目录
NASA有32,000多个数据集，并且NASA有兴趣了解这些数据集之间的联系，以及与NASA以外其他政府组织中其他重要数据集的联系。有关NASA数据集的元数据可以JSON格式在线获得。让我们使用tf-idf在描述字段中找到重要的单词，并将其与关键字联系起来。
获取和整理NASA元数据
让我们下载32,000多个NASA数据集的元数据。
library(jsonlite) library(dplyr) library(tidyr) metadata <- fromJSON("data.json") names(metadata$dataset) ## [1] "_id" "@type" "accessLevel" "accrualPeriodicity" ## [5] "bureauCode" "contactPoint" "description" "distribution" ## [9] "identifier" "issued" "keyword" "landingPage" ## [13] "language" "modified" "programCode" "publisher" ## [17] "spatial" "temporal" "theme" "title" ## [21] "license" "isPartOf" "references" "rights" ## [25] "describedBy" nasadesc <- data_frame(id = metadata$dataset$`_id`$`$oid`, desc = metadata$dataset$description) nasadesc ## # A tibble: 32,089 x 2 ## id ## ## 1 55942a57c63a7fe59b495a77## 2 55942a57c63a7fe59b495a78## 3 55942a58c63a7fe59b495a79## 4 55942a58c63a7fe59b495a7a## 5 55942a58c63a7fe59b495a7b## 6 55942a58c63a7fe59b495a7c## 7 55942a58c63a7fe59b495a7d## 8 55942a58c63a7fe59b495a7e## 9 55942a58c63a7fe59b495a7f## 10 55942a58c63a7fe59b495a80## # ... with 32,079 more rows, and 1 more variables: desc
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## # A tibble: 32,089 x 2 ## id ## ## 1 55942a57c63a7fe59b495a77## 2 55942a57c63a7fe59b495a78## 3 55942a58c63a7fe59b495a79## 4 55942a58c63a7fe59b495a7a## 5 55942a58c63a7fe59b495a7b## 6 55942a58c63a7fe59b495a7c## 7 55942a58c63a7fe59b495a7d## 8 55942a58c63a7fe59b495a7e## 9 55942a58c63a7fe59b495a7f## 10 55942a58c63a7fe59b495a80## # ... with 32,079 more rows, and 1 more variables: desc
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让我们打印出其中的一部分。
nasadesc %>% select(desc) %>% sample_n(5) ## # A tibble: 5 x 1 ## desc ## ## 1 A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL P ## 2 ML2CO is the EOS Aura Microwave Limb Sounder (MLS) standard product for carbon monoxide derived from radiances measured by the 640 GHz radiometer. The ## 3 Crew lock bag. Polygons: 405 Vertices: 514 ## 4 JEM Engineering proved the technical feasibility of the FlexScan array?a very low-cost, highly-efficient, wideband phased array antenna?in Phase I, an ## 5 MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the\nTerra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit aro
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这是关键词。
## # A tibble: 126,814 x 2 ## id keyword ## ## 1 55942a57c63a7fe59b495a77 EARTH SCIENCE ## 2 55942a57c63a7fe59b495a77 HYDROSPHERE ## 3 55942a57c63a7fe59b495a77 SURFACE WATER ## 4 55942a57c63a7fe59b495a78 EARTH SCIENCE ## 5 55942a57c63a7fe59b495a78 HYDROSPHERE ## 6 55942a57c63a7fe59b495a78 SURFACE WATER ## 7 55942a58c63a7fe59b495a79 EARTH SCIENCE ## 8 55942a58c63a7fe59b495a79 HYDROSPHERE ## 9 55942a58c63a7fe59b495a79 SURFACE WATER ## 10 55942a58c63a7fe59b495a7a EARTH SCIENCE ## # ... with 126,804 more rows
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最常见的关键字是什么？
