This is a general data frame. Load arrays and labels into it, and you will have a very powerful set of tools on your data set.
df = DataFrame.from_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csv') df.labels # => [:x, :y, :month, :day, :ffmc, :dmc, :dc, :isi, :temp, :rh, :wind, :rain, :area] df.dmc # => [26.2, 35.4, 43.7, 33.3, 51.3, 85.3,...] df.dmc.max # => 291.3 df.dmc.min # => 1.1 df.dmc.mean # => 110.872340425532 df.dmc.std # => 64.0464822492543 df = DataFrame.new(:list, :of, :things) # => #<DataFrame:0x24ec6e8 @items=[], @labels=[:list, :of, :things]> df.labels # => [:list, :of, :things] df << [1,2,3] # => [[1, 2, 3]] df.import([[2,3,4],[5,6,7]]) # => [[2, 3, 4], [5, 6, 7]] df.items # => [[1, 2, 3], [2, 3, 4], [5, 6, 7]] df.list # => [1, 2, 5] df.list.correlation(df.things) # => 1.0 df.list # => [1, 2, 5] df.things # => [3, 4, 7]
There are a few important features to know:
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DataFrame.from_csv works for a string, a filename, or a URL.
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FasterCSV parsing parameters can be passed to DataFrame.from_csv
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DataFrame looks for operations first on the column labels, then on the row labels, then on the items table. So don’t name things :mean, :standard_deviation, :min, and that sort of thing.
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CallbackArray allows you to set a callback anytime an array is tainted or untainted (taint, shift, pop, clear, map!, that sort of thing). This is generally useful and will probably be copied into the Repositories gem.
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TransposableArray is a subclass of CallbackArray, demonstrating how to use it. It creates a very simple approach to memoization. It caches the transpose of the table and resets it whenever it is tainted.
To get your feet wet, you may want to play with data sets found here:
http://www.liaad.up.pt/~ltorgo/Regression/DataSets.html
A lot of the work in the data frame is to transform the actual table. You may need to drop columns, filter results, replace values in a column or create a new data frame based on the existing one. Here’s how to do that:
> df = DataFrame.from_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csv')
# => DataFrame rows: 517 labels: [:x, :y, :month, :day, :ffmc, :dmc, :dc, :isi, :temp, :rh, :wind, :rain, :area]
> df.drop!(:ffmc)
# => DataFrame rows: 517 labels: [:x, :y, :month, :day, :dmc, :dc, :isi, :temp, :rh, :wind, :rain, :area]
> df.drop!(:dmc, :dc, :isi, :rh)
# => DataFrame rows: 517 labels: [:x, :y, :month, :day, :temp, :wind, :rain, :area]
> df.x
# => [7, 7, 7, 8, 8, 8, 8, 8, 8, 7, 7, 7, 6, 6, 6,...]
> df.replace!(:x) {|e| e * 3}
# => DataFrame rows: 517 labels: [:x, :y, :month, :day, :temp, :wind, :rain, :area]
> df.x
# => [21, 21, 21, 24, 24, 24, 24, 24, 24, 21, 21, 21, 18, 18, 18,...]
> df.filter!(:open_struct) {|row| row.x == 24}
# => DataFrame rows: 61 labels: [:x, :y, :month, :day, :temp, :wind, :rain, :area]
> df.x
# => [24, 24, 24, 24, 24, 24, 24, 24, 24,...]
> new_data_frame = df.subset_from_columns(:x, :y)
# => DataFrame rows: 61 labels: [:x, :y]
> new_data_frame.items
# => [[24, 6], [24, 6], [24, 6], [24, 6], ...]
Note: most of these transformations are not optimized. I’ll work with things for a while before I try to optimize this library. However, I should say that I’ve used some fairly large data sets (thousands of rows) and have been fine with things so far.
Data Frame can now create sub-models:
>> df = DataFrame.from_csv(‘archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csv’) => DataFrame rows: 517 labels: [:x, :y, :month, :day, :ffmc, :dmc, :dc, :isi, :temp, :rh, :wind, :rain, :area] >> df.model(:weekend) do |m| ?> m.day %w(sat sun) >> end => DataFrame rows: 179 labels: [:x, :y, :month, :day, :ffmc, :dmc, :dc, :isi, :temp, :rh, :wind, :rain, :area] >> df.models.weekend.day.uniq => [“sat”, “sun”] >> df.models => #<OpenStruct weekend=DataFrame rows: 179 labels: [:x, :y, :month, :day, :ffmc, :dmc, :dc, :isi, :temp, :rh, :wind, :rain, :area]>
I use data frame for a lot of things, and I’ve added some utilities for this gem in case you would like to as well. For instance, here is how I take the data in a data frame and load it into a neural network:
# Show mlp. Will probably need to add a row classifier for training and test data. Also, will probably want to
There are some really interesting things that have good command-line shortcuts:
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Make
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A
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List
# Now add some demos
sudo gem install davidrichards-data_frame
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ActiveSupport: sudo gem install activesupport
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JustEnumerableStats: sudo gem install davidrichards-just_enumerable_stats
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FasterCSV: sudo gem install fastercsv
Copyright © 2009 David Richards. See LICENSE for details.