From 1a8bddfaf5fbda12b2e30e36216a1a51c7fed3fa Mon Sep 17 00:00:00 2001 From: Sigrid Keydana Date: Fri, 4 Jan 2019 15:56:21 +0100 Subject: [PATCH 1/2] fixes due to #3, #4 --- .../sunspots-lstm.Rmd | 4 +- .../sunspots-lstm.html | 42 +++++++++++++++---- docs/index.html | 7 +++- docs/index.xml | 2 +- .../posts/2018-06-25-sunspots-lstm/index.html | 20 +++------ docs/posts/posts.json | 2 +- docs/sitemap.xml | 10 ++--- 7 files changed, 53 insertions(+), 34 deletions(-) diff --git a/_posts/2018-06-25-sunspots-lstm/sunspots-lstm.Rmd b/_posts/2018-06-25-sunspots-lstm/sunspots-lstm.Rmd index 4a16541e5..b719a0855 100644 --- a/_posts/2018-06-25-sunspots-lstm/sunspots-lstm.Rmd +++ b/_posts/2018-06-25-sunspots-lstm/sunspots-lstm.Rmd @@ -191,11 +191,11 @@ include_graphics("images/cowplot.png") When doing cross validation on sequential data, the time dependencies on preceding samples must be preserved. We can create a cross validation sampling plan by offsetting the window used to select sequential sub-samples. In essence, we're creatively dealing with the fact that there's no future test data available by creating multiple synthetic "futures" - a process often, esp. in finance, called "backtesting". -As mentioned in the introduction, the [rsample](https://cran.r-project.org/package=rsample) package includes facitlities for backtesting on time series. The vignette, ["Time Series Analysis Example"](https://topepo.github.io/rsample/articles/Applications/Time_Series.html), describes a procedure that uses the `rolling_origin()` function to create samples designed for time series cross validation. We'll use this approach. +As mentioned in the introduction, the [rsample](https://cran.r-project.org/package=rsample) package includes facitlities for backtesting on time series. The vignette, ["Time Series Analysis Example"](https://tidymodels.github.io/rsample/articles/Applications/Time_Series.html), describes a procedure that uses the `rolling_origin()` function to create samples designed for time series cross validation. We'll use this approach. #### Developing a backtesting strategy -The sampling plan we create uses 50 years (`initial` = 12 x 50 samples) for the training set and ten years (`assess` = 12 x 10) for the testing (validation) set. We select a `skip` span of about twenty years (`skip` = 12 x 20 - 1) to approximately evenly distribute the samples into 6 sets that span the entire 265 years of sunspots history. Last, we select `cumulative = FALSE` to allow the origin to shift which ensures that models on more recent data are not given an unfair advantage (more observations) over those operating on less recent data. The tibble return contains the `rolling_origin_resamples`. +The sampling plan we create uses 100 years (`initial` = 12 x 100 samples) for the training set and 50 years (`assess` = 12 x 50) for the testing (validation) set. We select a `skip` span of about 22 years (`skip` = 12 x 22 - 1) to approximately evenly distribute the samples into 6 sets that span the entire 265 years of sunspots history. Last, we select `cumulative = FALSE` to allow the origin to shift which ensures that models on more recent data are not given an unfair advantage (more observations) over those operating on less recent data. The tibble return contains the `rolling_origin_resamples`. ```{r} periods_train <- 12 * 100 diff --git a/_posts/2018-06-25-sunspots-lstm/sunspots-lstm.html b/_posts/2018-06-25-sunspots-lstm/sunspots-lstm.html index f9b4be051..b257eb138 100644 --- a/_posts/2018-06-25-sunspots-lstm/sunspots-lstm.html +++ b/_posts/2018-06-25-sunspots-lstm/sunspots-lstm.html @@ -66,6 +66,10 @@ width: 100%; } + .pandoc-table>caption { + margin-bottom: 10px; + } + .pandoc-table th:not([align]) { text-align: left; } @@ -82,6 +86,10 @@ padding-right: 16px; } + .l-screen .caption { + margin-left: 10px; + } + .shaded { background: rgb(247, 247, 247); padding-top: 20px; @@ -601,9 +609,9 @@ var fn = $('#' + id); var fn_p = $('#' + id + '>p'); fn_p.find('.footnote-back').remove(); - var text = fn_p.text(); + var text = fn_p.html(); var dtfn = $(''); - dtfn.text(text); + dtfn.html(text); $(this).replaceWith(dtfn); }); // remove footnotes @@ -629,7 +637,10 @@ var clz = ""; var language = pre.attr('class'); if (language) { - if ($.inArray(language, ["r", "cpp", "c", "java"]) != -1) + // map unknown languages to "clike" (without this they just dissapear) + if ($.inArray(language, ["bash", "clike", "css", "go", "html", + "javascript", "js", "julia", "lua", "markdown", + "markup", "mathml", "python", "svg", "xml"]) == -1) language = "clike"; language = ' language="' + language + '"'; var dt_code = $(''); @@ -844,9 +855,19 @@ // prevent underline for linked images $('a > img').parent().css({'border-bottom' : 'none'}); - // mark figures created by knitr chunks as 100% width + // mark non-body figures created by knitr chunks as 100% width $('.layout-chunk').each(function(i, val) { - $(this).find('img, .html-widget').css('width', '100%'); + var figures = $(this).find('img, .html-widget'); + if ($(this).attr('data-layout') !== "l-body") { + figures.css('width', '100%'); + } else { + figures.css('max-width', '100%'); + figures.filter("[width]").each(function(i, val) { + var fig = $(this); + fig.css('width', fig.attr('width') + 'px'); + }); + + } }); // auto-append index.html to post-preview links in file: protocol @@ -858,7 +879,7 @@ // get rid of index.html references in header if (window.location.protocol !== "file:") { - $('.radix-site-header a').each(function(i,val) { + $('.radix-site-header a[href]').each(function(i,val) { $(this).attr('href', $(this).attr('href').replace("index.html", "./")); }); } @@ -867,6 +888,8 @@ $('tr.header').parent('thead').parent('table').addClass('pandoc-table'); $('.kable-table').children('table').addClass('pandoc-table'); + // add figcaption style to table captions + $('caption').parent('table').addClass("figcaption"); // initialize posts list if (window.init_posts_list) @@ -878,6 +901,7 @@ $('#disqus_thread').toggleClass('hidden'); if (!$('#disqus_thread').hasClass('hidden')) { var offset = $(this).offset(); + $(window).resize(); $('html, body').animate({ scrollTop: offset.top - 35 }); @@ -910,7 +934,7 @@ @@ -1053,9 +1077,9 @@

