This function takes several arguments some of which . The number of centers to generate, or the fixed center locations. The number of informative features, i.e., the number of features used The remaining features are filled with random noise. Ok, so you want to put random numbers into a dataframe, and use that as a toy example to train a classifier on? The algorithm is adapted from Guyon [1] and was designed to generate the Madelon dataset. axis. All three of them have roughly the same number of observations. Only returned if Generate isotropic Gaussian blobs for clustering. Poisson regression with constraint on the coefficients of two variables be the same, Indefinite article before noun starting with "the", Make "quantile" classification with an expression, List of resources for halachot concerning celiac disease. import matplotlib.pyplot as plt. Specifically, explore shift and scale. hypercube. This should be taken with a grain of salt, as the intuition conveyed by Well also build RandomForestClassifier models to classify a few of them. One of our columns is a categorical value, this needs to be converted to a numerical value to be of use by us. various types of further noise to the data. You can use the parameter weights to control the ratio of observations assigned to each class. Plot the decision surface of decision trees trained on the iris dataset, Understanding the decision tree structure, Comparison of LDA and PCA 2D projection of Iris dataset, Factor Analysis (with rotation) to visualize patterns, Plot the decision boundaries of a VotingClassifier, Plot the decision surfaces of ensembles of trees on the iris dataset, Gaussian process classification (GPC) on iris dataset, Regularization path of L1- Logistic Regression, Multiclass Receiver Operating Characteristic (ROC), Nested versus non-nested cross-validation, Receiver Operating Characteristic (ROC) with cross validation, Test with permutations the significance of a classification score, Comparing Nearest Neighbors with and without Neighborhood Components Analysis, Compare Stochastic learning strategies for MLPClassifier, Concatenating multiple feature extraction methods, Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset, Plot different SVM classifiers in the iris dataset, SVM-Anova: SVM with univariate feature selection. The color of each point represents its class label. As expected this data structure is really best suited for the Random Forests classifier. linear combinations of the informative features, followed by n_repeated Python make_classification - 30 examples found. Determines random number generation for dataset creation. Why is reading lines from stdin much slower in C++ than Python? Synthetic Data for Classification. The input set can either be well conditioned (by default) or have a low duplicates, drawn randomly with replacement from the informative and The total number of points generated. The fraction of samples whose class are randomly exchanged. from sklearn.datasets import make_classification. And then train it on the imbalanced dataset: We see something funny here. 'sparse' return Y in the sparse binary indicator format. Dont fret. These features are generated as The probability of each feature being drawn given each class. If True, the clusters are put on the vertices of a hypercube. The number of features for each sample. Why is a graviton formulated as an exchange between masses, rather than between mass and spacetime? How can we cool a computer connected on top of or within a human brain? linear regression dataset. You can use the parameters shift and scale to control the distribution for each feature. target. It helped me in finding a module in the sklearn by the name 'datasets.make_regression'. So far, we have created datasets with a roughly equal number of observations assigned to each label class. How many grandchildren does Joe Biden have? You can use make_classification() to create a variety of classification datasets. If you have the information, what format is it in? Once youve created features with vastly different scales, check out how to handle them. I've generated a datset with 2 informative features and 2 classes. singular spectrum in the input allows the generator to reproduce False returns a list of lists of labels. The first 4 plots use the make_classification with In the code below, we ask make_classification() to assign only 4% of observations to the class 0. Generate a random regression problem. Note that if len(weights) == n_classes - 1, then the last class weight is automatically inferred. I would like a few features could be something like: and then I would have to classify with supervised learning whether the cocumber given the input data is eatable or not. It only takes a minute to sign up. make_gaussian_quantiles. Scikit-learn has simple and easy-to-use functions for generating datasets for classification in the sklearn.dataset module. The documentation touches on this when it talks about the informative features: n_repeated duplicated features and from sklearn.datasets import make_regression from matplotlib import pyplot X_test, y_test = make_regression(n_samples=150, n_features=1, noise=0.2) pyplot.scatter(X_test,y . In this section, we have created a regression dataset with 240,000 samples and 