Transfer predictions from NAGuideR

Contents

Transfer predictions from NAGuideR#

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import logging
from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

import pimmslearn
import pimmslearn.models
import pimmslearn.pandas
from pimmslearn.io import datasplits

pimmslearn.plotting.make_large_descriptors(5)

logger = pimmslearn.logging.setup_logger(logging.getLogger('pimmslearn'))

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# catch passed parameters
args = None
args = dict(globals()).keys()

Papermill script parameters:

# files and folders
# Datasplit folder with data for experiment
folder_experiment: str = 'runs/example'
folder_data: str = ''  # specify data directory if needed
file_format: str = 'csv'  # file format of create splits, default pickle (csv)
identifer_str: str = '_all_'  # identifier for prediction files to be filtered
dumps: list = None  # list of dumps to be used

# Parameters
dumps = "runs/alzheimer_study/preds/pred_all_BPCA.csv,runs/alzheimer_study/preds/pred_all_COLMEDIAN.csv,runs/alzheimer_study/preds/pred_all_IMPSEQ.csv,runs/alzheimer_study/preds/pred_all_IMPSEQROB.csv,runs/alzheimer_study/preds/pred_all_IRM.csv,runs/alzheimer_study/preds/pred_all_KNN_IMPUTE.csv,runs/alzheimer_study/preds/pred_all_LLS.csv,runs/alzheimer_study/preds/pred_all_MINDET.csv,runs/alzheimer_study/preds/pred_all_MINIMUM.csv,runs/alzheimer_study/preds/pred_all_MINPROB.csv,runs/alzheimer_study/preds/pred_all_MLE.csv,runs/alzheimer_study/preds/pred_all_PI.csv,runs/alzheimer_study/preds/pred_all_QRILC.csv,runs/alzheimer_study/preds/pred_all_RF.csv,runs/alzheimer_study/preds/pred_all_ROWMEDIAN.csv,runs/alzheimer_study/preds/pred_all_SVDMETHOD.csv,runs/alzheimer_study/preds/pred_all_TRKNN.csv,runs/alzheimer_study/preds/pred_all_ZERO.csv"
folder_experiment = "runs/alzheimer_study"

Some argument transformations

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args = pimmslearn.nb.get_params(args, globals=globals())
args = pimmslearn.nb.args_from_dict(args)
args

{'data': Path('runs/alzheimer_study/data'),
 'dumps': 'runs/alzheimer_study/preds/pred_all_BPCA.csv,runs/alzheimer_study/preds/pred_all_COLMEDIAN.csv,runs/alzheimer_study/preds/pred_all_IMPSEQ.csv,runs/alzheimer_study/preds/pred_all_IMPSEQROB.csv,runs/alzheimer_study/preds/pred_all_IRM.csv,runs/alzheimer_study/preds/pred_all_KNN_IMPUTE.csv,runs/alzheimer_study/preds/pred_all_LLS.csv,runs/alzheimer_study/preds/pred_all_MINDET.csv,runs/alzheimer_study/preds/pred_all_MINIMUM.csv,runs/alzheimer_study/preds/pred_all_MINPROB.csv,runs/alzheimer_study/preds/pred_all_MLE.csv,runs/alzheimer_study/preds/pred_all_PI.csv,runs/alzheimer_study/preds/pred_all_QRILC.csv,runs/alzheimer_study/preds/pred_all_RF.csv,runs/alzheimer_study/preds/pred_all_ROWMEDIAN.csv,runs/alzheimer_study/preds/pred_all_SVDMETHOD.csv,runs/alzheimer_study/preds/pred_all_TRKNN.csv,runs/alzheimer_study/preds/pred_all_ZERO.csv',
 'file_format': 'csv',
 'folder_data': '',
 'folder_experiment': Path('runs/alzheimer_study'),
 'identifer_str': '_all_',
 'out_figures': Path('runs/alzheimer_study/figures'),
 'out_folder': Path('runs/alzheimer_study'),
 'out_metrics': Path('runs/alzheimer_study'),
 'out_models': Path('runs/alzheimer_study'),
 'out_preds': Path('runs/alzheimer_study/preds')}