Visualizing sunspot data with cow

Backtesting: time series cross validation

When doing cross validation on sequential data, the time dependencies on preceding samples must be preserved. We can create a cross validation sampling plan by offsetting the window used to select sequential sub-samples. In essence, we’re creatively dealing with the fact that there’s no future test data available by creating multiple synthetic “futures” - a process often, esp. in finance, called “backtesting”.

-

As mentioned in the introduction, the rsample package includes facitlities for backtesting on time series. The vignette, “Time Series Analysis Example”, describes a procedure that uses the rolling_origin() function to create samples designed for time series cross validation. We’ll use this approach.

+

As mentioned in the introduction, the rsample package includes facitlities for backtesting on time series. The vignette, “Time Series Analysis Example”, describes a procedure that uses the rolling_origin() function to create samples designed for time series cross validation. We’ll use this approach.

Developing a backtesting strategy

-

The sampling plan we create uses 50 years (initial = 12 x 50 samples) for the training set and ten years (assess = 12 x 10) for the testing (validation) set. We select a skip span of about twenty years (skip = 12 x 20 - 1) to approximately evenly distribute the samples into 6 sets that span the entire 265 years of sunspots history. Last, we select cumulative = FALSE to allow the origin to shift which ensures that models on more recent data are not given an unfair advantage (more observations) over those operating on less recent data. The tibble return contains the rolling_origin_resamples.

