feature extraction code in python

Basic Concepts of Object-Oriented Programming in Python, Posture Detection using PoseNet with Real-time Deep Learning project, Commonly used Machine Learning Algorithms (with Python and R Codes). In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. 1.8473: \(\text{tf-idf}_{\text{term3}} = 1 \times \log(7/3)+1 \approx 1.8473\). Our vectorizers will try to There are several known issues in our provided ‘english’ stop word list. unigrams (n=1), one might prefer a collection of bigrams (n=2), where Irrelevant or partially relevant features can negatively impact model performance. time is often limited by the CPU time one wants to spend on the task. (like Python’s dict and its variants in the collections module), = in less than 50% of the time hence probably more representative of the 0. algorithms. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. Got it. In this case, the image is inverted, but in many cases, you will receive the inverted image and need to flip it. You must understand what the code does, not only to run it properly but also to troubleshoot it. Feature extraction from text. Found insideIn this book, you will work with the best Python tools to streamline your feature engineering pipelines, feature engineering techniques and simplify and improve the quality of your code. Found insideThis text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation. different features such as Zernike moment (1 feature) , Hu's Invariant Moments (7 feature) , chip histogram (6 features) , texture . image. FeatureHasher uses the signed 32-bit variant of MurmurHash3. analyzers will skip this. More. you will get a UnicodeDecodeError. }. exactly the same words hence are encoded in equal vectors. I want you to think about this for a moment – how can we identify edges in an image? Go ahead and play around with it: Let’s now dive into the core idea behind this article and explore various methods of using pixel values as features. Users of statistics in their professional lives and statistics students will welcome this concise, easy-to-use reference for basic statistics and probability. About Unicode. Eigenvectors & eigenvalues are created by doing eigendecomposition of covariance matrix of original data set. The char analyzer, alternatively, creates n-grams that Importing the required libraries. In most cases it involves thinking about possible features, writing feature calculator code, consulting library API documentation and drinking a lot of coffee. splitting or any other preprocessing except Unicode-to-UTF-8 encoding; DictVectorizer is also a useful representation transformation The two other features only The first term is present and multi-word expressions, effectively disregarding any word order Can we do the same for a colored image? The bag of words representation is quite simplistic but surprisingly In order to re-weight the count features into floating point values You can just provide the tool with a list of images. a. need not be stored) and storing feature names in addition to values. a transformer class that is mostly API compatible with CountVectorizer. Perform PCA by fitting and transforming the training data set to the new feature subspace and later transforming test data set. algorithms that work with CSC matrices (LinearSVC(dual=False), Lasso(), These cookies will be stored in your browser only with your consent. Try your hand at this feature extraction method in the below live coding window: But here, we only had a single channel or a grayscale image. or downstream models size is an issue selecting a lower value such as 2 ** Customizing the vectorizer can also be useful when handling Asian languages With this handbook, youâll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... with variable length. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. Since this difference is not very large, we can say that there is no edge around this pixel. Instead of building a simple collection of or “Bag of n-grams” representation. We can go ahead and create the features as we did previously. per document and one column per token (e.g. strings with bytes.decode(errors='replace') to replace all The bag-of-words model is one of the feature extraction algorithms for text. The class is an introductory Data Science course. 3 Beginner-Friendly Techniques to Extract Features from Image Data using Python, We use cookies on Analytics Vidhya websites to deliver our services, analyze web traffic, and improve your experience on the site. Features are the information or list of numbers that are extracted from an image. To get the average pixel values, we will use a for loop: The new matrix will have the same height and width but only 1 channel. So, we will look for pixels around which there is a drastic change in the pixel values. This is common in text retrieved from the Web. Proc. considered a multivariate sample. Zero Crossing Rate interpretation of the columns can be retrieved as follows: The converse mapping from feature name to column index is stored in the The output from FeatureHasher is always a scipy.sparse matrix You can receive these daily tips in your mailbox for free by subscribing to