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files_out = {}

load data splits

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data = datasplits.DataSplits.from_folder(
    args.data, file_format=args.file_format)

pimmslearn.io.datasplits - INFO     Loaded 'train_X' from file: runs/alzheimer_study/data/train_X.csv
pimmslearn.io.datasplits - INFO     Loaded 'val_y' from file: runs/alzheimer_study/data/val_y.csv
pimmslearn.io.datasplits - INFO     Loaded 'test_y' from file: runs/alzheimer_study/data/test_y.csv

Validation and test data split of simulated missing values

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val_pred_fake_na = data.val_y.to_frame(name='observed')
val_pred_fake_na
observed
Sample ID protein groups
Sample_158 Q9UN70;Q9UN70-2 14.630
Sample_050 Q9Y287 15.755
Sample_107 Q8N475;Q8N475-2 15.029
Sample_199 P06307 19.376
Sample_067 Q5VUB5 15.309
... ... ...
Sample_111 F6SYF8;Q9UBP4 22.822
Sample_002 A0A0A0MT36 18.165
Sample_049 Q8WY21;Q8WY21-2;Q8WY21-3;Q8WY21-4 15.525
Sample_182 Q8NFT8 14.379
Sample_123 Q16853;Q16853-2 14.504

12600 rows × 1 columns

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test_pred_fake_na = data.test_y.to_frame(name='observed')
test_pred_fake_na.describe()
observed
count 12,600.000
mean 16.339
std 2.741
min 7.209
25% 14.412
50% 15.935
75% 17.910
max 30.140

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# Find and load prediction files, filter for validation and test data

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if args.dumps is not None:
    entire_pred = [Path(s) for s in args.dumps.split(',')]
else:
    entire_pred = list(file for file in args.out_preds.iterdir()
                       if '_all_' in str(file))
entire_pred

[Path('runs/alzheimer_study/preds/pred_all_BPCA.csv'),
 Path('runs/alzheimer_study/preds/pred_all_COLMEDIAN.csv'),
 Path('runs/alzheimer_study/preds/pred_all_IMPSEQ.csv'),
 Path('runs/alzheimer_study/preds/pred_all_IMPSEQROB.csv'),
 Path('runs/alzheimer_study/preds/pred_all_IRM.csv'),
 Path('runs/alzheimer_study/preds/pred_all_KNN_IMPUTE.csv'),
 Path('runs/alzheimer_study/preds/pred_all_LLS.csv'),
 Path('runs/alzheimer_study/preds/pred_all_MINDET.csv'),
 Path('runs/alzheimer_study/preds/pred_all_MINIMUM.csv'),
 Path('runs/alzheimer_study/preds/pred_all_MINPROB.csv'),
 Path('runs/alzheimer_study/preds/pred_all_MLE.csv'),
 Path('runs/alzheimer_study/preds/pred_all_PI.csv'),
 Path('runs/alzheimer_study/preds/pred_all_QRILC.csv'),
 Path('runs/alzheimer_study/preds/pred_all_RF.csv'),
 Path('runs/alzheimer_study/preds/pred_all_ROWMEDIAN.csv'),
 Path('runs/alzheimer_study/preds/pred_all_SVDMETHOD.csv'),
 Path('runs/alzheimer_study/preds/pred_all_TRKNN.csv'),
 Path('runs/alzheimer_study/preds/pred_all_ZERO.csv')]