+

The sampling plan we create uses 100 years (initial = 12 x 100 samples) for the training set and 50 years (assess = 12 x 50) for the testing (validation) set. We select a skip span of about 22 years (skip = 12 x 22 - 1) to approximately evenly distribute the samples into 6 sets that span the entire 265 years of sunspots history. Last, we select cumulative = FALSE to allow the origin to shift which ensures that models on more recent data are not given an unfair advantage (more observations) over those operating on less recent data. The tibble return contains the rolling_origin_resamples.


 periods_train <- 12 * 100
diff --git a/docs/index.html b/docs/index.html
index 1330aba11..8dffdbc5e 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -1339,7 +1339,7 @@
   padding-bottom: 12px;
 }
 .posts-list {
-  margin-top: 60px;
+  margin-top: 24px;
   margin-bottom: 24px;
 }
 
@@ -1474,6 +1474,9 @@
   .posts-list .post-preview h2 {
     font-size: 26px;
   }
+  .posts-list {
+    margin-top: 60px;
+  }
   .posts-list .post-preview .thumbnail {
     float: right;
     width: 35%;
@@ -1825,7 +1828,7 @@ 
Classifying physical activity from smartphone data

 
 
 

-
June 25, 2018

+
June 24, 2018

 

 

 
diff --git a/docs/index.xml b/docs/index.xml
index 2f8e22249..269620a06 100644
--- a/docs/index.xml
+++ b/docs/index.xml
@@ -156,7 +156,7 @@ In this post, we explain the steps involved in coding a basic single-shot object
       https://blogs.rstudio.com/tensorflow/posts/2018-06-25-sunspots-lstm
       In this post we will examine making time series predictions using the sunspots dataset that ships with base R. Sunspots are dark spots on the sun, associated with lower temperature. Our post will focus on both how to apply deep learning to time series forecasting, and how to properly apply cross validation in this domain.
       https://blogs.rstudio.com/tensorflow/posts/2018-06-25-sunspots-lstm
-      Mon, 25 Jun 2018 00:00:00 +0000
+      Sun, 24 Jun 2018 00:00:00 +0000
       
     
     
diff --git a/docs/posts/2018-06-25-sunspots-lstm/index.html b/docs/posts/2018-06-25-sunspots-lstm/index.html
index 0b136f3fb..dbb7329bb 100644
--- a/docs/posts/2018-06-25-sunspots-lstm/index.html
+++ b/docs/posts/2018-06-25-sunspots-lstm/index.html
@@ -407,8 +407,9 @@
 
 
 
-
-
+
+
+
 
 
   
@@ -554,10 +555,6 @@
 /* Tweak code blocks (note that this CSS is repeated above in an injection
    into the d-code shadow dom) */
 
-d-code {
-  overflow-x: auto !important;
-}
-
 pre.d-code code.d-code {
   padding-left: 10px;
   font-size: 12px;
@@ -588,11 +585,6 @@
 }
 
 @media(min-width: 768px) {
-
-d-code {
-  overflow-x: visible !important;
-}
-
 pre.d-code code.d-code  {
     padding-left: 18px;
     font-size: 14px;
@@ -1307,7 +1299,7 @@
 
 
 
 
 
@@ -1470,9 +1462,9 @@ 
Visualizing sunspot data with cow
 

 
Backtesting: time series cross validation

 
When doing cross validation on sequential data, the time dependencies on preceding samples must be preserved. We can create a cross validation sampling plan by offsetting the window used to select sequential sub-samples. In essence, we’re creatively dealing with the fact that there’s no future test data available by creating multiple synthetic “futures” - a process often, esp. in finance, called “backtesting”.

-
As mentioned in the introduction, the rsample package includes facitlities for backtesting on time series. The vignette, “Time Series Analysis Example”, describes a procedure that uses the rolling_origin() function to create samples designed for time series cross validation. We’ll use this approach.