## # A tibble: 1,774 x 2 ## keyword n ## ## 1 EARTH SCIENCE 14362 ## 2 Project 7452 ## 3 ATMOSPHERE 7321 ## 4 Ocean Color 7268 ## 5 Ocean Optics 7268 ## 6 Oceans 7268 ## 7 completed 6452 ## 8 ATMOSPHERIC WATER VAPOR 3142 ## 9 OCEANS 2765 ## 10 LAND SURFACE 2720 ## # ... with 1,764 more rows
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看起来“已完成项目”对于某些目的来说可能不是有用的关键字，我们可能希望将所有这些都更改为小写或大写，以消除诸如“ OCEANS”和“ Oceans”之类的重复项。
计算文字的tf-idf
什么是tf-idf？评估文档中单词的重要性的一种方法可能是其术语频率（tf），即单词在文档中出现的频率。但是，一些经常出现的单词并不重要。在英语中，这些词可能是“ the”，“ is”，“ of”等词。另一种方法是查看术语的逆文档频率（idf），这会降低常用单词的权重，而增加在文档集中很少使用的单词的权重。
## # A tibble: 2,728,224 x 3 ## id word n ## ## 1 55942a88c63a7fe59b498280 amp 679 ## 2 55942a88c63a7fe59b498280 nbsp 655 ## 3 55942a8ec63a7fe59b4986ef gt 330 ## 4 55942a8ec63a7fe59b4986ef lt 330 ## 5 55942a8ec63a7fe59b4986ef p 327 ## 6 55942a8ec63a7fe59b4986ef the 231 ## 7 55942a86c63a7fe59b49803b amp 208 ## 8 55942a86c63a7fe59b49803b nbsp 204 ## 9 56cf5b00a759fdadc44e564a the 201 ## 10 55942a86c63a7fe59b4980a2 gt 191 ## # ... with 2,728,214 more rows
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这些是NASA说明字段中最常见的“单词”，是词频最高的单词。让我们看一下第一个数据集，例如：
## # A tibble: 1 x 1 ## desc ## ## 1
The objective of the Variable Oxygen Regulator Element is to develop an oxygen-rated, contaminant-tolerant oxygen regulator to control suit p
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tf-idf算法应该减少所有这些的权重，因为它们很常见，但是我们可以根据需要通过停用词将其删除。现在，让我们为描述字段中的所有单词计算tf-idf。
## # A tibble: 2,728,224 x 6 ## id word n tf idf tf_idf ## ## 1 55942a88c63a7fe59b498280 amp 679 0.35661765 3.1810813 1.134429711 ## 2 55942a88c63a7fe59b498280 nbsp 655 0.34401261 4.2066578 1.447143322 ## 3 55942a8ec63a7fe59b4986ef gt 330 0.05722213 3.2263517 0.184618705 ## 4 55942a8ec63a7fe59b4986ef lt 330 0.05722213 3.2903671 0.188281801 ## 5 55942a8ec63a7fe59b4986ef p 327 0.05670192 3.3741126 0.191318680 ## 6 55942a8ec63a7fe59b4986ef the 231 0.04005549 0.1485621 0.005950728 ## 7 55942a86c63a7fe59b49803b amp 208 0.32911392 3.1810813 1.046938133 ## 8 55942a86c63a7fe59b49803b nbsp 204 0.32278481 4.2066578 1.357845252 ## 9 56cf5b00a759fdadc44e564a the 201 0.06962245 0.1485621 0.010343258 ## 10 55942a86c63a7fe59b4980a2 gt 191 0.12290862 3.2263517 0.396546449 ## # ... with 2,728,214 more rows
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添加的列是tf，idf，这两个数量相乘在一起是tf-idf，这是我们感兴趣的东西。NASA描述字段中最高的tf-idf词是什么？
