Quick Start Guide
On this page we show examples of how to use the imputation methods of OptImpute on the echocardiogram dataset:
using CSV, DataFrames
df = CSV.read("echocardiogram.data", DataFrame,
missingstring="?",
pool=true,
header=[:survival, :alive, :age_at_heart_attack, :pe, :fs, :epss, :lvdd,
:wm_score, :wm_index, :mult, :name, :group, :alive_at_one],
)132×13 DataFrame
Row │ survival alive age_at_heart_attack pe fs epss lvdd ⋯
│ Float64? Int64? Float64? Int64? Float64? Float64? Floa ⋯
─────┼──────────────────────────────────────────────────────────────────────────
1 │ 11.0 0 71.0 0 0.26 9.0 4 ⋯
2 │ 19.0 0 72.0 0 0.38 6.0 4
3 │ 16.0 0 55.0 0 0.26 4.0 3
4 │ 57.0 0 60.0 0 0.253 12.062 4
5 │ 19.0 1 57.0 0 0.16 22.0 5 ⋯
6 │ 26.0 0 68.0 0 0.26 5.0 4
7 │ 13.0 0 62.0 0 0.23 31.0 5
8 │ 50.0 0 60.0 0 0.33 8.0 5
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
126 │ 17.0 0 missing 0 0.09 6.8 4 ⋯
127 │ 21.0 0 61.0 0 0.14 25.5 5
128 │ 7.5 1 64.0 0 0.24 12.9 4
129 │ 41.0 0 64.0 0 0.28 5.4 5
130 │ 36.0 0 69.0 0 0.2 7.0 5 ⋯
131 │ 22.0 0 57.0 0 0.14 16.1 4
132 │ 20.0 0 62.0 0 0.15 0.0 4
7 columns and 117 rows omittedThere are a number of missing values in the dataset:
using Statistics
DataFrame(col=propertynames(df),
missing_fraction=[mean(ismissing.(col)) for col in eachcol(df)])13×2 DataFrame
Row │ col missing_fraction
│ Symbol Float64
─────┼───────────────────────────────────────
1 │ survival 0.0151515
2 │ alive 0.00757576
3 │ age_at_heart_attack 0.0378788
4 │ pe 0.00757576
5 │ fs 0.0606061
6 │ epss 0.113636
7 │ lvdd 0.0833333
8 │ wm_score 0.030303
9 │ wm_index 0.00757576
10 │ mult 0.030303
11 │ name 0.0
12 │ group 0.166667
13 │ alive_at_one 0.439394Learner Interface
OptImpute uses the same learner interface as other IAI packages to allow you to properly conduct imputation on data that has been split into training and testing sets.
In this problem, we will use a survival framework and split the data into training and testing:
X = df[!, 3:end]
died = [ismissing(x) || x == 0 for x in df.alive]
times = df.survival
(train_X, train_died, train_times), (test_X, test_died, test_times) =
IAI.split_data(:survival, X, died, times, seed=1)First, create a learner and set parameters as normal:
lnr = IAI.OptKNNImputationLearner(random_seed=1)Unfitted OptKNNImputationLearner:
random_seed: 1Note that it is also possible to construct the learners with ImputationLearner and the method keyword argument (which can be useful when specifying the method programmatically):
lnr = IAI.ImputationLearner(method=:opt_knn, random_seed=1)Unfitted OptKNNImputationLearner:
random_seed: 1We can then train the imputation learner on the training dataset with fit!:
IAI.fit!(lnr, train_X)Fitted OptKNNImputationLearnerThe fitted learner can then be used to fill missing values with transform:
IAI.transform(lnr, test_X)40×11 DataFrame
Row │ age_at_heart_attack pe fs epss lvdd wm_score ⋯
│ Float64? Float64? Float64? Float64? Float64? Float64? ⋯
─────┼──────────────────────────────────────────────────────────────────────────
1 │ 72.0 0.0 0.38 6.0 4.1 14.0 ⋯
2 │ 57.0 0.0 0.16 22.0 5.75 18.0
3 │ 73.0 0.0 0.33 6.0 4.0 14.0
4 │ 85.0 1.0 0.18 19.0 5.46 13.83
5 │ 54.0 0.0 0.3 7.0 3.85 10.0 ⋯
6 │ 55.0 0.0 0.2315 7.0 4.611 2.0
7 │ 65.0 0.0 0.15 9.29387 5.05 10.0
8 │ 60.0 0.0 0.222 12.0 4.43909 6.0
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
34 │ 64.0 0.0 0.15 6.6 4.17 14.0 ⋯
35 │ 61.0 0.0 0.18 0.0 4.48 11.0
36 │ 48.0 0.0 0.15 12.0 3.66 10.0
37 │ 65.5 0.0 0.09 6.8 4.96 13.0
38 │ 61.0 0.0 0.14 25.5 5.16 14.0 ⋯
39 │ 64.0 0.0 0.24 12.9 4.72 12.0
40 │ 57.0 0.0 0.14 16.1 4.36 15.0
5 columns and 25 rows omittedWe commonly want to impute on the training set right after fitting the learner, so you can combine these two steps using fit_transform!:
IAI.fit_transform!(lnr, train_X)92×11 DataFrame
Row │ age_at_heart_attack pe fs epss lvdd wm_score ⋯
│ Float64? Float64? Float64? Float64? Float64? Float64? ⋯
─────┼──────────────────────────────────────────────────────────────────────────
1 │ 71.0 0.0 0.26 9.0 4.6 14.0 ⋯
2 │ 55.0 0.0 0.26 4.0 3.42 14.0
3 │ 60.0 0.0 0.253 12.062 4.603 16.0
4 │ 68.0 0.0 0.26 5.0 4.31 12.0
5 │ 62.0 0.0 0.23 31.0 5.43 22.5 ⋯
6 │ 60.0 0.0 0.33 8.0 5.25 14.0
7 │ 46.0 0.0 0.34 0.0 5.09 16.0
8 │ 54.0 0.0 0.14 13.0 4.49 15.5
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
86 │ 54.0 0.0 0.43 9.3 4.79 10.0 ⋯
87 │ 57.6429 0.0 0.23 19.1 5.49 12.0
88 │ 57.0 1.0 0.12 0.0 2.32 16.5
89 │ 61.0 1.0 0.19 13.2 5.04 19.0
90 │ 64.0 0.0 0.28 5.4 5.47 11.0 ⋯
91 │ 69.0 0.0 0.2 7.0 5.05 14.5
92 │ 62.0 0.0 0.15 0.0 4.51 15.5
5 columns and 77 rows omittedTo tune parameters, you can use the standard GridSearch interface, refer to the documentation on parameter tuning for more information.