+
As mentioned in the introduction, the rsample package includes facitlities for backtesting on time series. The vignette, “Time Series Analysis Example”, describes a procedure that uses the rolling_origin() function to create samples designed for time series cross validation. We’ll use this approach.

 
Developing a backtesting strategy

-
The sampling plan we create uses 50 years (initial = 12 x 50 samples) for the training set and ten years (assess = 12 x 10) for the testing (validation) set. We select a skip span of about twenty years (skip = 12 x 20 - 1) to approximately evenly distribute the samples into 6 sets that span the entire 265 years of sunspots history. Last, we select cumulative = FALSE to allow the origin to shift which ensures that models on more recent data are not given an unfair advantage (more observations) over those operating on less recent data. The tibble return contains the rolling_origin_resamples.

+
The sampling plan we create uses 100 years (initial = 12 x 100 samples) for the training set and 50 years (assess = 12 x 50) for the testing (validation) set. We select a skip span of about 22 years (skip = 12 x 22 - 1) to approximately evenly distribute the samples into 6 sets that span the entire 265 years of sunspots history. Last, we select cumulative = FALSE to allow the origin to shift which ensures that models on more recent data are not given an unfair advantage (more observations) over those operating on less recent data. The tibble return contains the rolling_origin_resamples.

 

 

 periods_train <- 12 * 100
diff --git a/docs/posts/posts.json b/docs/posts/posts.json
index c53da9063..4091d2b1a 100644
--- a/docs/posts/posts.json
+++ b/docs/posts/posts.json
@@ -323,7 +323,7 @@
       "Time Series"
     ],
     "preview": "posts/2018-06-25-sunspots-lstm/images/backtested_test.png",
-    "last_modified": "2018-09-12T12:45:46-04:00",
+    "last_modified": "2019-01-04T15:54:11+01:00",
     "preview_width": 800,
     "preview_height": 416
   },
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 584a1d0d2..579c7b8dd 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -2,19 +2,19 @@
 
   
     https://blogs.rstudio.com/tensorflow/about.html
-    2018-09-12T12:45:47-04:00
+    2018-11-15T08:25:53+01:00
   
   
     https://blogs.rstudio.com/tensorflow/contributing.html
-    2018-09-12T12:45:47-04:00
+    2018-11-15T08:25:53+01:00
   
   
     https://blogs.rstudio.com/tensorflow/gallery.html
-    2018-11-20T06:05:15-05:00
+    2018-11-15T08:25:54+01:00
   
   
     https://blogs.rstudio.com/tensorflow/
-    2018-10-09T09:43:20-04:00
+    2018-11-15T08:25:54+01:00
   
   
     https://blogs.rstudio.com/tensorflow/posts/2018-12-18-object-detection-concepts/
@@ -78,7 +78,7 @@
   
   
     https://blogs.rstudio.com/tensorflow/posts/2018-06-25-sunspots-lstm/
-    2018-09-12T12:45:46-04:00
+    2019-01-04T15:54:11+01:00
   
   
     https://blogs.rstudio.com/tensorflow/posts/2018-06-06-simple-audio-classification-keras/

From 4f1d3580f75b10a1549155fc3bba925f60985391 Mon Sep 17 00:00:00 2001
From: jjallaire 
Date: Mon, 7 Jan 2019 09:10:39 -0500
Subject: [PATCH 2/2] regenerate

---
 docs/index.html                                |  7 ++-----
 docs/index.xml                                 |  2 +-
 docs/posts/2018-06-25-sunspots-lstm/index.html | 16 ++++++++++++----
 docs/posts/posts.json                          |  2 +-
 docs/sitemap.xml                               | 10 +++++-----
 5 files changed, 21 insertions(+), 16 deletions(-)

diff --git a/docs/index.html b/docs/index.html
index 8dffdbc5e..1330aba11 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -1339,7 +1339,7 @@
   padding-bottom: 12px;
 }
 .posts-list {
-  margin-top: 24px;
+  margin-top: 60px;
   margin-bottom: 24px;
 }
 
