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[Utility & Helpers](/categories/utility) 4. / 5. zeeml/dataset ActiveLibrary[Utility & Helpers](/categories/utility) zeeml/dataset ============= A multi-purpose DataSet for Machine Learning algorithms training 01511PHP Since Jul 17Pushed 8y ago1 watchersCompare [ Source](https://github.com/Zeeml/Dataset)[ Packagist](https://packagist.org/packages/zeeml/dataset)[ RSS](/packages/zeeml-dataset/feed)WikiDiscussions master Synced today READMEChangelogDependenciesVersions (2)Used By (0) [![build](https://camo.githubusercontent.com/a5ddf9e3972d945bfa9b475bfe48dcffde03bceeaa969d747a04002180639cd3/68747470733a2f2f7472617669732d63692e6f72672f5a65656d6c2f446174617365742e7376673f6272616e63683d6d6173746572)](https://camo.githubusercontent.com/a5ddf9e3972d945bfa9b475bfe48dcffde03bceeaa969d747a04002180639cd3/68747470733a2f2f7472617669732d63692e6f72672f5a65656d6c2f446174617365742e7376673f6272616e63683d6d6173746572) Dataset ======= [](#dataset) A multi-purpose dataSet for Machine Learning algorithms training. Create a DataSet ---------------- [](#create-a-dataset) to create a DataSet to use for Zeeml Machine Learning, you need to specify a source : either a csv file or an array ### Create a dataSet from a csv file [](#create-a-dataset-from-a-csv-file) ``` $dataSet = DataSetFactory::create('/path/to/csv', ['name', 'Gender'], ['Height]); ``` The keys set in the header (first row of the CSV file) are used as keys for the dataSet ### Create a dataSet from an Array [](#create-a-dataset-from-an-array) ``` $dataSet = DataSetFactory::create( [ ['name' => 'Zac', 'gender' => 'Male', 'height' => 180], ['name' => 'Emily', 'gender' => 'Female', 'height' => 177], ['name' => 'Edward', 'gender' => 'Male', 'height' => 175], ['name' => 'Mark', 'gender' => 'Male', 'height' => 183], ['name' => 'Lesly', 'gender' => 'Female', 'height' => 170], ] ); ``` Any other array format will throw an exception Specify inputs and outputs ========================== [](#specify-inputs-and-outputs) **The prepare method must be called prior to any other call or an exception will be thrown.** ``` $mapper = new Mapper(['name', 'gendre'], ['height']); $dataSet->prepare($mapper); ``` where **\['name', 'gendre'\]** are the indexes to use as inputs and **\['height'\]** are the indexes to use as outputs. There is no limit to the number of inputs and outputs to pick from the entry **If a key does not exist it will throw an exception.** Manipulating the dataSet ======================== [](#manipulating-the-dataset) In order to manipulate and change the values of the dataSet (cleaning, renaming ...) you can apply a "Policy". A Policy is called when creating the Mapper. Each column can define multiple Policies : ``` $dataSet = DataSetFactory::create( [ [180, 'Male'], [177, 'Female'], [170, ''], [183, 'Male'], ] ); $mapper = new Mapper( [ 0 => [Policy::replaceWithAvg(), Policy::rename('height')], ], [ 1 => [Policy::skip()] ] ); $dataSet->prepare($mapper); ``` \###Supported policies : - **Policy::skip()** : If the value at the corresponding index is empty (NULL, false, '') the whole row will be skipped Example : ``` $data = [ [1, 2, 3], [4, null, 5], [6, 7, null], [null, 8, 9], ]; $dataSet = DataSetFactory::create($data); $mapper = new Mapper([0, 1 => Policy::skip()], [2 => Policy::skip()]); $dataSet->prepare($mapper); will use the following Inputs/Outputs : Inputs: [ [1, 2], [null, 8], //No policy applied on 0 ] Outputs: [ [3], [9], ] ``` - **Policy::replaceWith()** : If the value at the corresponding index is empty (NULL, false, '') it will be replaced with the given value