Merge #1023

1023: Feature: Added Boston Housing Dataset r=CarloLucibello a=pranjaldatta

[Boston Housing Dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/housing/) is one of the most common datasets that are used by beginners. It is as popular as other datasets like Iris etc. Hence, it feels only natural that this dataset is a part of Flux.

Added src/data/housing.jl: code for downloading and loading the dataset
Edited src/data/Data.jl: To include and export housing.jl
Edited test/data.jl: Added test for the dataset.

*All tests in test/data.jl are passing*

Co-authored-by: pranjaldatta <pranjaldatta99@gmail.com>
Co-authored-by: Pranjal  Datta <pranjaldatta99@gmail.com>

src/data/Data.jl

@@ -42,4 +42,7 @@ using .Sentiment
 include("iris.jl")
 export Iris
 
+include("housing.jl")
+export Housing
+
 end

src/data/housing.jl

@@ -0,0 +1,136 @@
+"""
+1. Title: Boston Housing Data
+
+2. Sources:
+   (a) Origin:  This dataset was taken from the StatLib library which is
+                maintained at Carnegie Mellon University.
+   (b) Creator:  Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the 
+                 demand for clean air', J. Environ. Economics & Management,
+                 vol.5, 81-102, 1978.
+   (c) Date: July 7, 1993
+
+3. Number of Instances: 506
+
+4. Number of Attributes: 13 continuous attributes (including "class"
+                            attribute "MEDV"), 1 binary-valued attribute.
+                            
+5. Attribute Information:
+   
+       1. CRIM      per capita crime rate by town
+       2. ZN        proportion of residential land zoned for lots over 
+                    25,000 sq.ft.
+       3. INDUS     proportion of non-retail business acres per town
+       4. CHAS      Charles River dummy variable (= 1 if tract bounds 
+                    river; 0 otherwise)
+       5. NOX       nitric oxides concentration (parts per 10 million)
+       6. RM        average number of rooms per dwelling
+       7. AGE       proportion of owner-occupied units built prior to 1940
+       8. DIS       weighted distances to five Boston employment centres
+       9. RAD       index of accessibility to radial highways
+       10. TAX      full-value property-tax rate per 10,000 dollars
+       11. PTRATIO  pupil-teacher ratio by town
+       12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks 
+                    by town
+       13. LSTAT    % lower status of the population
+       14. MEDV     Median value of owner-occupied homes in 1000's of dollars   
+
+       Downloaded From: https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data
+
+"""
+module Housing
+
+using DelimitedFiles
+using ..Data: deps, download_and_verify
+
+#Uncomment if package exists
+#const cache_prefix = "https://cache.julialang.org/"
+const cache_prefix = ""
+
+function load()
+    isfile(deps("housing.data")) && return
+    
+    @info "Downloading the Boston housing Dataset"
+    download_and_verify("$(cache_prefix)https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data",
+                        deps("housing.data"),
+                        "baadf72995725d76efe787b664e1f083388c79ba21ef9a7990d87f774184735a")
+    
+    #@info "Download complete. Working on the files"
+    path = deps()
+    isfile(deps("housing.data")) && touch(joinpath(path, "tempfile.data"))
+    open(joinpath(path, "tempfile.data"), "a") do fout
+        open(deps("housing.data"), "r") do fin
+            for line in eachline(fin)
+                line = replace(lstrip(line), r" +" => s",")
+                println(fout, line)
+            end
+        end
+    end
+    mv(joinpath(path, "tempfile.data"), deps("housing.data"), force=true)
+end
+
+"""
+Gets the targets for the Boston housing dataset, a 506 element array listing the targets for each example
+
+```jldoctest
+julia> using Flux
+
+julia> target = Flux.Data.Housing.targets()
+
+julia> summary(target)
+506×1 Array{Float64,2}
+
+julia> target[1]
+24.0
+
+"""
+function targets()
+    load()
+    housing = readdlm(deps("housing.data"), ',')
+    reshape(Vector{Float64}(housing[1:end,end]), (506, 1))           
+end
+
+
+"""
+Gets the names of the features provided in the dataset
+
+"""
+function feature_names()
+    ["crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","b","lstat"]
+end
+
+
+"""
+Gets the features of the Boston Housing Dataset. This is a 506x13 Matrix of Float64 datatypes.
+The values are in the order ["crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","b","lstat"].
+It has 506 examples.
+
+```jldoctest
+julia> using Flux
+
+julia> features = Flux.Data.Housing.features()
+
+julia> summary(features)
+506×13 Array{Float64,2}
+
+julia> features[1, :]
+13-element Array{Float64,1}:
+0.00632
+18.0    
+2.31   
+0.0    
+0.538  
+   ⋮      
+296.0    
+15.3    
+396.9    
+4.98   
+
+"""
+function features()
+    load()
+    housing = readdlm(deps("housing.data"), ',')
+    Matrix{Float64}(housing[1:end, 1:13])    
+end
+
+
+end

test/data.jl

@@ -16,7 +16,13 @@ using Test
 @test Data.Sentiment.train() isa Vector{Data.Tree{Any}}
 
 @test Iris.features() isa Matrix
-@test size(Iris.features()) == (4,150)
+@test size(Iris.features()) == (4,150) 
 
 @test Iris.labels() isa Vector{String}
 @test size(Iris.labels()) == (150,)
+
+@test Housing.features() isa Matrix
+@test size(Housing.features()) == (506, 13)
+
+@test Housing.targets() isa Array{Float64}
+@test size(Housing.targets()) == (506, 1)