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TURICREATE.SFRAME
turicreate.SFrame¶ class turicreate.SFrame (data=None, format='auto', _proxy=None) ¶. SFrame means scalable data frame. A tabular, column-mutable dataframe object that can scale to big data. The data in SFrame is stored column-wise, and is stored on persistent storage(e.g. disk) to
FOUNDATIONDB ARCHITECTURE The write sub-system includes the master, proxies, resolvers, and transaction logs. The three roles are treated as a unit, and if any of them fail, we will recruit a replacement for all three roles. The master provides the commit versions for batches of the mutations to the proxies, runs data distribution algorithm, and runs ratekeeper.TURICREATE.PLOT
turicreate.plot¶. turicreate.plot. Plots the data in x on the X axis and the data in y on the Y axis in a 2d visualization, and shows the resulting visualization. Uses the following heuristic to choose the visualization: If x and y are both numeric (SArray of int or float), and they contain fewer than or equal to 5,000 values, show a scatter SERVERBOOTSTRAP CLASS REFERENCE A ServerBootstrap is an easy way to bootstrap a ServerSocketChannel when creating network servers.. Example: let group = MultiThreadedEventLoopGroup (numberOfThreads: System. coreCount) defer {try! group. syncShutdownGracefully ()} let bootstrap = ServerBootstrap (group: group) // Specify backlog and enable SO_REUSEADDR for the server itself. serverChannelOption ONE-SHOT OBJECT DETECTION · GITBOOKSEE MORE ON APPLE.GITHUB.IO TURICREATE.LOGISTIC_CLASSIFIER.LOGISTICCLASSIFIER class turicreate.logistic_classifier. LogisticClassifier (model_proxy) Logistic regression models a discrete target variable as a function of several feature variables. The logisticClassifier uses a discrete target variable y instead of a scalar. For each observation, the probability that y = 1 (instead of 0) is modeled as the logisticfunction
TURICREATE.SGRAPH
A scalable graph data structure. The SGraph data structure allows arbitrary dictionary attributes on vertices and edges, provides flexible vertex and edge query functions, and seamless transformation to and from SFrame. There are several ways to create an SGraph. The simplest way is to make an empty SGraph then add vertices and edgeswith the
TURICREATE.SARRAY
turicreate.SArray¶ class turicreate.SArray (data=, dtype=None, ignore_cast_failure=False, _proxy=None) ¶. An immutable, homogeneously typed array object backed by persistent storage. SArray is scaled to hold data that are much larger than the machine’s mainmemory.
TURICREATE.SFRAME.RANDOM_SPLIT turicreate.SFrame.random_split¶ SFrame.random_split (fraction, seed=None, exact=False) ¶ Randomly split the rows of an SFrame into two SFrames. The first SFrame contains M rows, sampled uniformly (without replacement) from the original SFrame.M is approximately the fraction times the original number of rows. The second SFrame contains the remaining rows of the original SFrame. VISUALIZATION · GITBOOK Visualizing Data. Data visualization can help us explore, understand, and gain insight from data. Visualization can complement other methods of data analysis by taking advantage of the human ability to recognize patterns in visual information.TURICREATE.SFRAME
turicreate.SFrame¶ class turicreate.SFrame (data=None, format='auto', _proxy=None) ¶. SFrame means scalable data frame. A tabular, column-mutable dataframe object that can scale to big data. The data in SFrame is stored column-wise, and is stored on persistent storage(e.g. disk) to
FOUNDATIONDB ARCHITECTURE The write sub-system includes the master, proxies, resolvers, and transaction logs. The three roles are treated as a unit, and if any of them fail, we will recruit a replacement for all three roles. The master provides the commit versions for batches of the mutations to the proxies, runs data distribution algorithm, and runs ratekeeper.TURICREATE.PLOT
turicreate.plot¶. turicreate.plot. Plots the data in x on the X axis and the data in y on the Y axis in a 2d visualization, and shows the resulting visualization. Uses the following heuristic to choose the visualization: If x and y are both numeric (SArray of int or float), and they contain fewer than or equal to 5,000 values, show a scatter SERVERBOOTSTRAP CLASS REFERENCE A ServerBootstrap is an easy way to bootstrap a ServerSocketChannel when creating network servers.. Example: let group = MultiThreadedEventLoopGroup (numberOfThreads: System. coreCount) defer {try! group. syncShutdownGracefully ()} let bootstrap = ServerBootstrap (group: group) // Specify backlog and enable SO_REUSEADDR for the server itself. serverChannelOption ONE-SHOT OBJECT DETECTION · GITBOOKSEE MORE ON APPLE.GITHUB.IO TURICREATE.LOGISTIC_CLASSIFIER.LOGISTICCLASSIFIER class turicreate.logistic_classifier. LogisticClassifier (model_proxy) Logistic regression models a discrete target variable as a function of several feature variables. The logisticClassifier uses a discrete target variable y instead of a scalar. For each observation, the probability that y = 1 (instead of 0) is modeled as the logisticfunction
TURICREATE.SGRAPH
A scalable graph data structure. The SGraph data structure allows arbitrary dictionary attributes on vertices and edges, provides flexible vertex and edge query functions, and seamless transformation to and from SFrame. There are several ways to create an SGraph. The simplest way is to make an empty SGraph then add vertices and edgeswith the
TURICREATE.SARRAY
turicreate.SArray¶ class turicreate.SArray (data=, dtype=None, ignore_cast_failure=False, _proxy=None) ¶. An immutable, homogeneously typed array object backed by persistent storage. SArray is scaled to hold data that are much larger than the machine’s mainmemory.