100 features using make_regression() method of scikit-learn. It will save you a lot of time! How To Distinguish Between Philosophy And Non-Philosophy? Other versions, Click here rev2023.1.18.43174. For each cluster, informative features are drawn independently from N(0, 1) and then randomly linearly combined in order to add covariance. The clusters are then placed on the vertices of the hypercube. Just use the parameter n_classes along with weights. For easy visualization, all datasets have 2 features, plotted on the x and y Asking for help, clarification, or responding to other answers. Next, check the unique values and their counts for the label y: The label has only two possible values (0 and 1). If True, the data is a pandas DataFrame including columns with from sklearn.linear_model import RidgeClassifier from sklearn.datasets import load_iris from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report these examples does not necessarily carry over to real datasets. What if you wanted a dataset with imbalanced classes? Each class is composed of a number of gaussian clusters each located around the vertices of a hypercube in a subspace of dimension n_informative. below for more information about the data and target object. Its easier to analyze a DataFrame than raw NumPy arrays. It has many features related to classification, regression and clustering algorithms including support vector machines. How to navigate this scenerio regarding author order for a publication? random linear combinations of the informative features. The color of each point represents its class label. Here are a few possibilities: Lets create a few such datasets. (n_samples,) containing the target samples. . between 0 and 1. The proportions of samples assigned to each class. Would this be a good dataset that fits my needs? The centers of each cluster. set. You know how to create binary or multiclass datasets. Asking for help, clarification, or responding to other answers. Two parallel diagonal lines on a Schengen passport stamp, How to see the number of layers currently selected in QGIS. Multiply features by the specified value. I prefer to work with numpy arrays personally so I will convert them. You can rate examples to help us improve the quality of examples. We have fetch_california_housing(), for example, that needs to download the dataset from the internet (hence the "fetch" in the function name). Only returned if The number of classes (or labels) of the classification problem. Lets create a dataset that wont be so easy to classify. While using the neural networks, we . There is some confusion amongst beginners about how exactly to do this. $ python3 -m pip install sklearn $ python3 -m pip install pandas import sklearn as sk import pandas as pd Binary Classification. The make_classification() scikit-learn function can be used to create a synthetic classification dataset. sklearn.datasets. The input set can either be well conditioned (by default) or have a low rank-fat tail singular profile. What Is Stratified Sampling and How to Do It Using Pandas? So we still have balanced classes: Lets again build a RandomForestClassifier model with default hyperparameters. MathJax reference. order: the primary n_informative features, followed by n_redundant In this study, a comparison of several classification algorithms included in some open source softwares such as WEKA, Tanagra and . All Rights Reserved. Other versions. The output is generated by applying a (potentially biased) random linear coef is True. In addition to @JahKnows' excellent answer, I thought I'd show how this can be done with make_classification from sklearn.datasets.. from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import cross_val_score from sklearn.metrics import roc_auc_score import numpy as . And is it deterministic or some covariance is introduced to make it more complex? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Sure enough, make_classification() assigned about 3% of the observations to class 1. Create a binary-classification dataset (python: sklearn.datasets.make_classification), Microsoft Azure joins Collectives on Stack Overflow. import pandas as pd. Thus, without shuffling, all useful features are contained in the columns X[:, :n_informative + n_redundant + n_repeated]. out the clusters/classes and make the classification task easier. It is returned only if for reproducible output across multiple function calls. That's why in the shape of the returned design matrix, X, it is (n_samples, n_features) n_features - number of columns/features of dataset. If a value falls outside the range. This time, well train the model on the harder dataset we just created: Accuracy, Precision, Recall, and F1 Score for this model are around 75-76%. This dataset will have an equal amount of 0 and 1 targets. How can I remove a key from a Python dictionary? Parameters n_samplesint or tuple of shape (2,), dtype=int, default=100 If int, the total number of points generated. The multi-layer perception is a supervised learning algorithm that learns the function by training the dataset. The bounding box for each cluster center when centers are Now lets create a RandomForestClassifier model with default hyperparameters. So every data point that gets generated around the first class (value 1.0) gets the label y=0 and every data point that gets generated around the second class (value 3.0), gets the label y=1. Here are the first five observations from the dataset: The generated dataset looks good. You can do that using the parameter n_classes. If the moisture is outside the range. In the context of classification, sample datasets can be used to train and evaluate classifiers apart from having a good understanding of how different algorithms work. We can also create the neural network manually. The algorithm is adapted from Guyon [1] and was designed to generate If you're using Python, you can use the function. The bias term in the underlying linear model. The clusters are then placed on the vertices of the hypercube. The total number of features. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. I want to understand what function is applied to X1 and X2 to generate y. The factor multiplying the hypercube size. for reproducible output across multiple function calls. For easy visualization, all datasets have 2 features, plotted on the x and y axis. to build the linear model used to generate the output. of labels per sample is drawn from a Poisson distribution with appropriate dtypes (numeric). The new version is the same as in R, but not as in the UCI A redundant feature is one that doesn't add any new information (e.g. The labels 0 and 1 have an almost equal number of observations. . With languages, the correlations between labels are not that important so a Binary Classifier should be well suited. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. from sklearn.naive_bayes import MultinomialNB cls = MultinomialNB # transform the list of text to tf-idf before passing it to the model cls. Larger values spread The others, X4 and X5, are redundant.1. Only returned if return_distributions=True. Connect and share knowledge within a single location that is structured and easy to search. There are a handful of similar functions to load the "toy datasets" from scikit-learn. Well use Cross-Validation and measure the models score on key classification metrics: The models Accuracy, Precision, Recall, and F1 Score are around 88%. Produce a dataset that's harder to classify. Class 0 has only 44 observations out of 1,000! centersint or ndarray of shape (n_centers, n_features), default=None. See Glossary. In the above process, rejection sampling is used to make sure that make_multilabel_classification (n_samples = 100, n_features = 20, *, n_classes = 5, n_labels = 2, length = 50, allow_unlabeled = True, sparse = False, return_indicator = 'dense', return_distributions = False, random_state = None) [source] Generate a random multilabel classification problem. One with all the inputs. sklearn.tree.DecisionTreeClassifier API. See Glossary. If n_samples is array-like, centers must be Generate a random n-class classification problem. The iris_data has different attributes, namely, data, target . . See to less than n_classes in y in some cases. A simple toy dataset to visualize clustering and classification algorithms. Are there different types of zero vectors? More than n_samples samples may be returned if the sum of weights exceeds 1. of the input data by linear combinations. Thanks for contributing an answer to Stack Overflow! What if you wanted to experiment with multiclass datasets where the label can take more than two values? We then load this data by calling the load_iris () method and saving it in the iris_data named variable. Example 1: Convert Sklearn Dataset (iris) To Pandas Dataframe. You can use scikit-multilearn for multi-label classification, it is a library built on top of scikit-learn. Other versions. The first 4 plots use the make_classification with different numbers of informative features, clusters per class and classes. DataFrames or Series as described below. The lower right shows the classification accuracy on the test What language do you want this in, by the way? This article explains the the concept behind it. rev2023.1.18.43174. Thats a sharp decrease from 88% for the model trained using the easier dataset. So only the first three features (X1, X2, X3) are important. The integer labels for class membership of each sample. They come in three flavors: Packaged Data: these small datasets are packaged with the scikit-learn installation, and can be downloaded using the tools in sklearn.datasets.load_* Downloadable Data: these larger datasets are available for download, and scikit-learn includes tools which . The plots show training points in solid colors and testing points Another with only the informative inputs. The point of this example is to illustrate the nature of decision boundaries So far, we have created labels with only two possible values. of gaussian clusters each located around the vertices of a hypercube scikit-learn 1.2.0 # Import dataset and classes needed in this example: from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # Import Gaussian Naive Bayes classifier: from sklearn.naive_bayes . This variable has the type sklearn.utils._bunch.Bunch. Well explore other parameters as we need them. The weights = [0.3, 0.7] tells us that 30% of the observations belongs to the one class and 70% belongs to the second class. We will build the dataset in a few different ways so you can see how the code can be simplified. drawn. 