere are opportunities to make use of this to provide better user exp... |
17,758 | the first step will be to load the image from the diskwhere it is typically stored in an image-specific format such as png or jpegthe former being lossless compression format and the latter lossy compression one that is optimized for subjective appreciation of photographs thenwe may wish to perform preprocessing on the... |
17,759 | howeversome specialized equipment (mostly in scientific fieldscan take images with more bit resolution or bits are common mahotas can deal with all these typesincluding floating point images (not all operations make sense with floating point numbersbut when they domahotas supports themin many computationseven if the or... |
17,760 | this screenshot of building is one of the images in the dataset we will use this screenshot as an example as you may be awareimage processing is large field here we will only be looking at some very basic operations we can perform on our images some of the most basic operations can be performed using numpy onlybut othe... |
17,761 | instead of rgb graywe can also have just the mean value of the redgreenand blue channels by calling image mean( the resulthoweverwill not be the same because rgb gray uses different weights for the different colors to give subjectively more pleasing result our eyes are not equally sensitive to the three basic colors im... |
17,762 | the result may be useful on its own (if you are measuring some properties of the thresholded imageor it can be useful for further processing the result is binary image that can be used to select region of interest the result is still not very good we can use operations on this screenshot to further refine it for exampl... |
17,763 | this is still not perfect as there are few bright objects in the parking lot that are not picked up we will improve it bit later in the the otsu threshold was able to identify the region of the sky as brighter than the building an alternative thresholding method is the ridley-calvard method (also named after its invent... |
17,764 | notice how we did not convert the gray screenshot to unsigned integerswe just made use of the floating point result as it is the second argument to the gaussian_filter function is the size of the filter (the standard deviation of the filterlarger values result in more blurringas can be seen in the following screenshot ... |
17,765 | filtering for different effects the use of image processing to achieve pleasing effects in images dates back to the beginning of digital imagesbut it has recently been the basis of number of interesting applicationsthe most well-known of which is probably instagram we are going to use traditional image in image process... |
17,766 | we now add the salt (which means some values will be almost whiteand pepper noise (which means some values will be almost black)lenna mh stretch(lennalenna np maximum(salt* seplenna np minimum(pepper* lenna*(~pepper)lennawe used the values and as white and black this is slightly smoother than the more extreme choices o... |
17,767 | now we filter the channels separately and build composite image out of it with mh as_rgb this function takes two-dimensional arraysperforms contrast stretching to make each an -bit integer arrayand then stacks themr mh gaussian_filter( mh gaussian_filter( mh gaussian_filter( im mh as_rgbr , , we then blend the two imag... |
17,768 | finallywe can combine the two images to have the center in sharp focus and the edges softer ringed mh stretch(im* ( - )*im now that you know some of the basic techniques of filtering imagesyou can build upon this to generate new filters it is more of an art than science after this point pattern recognition when classif... |
17,769 | we previously used an example of the buildings class here are examples of the text and scene classespattern recognition is just classification of images for historical reasonsthe classification of images has been called pattern recognition howeverthis is nothing more than the application of classification methods to im... |
17,770 | there are few other feature sets implemented in mahotas linear binary patterns is another texture-based feature set that is very robust against illumination changes there are other types of featuresincluding local featuresthat we will discuss later in this features are not just for classification the feature-based appr... |
17,771 | howeverit is also possible that your particular use case would benefit from few specially designed features for examplewe may think that in order to distinguish text from natural imagesit is an important defining feature of text that it is "edgy we do not mean what the text says (that may be edgy or square)but rather t... |
17,772 | the just_filter=true argument is necessaryotherwise thresholding is performed and you get an estimate of where the edges are the following screenshot shows the result of applying the filter (so that lighter areas are edgieron the left and the result of thresholding on the rightbased on this operatorwe may want to defin... |
17,773 | feature sets may be combined easily using this structure by using all of these featureswe get percent accuracy this is perfect illustration of the principle that good algorithms are the easy part you can always use an implementation of state-of-the-art classification the real secret and added value often comes in featu... |
17,774 | both objects are against natural backgrounds and with large smooth areas inside the objects we therefore expect that textures will not be very good when we use the same features as beforewe achieve percent accuracy in cross-validation using logistic regression this is not too bad on four classesbut not spectacular eith... |
17,775 | the descriptors_only=true flag means that we are only interested in the descriptors themselvesand not in their pixel locationsizeand other method information alternativelywe could have used the dense sampling methodusing the surf dense functionfrom mahotas features import surf descriptors surf dense(imagespacing= this ... |
17,776 | the number of words used does not usually have big impact on the final performance of the algorithm naturallyif the number is extremely small (ten or twentywhen you have few thousand images)then the overall system will not perform well similarlyif you have too many words (many more than the number of images for example... |
17,777 | the result is that each image is now represented by single array of features of the same size (the number of clustersin our case thereforewe can use our standard classification methods using logistic regression againwe now get percenta percent improvement we can combine all of the features together and we obtain percen... |
17,778 | we focused on mahotaswhich is one of the major computer vision libraries in python there are others that are equally well maintained skimage (scikit-imageis similar in spiritbut has different set of features opencv is very good +library with python interface all of these can work with numpy arrays and can mix and match... |