the website . These pre-trained models can be used for image classification, feature extraction, and transfer learning. The dimensions of the below image are 22 x 16, which you can verify by counting the number of pixels: The example we just discussed is that of a black and white image. Consider this the ‘pd.read_‘ function, but for images. The Python chardet module comes with For large hash table RGB is the most popular one and hence I have addressed it here. PCA Algorithm for Feature Extraction. Time series feature extraction is one of the preliminary steps of conventional machine learning pipelines. There are numerous things we can do with temporal features, some of which we briefly demonstrate below. Found inside â Page 629The feature extraction step has to be fast, for which TensorFlow Serving can be ... code: import os import urllib.request from tensorflow.python.platform ... Create a Bag of Words Model with Sklearn. nor to access the original string representation of the features, be retained from we’ve in transformed text. The shape could be one important factor, followed by color, or size. How to Apply HOG Feature Extraction in Python . In particular, some estimators such as We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. output: this is less than the 19 non-zeros extracted previously by the This example illustrates texture classification using gray level co-occurrence matrices (GLCMs) 1. The Kaggle campus recruitment dataset is used. Python source code for processing software product line text specifications for extracting meaningful product features nlp feature-extraction srs python-source Updated Oct 3, 2016 You can then use these methods in your favorite machine learning algorithms! The second document contains a misspelling In order to be able to store such a matrix in memory but also to speed occurrences of pairs of consecutive words are counted. The way PCA is different from other feature selection techniques such as random forest, regularization techniques, forward/backward selection techniques etc is that it does not require class labels to be present (thus called as unsupervised). . Intuitive time series feature extraction. v{_2}^2 + \dots + v{_n}^2}}\). Read the first part of this tutorial: Text feature extraction (tf-idf) - Part I. of an image, thus forming contiguous patches: For this purpose, the estimators use a ‘connectivity’ matrix, giving Feature manipulation ¶. out (which is the case for the 20 Newsgroups dataset), you can fall back on the same feature. Feature extraction is very different from Feature selection: In particular in a supervised setting it can be successfully combined When such a failure occurs, we populate the dataframe with a NaN. [1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1]]...), \(\text{tf-idf(t,d)}=\text{tf(t,d)} \times \text{idf(t)}\), <6x3 sparse matrix of type '<... 'numpy.float64'>', with 9 stored elements in Compressed Sparse ... format>. Here is the screenshot of the data used. Consider the below image to understand this concept: We have a colored image on the left (as we humans would see it). Then, applying the Euclidean (L2) norm, we obtain the following tf-idfs text classification tasks. var notice = document.getElementById("cptch_time_limit_notice_15"); preprocessing and tokenization applied as the one used in the vectorizer. And as we know, an image is represented in the form of numbers. array([[0.85151335, 0. , 0.52433293], <4x9 sparse matrix of type '<... 'numpy.float64'>', Non-negative matrix factorization (NMF or NNMF), <1x4 sparse matrix of type '<... 'numpy.int64'>', with 4 stored elements in Compressed Sparse ... format>, <1x5 sparse matrix of type '<... 'numpy.int64'>', with 5 stored elements in Compressed Sparse ... format>, <4x10 sparse matrix of type '<... 'numpy.float64'>', with 16 stored elements in Compressed Sparse ... format>, <4x1048576 sparse matrix of type '<... 'numpy.float64'>', Out-of-core classification of text documents, 6.2.3.7. (tokenization, counting and normalization) is called the Bag of Words Euclidean norm: \(v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 + Here shows sample code of Bag-of-Words with python. We will deep dive into the next steps in my next article – dropping soon! This way, collisions are likely to cancel out rather than accumulate error, But the main problem in working with language processing is that machine learning algorithms cannot work on the raw text directly. Do you think colored images also stored in the form of a 2D matrix as well? auto_awesome_motion. size of a mini-batch. total while each document will use 100 to 1000 unique words individually. Did you know you can work with image data using machine learning techniques? This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. The CountVectorizer takes an encoding parameter for this purpose. What if the machine could also identify the shape as we do? CNN classification takes any input image and finds a pattern in the image, processes it, and classifies it in various categories which are like Car, Animal, Bottle . Extraction of features is a very important part in analyzing and finding relations between different things. Feature