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mask = data.train_X.unstack().isna().stack()
idx_real_na = mask.index[mask]
idx_real_na = (idx_real_na
               .drop(val_pred_fake_na.index)
               .drop(test_pred_fake_na.index))

for fpath in entire_pred:
    logger.info(f"Load {fpath = }")
    col_name = fpath.stem.split('_all_')[-1]
    pred = pd.read_csv(fpath, index_col=[1, 0])
    val_pred_fake_na[col_name] = pred
    fname = args.out_preds / f'pred_val_{col_name}.csv'
    files_out[fname.name] = fname.as_posix()
    val_pred_fake_na[['observed', col_name]].to_csv(fname)
    logger.info(f"Save {fname = }")

    test_pred_fake_na[col_name] = pred
    fname = args.out_preds / f'pred_test_{col_name}.csv'
    files_out[fname.name] = fname.as_posix()
    test_pred_fake_na[['observed', col_name]].to_csv(fname)
    logger.info(f"Save {fname = }")
    # hacky, but works:
    pred_real_na = (pd.Series(0, index=idx_real_na, name='placeholder')
                    .to_frame()
                    .join(pred, how='left')
                    .drop('placeholder', axis=1))
    # pred_real_na.name = 'intensity'
    fname = args.out_preds / f'pred_real_na_{col_name}.csv'
    files_out[fname.name] = fname.as_posix()
    pred_real_na.to_csv(fname)
    logger.info(f"Save {fname = }")

# del pred

pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_BPCA.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_BPCA.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_BPCA.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_BPCA.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_COLMEDIAN.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_COLMEDIAN.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_COLMEDIAN.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_COLMEDIAN.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_IMPSEQ.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_IMPSEQ.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_IMPSEQ.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_IMPSEQ.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_IMPSEQROB.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_IMPSEQROB.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_IMPSEQROB.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_IMPSEQROB.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_IRM.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_IRM.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_IRM.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_IRM.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_KNN_IMPUTE.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_KNN_IMPUTE.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_KNN_IMPUTE.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_KNN_IMPUTE.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_LLS.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_LLS.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_LLS.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_LLS.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_MINDET.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_MINDET.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_MINDET.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_MINDET.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_MINIMUM.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_MINIMUM.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_MINIMUM.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_MINIMUM.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_MINPROB.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_MINPROB.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_MINPROB.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_MINPROB.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_MLE.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_MLE.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_MLE.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_MLE.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_PI.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_PI.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_PI.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_PI.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_QRILC.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_QRILC.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_QRILC.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_QRILC.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_RF.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_RF.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_RF.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_RF.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_ROWMEDIAN.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_ROWMEDIAN.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_ROWMEDIAN.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_ROWMEDIAN.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_SVDMETHOD.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_SVDMETHOD.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_SVDMETHOD.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_SVDMETHOD.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_TRKNN.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_TRKNN.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_TRKNN.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_TRKNN.csv')
pimmslearn - INFO     Load fpath = Path('runs/alzheimer_study/preds/pred_all_ZERO.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_val_ZERO.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_test_ZERO.csv')
pimmslearn - INFO     Save fname = Path('runs/alzheimer_study/preds/pred_real_na_ZERO.csv')

