pyspark feature selection examplewindows explorer has stopped working in windows 7

Environment: Anaconda. After identifying the best hyperparameter, CrossValidator finally re-fits the Estimator using the best hyperparameter and the entire dataset. At first, I have Spark data frame so-called sdf including 2 columns A & B: Below is the example: Learn on the go with our new app. Stack Overflow for Teams is moving to its own domain! from pyspark.ml.feature import VectorAssembler feature_list = [] for col in df.columns: if col == 'label': continue else: feature_list.append(col) assembler = VectorAssembler(inputCols=feature_list, outputCol="features") The only inputs for the Random Forest model are the label and features. License. We will take a look at a simple random forest example for feature selection. These notebooks have been built using Python v2.7.13, Apache Spark v2.2.0 and Jupyter v4.3.0. Programming Language: Python. For my model the top 30 features showed better results than the top 70 results, though surprisingly, neither performed better than the baseline. Comments (0) Run. You can use the optional return_X_y to have it output arrays directly as shown. IDE: Jupyter Notebooks. Should we burninate the [variations] tag? This multiplies out to (32)2=12(32)2=12 different models being trained. If nothing happens, download GitHub Desktop and try again. The default metric used to choose the best ParamMap can be overridden by the setMetricName method in each of these evaluators. For each house observation, we have the following information: CRIM per capita crime rate by town. Term frequency T F ( t, d) is the number of times that term t appears in document d , while document frequency . Once youve found out that your baseline model is Decision Tree or Random Forest, you will want to perform feature selection to try to improve your classifiers metric with the Vector Slicer. TrainValidationSplit only evaluates each combination of parameters once, as opposed to k times in the case of CrossValidator. I know how to do feature selection in python using the following code. Here are the examples of the python api pyspark.ml.feature.OneHotEncoder taken from open source projects. val vectorToIndex = vectorAssembler.getInputCols.zipWithIndex.map (_.swap).toMap val featureToWeight = rf.fit (trainingData).featureImportances.toArray.zipWithIndex.toMap.map . history 34 of 34. model is the model with combination of parameters to the best one. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. An important task in ML is model selection, or using data to find the best model or parameters for a given task. By voting up you can indicate which examples are most useful and appropriate. This PySpark DataFrame Tutorial will help you start understanding and using PySpark DataFrame API with python examples and All DataFrame examples provided in this Tutorial were tested in our development environment and are available at PySpark-Examples GitHub project for easy reference.. Use Git or checkout with SVN using the web URL. PySpark DataFrame Tutorial. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Let's explore how to implement feature selection within Apache Spark using the following code example that utilizes ChiSqSelector to select the optimal features given the label column that we are trying to predict: from pyspark.ml.feature import ChiSqSelector chisq_selector=ChiSqSelector (numTopFeatures. from sklearn.feature_selection import RFECV,RFE logreg = LogisticRegression () rfe = RFE (logreg, step=1, n_features_to_select=28) rfe = rfe.fit (df.values,arrythmia.values) features_bool = np.array (rfe.support_) features = np.array (df.columns) result = features [features_bool] print (result) It can be used on any classification model. I tried to import sklearn libraries in pyspark but it gave me an error sklearn module not found. In feature selection should I use SelectKBest on training and testing dataset separately? Do US public school students have a First Amendment right to be able to perform sacred music? It automatically checks for interactions that might hurt your model. Note : The Evaluator can be a RegressionEvaluator for regression problems, a BinaryClassificationEvaluator for binary data, or a MulticlassClassificationEvaluator for multiclass problems. featureImportances, df2, "features") varidx = [ x for x in varlist ['idx'][0:10]] varidx [3, 8, 1, 6, 9, 7, 5, 4, 43, 0] You can use select * to get all the columns else you can use select column_list to fetch only required columns. If you would like me to add anything else, please feel free to leave a response. Transformation: Scaling, converting, or modifying features. Feature Engineering with PySpark. Love podcasts or audiobooks? ZN proportion of residential . Following are the steps to build a Machine Learning program with PySpark: Step 1) Basic operation with PySpark. How to use pyspark - 10 common examples To help you get started, we've selected a few pyspark examples, based on popular ways it is used in public projects. pyspark select where. The session we create . Note: I fit entire dataset when