TURICREATE.SFRAME.RANDOM_SPLIT turicreate.SFrame.random_split¶ SFrame.random_split (fraction, seed=None, exact=False) ¶ Randomly split the rows of an SFrame into two SFrames. The first SFrame contains M rows, sampled uniformly (without replacement) from the original SFrame.M is approximately the fraction times the original number of rows. The second SFrame contains the remaining rows of the original SFrame.PERFORMANCE
Concurrency. FoundationDB is designed to achieve great performance under high concurrency from a large number of clients. Its asynchronous design allows it to handle very high concurrency, and for a typical workload with 90% reads and 10% writes, maximum throughput is reached at about 200 concurrent operations. CLIENTBOOTSTRAP CLASS REFERENCE A ClientBootstrap is an easy way to bootstrap a SocketChannel when creating network clients.. Usually you re-use a ClientBootstrap once you set it up and called connect multiple times on it. This way you ensure that the same EventLoops will be shared across all your connections.. Example: let group = MultiThreadedEventLoopGroup (numberOfThreads: 1) defer {try! group. SERVERBOOTSTRAP CLASS REFERENCE A ServerBootstrap is an easy way to bootstrap a ServerSocketChannel when creating network servers.. Example: let group = MultiThreadedEventLoopGroup (numberOfThreads: System. coreCount) defer {try! group. syncShutdownGracefully ()} let bootstrap = ServerBootstrap (group: group) // Specify backlog and enable SO_REUSEADDR for the server itself. serverChannelOption FOUNDATIONDB ARCHITECTURE The write sub-system includes the master, proxies, resolvers, and transaction logs. The three roles are treated as a unit, and if any of them fail, we will recruit a replacement for all three roles. The master provides the commit versions for batches of the mutations to the proxies, runs data distribution algorithm, and runs ratekeeper. ONE-SHOT OBJECT DETECTION · GITBOOK One-Shot Object Detection. One-Shot object detection (OSOD) is the task of detecting an object from as little as one example per category. Unlike the Object Detector which requires many varied examples of objects in the real world, the One-Shot Object Detector requires a very small (sometimes even just one) canonical example of the object. The One-shot Object Detector is best suited for two TURI CREATE API DOCUMENTATION Turi Create API Documentation¶. Turi Create simplifies the development of custom machine learning models. You don’t have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification toyour app.
OBJECT DETECTION · GITBOOK Object Detection. Object detection is the task of simultaneously classifying (what) and localizing (where) object instances in an image.Given an image, a detector will produce instance predictions that may look something like this:RECOMMENDER
item_content_recommender.create: Create a content-based recommender model in which the similarity between the items recommended is determined by the content of those items rather than learned from userinteraction data.
TURICREATE.LOGISTIC_CLASSIFIER.LOGISTICCLASSIFIER.EVALUATE turicreate.logistic_classifier.LogisticClassifier.evaluate. Evaluate the model by making predictions of target values and comparing these to actual values. Dataset of new observations. Must include columns with the same names as the target and features used for model training. Additional columns are ignored. Name of the evaluationmetric.