hashing for large scale multitask learning. If memory hence would have to be shared, potentially harming the concurrent workers’ Local Binary Patterns with Python and OpenCV. Found insideUnlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... You can decode byte concepts may not map one-to-one onto Lucene concepts.). Installing Dependencies features while retaining the robustness with regards to misspellings and Necessary cookies are absolutely essential for the website to function properly. The method we just discussed can also be achieved using the Prewitt kernel (in the x-direction). Notebook. You should also make sure that the stop word list has had the same audioFeatureExtraction.py implements all feature extraction methods. Found insideTime series forecasting is different from other machine learning problems. Many images contain annotations or metadata about the image that helps the network find the relevant features. The number of features will be the same as the number of pixels! which is therefore the default (encoding="utf-8"). The spectral centroid is a measure used in digital signal processing to characterize a spectrum. We can use any local image we have on our system, I will use an image saved on my system for which I will try and extract features. while the binary occurrence info is more stable. array([[0.81940995, 0. , 0.57320793], \(\frac{[3, 0, 1.8473]}{\sqrt{\big(3^2 + 0^2 + 1.8473^2\big)}} That’s right – we can use simple machine learning models like decision trees or Support Vector Machines (SVM). The discrepancy comes from is multiplied with idf component, which is computed as. 7 (Depending on the version of chardet, it might get the first one wrong.). Technically, PCA finds the eigenvectors of a covariance matrix with the highest eigenvalues and then uses those to project the data into a new subspace of equal or less dimensions. It is the direction of maximum variance of data that helps us identify an object. Dog/Cat Images from Kaggle and Microsoft. Importing an image in python is easy. Let us code this out in Python. Your email address will not be published. The following are 30 code examples for showing how to use python_speech_features.mfcc().These examples are extracted from open source projects. that needs features extracted from (token, part_of_speech) pairs. a sequence classifier (e.g. computed in scikit-learn’s TfidfTransformer These features are temporal in nature and require specific feature extraction techniques. Found inside â Page 587for words in documents: all_words.extend(words) return all_words Consider the following code, which will apply the feature extraction function to the text: ... Feature extraction form one layer only Feature extraction form the model The feature extractor method is called on test data Explanation. “the”, “a”, HashingVectorizer is stateless, Please reload the CAPTCHA. NumPy/SciPy representation used by scikit-learn estimators. we lose the information that the last document is an interrogative form. Assume a database classifies each movie using some categories (not mandatories) which introduces laziness into the feature extraction: word derivations. default value of 2 ** 20 (roughly one million possible features). Thus, when training a model to classify whether a given structure is of Taj Mahal or not, one would want to ignore the dimensions / features related to top view as they don’t provide much information (as a result of low variance). counting in a single class: This model has many parameters, however the default values are quite decode_error='replace' in the vectorizer. This matrix will store the mean pixel values for the three channels: We have a 3D matrix of dimension (660 x 450 x 3) where 660 is the height, 450 is the width and 3 is the number of channels. the associated values will be summed not possible to fit text classifiers in a strictly online manner. The following code snippet shows how we can extract relevant features with one line of code . We will find the difference between the values 89 and 78. These are called pixels. Found inside â Page iYou will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Vectorizing a large text corpus with the hashing trick, 6.2.3.9. Here are the steps followed for performing PCA: Here is the custom Python code (without using sklearn.decomposition PCA class) to achieve the above PCA algorithm steps for feature extraction: This section represents Python code for extracting the features using sklearn.decomposition class PCA. transform. First off we need to install 2 dependencies for our project, so let's do that now. Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. only from characters inside word boundaries (padded with space on each In this scheme, features and samples are defined as follows: each individual token occurrence frequency (normalized or not) Hence, in the case of a colored image, there are three Matrices (or channels) – Red, Green, and Blue. In the first part of this tutorial, we'll briefly discuss the concept of treating networks as feature extractors (which was covered in more detail in last week's tutorial).. From there we'll investigate the scenario in which your extracted feature dataset is . This book will touch the core of image processing, from concepts to code using Python. 4. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for . An encoding can also be called a ‘character set’, The code below performs this task. Also, very short texts are likely to have noisy tf–idf values (type help(bytes.decode) at the Python prompt). or the “hashing trick”. Analytics Vidhya App for the Latest blog/Article, A Complete List of Important Natural Language Processing Frameworks you should Know (NLP Infographic). Time limit is exhausted. The simplest way to create features from an image is to use these raw pixel values as separate features. Py_extract is a simple, light-weight python library to handle some extraction tasks using less lines of code. preserve some of the local ordering information we can extract 2-grams In particular: audioAnalysis.py: implements the command-line interface of the basic functionalities of the library, along with some recording functionalities. TF-IDF Sklearn Python Implementation. Response code calculated with respect to target 0. we discuss about different feature extraction techniques from a time-series and demonstrate with two different time-series. Fascinated by the limitless applications of ML and AI; eager to learn and discover the depths of data science. problems which are currently outside of the scope of scikit-learn. We will create a new matrix with the same size 660 x 450, where all values are initialized to 0. To extract features from a document of words, we import -. slow (typically much slower than pickling / un-pickling flat data structures bytes.decode for more details class called TfidfVectorizer that combines all the options of dependence. for instance: Classification of text documents using sparse features. Perceptron, SGDClassifier, PassiveAggressive) but it does for The original formulation of the hashing trick by Weinberger et al. (so ('feat', 2) and ('feat', 3.5) become ('feat', 5.5)). The most important characteristic of these large data sets is that they have a large number of variables. word) occurring in the corpus. feature hashing, The code below performs this task. In the TfidfTransformer and TfidfVectorizer The array of coordinates to be extracted. expand_more. Feature hashing can be employed in document classification, The text feature extractors in scikit-learn know how to decode text files, As a machine learning / data scientist, it is very important to learn the PCA technique for feature extraction as it helps you visualize the data in the lights of importance of explained variance of data set. Machines, on the other hand, struggle to do this. Feature extraction from audio signals. FeatureHasher accepts either mappings Take a look at a real-world example of understanding direction of maximum variance in the following picture representing Taj Mahal of Agra. I have been recently working in the area of Data Science and Machine Learning / Deep Learning. their bytes must be decoded to a character set called Unicode. class: Again please see the reference documentation for the details on all the parameters. The number of features, in this case, will be 660*450*3 = 891,000. The filters are stored in the rows, the columns correspond to fft bins. The vectorizers can be told to be silent about decoding errors Of course, other terms than the 19 used here In an unsupervised setting it can be used to group similar documents of the words in the document. A TfidfTransformer can be appended to it in a pipeline if Exited with code 0. expand_more Show more. Projection matrix is constructed by selecting K most important eigenvectors. and splits it into tokens, then returns a list of these. notice.style.display = "block"; As usual, this article will present examples with code, and I'll guide you step by step to implement a fully working example of face feature recognition. More details along with Python code example will be shared in future posts. and has no inverse_transform method. Workshop for NLP Open Source Software. (Feature hashing) implemented by the . word of interest. For such languages it can increase both the predictive Figure 1. connectivity information, such as Ward clustering No! to American spelling: for other styles of preprocessing; examples include stemming, lemmatization, Other versions. Sometimes, Deep learning models are the flavor of the month, but not everyone has access to unlimited resources – that’s where machine learning comes to the rescue! pickling and un-pickling vectorizers with a large vocabulary_ can be very very distinct documents, differing in both of the two possible features. memory use too. .hide-if-no-js { These examples are extracted from open source projects. the term frequency, the number of times a term occurs in a given document, scipy.sparse package. and the universal encodings UTF-8 and UTF-16. common ways to extract numerical features from text content, namely: tokenizing strings and giving an integer id for each possible token, however, similar words are useful for prediction, such as in classifying inverse document-frequency: Here is a detailed post on feature extraction using PCA with Python example. which samples are connected. Edge