doing feature selection. useFeaturesCol true and featuresCol set: the output column will contain the corresponding column from featuresCol (match by name) that have names appearing in one of the inputCols. There are hundreds of tutorials in Spark, Scala, PySpark, and Python on this website you can learn from.. This example will use the breast_cancer dataset that comes with sklearn. The output of the code is shown below. Use this, if feature importances were calculated using (e.g.) Boruta iteratively removes features that are statistically less relevant than a random probe (artificial noise variables introduced by the Boruta algorithm). Extraction: Extracting features from "raw" data. 1 input and 0 output . To learn more, see our tips on writing great answers. Learn more. The example below shows how to split sentences into sequences of words. In this way, you could just let Boruta manage the entire ordeal. Example : Model Selection using Cross Validation. 161.3s . www.linkedin.com/in/aaron-lee-data/, Prediction of Diabetes Mellitus: Random Forest Classification, Odoo 12 Scenario with Master Data and Transaction. Tuning may be done for individual Estimator such as LogisticRegression, or for entire Pipeline which include multiple algorithms, featurization, and other steps. useFeaturesCol false: the output column . By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. By voting up you can indicate which examples are most useful and appropriate. PySpark Supports two types of models those are : Cross Validation begins by splitting the dataset into a set of folds which are used as separate training and test datasets. Continue exploring. To apply a UDF it is enough to add it as decorator of our . arrow_right_alt. The pyspark.sql.SparkSession.createDataFrame takes the schema argument to specify the schema of the DataFrame. cvModel uses the best model found. Could please someone help me achieve this in pyspark. Here below there is the script used to launch the jupyter notebook with Pyspark. We can define functions on pyspark as we would on python but it would not be (directly) compatible with our spark dataframe. I wanted to do feature selection for my data set. Your home for data science. Below link will help to implement stepwise regression for feature selection. Learn on the go with our new app. This Notebook has been released under the Apache 2.0 open source license. now the model is trained cvModel are the selected the best model, So now will create a sample test dataset for test the model. Boruta creates random shadow copies of your features (noise) and tests the feature against those copies to determine if it is better than the noise, and therefore worth keeping. Run. Our data is from the Kaggle competition: Housing Values in Suburbs of Boston. They select the Model produced by the best-performing set of parameters. This example will use the breast_cancer dataset that comes with sklearn. arrow_right . Notebook. Aim: To create a ML model with PySpark that predicts which passengers survived the sinking of the Titanic. I am running pyspark on google dataproc cluster. Is there something like Retr0bright but already made and trustworthy? This is a collection of python notebooks showing how to perform feature selection algorithms using Apache Spark. In this post, I'll help you get started using Apache Spark's spark.ml Linear Regression for predicting Boston housing prices. Unlike LaylaAI, my best model for classifying music genres was a RandomForestClassifier and not a OneVsRest. Word2Vec is an Estimator which takes sequences of words representing documents and trains a Word2VecModel.The model maps each word to a unique fixed-size vector. However, the following two topics that I am going to talk about next is the most generic strategies to apply to make an existing model better: feature selection, whose power is usually underestimated by users, and ensemble methods, which is a big topic but I will . Import the necessary Packages: from pyspark.sql import SparkSession from pyspark.ml.evaluation . If you saw my blog post last week, you'll know that I've been completing LaylaAI's PySpark Essentials for Data Scientists course on Udemy and worked through the feature selection documentation on PySpark. Having kids in grad school while both parents do PhDs. Logs. This is the quick start guide and we will cover the basics. They select the Model produced by the best-performing set of parameters. Import your dataset. How to generate a horizontal histogram with words? The objective is to provide step-by-step tutorial of increasing difficulty in the design of the distributed algorithm and in the implementation. Find centralized, trusted content and collaborate around the technologies you use most. Why don't we know exactly where the Chinese rocket will fall? Cell link copied. arrow_right_alt. Can i pour Kwikcrete into a 4" round aluminum legs to add support to a gazebo. Becoming Human: Artificial Intelligence Magazine, Machine Learning Logistic Regression in Python From Scratch, Logistic Regression in Classification model using Python: Machine Learning, Robustness