/
ames_test.clj
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/
ames_test.clj
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(ns tech.ml.dataset.ames-test
(:require [tech.ml.dataset.pipeline
:refer [m= col int-map]
:as dsp]
[tech.ml.dataset.column :as ds-col]
[tech.ml.dataset.pipeline :as ds-pipe]
[tech.ml.dataset.pipeline.base
:refer [with-ds]]
[tech.ml.dataset :as ds]
[tech.ml.dataset.pipeline.column-filters
:as cf]
[tech.v2.datatype :as dtype]
[tech.v2.datatype.functional :as dfn]
[clojure.set :as c-set]
[clojure.test :refer :all]))
(deftest tablesaw-col-subset-test
(let [test-col (ds-col/new-column "unnamed" (range 10))
select-vec [3 5 7 3 2 1]
new-col (ds-col/select test-col select-vec)]
(is (= select-vec
(dtype/->vector new-col)))))
(def src-ds (ds/->dataset "data/ames-house-prices/train.csv"))
(defn missing-pipeline
[dataset]
(-> (ds/->dataset dataset)
(ds/remove-column "Id")
(dsp/replace-missing cf/string? "NA")
(dsp/replace cf/string? {"" "NA"})
(dsp/replace-missing cf/numeric? 0)
(dsp/replace-missing cf/boolean? false)
(dsp/->datatype #(cf/or cf/numeric?
cf/boolean?))))
(deftest basic-pipeline-test
(let [dataset (missing-pipeline src-ds)]
(is (= 19 (count (ds/columns-with-missing-seq src-ds))))
(is (= 0 (count (ds/columns-with-missing-seq dataset))))
(is (= 42 (count (cf/categorical? dataset))))
(is (= #{:string :float64}
(->> (ds/columns dataset)
(map dtype/get-datatype)
set)))))
(deftest log1p-changes-datatype
;;This causes actual data corruption--if the column datatype gets clipped
;;back to an integer type you get values like 12 instead of 12.5. For this
;;dataset that destroys the accuracy so we make sure the log1p operation does
;;in fact change the datatype correctly.
(is (dfn/equals [12.24769911637256
12.109016442313738
12.317171167298682
11.849404844423074
12.429220196836383]
(->> (dsp/m= src-ds "SalePrice" #(dfn/log1p (col)))
(#(ds/column % "SalePrice"))
(take 5)
(vec)))))
(defn skew-column-filter
[dataset]
(with-ds dataset
(cf/and cf/numeric-and-non-categorical-and-not-target
#(cf/not "SalePrice")
(fn []
(cf/> #(let [skewness (dfn/skewness
(dtype/->reader (col)
:float64
{:missing-policy :elide}))]
(dfn/abs skewness))
0.5)))))
;;This test fails if col-filters/and (intersection) isn't
;;implemented correctly
(deftest and-is-lazy
(is (= 29 (count (skew-column-filter src-ds)))))
(defn string-and-math
[dataset]
(let [initial-ds
(-> dataset
(dsp/string->number "Utilities" [["NA" -1] "ELO" "NoSeWa" "NoSewr" "AllPub"])
(dsp/string->number "LandSlope" ["Gtl" "Mod" "Sev" "NA"])
(dsp/string->number ["ExterQual"
"ExterCond"
"BsmtQual"
"BsmtCond"
"HeatingQC"
"KitchenQual"
"FireplaceQu"
"GarageQual"
"GarageCond"
"PoolQC"] ["Ex" "Gd" "TA" "Fa" "Po" "NA"])
(dsp/assoc-metadata ["MSSubClass" "OverallQual" "OverallCond"]
:categorical? true)
(dsp/string->number "MasVnrType" {"BrkCmn" 1
"BrkFace" 1
"CBlock" 1
"Stone" 1
"None" 0
"NA" -1})
(dsp/string->number "SaleCondition" {"Abnorml" 0
"Alloca" 0
"AdjLand" 0
"Family" 0
"Normal" 0
"Partial" 1
"NA" -1})
;; ;;Auto convert the rest that are still string columns
(dsp/string->number))]
(if (ds/has-column? initial-ds "SalePrice")
(-> initial-ds
(dsp/new-column "SalePriceDup" #(ds/column % "SalePrice"))
(dsp/update-column "SalePrice" dfn/log1p)
(ds/set-inference-target "SalePrice"))
initial-ds)))
(deftest base-etl-test
(let [src-dataset src-ds
;;For inference, we won't have the target but we will have everything else.