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val_pred_fake_na
observed BPCA COLMEDIAN IMPSEQ IMPSEQROB IRM KNN_IMPUTE LLS MINDET MINIMUM MINPROB MLE PI QRILC RF ROWMEDIAN SVDMETHOD TRKNN ZERO
Sample ID protein groups
Sample_158 Q9UN70;Q9UN70-2 14.630 15.469 16.800 NaN 58.276 15.871 15.937 15.858 11.916 7.068 11.759 2,513.638 13.686 14.684 15.652 15.752 17.206 15.700 0
Sample_050 Q9Y287 15.755 16.453 17.288 NaN 16.993 17.472 16.961 17.058 12.900 7.068 11.661 19.829 12.756 15.985 16.844 17.221 17.807 16.738 0
Sample_107 Q8N475;Q8N475-2 15.029 13.110 17.187 NaN -78.084 12.680 15.437 14.397 12.313 7.068 11.758 2,582.130 12.748 13.426 14.501 14.846 17.434 13.776 0
Sample_199 P06307 19.376 19.639 16.711 NaN 102.283 19.632 18.861 19.005 12.285 7.068 12.699 2,483.120 12.973 17.354 19.224 18.973 17.111 19.015 0
Sample_067 Q5VUB5 15.309 15.465 16.743 NaN -36.470 15.976 15.079 15.104 11.827 7.068 11.790 2,569.564 13.254 12.685 14.943 14.726 17.031 14.699 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Sample_111 F6SYF8;Q9UBP4 22.822 22.994 17.042 NaN 104.484 22.983 22.837 22.836 12.161 7.068 12.253 2,634.108 12.200 22.290 22.864 22.918 17.330 22.872 0
Sample_002 A0A0A0MT36 18.165 15.882 16.792 NaN -18.408 17.237 15.446 15.579 12.586 7.068 12.826 2,448.503 11.867 13.208 15.572 15.877 16.879 15.671 0
Sample_049 Q8WY21;Q8WY21-2;Q8WY21-3;Q8WY21-4 15.525 15.406 17.032 NaN -27.128 15.313 15.995 15.903 12.352 7.068 11.813 2,487.550 14.168 14.056 15.662 16.278 17.215 15.574 0
Sample_182 Q8NFT8 14.379 14.322 16.764 NaN -12.434 14.873 14.675 13.456 12.504 7.068 11.421 2,426.191 11.618 10.753 13.788 13.995 17.125 14.518 0
Sample_123 Q16853;Q16853-2 14.504 14.582 16.686 NaN 78.799 14.748 14.824 14.931 12.689 7.068 11.676 2,461.806 11.338 14.021 14.730 14.849 16.981 14.485 0

12600 rows × 19 columns

Metrics for simulated missing values (NA)

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# papermill_description=metrics
d_metrics = pimmslearn.models.Metrics()

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added_metrics = d_metrics.add_metrics(val_pred_fake_na.dropna(how='all', axis=1), 'valid_fake_na')
pd.DataFrame(added_metrics)

Selected as truth to compare to: observed
BPCA COLMEDIAN IMPSEQROB IRM KNN_IMPUTE LLS MINDET MINIMUM MINPROB MLE PI QRILC RF ROWMEDIAN SVDMETHOD TRKNN ZERO
MSE 0.429 7.554 741,117.141 0.751 0.753 3,023.655 23.897 93.346 24.245 5,469,016.990 21.437 4.278 0.494 0.766 8.005 0.469 274.368
MAE 0.422 2.210 333.478 0.588 0.554 1.329 4.108 9.272 4.126 2,172.384 3.801 1.639 0.461 0.598 2.309 0.450 16.340
N 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000
prop 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Test Datasplit#

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added_metrics = d_metrics.add_metrics(test_pred_fake_na.dropna(how='all', axis=1), 'test_fake_na')
pd.DataFrame(added_metrics)

Selected as truth to compare to: observed
BPCA COLMEDIAN IMPSEQROB IRM KNN_IMPUTE LLS MINDET MINIMUM MINPROB MLE PI QRILC RF ROWMEDIAN SVDMETHOD TRKNN ZERO
MSE 0.455 7.704 741,503.932 0.763 0.772 838.462 23.999 93.458 24.430 5,509,003.848 21.699 4.262 0.512 0.776 8.159 0.500 274.464
MAE 0.432 2.223 334.546 0.587 0.558 0.874 4.109 9.271 4.131 2,186.302 3.818 1.629 0.465 0.602 2.330 0.458 16.339
N 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000 12,600.000
prop 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

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metrics_df = pimmslearn.models.get_df_from_nested_dict(
    d_metrics.metrics, column_levels=['model', 'metric_name']).T
metrics_df
subset valid_fake_na test_fake_na
model metric_name
BPCA MSE 0.429 0.455
MAE 0.422 0.432
N 12,600.000 12,600.000
prop 1.000 1.000
COLMEDIAN MSE 7.554 7.704
... ... ... ...
TRKNN prop 1.000 1.000
ZERO MSE 274.368 274.464
MAE 16.340 16.339
N 12,600.000 12,600.000
prop 1.000 1.000