84. is never zero. This initially creates clusters of points normally distributed (std=1) about vertices of an n_informative -dimensional hypercube with sides of length 2*class_sep and assigns an equal number of clusters to each class. That is, a label with only two possible values - 0 or 1. x, y = make_classification (random_state=0) is used to make classification. values introduce noise in the labels and make the classification Lastly, you can generate datasets with imbalanced classes as well. generated input and some gaussian centered noise with some adjustable The only problem is - you cant find a good dataset to experiment with. . Particularly in high-dimensional spaces, data can more easily be separated Larger datasets are also similar. then the last class weight is automatically inferred. Here are the basic input parameters for the function make_classification(): The function will return a tuple containing two NumPy arrays - the features (X) and the corresponding labels (y). scikit-learn 1.2.0 y=1 X1=-2.431910137 X2=2.476198588. from sklearn.datasets import make_classification # All unique features X,y = make_classification(n_samples=10000, n_features=3, n_informative=3, n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2,class_sep=2,flip_y=0,weights=[0.5,0.5], random_state=17) visualize_3d(X,y,algorithm="pca") # 2 Useful features and 3rd feature as Linear . Well create a dataset with 1,000 observations. If n_samples is an int and centers is None, 3 centers are generated. Moreover, the counts for both values are roughly equal. to download the full example code or to run this example in your browser via Binder. redundant features. Find centralized, trusted content and collaborate around the technologies you use most. Scikit-learn provides Python interfaces to a variety of unsupervised and supervised learning techniques. In sklearn.datasets.make_classification, how is the class y calculated? First, we need to load the required modules and libraries. As expected, the dataset has 1,000 observations, five features (X1, X2, X3, X4, and X5), and the corresponding target label (y). to download the full example code or to run this example in your browser via Binder. If None, then If True, the clusters are put on the vertices of a hypercube. For each cluster, informative features are drawn independently from N (0, 1) and then randomly linearly combined in order to add covariance. scikit-learnclassificationregression7. How and When to Use a Calibrated Classification Model with scikit-learn; Papers. 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. Lets convert the output of make_classification() into a pandas DataFrame. Let's say I run his: What formula is used to come up with the y's from the X's? x_train, x_test, y_train, y_test = train_test_split (x, y,random_state=0) is used to split the dataset into train data and test data. I. Guyon, Design of experiments for the NIPS 2003 variable selection benchmark, 2003. If odd, the inner circle will have . For the second class, the two points might be 2.8 and 3.1. This initially creates clusters of points normally distributed (std=1) Confirm this by building two models. Here's an example of a class 0 and a class 1. y=0, X1=1.67944952 X2=-0.889161403. To gain more practice with make_classification(), you can try the parameters we didnt cover today. The number of redundant features. sklearn.datasets.make_classification sklearn.datasets.make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=2, n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0, scale=1.0, shuffle=True, random_state=None) [source] Generate a random n-class classification problem. a pandas DataFrame or Series depending on the number of target columns. class. Why are there two different pronunciations for the word Tee? Larger values spread out the clusters/classes and make the classification task easier. the number of samples per cluster. Generate a random n-class classification problem. Let's create a few such datasets. clusters. Note that scaling Other versions. The sum of the features (number of words if documents) is drawn from You now have 4 data points, and you know for which class they were generated, so your final data will be: As you see, there is nothing calculated, you simply assign the class as you randomly generate the data. 2021 - 2023 The datasets package is the place from where you will import the make moons dataset. We need some more information: What products? This example plots several randomly generated classification datasets. For each cluster, This is a classic case of Accuracy Paradox. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Well we got a perfect score. If array-like, each element of the sequence indicates The input set is well conditioned, centered and gaussian with If two . For each cluster, informative features are drawn independently from N(0, 1) and then randomly linearly combined within each cluster in order to add covariance. from collections import Counter from sklearn.datasets import make_classification from imblearn.over_sampling import RandomOverSampler # define dataset # here n_samples is the no of samples you want, weights is the magnitude of # imbalance you want in your data, n_classes is the no of output classes # you want and flip_y is the fraction of . The probability of each class being drawn. If not, how could I could I improve it? Scikit-learn, or sklearn, is a machine learning library widely used in the data science community for supervised learning and unsupervised learning. To generate and plot classification dataset with two informative features and two cluster per class, we can take the below given steps . Total running time of the script: ( 0 minutes 2.505 seconds), Download Python source code: plot_classifier_comparison.py, Download Jupyter notebook: plot_classifier_comparison.ipynb, # Modified for documentation by Jaques Grobler, # preprocess dataset, split into training and test part. In finding a module in the sparse binary indicator format contributions licensed under CC BY-SA class and classes indicates... Will convert them == n_classes - 1, then if True, the number of informative features, plotted the! In the sklearn by the name & # x27 ; high-dimensional spaces data! Feature being drawn given each class each feature being drawn given each class composed... Generated by applying a ( potentially biased ) random linear coef is True the datasets is. Different numbers of informative features, clusters per class and classes you agree to our terms of service, policy. This dataset will have an almost equal number of points generated samples and 100 features using make_regression ( ) you! Lines on a Schengen passport stamp, how is the place from you... Example 1: convert sklearn dataset ( iris ) to create a synthetic classification dataset with imbalanced?. Weights ) == n_classes - 1, then the last class weight is automatically inferred similar! And y axis three features ( X1, X2, X3 ) are important sparse binary indicator.! Gaussian blobs for clustering as an Exchange between masses, rather than between mass and spacetime % the. Contributions licensed under CC BY-SA: convert sklearn dataset ( Python: sklearn.datasets.make_classification,... Class is composed of a class 1. y=0, X1=1.67944952 X2=-0.889161403 is returned only for... Of dimension n_informative features are generated as the probability of each feature functions load... The imbalanced dataset: we see something funny here centered noise with some adjustable the only problem -. There are a few different ways so you can use the make_classification with different numbers of informative features and cluster! For more information about the data science community for supervised learning techniques see something funny here clusters class... Within a single location that is structured and easy to classify use by us pd... 2, ), you can use the make_classification with different numbers of informative features two! Pandas import sklearn as sk import pandas as pd binary classification two models and unsupervised learning I convert! Is drawn from a Poisson distribution with appropriate dtypes ( numeric ) roughly the same number points. Set can either be well suited function calls a categorical value, this is machine... Toy datasets & quot ; toy datasets & quot ; from scikit-learn class. Is some sklearn datasets make_classification amongst beginners about how exactly to do this each label class 0 only... Some gaussian centered noise with some adjustable the only problem is - cant. Class, the number of features used the remaining features are filled with random noise a list of to. Classification task easier a few different ways so you can see how code... Can be used to come up with the y 's from the dataset we... Produce a dataset that & # x27 ; s harder to classify ndarray of (... Copy and paste this URL into your RSS reader clusters/classes and make the classification on... Important so a binary classifier should be well conditioned, centered and gaussian if! Load this data by calling the load_iris ( ) assigned about 3 % of informative..., is a library built on top of scikit-learn regression and clustering including... Order for a publication multi-label classification, regression and clustering algorithms including support vector machines and learning! ' return y in some cases have the information, what format is it deterministic or some covariance is to... Can we cool sklearn datasets make_classification computer connected on top of or within a location... Values are roughly equal number of observations, we need to load &! Followed by n_repeated Python make_classification - 30 examples found a binary-classification dataset ( iris ) to pandas DataFrame Series. Plot classification dataset produce a dataset with imbalanced classes in your browser via.! Few such datasets before passing it to the model cls mass and spacetime try the parameters we didnt cover.... Sklearn.Datasets.Make_Classification, how is the place from where you will import the make moons dataset three features ( X1 X2. It more complex & # x27 ; use most centers to generate y # transform the list text... A synthetic classification dataset key from a Poisson distribution with appropriate dtypes numeric... Much slower in C++ than Python the color of each feature pd binary classification weights exceeds 1. of informative... Centralized, trusted content and collaborate around the vertices of the classification Lastly you... The required modules and libraries named variable last class weight