Quick Start Guide: Optimal Imputation
This is a Python version of the corresponding OptImpute quick start guide.
On this page we show examples of how to use the imputation methods of OptImpute on the echocardiogram dataset:
import pandas as pd
df = pd.read_csv(
"echocardiogram.data",
na_values="?",
header=None,
names=['survival', 'alive', 'age_at_heart_attack', 'pe', 'fs', 'epss',
'lvdd', 'wm_score', 'wm_index', 'mult', 'name', 'group',
'alive_at_one'],
)
df.name = df.name.astype('category')
survival alive age_at_heart_attack ... name group alive_at_one
0 11.0 0.0 71.0 ... name 1.0 0.0
1 19.0 0.0 72.0 ... name 1.0 0.0
2 16.0 0.0 55.0 ... name 1.0 0.0
3 57.0 0.0 60.0 ... name 1.0 0.0
4 19.0 1.0 57.0 ... name 1.0 0.0
5 26.0 0.0 68.0 ... name 1.0 0.0
6 13.0 0.0 62.0 ... name 1.0 0.0
.. ... ... ... ... ... ... ...
125 17.0 0.0 NaN ... name NaN NaN
126 21.0 0.0 61.0 ... name NaN NaN
127 7.5 1.0 64.0 ... name NaN NaN
128 41.0 0.0 64.0 ... name NaN NaN
129 36.0 0.0 69.0 ... name NaN NaN
130 22.0 0.0 57.0 ... name NaN NaN
131 20.0 0.0 62.0 ... name NaN NaN
[132 rows x 13 columns]There are a number of missing values in the dataset:
df.isnull().sum() / len(df)
survival 0.015152
alive 0.007576
age_at_heart_attack 0.037879
pe 0.007576
fs 0.060606
epss 0.113636
lvdd 0.083333
wm_score 0.030303
wm_index 0.007576
mult 0.030303
name 0.000000
group 0.166667
alive_at_one 0.439394
dtype: float64Simple Imputation
We can use impute to simply fill the missing values in a DataFrame :
from interpretableai import iai
df_imputed = iai.impute(df)
survival alive age_at_heart_attack ... name group alive_at_one
0 11.0 0.0 71.000 ... name 1.0 0.000000
1 19.0 0.0 72.000 ... name 1.0 0.000000
2 16.0 0.0 55.000 ... name 1.0 0.000000
3 57.0 0.0 60.000 ... name 1.0 0.000000
4 19.0 1.0 57.000 ... name 1.0 0.000000
5 26.0 0.0 68.000 ... name 1.0 0.000000
6 13.0 0.0 62.000 ... name 1.0 0.000000
.. ... ... ... ... ... ... ...
125 17.0 0.0 61.875 ... name 2.0 0.000004
126 21.0 0.0 61.000 ... name 2.0 0.000003
127 7.5 1.0 64.000 ... name 2.0 0.893597
128 41.0 0.0 64.000 ... name 2.0 0.000004
129 36.0 0.0 69.000 ... name 2.0 0.000003
130 22.0 0.0 57.000 ... name 2.0 0.000003
131 20.0 0.0 62.000 ... name 2.0 0.000003
[132 rows x 13 columns]We can control the method to use for imputation by passing the method:
df_imputed = iai.impute(df, 'opt_svm')
If you don't know which imputation method or parameter values are best, you can define the grid of parameters to be searched over:
df_imputed = iai.impute(df, {'method': ['opt_knn', 'opt_svm']})
You can also use impute_cv to conduct the search with cross-validation:
df_imputed = iai.impute_cv(df, {'method': ['opt_knn', 'opt_svm']})
Learner Interface
You can also use OptImpute using the same learner interface as other IAI packages. In particular, you should use this approach when you want to properly conduct an out-of-sample evaluation of performance.
We will split the data into training and testing:
(train_df,), (test_df,) = iai.split_data('imputation', df, 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:
lnr.fit(train_df)
Fitted OptKNNImputationLearnerThe fitted learner can then be used to fill missing values with transform:
lnr.transform(test_df)
survival alive age_at_heart_attack pe ... mult name group alive_at_one
0 26.00 0.0 68.0 0.0 ... 0.857 name 1.0 0.000000
1 25.00 0.0 54.0 0.0 ... 0.930 name 1.0 0.000000
2 10.00 1.0 77.0 0.0 ... 0.714 name 1.0 1.000000
3 52.00 0.0 73.0 0.0 ... 1.000 name 1.0 0.000000
4 44.00 0.0 60.0 0.0 ... 1.000 name 1.0 0.000000
5 0.50 1.0 69.0 1.0 ... 0.784 name 1.0 1.000000
6 0.75 1.0 69.0 0.0 ... 0.857 name 1.0 1.000000
.. ... ... ... ... ... ... ... ... ...
33 31.00 0.0 53.0 0.0 ... 0.710 name 2.0 0.000087
34 25.00 0.0 62.0 0.0 ... 0.786 name 2.0 0.000083
35 25.00 0.0 57.0 0.0 ... 0.786 name 2.0 0.000000
36 3.00 1.0 62.0 0.0 ... 1.000 name 2.0 1.000000
37 25.00 0.0 59.0 1.0 ... 0.857 name 1.8 0.000040
38 36.00 0.0 48.0 0.0 ... 0.714 name 2.0 0.000061
39 22.00 0.0 57.0 0.0 ... 0.786 name 2.0 0.000084
[40 rows x 13 columns]We commonly want to impute on the training set right after fitting the learner, so you can combine these two steps using fit_transform:
lnr.fit_transform(train_df)
survival alive age_at_heart_attack pe ... mult name group alive_at_one
0 11.0 0.0 71.000000 0.0 ... 1.000 name 1.0 0.000000
1 19.0 0.0 72.000000 0.0 ... 0.588 name 1.0 0.000000
2 16.0 0.0 55.000000 0.0 ... 1.000 name 1.0 0.000000
3 57.0 0.0 60.000000 0.0 ... 0.788 name 1.0 0.000000
4 19.0 1.0 57.000000 0.0 ... 0.571 name 1.0 0.000000
5 13.0 0.0 62.000000 0.0 ... 0.857 name 1.0 0.000000
6 50.0 0.0 60.000000 0.0 ... 1.000 name 1.0 0.000000
.. ... ... ... ... ... ... ... ... ...
85 12.0 0.0 61.000000 1.0 ... 0.786 name 2.0 0.000064
86 17.0 0.0 62.705882 0.0 ... 0.857 name 2.0 0.000115
87 21.0 0.0 61.000000 0.0 ... 0.786 name 2.0 0.000074
88 7.5 1.0 64.000000 0.0 ... 0.857 name 2.0 0.999997
89 41.0 0.0 64.000000 0.0 ... 0.714 name 2.0 0.000091
90 36.0 0.0 69.000000 0.0 ... 0.857 name 2.0 0.000095
91 20.0 0.0 62.000000 0.0 ... 0.786 name 2.0 0.000110
[92 rows x 13 columns]To tune parameters, you can use the standard GridSearch interface, refer to the documentation on parameter tuning for more information.