17,779 | garbage ingarbage outthat' what we know from real life throughout this bookwe have seen that this pattern also holds true when applying machine learning methods to training data looking backwe realize that the most interesting machine learning challenges always involved some sort of feature engineeringwhere we tried to... |
17,780 | sketching our roadmap dimensionality reduction can be roughly grouped into feature selection and feature extraction methods we have already employed some kind of feature selection in almost every when we inventedanalyzedand then probably dropped some features in this we will present some ways that use statistical metho... |
17,781 | detecting redundant features using filters filters try to clean up the feature forest independent of any machine learning method used later they rely on statistical methods to find out which of the features are redundant (in which casewe need to keep only one per redundant feature groupor irrelevant in generalthe filte... |
17,782 | howeverthe -value basically tells us that whatever the correlation coefficient iswe should not pay attention to it the following output in the screenshot illustrates the samein the first three cases that have high correlation coefficientswe would probably want to throw out either or since they seem to convey similar if... |
17,783 | although the human eye immediately sees the relationship between and in all but the bottom-right graphthe correlation coefficient does not it is obvious that correlation is useful to detect linear relationshipsbut fails for everything else for non-linear relationshipsmutual information comes to the rescue mutual inform... |
17,784 | to understand thatlet us pretend we want to use features from the feature set house_sizenumber_of_levelsand avg_rent_price to train classifier that outputs whether the house has an elevator or not in this examplewe intuitively see that knowing house_size means we don' need number_of_levels anymoresince it somehow conta... |
17,785 | we can see that this situation is less uncertain the uncertainty will decrease the farther we get from reaching the extreme value of for either percent or percent chance of the head showing upas we can see in the following graphwe will now modify the entropy by applying it to two features instead of onesuch that it mea... |
17,786 | in order to restrict mutual information to the interval [ , ]we have to divide it by their added individual entropywhich gives us the normalized mutual informationthe nice thing about mutual information is that unlike correlationit is not looking only at linear relationshipsas we can see in the following graphs |
17,787 | hencewe have to calculate the normalized mutual information for all feature pairs for every pair having very high value (we would have to determine what that means)we would then drop one of them in case we are doing regressionwe could drop the feature that has very low mutual information with the desired result value t... |
17,788 | asking the model about the features using wrappers while filters can tremendously help in getting rid of useless featuresthey can go only so far after all the filteringthere might still be some features that are independent among themselves and show some degree of dependence with the result variablebut yet they are tot... |
17,789 | coming back to scikit-learnwe find various excellent wrapper classes in the sklearn feature_selection package real workhorse in this field is rfewhich stands for recursive feature elimination it takes an estimator and the desired number of features to keep as parameters and then trains the estimator with various featur... |
17,790 | n_ features_ to_select support_ ranking_ [false true false true false false false false true true[ [false true true true false false false false true true[ true true true true false false false false true true[ true true true true false true false false true true[ true true true true false true false true true true[ tr... |
17,791 | feature extraction at some pointafter we have removed the redundant features and dropped the irrelevant oneswe often still find that we have too many features no matter what learning method we usethey all perform badlyand given the huge feature spacewe understand that they actually cannot do better we realize that we h... |
17,792 | sketching pca pca involves lot of linear algebrawhich we do not want to go into neverthelessthe basic algorithm can be easily described with the help of the following steps center the data by subtracting the mean from it calculate the covariance matrix calculate the eigenvectors of the covariance matrix if we start wit... |
17,793 | scikit-learn provides the pca class in its decomposition package in this examplewe can clearly see that one dimension should be enough to describe the data we can specify that using the n_components parameterfrom sklearn import linear_modeldecompositiondatasets pca decomposition pca(n_components= here we can also use p... |
17,794 | limitations of pca and how lda can help being linear methodpca has its limitations when we are faced with data that has non-linear relationships we won' go into details herebut it will suffice to say that there are extensions of pcafor example kernel pcawhich introduce non-linear transformation so that we can still use... |
17,795 | that' all note that in contrast to the previous pca examplewe provide the class labels to the fit_transform(method thuswhereas pca is an unsupervised feature extraction methodlda is supervised one the result looks as expectedthen why to consider pca in the first place and not use lda onlywellit is not that simple with ... |
17,796 | nowmds tries to position the individual data points in the lower dimensional space such that the new distance there resembles as much as possible the distances in the original space as mds is often used for visualizationthe choice of the lower dimension is most of the time two or three let us have look at the following... |
17,797 | let us have look at slightly more complex iris dataset we will use it later to contrast lda with pca the iris dataset contains four attributes per flower with the previous codewe would project it into three-dimensional space while keeping the relative distances between the individual flowers as much as possible in the ... |
17,798 | of courseusing mds requires an understanding of the individual feature' unitsmaybe we are using features that cannot be compared using the euclidean metric for instancea categorical variableeven when encoded as an integer ( red circle blue star green triangleand so on)cannot be compared using euclidean (is red closer t... |
17,799 | while computers keep getting faster and have more memorythe size of the data has grown as well in factdata has grown faster than computational speedand this means that it has grown faster than our ability to process it it is not easy to say what is big data and what is notso we will adopt an operational definitionwhen ... |
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