inference-columns (c-set/difference
(set (map ds-col/column-name
(ds/columns src-dataset)))
#{"SalePrice"})
inference-dataset (-> (ds/select src-dataset
inference-columns
(range 10))
(ds/->flyweight :error-on-missing-values? false))
dataset (-> src-ds
missing-pipeline
string-and-math)
post-pipeline-columns (c-set/difference inference-columns #{"Id"})
sane-dataset-for-flyweight (ds/select dataset post-pipeline-columns
(range 10))
final-flyweight (-> sane-dataset-for-flyweight
(ds/->flyweight))]
(is (= [81 1460] (dtype/shape src-dataset)))
(is (= [81 1460] (dtype/shape dataset)))
(is (= 45 (count (cf/categorical? dataset))))
(is (= #{"MSSubClass" "OverallQual" "OverallCond"}
(c-set/intersection #{"MSSubClass" "OverallQual" "OverallCond"}
(set (cf/categorical? dataset)))))
(is (= []
(vec (cf/string? dataset))))
(is (= ["SalePrice"]
(vec (cf/target? dataset))))
(is (= []
(vec (cf/not cf/numeric? dataset))))
(let [sale-price (ds/column dataset "SalePriceDup")
sale-price-l1p (ds/column dataset "SalePrice")
sp-stats (ds-col/stats sale-price [:mean :min :max])
sp1p-stats (ds-col/stats sale-price-l1p [:mean :min :max])]
(is (dfn/equals (mapv sp-stats [:mean :min :max])
[180921.195890 34900 755000]
0.01))
(is (dfn/equals (mapv sp1p-stats [:mean :min :max])
[12.024 10.460 13.534]
0.01)))
(is (= 10 (count inference-dataset)))
(is (= 10 (count final-flyweight)))
(let [pre-pipeline (map ds-col/metadata (ds/columns src-ds))
col-dtype-map (->> pre-pipeline
(map (fn [{:keys [name datatype]}]
[name datatype]))
(into {}))
exact-columns (ds/->dataset
inference-dataset
{:parser-fn col-dtype-map})
;;Just checking that this works at all..
autoscan-columns (ds/->dataset inference-dataset {})]
;;And the definition of exact is...
(is (every? #(= (dtype/get-datatype %)
(get col-dtype-map
(ds-col/column-name %)))
(ds/columns exact-columns)))
(let [inference-ds (-> exact-columns
missing-pipeline
string-and-math)]
;;spot check a few of the items
(is (dfn/equals (dtype/->vector (ds/column sane-dataset-for-flyweight
"MSSubClass"))
(dtype/->vector (ds/column inference-ds "MSSubClass"))))
;;did categorical values get encoded identically?
(is (dfn/equals (dtype/->vector (ds/column sane-dataset-for-flyweight
"OverallQual"))
(dtype/->vector (ds/column inference-ds "OverallQual"))))))))
(defn full-ames-pt-1
[dataset]
(-> (missing-pipeline dataset)
(dsp/string->number "Utilities" [["NA" -1] "ELO" "NoSeWa" "NoSewr" "AllPub"])
(dsp/string->number "LandSlope" ["Gtl" "Mod" "Sev" "NA"])
(dsp/string->number ["ExterQual"
"ExterCond"
"BsmtQual"
"BsmtCond"
"HeatingQC"
"KitchenQual"
"FireplaceQu"
"GarageQual"
"GarageCond"
"PoolQC"] ["Ex" "Gd" "TA" "Fa" "Po" "NA"])
(dsp/assoc-metadata ["MSSubClass" "OverallQual" "OverallCond"]
:categorical? true)
(dsp/string->number "MasVnrType" {"BrkCmn" 1
"BrkFace" 1
"CBlock" 1
"Stone" 1
"None" 0
"NA" -1})
(dsp/string->number "SaleCondition" {"Abnorml" 0
"Alloca" 0
"AdjLand" 0
"Family" 0
"Normal" 0
"Partial" 1
"NA" -1})
;; ;;Auto convert the rest that are still string columns
(dsp/string->number)
(dsp/update-column "SalePrice" dfn/log1p)
(ds/set-inference-target "SalePrice")
(m= "OverallGrade" #(dfn/* (col "OverallQual") (col "OverallCond")))
;; Overall quality of the garage
(m= "GarageGrade" #(dfn/* (col "GarageQual") (col "GarageCond")))
;; Overall quality of the exterior
(m= "ExterGrade" #(dfn/* (col "ExterQual") (col "ExterCond")))
;; Overall kitchen score
(m= "KitchenScore" #(dfn/* (col "KitchenAbvGr") (col "KitchenQual")))
;; Overall fireplace score
(m= "FireplaceScore" #(dfn/* (col "Fireplaces") (col "FireplaceQu")))
;; Overall garage score
(m= "GarageScore" #(dfn/* (col "GarageArea") (col "GarageQual")))
;; Overall pool score
(m= "PoolScore" #(dfn/* (col "PoolArea") (col "PoolQC")))
;; Simplified overall quality of the house
(m= "SimplOverallGrade" #(dfn/* (col "OverallQual") (col "OverallCond")))
;; Simplified overall quality of the exterior
(m= "SimplExterGrade" #(dfn/* (col "ExterQual") (col "ExterCond")))
;; Simplified overall pool score
(m= "SimplPoolScore" #(dfn/* (col "PoolArea") (col "PoolQC")))
;; Simplified overall garage score
(m= "SimplGarageScore" #(dfn/* (col "GarageArea") (col "GarageQual")))
;; Simplified overall fireplace score
(m= "SimplFireplaceScore" #(dfn/* (col "Fireplaces") (col "FireplaceQu")))
;; Simplified overall kitchen score
(m= "SimplKitchenScore" #(dfn/* (col "KitchenAbvGr") (col "KitchenQual")))
;; Total number of bathrooms
(m= "TotalBath" #(dfn/+ (col "BsmtFullBath")
(dfn/* 0.5 (col "BsmtHalfBath"))
(col "FullBath")
(dfn/* 0.5 (col "HalfBath"))))
;; Total SF for house (incl. basement)
(m= "AllSF" #(dfn/+ (col "GrLivArea") (col "TotalBsmtSF")))
;; Total SF for 1st + 2nd floors
(m= "AllFlrsSF" #(dfn/+ (col "1stFlrSF") (col "2ndFlrSF")))
;; Total SF for porch
(m= "AllPorchSF" #(dfn/+ (col "OpenPorchSF") (col "EnclosedPorch")
(col "3SsnPorch") (col "ScreenPorch")))))
(def ames-top-columns
["SalePrice"
"OverallQual"
"AllSF"
"AllFlrsSF"
"GrLivArea"
"GarageCars"
"ExterQual"
"TotalBath"
"KitchenQual"
"GarageArea"
"ExterGrade"])
(defn full-ames-pt-2
[dataset]
;;Drop SalePrice column of course.