@@ -1474,9 +1474,6 @@
   .posts-list .post-preview h2 {
     font-size: 26px;
   }
-  .posts-list {
-    margin-top: 60px;
-  }
   .posts-list .post-preview .thumbnail {
     float: right;
     width: 35%;
@@ -1828,7 +1825,7 @@ 
Classifying physical activity from smartphone data

 
 
 

-
June 24, 2018

+
June 25, 2018

 

 

 
diff --git a/docs/index.xml b/docs/index.xml
index 269620a06..2f8e22249 100644
--- a/docs/index.xml
+++ b/docs/index.xml
@@ -156,7 +156,7 @@ In this post, we explain the steps involved in coding a basic single-shot object
       https://blogs.rstudio.com/tensorflow/posts/2018-06-25-sunspots-lstm
       In this post we will examine making time series predictions using the sunspots dataset that ships with base R. Sunspots are dark spots on the sun, associated with lower temperature. Our post will focus on both how to apply deep learning to time series forecasting, and how to properly apply cross validation in this domain.
       https://blogs.rstudio.com/tensorflow/posts/2018-06-25-sunspots-lstm
-      Sun, 24 Jun 2018 00:00:00 +0000
+      Mon, 25 Jun 2018 00:00:00 +0000
       
     
     
diff --git a/docs/posts/2018-06-25-sunspots-lstm/index.html b/docs/posts/2018-06-25-sunspots-lstm/index.html
index dbb7329bb..45cc43c12 100644
--- a/docs/posts/2018-06-25-sunspots-lstm/index.html
+++ b/docs/posts/2018-06-25-sunspots-lstm/index.html
@@ -407,9 +407,8 @@
 
 
 
-
-
-
+
+
 
 
   
@@ -555,6 +554,10 @@
 /* Tweak code blocks (note that this CSS is repeated above in an injection
    into the d-code shadow dom) */
 
+d-code {
+  overflow-x: auto !important;
+}
+
 pre.d-code code.d-code {
   padding-left: 10px;
   font-size: 12px;
@@ -585,6 +588,11 @@
 }
 
 @media(min-width: 768px) {
+
+d-code {
+  overflow-x: visible !important;
+}
+
 pre.d-code code.d-code  {
     padding-left: 18px;
     font-size: 14px;
@@ -1299,7 +1307,7 @@
 
 
 
 
 
diff --git a/docs/posts/posts.json b/docs/posts/posts.json
index 4091d2b1a..f0de7b7ff 100644
--- a/docs/posts/posts.json
+++ b/docs/posts/posts.json
@@ -323,7 +323,7 @@
       "Time Series"
     ],
     "preview": "posts/2018-06-25-sunspots-lstm/images/backtested_test.png",
-    "last_modified": "2019-01-04T15:54:11+01:00",
+    "last_modified": "2019-01-07T09:09:45-05:00",
     "preview_width": 800,
     "preview_height": 416
   },
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 579c7b8dd..2c7731b82 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -2,19 +2,19 @@
 
   
     https://blogs.rstudio.com/tensorflow/about.html
-    2018-11-15T08:25:53+01:00
+    2018-09-12T12:45:47-04:00
   
   
     https://blogs.rstudio.com/tensorflow/contributing.html
-    2018-11-15T08:25:53+01:00
+    2018-09-12T12:45:47-04:00
   
   
     https://blogs.rstudio.com/tensorflow/gallery.html
-    2018-11-15T08:25:54+01:00
+    2018-11-20T06:05:15-05:00
   
   
     https://blogs.rstudio.com/tensorflow/
-    2018-11-15T08:25:54+01:00
+    2018-10-09T09:43:20-04:00
   
   
     https://blogs.rstudio.com/tensorflow/posts/2018-12-18-object-detection-concepts/
@@ -78,7 +78,7 @@
   
   
     https://blogs.rstudio.com/tensorflow/posts/2018-06-25-sunspots-lstm/
-    2019-01-04T15:54:11+01:00
+    2019-01-07T09:09:45-05:00
   
   
     https://blogs.rstudio.com/tensorflow/posts/2018-06-06-simple-audio-classification-keras/