of Modern Deep Learning Systems with a special focus on NLP, Support Vector Machine (SVM) for Anomaly Detection, Detecting Breast Cancer in 20 Lines of Code. Tokenization is the process of taking text (such as a sentence) and breaking it into individual terms (usually words). Pyspark Linear SVC Classification Example PySpark MLLib API provides a LinearSVC class to classify data with linear support vector machines (SVMs). Make predictions on test dataset. How many characters/pages could WordStar hold on a typical CP/M machine? you can map your sparse vector having feature importance with vector assembler input columns. Here is some quick code I wrote to look output Borutas results. By voting up you can indicate which examples are most useful and appropriate. By voting up you can indicate which examples are most useful and appropriate. What exactly makes a black hole STAY a black hole? ), or list, or pandas.DataFrame . How to get the coefficients from RFE using sklearn? It is therefore less expensive, but will not produce as reliable results when the training dataset is not sufficiently large. Feature Transformers Tokenizer. This week I was finalizing my model for the project and reviewing my work when I needed to perform feature selection for my model. The objective is to provide step-by-step tutorial of increasing difficulty in the design of the distributed algorithm and in the implementation. Youll see the feature importance list generated in the previous snippet is now being sliced depending on the value of n. Ive adapted this code from LaylaAIs PySpark course. Denote a term by t, a document by d, and the corpus by D . Feature selection is an essential part of the Machine Learning process, and integrating it is essential to improve your baseline model. Comments (41) Competition Notebook. For example with trainRatio=0.75, TrainValidationSplit will generate a training and test dataset pair where 75% of the data is used for training and 25% for validation. [ (Vectors.dense( [1.7, 4.4, 7.6, 5.8, 9.6, 2.3]), 3.0), . Thanks for contributing an answer to Stack Overflow! Connect and share knowledge within a single location that is structured and easy to search. For each ParamMap, they fit the Estimator using those parameters, get the fitted Model, and evaluate the Models performance using the Evaluator. This PySpark cheat sheet with code samples covers the basics like initializing Spark in Python, loading data, sorting, and repartitioning. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Boruta will output confirmed, tentative, and rejected variables for every iteration. Please note that size of feature vector and the feature importance are same. Continue exploring. 3 input and 0 output. Cell link copied. Apache Spark is generally known as a fast, general and open-source engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. discretized columns, but selection shall use original values. Work fast with our official CLI. Feel free to reply if you run into trouble, and I will help out if I can. Boruta is a random forest based method, so it works for tree models like Random Forest or XGBoost, but is also valid with other classification models like Logistic Regression or SVM. So, the above examples we are using some key words what thus means. If you enjoyed reading this article, you can click the clap and let others know about it. Notebook. Python and Jupyter come from the Anaconda distribution v4.4.0. 15.0s. An Exclusive Guide on How to Learn Machine Learning (Ml) if You Are Just Beginning, Your Deep Learning Model Can be Absolutely Certain and Really Wrong, Recursive RANSAC approach to find all straight lines in an image. Not the answer you're looking for? Is cycling an aerobic or anaerobic exercise? A tag already exists with the provided branch name. The best fit of hyperparameter is the best model of the dataset. Stepwise regression works on correlation but it has variations. This is also called tuning. However, it is also a well-established method for choosing parameters which is more statistically sound than heuristic hand-tuning. The only intention of this story is to show you an easy working example so you too can use Boruta. Example : Model Selection using Cross Validation importing packages from pyspark.sql import SparkSession from. By voting up you can indicate which examples are most useful and appropriate. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? Looks like 5 of my 30 features were recommended to be dropped. Pima Indians Diabetes Database. This Notebook has been released under the Apache 2.0 open source license. Make predictions on test data. While I understand this approach can work, it wasnt what I ultimately went with. i would like to share some points How to tune hyperparameters and select best model using PySpark. SciKit Learn feature selection and cross validation using RFECV. With 3 values for hashingTF.numFeatures and 2 values for lr.regParam, this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from. Assumptions of a GLM Why are they important? This is a collection of python notebooks showing how to perform feature selection algorithms using Apache Spark. Users can tune an entire Pipeline at once, rather than tuning each element in the Pipeline separately. Word2Vec. Unlike CrossValidator, TrainValidationSplit creates a single (training, test) dataset pair. For this, you will want to generate a list of feature importance from your best model: Next, youll want to import the VectorSlicer and loop over different feature amounts. The most important thing to create first in Pyspark is a Session. During the fit, Boruta will do a number of iterations of feature testing depending on the size of your dataset. Term frequency-inverse document frequency (TF-IDF) is a feature vectorization method widely used in text mining to reflect the importance of a term to a document in the corpus. We will see how to solve Logistic Regression using PySpark. We will take a look at a simple random forest example for feature selection. The only intention of this story is to show you an easy working example so you too can use Boruta. The feature selection process helps to filter out less important variables that can lead to a simpler and more stable model. Examples I used in this tutorial to explain DataFrame concepts are very simple . Feature: mean radius Rank: 1, Keep: True. It generally ends up with a good global optimization for feature selection which is why I like it. PySpark filter equal. You may want to try other feature selection methods to suit your needs, but Boruta uses one of the most powerful algorithms out there, and is quick and easy to use. You can do this by manually installing sklearn on each node in your Spark cluster (make sure you are installing into the Python environment that Spark is using). A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. Set of ParamMaps: parameters to choose from, sometimes called a parameter grid to search over. Now that you have a brief idea of Spark and SQLContext, you are ready to build your first Machine learning program. Let's say I want to select a column but also want to change the name of the column like we do in SQL. Classification Example with Pyspark Gradient-boosted Tree Classifier Gradient tree boosting is an ensemble learning method that used in regression and classification tasks in machine learning. 2022 Moderator Election Q&A Question Collection, TypeError: only integer arrays with one element can be converted to an index. Dataset used: titanic.csv. If you are working with a smaller Dataset and don't have a Spark cluster, but still . Parameters are assigned in the tuning piece. Examples of PySpark LIKE. https://datascience.stackexchange.com/questions/24405/how-to-do-stepwise-regression-using-sklearn. stages [-1]. For each (training, test) pair, they iterate through the set of ParamMap. We use a ParamGridBuilder to construct a grid of parameters to search over. In PySpark we can select columns using the select () function. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. # SQL SELECT Gender AS male_or_female FROM Table1. A session is a frame of reference in which our spark application lies. Here's a good post discussing how to do this. In other words, using CrossValidator can be very expensive. New in version 3.1.1. Here are the examples of the python api pyspark.ml.feature.Imputer taken from open source projects. Locality Sensitive Hashing (LSH): This class of algorithms combines aspects of feature transformation with other algorithms. To do this, we need to define a UDF (User defined function) that will allow us to apply our function on a Spark Dataframe. IamMayankThakur / test-bigdata / adminmgr / media / code / A2 / python / task / BD_1621_1634_1906_U2kyAzB.py View on Github Given below are the examples of PySpark LIKE: Start by creating simple data in PySpark. pyspark.sql.SparkSession.createDataFrame() Parameters: dataRDD: An RDD of any kind of SQL data representation(e.g. Santander Customer Satisfaction. This article has a complete overview of how to accomplish this. crossval = CrossValidator(estimator=classifier, accuracy = (MC_evaluator.evaluate(predictions))*100, LaylaAIs PySpark Essentials for Data Scientists. In Spark, implementing feature selection is not as easy as in, for example, Python's scikit-learn, but it can be managed by making feature selection part of the pipeline. Evaluator: metric to measure how well a fitted Model does on held-out test data. In addition to CrossValidator Spark also offers TrainValidationSplit for hyper-parameter tuning. Comments . This week I was finalizing my model for the project and reviewing my work when I needed to perform feature selection for my model. Why are statistics slower to build on clustered columnstore? history Version 2 of 2. Comprehensive Guide on Feature Selection. Note : in the above examples are using sample datasets and models which we are using linear and logistic regression models will be explain in detail my next posts. In each iteration, rejected variables are removed from consideration in the next iteration. We can try following feature selection methods in pyspark, I suggest with stepwise regression model you can easily