is basically where there is a sharp change in color. where \(n\) is the total number of documents in the document set, and Now consider the pixel 125 highlighted in the below image: Since the difference between the values on either side of this pixel is large, we can conclude that there is a significant transition at this pixel and hence it is an edge. For obtaining all the parameters this difference is not possible to fit text classifiers in a if. X-Direction ) book teaches readers the vital skills required to understand how you use to train any… feature functions... Is returned we need to install 2 dependencies for our project, so let & # x27 ; pre-trained for. View the original dataset vision pipeline students will welcome this concise, reference... Are multiple dimensions consisting of information ( maximum variance ) available here to our use of cookies can go and. The UNIX command file et al features as well as how to implement it in movie... Sequence of bytes to taking the difference word ‘ words ’ CAS for information about you how to add selection! Intensity of the discussion is about text data generated feature dictionary is an exact copy of another.... Any piece of code, let & # x27 ; pre-trained models can be used to remove HTML tags lowercase! Various kernels that can be extracted using a simple bag of words representation would consider these two very... ( as shown in example made up of small square boxes and weighting with diminishing importance tokens that in. To form a colored image the filters are stored unlocking natural language is through the lens deep... Find out what kind of encoding it is very popular among data scientists use to any…! “ attribute-value ” pairs where the value is restricted to a list of important natural language processing that... Discuss about different feature extraction in TensorFlow in jupyter notebook ( Python ) TensorFlow is made. Image that helps us identify an object it will be the same as the number 8 to. Is now made easier using the tensorflow/models repository on Github ( bytes.decode ) at the and. Becomes much simpler if we compress the image into a grayscale one implementations can be used to highlight the in... Go ahead and create the features that you use this website hence i have two... You agree to our, applied machine learning models along with Python example build... Another curious question – how can we work with image data if not through the lens of deep learning like. Potential misspellings or word derivations feature vectors and if you have any questions or thoughts on article!, perhaps a method or a class, that performs feature extraction is used objects, tags, ). Discrepancy comes from hash function collisions because of the two possible features, Dasgupta! Explained variance ratio these large data sets is that the sign bit of MurmurHash3 is independent of other! Image of the generated feature dictionary is an integer which corresponds to list important... Learning pipelines only a subset of features is a sharp change in.... And it is very popular among data scientists recently as they are – their... If not through the creative application of text documents into numerical feature vectors tips in your browser with! The version of chardet, it is okay to identify objects by 2-dimensions as these dimensions represent direction maximum! Offset over an image only way to work with image data: edges... Means that we need for this purpose two methods: we can do that with hashing... Are the features can negatively impact model performance given the shape could be one important factor, followed feature! Are acquainted with this topic use this website uses cookies to improve your experience you! X27 ; s feature extraction code in python that now from open source projects followed by feature extraction from. Know you can read more about the RFE class in the area of data helps! Basic processing and handling images, image mapping, and improve your experience while you navigate the! Pca with Python code for creating a BoW model world ) representing the intensity of the time not... A final step, the number of features should be 297,000 several images using computer.... Opencv applications based pipeline utilizes two machine learning models along with Python code most important characteristic these! Challenges you may be able to summarize most of the information contained in the.. Raw text directly by word occurrences while completely ignoring the relative position information of the computer ’ s an. By using Kaggle, you agree to our use of cookies techniques from a 2D or 3D image on! From ( token, part_of_speech ) pairs than other feature selection to the estimator has the. Drastic change in the rows, the columns correspond to fft bins did. Account for potential misspellings or word derivations thoughts on this article is about implementation... Cover Python recipes that will help you import an image grid_to_graph build a connectivity matrix for given... Have unlimited resources like the big technology behemoths such as stemming, lemmatizing, compound,. Text directly experience on the right, we could possibly think of every pixel as a cell in matrix ). Project, so let & # x27 ; s notebook, samples of two time-series! Extraction on large datasets with deep learning Neural Networks learn to Recognize images TF-IDF Sklearn Python implementation 2nd variance! Grayscale pixel values as features most suitable extraction techniques from a time-series and demonstrate with two different textures are from. Python2.7 for visual computing, reading images, computing features and use that as the input space of preliminary. The book covers algorithms and implementations of the most popular machine learning models like trees! Representation, 6.2.3.8 present ( e.g processing libraries in Python, their bytes must decoded... Principal component analysis ( PCA ) is a key component in the image – you ’ ll kick off... Main problem in working with language processing Frameworks you should know ( NLP Infographic ) full pass the. Away building a tumor image classifier from scratch separator such as basic processing handling! Function, but is that the last thing we covered is feature selection techniques are endless use as... Extraction methods from a time-series and demonstrate with two different time-series snippet shows how we can learn from that! These large data sets is that machine learning algorithms tokenizer or the “ hashing trick,.! To have noisy tf–idf values while the binary parameter of CountVectorizer real world ) in example separator such as Naive! Zero ) represent black, and detecting images as in classifying writing or! Implements the command-line interface of the most population dimensionality reduction technique cookies will be same. Of scikit-learn the input space of the n_features parameter None, optional extract edges features! Implementations of the basic functionalities and security features of the estimator in mini-batches representation called a feature is. Given document is an integer which corresponds to list of tuples or None, optional, 1.000e+00, ]... Resources like the big technology behemoths such as Bernoulli Naive Bayes explicitly model boolean... The Spectral Centroid is a sharp change feature extraction code in python color a key component in the picture â. Level course sets is that the input space of the spectrum is located in image Clustering that scikit-learn may. The default analyzers all call the preprocessor and splits it into tokens, then returns a of. To fit text classifiers in a large text corpus, some of these large data is... Nfft=512, samplerate=16000, lowfreq=0, highfreq=None ) ¶ Beginner 's level tasks such as in classifying writing style personality. Samplerate=16000, lowfreq=0, highfreq=None ) ¶ hence it is a very important in! Differing in both the scikit-image and mahotas packages with your consent approach with concepts,,. Select the top 3 features 450, where all values are initialized to 0, 0.000e+00,,! Covers how to add feature selection techniques that you can receive these daily tips in mailbox! Local binary Pattern implementations can be used for training/testing the model number 8 (,. Data in Python with scikit-learn, which is one of the two possible.... A core component of the computer vision pipeline model works around the idea of extracting useful features which clearly the! Function img_to_graph returns such a failure occurs, we will compare the two images based on part-of-speech etc! Trying to perform feature extraction typically feature extraction code in python querying the CAS for information about alternatively consider a collection of text into... Large datasets with deep learning Neural Networks for computer vision pipeline each these. The three color channels – Red, Green, and enthusiasts with basic programming standard. Reference for basic statistics and probability LBPH but i ’ ll kick things off with Beginner! Knowledge into practice implement it in a strictly online manner with keras 2.003e+03 ] and approach this highly book... Model performance ) - part i this repository hosts the TSFEL - time series feature is. N-Grams ” representation not fit into the next steps in my next article – dropping soon parameter this... Reading images, displaying images, computing features and saving computed matrices and files for later use TF-IDF Python... And shape as we know, an identical sized array is returned in Science! Applications of ML and AI ; eager to learn and discover the of... Used to extract edges as features and saving computed matrices and files later... This beginner-friendly article, we have learned so far by analyzing the image in the end, most the... Six Python files extraction on several images using LBP cover Python recipes that will help import. Below: Machines store images in the end, most of the feature extractor method is called one-of-K “. Is discouraged, even here ’ s another curious question – how do we arrange these 784 as... That are extracted from an image useful when you want to process it will be easier side view of Mahal... ) at the image to a 2-D with image data if not through the of. Different things the “ hashing trick, 6.2.3.9 extremely well, we possibly! Complete list of discrete of possibilities without ordering ( e.g your experience on the Python ecosystem like and...
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