add boston housing

Author: CarloLucibello

.travis.yml

@@ -28,7 +28,7 @@ addons:
 jobs:
   include:
     - stage: "Documentation"
-      julia: 1.0
+      julia: 1
       os: linux
       script:
         - julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd()));

Project.toml

@@ -1,6 +1,6 @@
 name = "MLDatasets"
 uuid = "eb30cadb-4394-5ae3-aed4-317e484a6458"
-version = "0.5.0"
+version = "0.5.1"
 
 [deps]
 BinDeps = "9e28174c-4ba2-5203-b857-d8d62c4213ee"

src/BostonHousing/BostonHousing.jl

@@ -0,0 +1,115 @@
+export BostonHousing
+
+"""
+Boston Housing Dataset.
+
+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
+
+Number of Instances: 506
+
+Number of Attributes: 13 continuous attributes (including target
+                            attribute "MEDV"), 1 binary-valued attribute.
+                            
+Features:
+       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
+       
+Target:
+       14. MEDV     Median value of owner-occupied homes in 1000's of dollars   
+
+Note: Variable #14 seems to be censored at 50.00 (corresponding to a median price of \$50,000); 
+Censoring is suggested by the fact that the highest median price of exactly \$50,000 is reported in 16 cases, 
+while 15 cases have prices between \$40,000 and \$50,000, with prices rounded to the nearest hundred. 
+Harrison and Rubinfeld do not mention any censoring.
+
+The data file stored in this repo is a copy of the This is a copy of UCI ML housing dataset. 
+https://archive.ics.uci.edu/ml/machine-learning-databases/housing/
+
+## Interface
+
+- [`BostonHousing.features`](@ref)
+- [`BostonHousing.targets`](@ref)
+- [`feature_names`](@ref)
+"""
+module BostonHousing
+
+using DataDeps
+using DelimitedFiles
+
+export features, targets, feature_names
+
+const DATA = joinpath(@__DIR__, "boston_housing.csv")
+
+"""
+    targets(; dir = nothing)
+
+Get the targets for the Boston housing dataset, 
+a 506 element array listing the targets for each example.
+
+```jldoctest
+julia> using MLDatasets: BostonHousing
+
+julia> target = BostonHousing.targets()
+
+julia> summary(target)
+1x506 Array{Float64,2}
+
+julia> target[1]
+24.0
+```     
+"""
+function targets(; dir = nothing)
+    housing = readdlm(DATA, ',')
+    reshape(Vector{Float64}(housing[2:end,end]), (1, 506))
+end
+
+"""
+    feature_names()
+
+Return the  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
+
+
+"""
+    features()
+
+Return the features of the Boston Housing dataset. This is a 13x506 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 MLDatasets: BostonHousing
+
+julia> features = BostonHousing.features()
+
+julia> summary(features)
+13x506 Array{Float64,2}
+```
+"""
+function features()
+    housing = readdlm(DATA, ',')
+    Matrix{Float64}(housing[2:end,1:13])' |> collect
+end
+
+end # module
+