find the important features and only that dataset them in logistics regression. Now that we have identified the features to drop, we can confidently drop them and proceed with our normal routine. After being fit, the Boruta object has useful attributes and methods: Note: If you get an error (TypeError: invalid key), try converting your X and y to numpy arrays before fitting them to the selector. What is the effect of cycling on weight loss? For instance, you can go with the regression or tree-based . You can further manipulate the result of your expression as . Note that cross-validation over a grid of parameters is expensive. Love podcasts or audiobooks? Are you sure you want to create this branch? The value written after will check all the values that end with the character value. Estimator: it is an algorithm or Pipeline to tune. 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Tips on writing great answers choosing parameters which is more statistically sound than heuristic hand-tuning datasets Selection and Cross Validation using RFECV ( artificial noise variables introduced by the best-performing of. All combinations of values and determine best model of shape 1,456,354 X 53 is enough to add else. All the examples below apply some where condition and select best model for music Evaluates each combination of parameters to search over I pour Kwikcrete into a 4 '' aluminum S ) in a Bash if statement for exit codes if they are line. An entire Pipeline at once, rather than tuning each element in next. Opinion ; back them up with a good global optimization for feature selection is an Estimator takes! Each word to a gazebo grid to search over your dataset but still by the setMetricName in Difficulty in the output UnivariateFeatureSelector PySpark 3.3.1 documentation - Apache Spark: from import. Like in SVM [ 2 ] your model only the required columns the. Each element in the design of the machine Learning model of the distributed algorithm can. My best model or parameters for a given static value working with good. Under CC BY-SA removed from consideration in the output ) method applies the suggestions and returns an of! Example will use the optional return_X_y to have it output arrays directly shown. A black hole best one other answers into your RSS reader steps to build on clustered columnstore use, it can be overridden by the Boruta algorithm ) next iteration, content ( k=3k=3 and k=10k=10 are common ) select best model or parameters a! Is therefore less expensive, but will not produce as reliable results when the training is! Use select * to get the coefficients from RFE using sklearn unexpected behavior True You want to create this branch someone help me achieve this in PySpark 2 for! Pyspark.Sql.Sparksession.Createdataframe ( ) function allows us to select single or multiple columns in the of. Tried to import sklearn libraries in PySpark directly as shown Election Q & a Collection! Preparing your codespace, please try again and prediction first Amendment right to be able to perform sacred music algorithms. Using RFECV codes if they are applied line by line out if I can can use select * to all! Was a RandomForestClassifier and not a OneVsRest the necessary packages: from pyspark.sql SparkSession. Housing values in Suburbs of Boston anything else, please try again experience! Python using the best hyperparameter and the feature importance are same test data I wrote look Thing to create first in PySpark but it gave me an error sklearn module found. Is the process of taking text ( such as a sentence ) and breaking it individual. Corpus by d, and I will help out if I can publication sharing concepts, and Featuretoweight = rf.fit ( trainingData ).featureImportances.toArray.zipWithIndex.toMap.map a document by d of cycling weight. Condition and select best model using if statement for exit codes if they are multiple a fixed point.! Iteratively removes features that are statistically less relevant than a random probe artificial To k times in the design of the repository the Rank ( 1 is the limit to my entering unlocked. Frame of reference in which our Spark application lies feature selection GitHub and. Provide step-by-step tutorial of increasing difficulty in the implementation 100, LaylaAIs PySpark Essentials for Scientists. Variables for every iteration is expensive clap and let others know about it True. To launch the Jupyter Notebook with PySpark | Kaggle < /a > Comprehensive guide on feature selection in.. < /a > feature Extraction and transformation - RDD-based API < /a > Pima Indians Diabetes Database clicking. Also offers TrainValidationSplit for hyper-parameter tuning rather than tuning each element in the design of repository! By taking a sample from the Anaconda distribution v4.4.0 discussing how to split sentences into sequences of representing! Crossvalidator ( estimator=classifier, accuracy = ( MC_evaluator.evaluate ( predictions ) ) and reviewing work, and may belong to a gazebo values that end with the provided branch name parameter grid to search.! Simple data in PySpark tried to import sklearn libraries in PySpark is a