(->> (rest ames-top-columns)
(reduce (fn [dataset colname]
(-> dataset
(m= (str colname "-s2") #(dfn/pow (col colname) 2))
(m= (str colname "-s3") #(dfn/pow (col colname) 3))
(m= (str colname "-sqrt") #(dfn/sqrt (col colname)))))
dataset)))
(defn full-ames-pt-3
[dataset]
(-> dataset
(m= (skew-column-filter dataset)
#(dfn/log1p (col)))
(dsp/std-scale)))
(deftest full-ames-pipeline-test
(let [src-dataset src-ds]
(testing "Pathway through ames pt one is sane. Checking skew."
(let [dataset (full-ames-pt-1 src-dataset)]
(is (= ames-top-columns
(->> (get (ds/correlation-table dataset) "SalePrice")
(take 11)
(mapv first))))
(let [[n-cols n-rows] (dtype/shape src-dataset)
[n-new-cols n-new-rows] (-> (dsp/filter src-dataset
"GrLivArea"
#(dfn/< (col) 4000))
dtype/shape)
num-over-the-line (->> (ds/column src-dataset "GrLivArea")
(dtype/->reader)
(filter #(>= (int %) 4000))
count)]
;;Ensure our test isn't pointless.
(is (not= 0 num-over-the-line))
(is (= n-new-rows
(- n-rows num-over-the-line))))
(let [new-ds (m= src-dataset "SimplOverallQual"
#(int-map {1 1 2 1 3 1
4 2 5 2 6 2
7 3 8 3 9 3 10 3}
(col "OverallQual")))]
(is (= #{1 2 3}
(->> (ds/column new-ds "SimplOverallQual")
(ds-col/unique)
(map int)
set))))))
(testing "Pathway through ames pt 2 is sane. Checking skew."
(let [dataset (-> src-ds
full-ames-pt-1
full-ames-pt-2)
skewed-set (set (skew-column-filter dataset))]
;;This count seems rather high...a diff against the python stuff would be wise.
(is (= 64 (count skewed-set)))
(is (= 45 (count (cf/categorical? dataset))))
;;Sale price cannot be in the set as it was explicitly removed.
(is (not (contains? skewed-set "SalePrice")))))
(testing "Full ames pathway is sane"
(let [dataset (-> src-ds
full-ames-pt-1
full-ames-pt-2
full-ames-pt-3)
std-set (set (cf/numeric-and-non-categorical-and-not-target dataset))
mean-var-seq (->> std-set
(map (comp #(ds-col/stats % [:mean :variance])
(partial ds/column dataset))))]
;;Are means 0?
(is (dfn/equals (mapv :mean mean-var-seq)
(vec (repeat (count mean-var-seq) 0))
0.001))
(let [pca-ds (dsp/pca dataset)]
(is (= 127 (count (ds/columns dataset))))
(is (= 45 (count (cf/categorical? pca-ds))))
(is (= 75 (count (ds/columns pca-ds))))
(is (= 1 (count (cf/target? pca-ds)))))
(let [pca-ds (dsp/pca dataset
cf/numeric-and-non-categorical-and-not-target
:n-components 10)]
(is (= 45 (count (cf/categorical? dataset))))
(is (= 45 (count (cf/categorical? pca-ds))))
(is (= 127 (count (ds/columns dataset))))
(is (= 56 (count (ds/columns pca-ds))))
(is (= 1 (count (cf/target? pca-ds)))))))))
(deftest tostring-regression
(testing "tostring has to work with missing values"
(is (string?
(.toString ^Object src-ds)))))
(deftest desc-stats-and-correlation
[]
(let [stats-data (ds/descriptive-stats src-ds)
corr-data (ds-pipe/correlation-table src-ds :colname-seq ["SalePrice"])]
(is (= #{:min :n-missing :col-name :mean :datatype :skew :mode
:standard-deviation :n-valid :max}
(set (ds/column-names stats-data))))
(is (= 81
(->> corr-data
first
second
count)))))