{"id":161281,"date":"2022-10-11T10:44:39","date_gmt":"2022-10-11T14:44:39","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=161281"},"modified":"2022-11-21T09:58:58","modified_gmt":"2022-11-21T14:58:58","slug":"r-code-back-transform-from-carets-preprocess","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-back-transform-from-carets-preprocess\/","title":{"rendered":"R code: Back Transform from Caret&#8217;s preProcess()"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">This post gives a small R code for the&nbsp;<strong>back transformation of the caret&#8217;s preProcess() function<\/strong>, which is not implemented in caret R package yet. This is useful , for example, when we forecast stock prices using deep learning techniques such as the LSTM which requires normalized input data but we want to back transform it to the original scale.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-reverse-transform-from-caret-s-preprocess\">Reverse Transform from Caret&#8217;s preProcess()<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Caret R package provides a very convenient function,&nbsp;<strong>preProcess()<\/strong>, which transform a given data to a normalized or standardized one. However,&nbsp;<strong>it does not provide the back (or reverse) transformation function<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-transformation\">Transformation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>method = &#8220;center&#8221; or &#8220;scale&#8221; or c(&#8220;center&#8221;, &#8220;scale&#8221;)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">x\u2032 = (x\u2212\u03bc<sub>x<\/sub>)\/\u03c3<sub>x<\/sub><\/p>\n\n\n\n<figure class=\"wp-block-image size-large img-twothird\"><img decoding=\"async\" width=\"565\" height=\"55\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-1.png\" alt=\"\" class=\"wp-image-161296 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-1.png 565w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-1-300x29.png 300w\" data-sizes=\"(max-width: 565px) 100vw, 565px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 565px; aspect-ratio: 565\/55;\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>method = &#8220;range&#8221;, rangeBounds = c(a, b)<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large img-twothird\"><img decoding=\"async\" width=\"562\" height=\"59\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-2.png\" alt=\"\" class=\"wp-image-161307 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-2.png 562w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-2-300x31.png 300w\" data-sizes=\"(max-width: 562px) 100vw, 562px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 562px; aspect-ratio: 562\/59;\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">These transformations are done by using&nbsp;<strong>preProcess()<\/strong>&nbsp;function in caret R package.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>preProc &lt;- preProcess(training, \n                      method = c(\"center\", \"scale\"))\ntransformed &lt;- predict(preProc, training)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Back Transformation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>method = &#8220;center&#8221; or &#8220;scale&#8221; or c(&#8220;center&#8221;, &#8220;scale&#8221;)<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large img-twothird\"><img decoding=\"async\" width=\"549\" height=\"50\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-3.png\" alt=\"\" class=\"wp-image-161315 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-3.png 549w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-3-300x27.png 300w\" data-sizes=\"(max-width: 549px) 100vw, 549px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 549px; aspect-ratio: 549\/50;\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>method = &#8220;range&#8221;, rangeBounds = c(a, b)<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large img-twothird\"><img decoding=\"async\" width=\"519\" height=\"57\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-4.png\" alt=\"\" class=\"wp-image-161320 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-4.png 519w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-4-300x33.png 300w\" data-sizes=\"(max-width: 519px) 100vw, 519px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 519px; aspect-ratio: 519\/57;\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">These back transformations can accomplished by the following R code<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#===========================================================\n# back transform using the object from the caret preProcess\n#===========================================================\n \nback_preProc &lt;- function(preProc, df_trans, digits = 10) {\n    \n    pp &lt;- preProc\n    nc &lt;- ncol(df_trans); nr &lt;- nrow(df_trans)\n    av &lt;- t(replicate(nr, pp$mean))\n    st &lt;- t(replicate(nr, pp$std))\n    a  &lt;- pp$rangeBounds\n    x_max &lt;- t(replicate(nr, pp$ranges&#91;2,]))\n    x_min &lt;- t(replicate(nr, pp$ranges&#91;1,]))\n    \n    if(sum(!is.na(match(c(\"center\", \"scale\"), \n                        names(pp$method)))) == 2) {\n        df &lt;- df_trans*st + av\n    } else if(sum(!is.na(match(\"center\", \n                               names(pp$method)))) == 1) {\n        df &lt;- df_trans + av\n    } else if(sum(!is.na(match(\"scale\", \n                               names(pp$method)))) == 1) {\n        df &lt;- df_trans*st\n    } else {\n        df &lt;- (df_trans-a&#91;1])\/(a&#91;2]-a&#91;1])*(x_max - x_min) + x_min\n    }\n    \n    return(round(df, digits))\n}<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Excercise<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">An exercise is a range transformation between -1 and 1 with training and test sample data.