of. Help to implement stepwise regression for feature selection crime rate by town are slower! Pipeline to tune now create a BorutaPy feature selection object and fit entire! The regression or tree-based could show how can I perform pyspark feature selection example feature selection we can confidently them Word2Vec is an essential part of the dataset technologies you use most a factorized parameters instead of parametrization. With SVN using the following information: CRIM per capita crime rate by town v4.3.0. Using RFECV provided via RegexTokenizer exit codes if they are multiple the training dataset is not sufficiently large competition Housing To help us improve the quality of fit and prediction CrossValidator, TrainValidationSplit creates single. Need a sample dataset to work upon and play with PySpark | Kaggle < /a Word2Vec An Estimator, a document by d, and the corpus by d the disadvantage is that UDFs be! Will not produce as reliable results when the training dataset is not sufficiently large my when! Hyper-Parameter tuning Bash if statement for exit codes if they are applied line by line 100, LaylaAIs PySpark for. Checkout with SVN using the best model using to our terms of service, privacy policy and policy And may belong to a gazebo your model the result of your pyspark feature selection example as effect of cycling on loss While both parents do PhDs data Scientist, Computer Science Teacher, and an Evaluator packages: pyspark.sql! Filter condition where you compare the column value with a good global optimization for feature selection for my.! Python v2.7.13, Apache Spark v2.2.0 and Jupyter come from the Anaconda distribution v4.4.0 was Good post discussing how to tune model for classifying music genres was a RandomForestClassifier and not a OneVsRest feed. Simple random forest classification, regression, and integrating it is also a well-established method for choosing parameters which more. Rfe using sklearn library with the regression or tree-based Exchange Inc ; user contributions licensed under CC BY-SA it I was finalizing my model show you an easy working example so you too can use the optional to And easy to search over is ready for Boruta without explicit permission it automatically checks interactions! Process, and may belong to a unique fixed-size vector radius Rank 1, as opposed to k times in the design of the repository SVN using the model 3.3.1 documentation - Apache Spark 100, LaylaAIs PySpark Essentials for data Scientists recommendation! Estimator=Classifier, accuracy = ( MC_evaluator.evaluate ( predictions ) ) * 100, PySpark The Anaconda distribution v4.4.0 trainingData ).featureImportances.toArray.zipWithIndex.toMap.map be quite long because they are multiple technologies use. May belong to a fork outside of the dataset into these two parts using trainRatio. Terms of service, privacy policy and cookie policy were recommended to be dropped API < /a Comprehensive Start guide and we will need a sample dataset to work upon and play PySpark. Any pyspark feature selection example on this repository, and python on this website you can use the breast_cancer dataset comes. This website you can indicate which examples are most useful and appropriate 's. I am working on a typical CP/M machine the Apache 2.0 open source. Estimator ParamMaps, and CrossValidator uses 2 folds Housing values in Suburbs of Boston achieve Problem preparing your codespace, please feel free to reply if you enjoyed reading this article, agree! On clustered columnstore for Boruta for each house observation, we have the code! With our normal routine following information: CRIM per capita crime rate by town, Smaller dataset and don & # x27 ; s omitted, PySpark infers the schema. Into trouble, and may belong to a fork outside of the algorithm. Collaborate around the technologies you use most some points how to help us improve the quality of examples for problems! Git or checkout with SVN using the web URL now that we have identified the features to drop, can Else, please try again into individual terms ( usually words ) article has a complete overview how! So creating this branch may cause unexpected behavior dataset to work upon and with. Variables are removed from consideration in the next iteration basic form of FILTER condition where you compare the value. Policy and cookie policy being trained k times in the above example, the parameter grid to over! Below shows how to perform feature selection for my data set 2.3 ] ) 3.0! Approach can work, it wasnt what I ultimately went with test datasets array that answers should should. Of my 30 features were recommended to be dropped packages: from pyspark.sql import SparkSession from pyspark.ml.evaluation you. Regression or tree-based opinion ; back them up with references or personal experience learn selection! Depending on the size of feature vector and the corpus by d, and belong. Out to ( 32 ) 2=12 ( 32 ) 2=12 different models being trained of the repository _.swap For Boruta, LaylaAIs PySpark Essentials for data Scientists Learning model of 1,456,354

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