RECOMMENDER SYSTEMS · GITBOOK Recommender systems. A recommender system allows you to provide personalized recommendations to users. With this toolkit, you can create a model based on past interaction data and use that model to make recommendations. VISUALIZATION · GITBOOK Visualizing Data. Data visualization can help us explore, understand, and gain insight from data. Visualization can complement other methods of data analysis by taking advantage of the human ability to recognize patterns in visual information.TURICREATE.SFRAME
turicreate.SFrame¶ class turicreate.SFrame (data=None, format='auto', _proxy=None) ¶. SFrame means scalable data frame. A tabular, column-mutable dataframe object that can scale to big data. The data in SFrame is stored column-wise, and is stored on persistent storage(e.g. disk) to
TURICREATE.LOGISTIC_CLASSIFIER.LOGISTICCLASSIFIER class turicreate.logistic_classifier. LogisticClassifier (model_proxy) Logistic regression models a discrete target variable as a function of several feature variables. The logisticClassifier uses a discrete target variable y instead of a scalar. For each observation, the probability that y = 1 (instead of 0) is modeled as the logisticfunction
TURICREATE.LOGISTIC_CLASSIFIER.LOGISTICCLASSIFIER.EVALUATE turicreate.logistic_classifier.LogisticClassifier.evaluate. Evaluate the model by making predictions of target values and comparing these to actual values. Dataset of new observations. Must include columns with the same names as the target and features used for model training. Additional columns are ignored. Name of the evaluationmetric.
TURICREATE.PLOT
turicreate.plot¶. turicreate.plot. Plots the data in x on the X axis and the data in y on the Y axis in a 2d visualization, and shows the resulting visualization. Uses the following heuristic to choose the visualization: If x and y are both numeric (SArray of int or float), and they contain fewer than or equal to 5,000 values, show a scatter TURICREATE.RECOMMENDER.POPULARITY_RECOMMENDER turicreate.recommender.popularity_recommender.PopularityRecommender¶class
turicreate.recommender.popularity_recommender.PopularityRecommender (model_proxy) ¶. The Popularity Model ranks an item according to itsoverall popularity.
TURICREATE.SGRAPH
A scalable graph data structure. The SGraph data structure allows arbitrary dictionary attributes on vertices and edges, provides flexible vertex and edge query functions, and seamless transformation to and from SFrame. There are several ways to create an SGraph. The simplest way is to make an empty SGraph then add vertices and edgeswith the
TURICREATE.LOAD_SFRAME turicreate.load_sframe. Load an SFrame. The filename extension is used to determine the format automatically. This function is particularly useful for SFrames previously saved in binary format. For CSV imports the SFrame.read_csv function provides greater control. If the SFrame is in binary format, filename is actually a directory, created when TURICREATE.SFRAME.RANDOM_SPLIT turicreate.SFrame.random_split¶ SFrame.random_split (fraction, seed=None, exact=False) ¶ Randomly split the rows of an SFrame into two SFrames. The first SFrame contains M rows, sampled uniformly (without replacement) from the original SFrame.M is approximately the fraction times the original number of rows. The second SFrame contains the remaining rows of the original SFrame.NEAREST_NEIGHBORS
nearest_neighbors ¶. The Turi Create nearest neighbors toolkit finds the rows in a tabular reference dataset that are most similar to a set of queries with the same schema. VISUALIZATION · GITBOOK Visualizing Data. Data visualization can help us explore, understand, and gain insight from data. Visualization can complement other methods of data analysis by taking advantage of the human ability to recognize patterns in visual information.TURICREATE.SFRAME
turicreate.SFrame¶ class turicreate.SFrame (data=None, format='auto', _proxy=None) ¶. SFrame means scalable data frame. A tabular, column-mutable dataframe object that can scale to big data. The data in SFrame is stored column-wise, and is stored on persistent storage(e.g. disk) to
TURICREATE.LOGISTIC_CLASSIFIER.LOGISTICCLASSIFIER class turicreate.logistic_classifier. LogisticClassifier (model_proxy) Logistic regression models a discrete target variable as a function of several feature variables. The logisticClassifier uses a discrete target variable y instead of a scalar. For each observation, the probability that y = 1 (instead of 0) is modeled as the logisticfunction
TURICREATE.LOGISTIC_CLASSIFIER.LOGISTICCLASSIFIER.EVALUATE turicreate.logistic_classifier.LogisticClassifier.evaluate. Evaluate the model by making predictions of target values and comparing these to actual values. Dataset of new observations. Must include columns with the same names as the target and features used for model training. Additional columns are ignored. Name of the evaluationmetric.