## # A tibble: 2,728,224 x 6 ## id word n tf idf ## ## 1 55942a7cc63a7fe59b49774a rdr 1 1 10.376269 ## 2 55942ac9c63a7fe59b49b688 palsar_radiometric_terrain_corrected_high_res 1 1 10.376269 ## 3 55942ac9c63a7fe59b49b689 palsar_radiometric_terrain_corrected_low_res 1 1 10.376269 ## 4 55942a7bc63a7fe59b4976ca lgrs 1 1 8.766831 ## 5 55942a7bc63a7fe59b4976d2 lgrs 1 1 8.766831 ## 6 55942a7bc63a7fe59b4976e3 lgrs 1 1 8.766831 ## 7 55942ad8c63a7fe59b49cf6c template_proddescription 1 1 8.296827 ## 8 55942ad8c63a7fe59b49cf6d template_proddescription 1 1 8.296827 ## 9 55942ad8c63a7fe59b49cf6e template_proddescription 1 1 8.296827 ## 10 55942ad8c63a7fe59b49cf6f template_proddescription 1 1 8.296827 ## tf_idf ## ## 1 10.376269 ## 2 10.376269 ## 3 10.376269 ## 4 8.766831 ## 5 8.766831 ## 6 8.766831 ## 7 8.296827 ## 8 8.296827 ## 9 8.296827 ## 10 8.296827 ## # ... with 2,728,214 more rows
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因此，这些是用tf-idf衡量的描述字段中最“重要”的词，这意味着它们很常见，但不太常用。
## # A tibble: 1 x 1 ## desc ## ## 1 RDR
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tf-idf算法将认为这是一个非常重要的词。
连接关键字和描述
因此，现在我们知道描述中的哪个词具有较高的tf-idf，并且在关键字中也有这些描述的标签。
## # A tibble: 11,013,838 x 7 ## id word n tf idf tf_idf keyword ## ## 1 55942a88c63a7fe59b498280 amp 679 0.35661765 3.181081 1.1344297 ELEMENT ## 2 55942a88c63a7fe59b498280 amp 679 0.35661765 3.181081 1.1344297 JOHNSON SPACE CENTER ## 3 55942a88c63a7fe59b498280 amp 679 0.35661765 3.181081 1.1344297 VOR ## 4 55942a88c63a7fe59b498280 amp 679 0.35661765 3.181081 1.1344297 ACTIVE ## 5 55942a88c63a7fe59b498280 nbsp 655 0.34401261 4.206658 1.4471433 ELEMENT ## 6 55942a88c63a7fe59b498280 nbsp 655 0.34401261 4.206658 1.4471433 JOHNSON SPACE CENTER ## 7 55942a88c63a7fe59b498280 nbsp 655 0.34401261 4.206658 1.4471433 VOR ## 8 55942a88c63a7fe59b498280 nbsp 655 0.34401261 4.206658 1.4471433 ACTIVE ## 9 55942a8ec63a7fe59b4986ef gt 330 0.05722213 3.226352 0.1846187 JOHNSON SPACE CENTER ## 10 55942a8ec63a7fe59b4986ef gt 330 0.05722213 3.226352 0.1846187 PROJECT ## # ... with 11,013,828 more rows
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可视化结果
让我们来看几个示例关键字中最重要的单词。
## # A tibble: 122 x 7 ## id word n tf idf tf_idf keyword ## ## 1 55942a60c63a7fe59b49612f estimates 1 0.5000000 3.172863 1.586432 CLOUDS ## 2 55942a76c63a7fe59b49728d ncdc 1 0.1666667 7.603680 1.267280 CLOUDS ## 3 55942a60c63a7fe59b49612f cloud 1 0.5000000 2.464212 1.232106 CLOUDS ## 4 55942a5ac63a7fe59b495bd8 fife 1 0.2000000 5.910360 1.182072 CLOUDS ## 5 55942a5cc63a7fe59b495deb allometry 1 0.1428571 7.891362 1.127337 VEGETATION ## 6 55942a5dc63a7fe59b495ede tgb 3 0.1875000 5.945452 1.114772 VEGETATION ## 7 55942a5ac63a7fe59b495bd8 tovs 1 0.2000000 5.524238 1.104848 CLOUDS ## 8 55942a5ac63a7fe59b495bd8 received 1 0.2000000 5.332843 1.066569 CLOUDS ## 9 55942a5cc63a7fe59b495dfd sap 1 0.1250000 8.430358 1.053795 VEGETATION ## 10 55942a60c63a7fe59b496131 abstract 1 0.3333333 3.118561 1.039520 CLOUDS ## # ... with 112 more rows
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## # A tibble: 1 x 1 ## desc ## ## 1 Cloud estimates
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tf-idf算法在仅2个字长的描述中无法很好地工作，或者至少它将对这些字进行非常重的加权。实际上，也许这是不合适的。
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