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#========================================================#\n# Quantitative ALM, Financial Econometrics &amp; Derivatives \n# ML\/DL using R, Python, Tensorflow by Sang-Heon Lee \n#\n# https:\/\/kiandlee.blogspot.com\n#--------------------------------------------------------#\n# backtransform of caret::preProcess\n#========================================================#\n \ngraphics.off(); rm(list = ls())\n \nlibrary(caret)\n \n#-----------------------------------------\n# sample data\n#-----------------------------------------\ndf &lt;- data.frame(x = -10:10, y = -10:10*0.001)\n \n#-----------------------------------------\n# train\/test splitting data\n#-----------------------------------------\n# In case of one-column dataframe, sub rows become a vector. \n# To avoid this and preserve a single-column data frame, \n# use drop=F option. \ndf_train &lt;- df&#91;1:15,,drop=F]\ndf_test &lt;- df&#91;16:21,,drop=F]\n \n \n#-----------------------------------------\n# create transform funtion\n#-----------------------------------------\npreProc &lt;- preProcess(df_train, method = \"range\", \n                      rangeBounds = c(-1, 1))\n \n#=====================================================\n# transform\n#=====================================================\ndf_train_trans &lt;- predict(preProc, df_train)\ndf_test_trans  &lt;- predict(preProc, df_test)\n \n    \n#=====================================================\n# back transform of train data\n#=====================================================\ndf_train_back &lt;- back_preProc(preProc, df_train_trans)\ndf_test_back  &lt;- back_preProc(preProc, df_test_trans)\n \n \n#-----------------------------------------\n# print comparisons of returns\n#-----------------------------------------\ntemp &lt;- cbind(df_train, df_train_trans, df_train_back)\n \nprint(\"========= Train Data =========\")\ncolnames(temp) &lt;- c(\n    paste0(\"raw_\",colnames(df_train)),\n    paste0(\"trans_\",colnames(df_train_trans)),\n    paste0(\"back_\",colnames(df_back)))\nprint(temp)\n    \nprint(\"========= Test Data  =========\")\ntemp &lt;- cbind(df_test, df_test_trans, df_test_back)\ncolnames(temp) &lt;- c(\n    paste0(\"raw_\",colnames(df_test)),\n    paste0(\"trans_\",colnames(df_test_trans)),\n    paste0(\"back_\",colnames(df_back)))\nprint(temp)<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Comparisons of the original, transformed, and back transformed data delivers the expected results.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"586\" height=\"569\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-5.png\" alt=\"\" class=\"wp-image-161327 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-5.png 586w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2022\/10\/sh-fintech-caret-preprocess-5-300x291.png 300w\" data-sizes=\"(max-width: 586px) 100vw, 586px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 586px; aspect-ratio: 586\/569;\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The upper and lower bounds of the transfomed test data is not 1 and -1 since the raw data has a trend. To show a distinct result, I use a trending sample data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>For additional insight on this topic and to download the R scripts, visit <a href=\"https:\/\/kiandlee.blogspot.com\/2022\/10\/r-code-back-transform-from-carets.html\">https:\/\/kiandlee.blogspot.com\/2022\/10\/r-code-back-transform-from-carets.html<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This post gives a small R code for the\u00a0back transformation of the caret&#8217;s preProcess() function, which is not implemented in caret R package yet. <\/p>\n","protected":false},"author":662,"featured_media":161338,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,338,350,341,344,342],"tags":[12913,806,487,6591],"contributors-categories":[13728],"class_list":["post-161281","post","type-post","status-publish","format-standard","has-post-thumbnail","category-data-science","category-programing-languages","category-ibkr-quant-news","category-quant-asia-pacific","category-quant-development","category-quant-regions","category-r-development","tag-caret-r-package","tag-data-science","tag-r","tag-rstats","contributors-categories-sh-fintech-modeling"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v27.8) - 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