Example : ``` $data = [ [1, 2, 3], [4, null, 5], [6, 7, null], [null, 8, 9], ]; $dataSet = DataSetFactory::create($data); $mapper = new Mapper([0, 1 => Policy::replaceWith('Unknown')], [2 => Policy::replaceWith(-1)]); $dataSet->prepare($mapper); will use the following Inputs/Outputs : Inputs: [ [1, 2], [4, 'Unknown'], [6, 7], [null, 8], //No policy applied on 0 ] Outputs: [ [3], [5], [-1], [9] ] ``` - **Policy::replaceWithAvg()** : The empty values will be replaced with the average value of that column calculated from the original DataSet. Example : ``` $data = [ [1, 2, 3], [4, null, 5], [6, 7, null], [null, 8, 9], ]; $dataSet = DataSetFactory::create($data); $mapper = new Mapper([0 => Policy::replaceWithAvg(), 1 => Policy::skip()], [2 => Policy::replaceWithAvg()]); $dataSet->prepare($mapper); will use the following Inputs/Outputs : Inputs: [ [1, 2], [6, 7], [2.75, 8], // Avg(0) = 1 + 4 + 6 + 0 = 11 / 4 = 2.75 ] Outputs: [ [3], [-1], [9], ] ] ``` - **Policy::replaceWithMostCommon()** : The empty values will be replaced with the most common value (the value that occurs the most) If multiple values have the same frequency, one is taken randomly. Example : ``` $data = [ [1, 2, 3], [1, null, 5], [6, 7, null], [null, 8, 9], ]; $dataSet = DataSetFactory::create($data); $mapper = new Mapper([0=> Policy::replaceWithMostCommon(), 1 => Policy::skip()], [2]); $dataSet->prepare($mapper); will use the following Inputs/Outputs : Inputs: [ [1, 2], [6, 7], [1, 8], ] Outputs: [ [3], [null], [9], ] ``` - **Policy::custom()** : create your own Policy the callable function is only called when the value is empty. The callable must : - Take in a first parameter by reference which corresponds to the value of the column upon each iteration - Take in a second parameter which corresponds to the line - Return true to keep the line, false to skip it Example : ``` $data = [ [180, 'Male'], [177, 'Female'], [170, ''], [183, 'Male'], ]; $dataSet = DataSetFactory::create($data); $genderCleaner = function(&$value, $line) { if ($line[0] > 175) { $value = 'Male' ; } else { $value = 'Female'; } return true; } $mapper = new Mapper([0], [1 => Policy::custom($genderCleaner)]); $dataSet->prepare($mapper); will use the following Inputs/Outputs : Inputs: [ [180], [177], [170], [183], ] Outputs: [ ['Male'], ['Female'], ['Female'], ['Male'], ] ``` Renaming keys of dataSet ------------------------ [](#renaming-keys-of-dataset) You can rename the dataSet keys : ``` $data = [ ['Zac', 'Male', 180], ['Emily', 'Female', 177], ['Edward', 'Male', 175], ['Mark', 'Male', 183], ['Lesly', 'Female', 170], ]; $dataSet = DataSetFactory::create($data); $mapper = new Mapper([0, 1], [2]); $dataSet->prepare($mapper); $dataSet->rename([0 => 'Name', 1 => 'Gender', 2 => 'Height']); and the inputs/outputs matrices used are : Inputs : [ ['Name' => 'Zac', 'Gender' => 'Male'], ['Name' => 'Emily', 'Gender' => 'Female'], ['Name' => 'Edward', 'Gender' => 'Male'], ['Name' => 'Mark', 'Gender' => 'Male'], ['Name' => 'Lesly', 'Gender' => 'Female'], ] Outputs : [ ['Height' => 180], ['Height' => 177], ['Height' => 175], ['Height' => 183], ['Height' => 170], ] ``` ### Health Score 23 — LowBetter than 26% of packages Maintenance20 Infrequent updates — may be unmaintained Popularity13 Limited adoption so far Community10 Small or concentrated contributor base Maturity43 Maturing project, gaining track record Bus Factor1 Top contributor holds 55.2% of commits — single point of failure How is this calculated?**Maintenance (25%)** — Last commit recency, latest release date, and issue-to-star ratio. 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