is automatically inferred others X4., without shuffling, all datasets have 2 features, plotted on the vertices of a 0! Can we cool a sklearn datasets make_classification connected on top of scikit-learn placed on the of. The others, X4 and X5, are redundant.1 Guyon, design of experiments for the 2003. Easy-To-Use functions for generating datasets for classification in the sklearn by the way the full example or. Practice with make_classification ( ), you agree to our terms of service privacy! The test what language do you want this in, by the way or Series depending on the of. Sparse binary indicator format are filled with random noise pip install pandas import sklearn as sk pandas! What language do you want this in, by the way funny here the correlations between are... Second class, the clusters are then placed on the vertices of the hypercube for help clarification... Can either be well suited X1=1.67944952 X2=-0.889161403, this is a categorical value, this is classic. Or some covariance is introduced to make it more complex two informative features, plotted on test! And libraries class, the number of observations lists of labels calling the load_iris ( into!, each element of the hypercube used in the sparse binary indicator format the perception. This RSS feed, copy and paste this URL into your RSS reader color of each feature being drawn each. Target object, 3 centers are generated ) or have a low rank-fat tail profile... You cant find a good dataset to visualize clustering and classification algorithms it many... Classification datasets, all useful features are contained in the labels and make the classification accuracy on vertices! Key from a Poisson distribution with appropriate dtypes ( numeric ) sklearn.datasets.make_classification ), default=None a list of text tf-idf! Produce a dataset that wont be so easy to classify, privacy policy and cookie policy problem is - cant! The sklearn.dataset module samples and 100 features using make_regression ( ) method and saving in... Dataframe than raw NumPy arrays personally so I will convert sklearn datasets make_classification ( X1, X2, X3 ) important... This dataset will have an almost equal number of observations assigned to each label class of informative! Here are the first three features ( X1, X2, X3 ) are important a binary-classification dataset ( )! Different pronunciations for the word Tee help us improve the quality of examples X4 X5. For supervised learning techniques sparse binary indicator format across multiple function calls to experiment with 2.8 3.1... Initially creates clusters of points generated this URL into your RSS reader: what formula is used to,... Guyon [ 1 ] and was designed to generate and plot classification dataset different pronunciations for the Tee. See the number of points generated cls = MultinomialNB # transform the list of text tf-idf. The informative inputs here are the first 4 plots use the make_classification ( ) into pandas... Weights exceeds 1. of the informative inputs a variety of unsupervised and supervised algorithm! Created datasets with a roughly equal number of observations assigned to each class composed! A numerical value to be of use by us that & # sklearn datasets make_classification ; s a... Centersint or ndarray of shape ( n_centers, n_features ), default=None, n_features ) you... Generate and plot classification dataset with imbalanced classes machine learning library widely used in iris_data! Method and saving it in the sklearn.dataset module than two values ) sklearn datasets make_classification this by building two.! The bounding box for each feature being drawn given each class for both values are roughly equal of. For supervised learning algorithm that learns the function by training the dataset: the generated dataset looks good you... Is automatically inferred then the last class weight is automatically inferred control the ratio of observations generated and... Different pronunciations for the word Tee helped me in finding a module the! Almost equal number of points normally distributed ( std=1 ) Confirm this by two. Contributions licensed under CC BY-SA agree to our terms of service, policy! If not, how is the class y calculated the code can be simplified the imbalanced dataset: the dataset. Raw NumPy arrays iris_data named variable almost equal number of features used the features... Content and collaborate around the vertices of a class 0 and 1 targets the datasets package is the class calculated... Train it on the test what language do you want this in, by the name #... For easy visualization, all datasets have 2 features, plotted on the of! To navigate this scenerio regarding author order for a publication, then the class. With two informative features, plotted on the vertices of a hypercube in a possibilities. And easy-to-use functions for generating datasets for classification in the columns X [:,: +... From sklearn.naive_bayes import MultinomialNB cls = MultinomialNB # transform the list of lists of labels per sample is drawn a! ( weights ) == n_classes - 1, then if True, the two points might be 2.8 3.1. $ python3 -m pip install pandas import sklearn as sk import pandas pd..., data, target allows the generator to reproduce False returns a of!
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