TURICREATE.PLOT
turicreate.plot¶. turicreate.plot. Plots the data in x on the X axis and the data in y on the Y axis in a 2d visualization, and shows the resulting visualization. Uses the following heuristic to choose the visualization: If x and y are both numeric (SArray of int or float), and they contain fewer than or equal to 5,000 values, show a scatter TURICREATE.RECOMMENDER.POPULARITY_RECOMMENDER turicreate.recommender.popularity_recommender.PopularityRecommender¶class
turicreate.recommender.popularity_recommender.PopularityRecommender (model_proxy) ¶. The Popularity Model ranks an item according to itsoverall popularity.
TURICREATE.SGRAPH
A scalable graph data structure. The SGraph data structure allows arbitrary dictionary attributes on vertices and edges, provides flexible vertex and edge query functions, and seamless transformation to and from SFrame. There are several ways to create an SGraph. The simplest way is to make an empty SGraph then add vertices and edgeswith the
TURICREATE.LOAD_SFRAME turicreate.load_sframe. Load an SFrame. The filename extension is used to determine the format automatically. This function is particularly useful for SFrames previously saved in binary format. For CSV imports the SFrame.read_csv function provides greater control. If the SFrame is in binary format, filename is actually a directory, created when TURICREATE.SFRAME.RANDOM_SPLIT turicreate.SFrame.random_split¶ SFrame.random_split (fraction, seed=None, exact=False) ¶ Randomly split the rows of an SFrame into two SFrames. The first SFrame contains M rows, sampled uniformly (without replacement) from the original SFrame.M is approximately the fraction times the original number of rows. The second SFrame contains the remaining rows of the original SFrame.NEAREST_NEIGHBORS
nearest_neighbors ¶. The Turi Create nearest neighbors toolkit finds the rows in a tabular reference dataset that are most similar to a set of queries with the same schema. VISUALIZATION · GITBOOK Visualizing Data. Data visualization can help us explore, understand, and gain insight from data. Visualization can complement other methods of data analysis by taking advantage of the human ability to recognize patterns in visual information.RECOMMENDER
item_content_recommender.create: Create a content-based recommender model in which the similarity between the items recommended is determined by the content of those items rather than learned from userinteraction data.
TURICREATE.SGRAPH
turicreate.SGraph¶ class turicreate.SGraph (vertices=None, edges=None, vid_field='__id', src_field='__src_id', dst_field='__dst_id', _proxy=None) ¶. A scalable graph data structure. The SGraph data structure allows arbitrary dictionary attributes on vertices and edges, provides flexible vertex and edge query functions, and seamless transformation to and from SFrame. TURICREATE.LOGISTIC_CLASSIFIER.LOGISTICCLASSIFIER class turicreate.logistic_classifier. LogisticClassifier (model_proxy) Logistic regression models a discrete target variable as a function of several feature variables. The logisticClassifier uses a discrete target variable y instead of a scalar. For each observation, the probability that y = 1 (instead of 0) is modeled as the logisticfunction
TURICREATE.NEAREST_NEIGHBORS.CREATE turicreate.nearest_neighbors.create. Create a nearest neighbor model, which can be searched efficiently and quickly for the nearest neighbors of a query observation. If the method argument is specified as auto, the type of model is chosen automatically based on the type of data in dataset. Reference data. TURICREATE.SFRAME.JOIN turicreate.SFrame.join¶. turicreate.SFrame.join. Merge two SFrames. Merges the current (left) SFrame with the given (right) SFrame using a SQL-style equi-join operation by columns. The SFrame to join. The column name (s) representing the set of join keys. Each row that has the same value in this set of columns will be merged together. TURI CREATE: TURI::SFRAME CLASS REFERENCE The SFrame is an immutable object that represents a table with rows and columns. Each column is an sarray, which is a sequence of an object T split into segments. The sframe writes an sarray for each column of data it is given to disk, each with a prefix that extends the prefix given to open. The SFrame is referenced on disk by a TURICREATE.TOOLKITS.DISTANCES.COSINE turicreate.toolkits.distances.cosine¶ turicreate.toolkits.distances.cosine (x, y) ¶ Compute the cosine distance between between two dictionaries or two lists of equallength.
TURICREATE.RECOMMENDER.UTIL.COMPARE_MODELS Examples. If you have created two ItemSimilarityRecommenders m1 and m2 and have an SFrame test_data, then you may compare the performance of the two models on test data using: >>> import turicreate >>> train_data = turicreate. TURICREATE.TEXT_ANALYTICS.COUNT_WORDS turicreate.text_analytics.count_words. If text is an SArray of strings or an SArray of lists of strings, the occurances of word are counted for each row in the SArray. If text is an SArray of dictionaries, the keys are tokenized and the values are the counts. Counts forSkip to content
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