68 rows × 2 columns

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order_methods = metrics_df.loc[pd.IndexSlice[:,
                                             'MAE'], 'valid_fake_na'].sort_values()
order_methods

model       metric_name
BPCA        MAE               0.422
TRKNN       MAE               0.450
RF          MAE               0.461
KNN_IMPUTE  MAE               0.554
IRM         MAE               0.588
ROWMEDIAN   MAE               0.598
LLS         MAE               1.329
QRILC       MAE               1.639
COLMEDIAN   MAE               2.210
SVDMETHOD   MAE               2.309
PI          MAE               3.801
MINDET      MAE               4.108
MINPROB     MAE               4.126
MINIMUM     MAE               9.272
ZERO        MAE              16.340
IMPSEQROB   MAE             333.478
MLE         MAE           2,172.384
Name: valid_fake_na, dtype: float64

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top_5 = ['observed', *order_methods.droplevel(-1).index[:6]]
top_5

['observed', 'BPCA', 'TRKNN', 'RF', 'KNN_IMPUTE', 'IRM', 'ROWMEDIAN']

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fig, ax = plt.subplots(figsize=(8, 2))
ax, errors_bind = pimmslearn.plotting.errors.plot_errors_binned(
    val_pred_fake_na[top_5],
    ax=ax,
)
fname = args.out_figures / 'NAGuideR_errors_per_bin_val.png'
files_out[fname.name] = fname.as_posix()
pimmslearn.savefig(ax.get_figure(), fname)

pimmslearn.plotting - INFO     Saved Figures to runs/alzheimer_study/figures/NAGuideR_errors_per_bin_val.png

/home/runner/work/pimms/pimms/project/.snakemake/conda/924ec7e362d761ecf0807b9074d79999_/lib/python3.12/site-packages/pimmslearn/plotting/errors.py:45: FutureWarning: 

The `errwidth` parameter is deprecated. And will be removed in v0.15.0. Pass `err_kws={'linewidth': 1.2}` instead.

  ax = sns.barplot(data=errors_binned, ax=ax,
_images/b656e94654bd3bdab242175a745a70bb6b22e43fddd9d41414789ee927f32469.png

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files_out

{'pred_val_BPCA.csv': 'runs/alzheimer_study/preds/pred_val_BPCA.csv',
 'pred_test_BPCA.csv': 'runs/alzheimer_study/preds/pred_test_BPCA.csv',
 'pred_real_na_BPCA.csv': 'runs/alzheimer_study/preds/pred_real_na_BPCA.csv',
 'pred_val_COLMEDIAN.csv': 'runs/alzheimer_study/preds/pred_val_COLMEDIAN.csv',
 'pred_test_COLMEDIAN.csv': 'runs/alzheimer_study/preds/pred_test_COLMEDIAN.csv',
 'pred_real_na_COLMEDIAN.csv': 'runs/alzheimer_study/preds/pred_real_na_COLMEDIAN.csv',
 'pred_val_IMPSEQ.csv': 'runs/alzheimer_study/preds/pred_val_IMPSEQ.csv',
 'pred_test_IMPSEQ.csv': 'runs/alzheimer_study/preds/pred_test_IMPSEQ.csv',
 'pred_real_na_IMPSEQ.csv': 'runs/alzheimer_study/preds/pred_real_na_IMPSEQ.csv',
 'pred_val_IMPSEQROB.csv': 'runs/alzheimer_study/preds/pred_val_IMPSEQROB.csv',
 'pred_test_IMPSEQROB.csv': 'runs/alzheimer_study/preds/pred_test_IMPSEQROB.csv',
 'pred_real_na_IMPSEQROB.csv': 'runs/alzheimer_study/preds/pred_real_na_IMPSEQROB.csv',
 'pred_val_IRM.csv': 'runs/alzheimer_study/preds/pred_val_IRM.csv',
 'pred_test_IRM.csv': 'runs/alzheimer_study/preds/pred_test_IRM.csv',
 'pred_real_na_IRM.csv': 'runs/alzheimer_study/preds/pred_real_na_IRM.csv',
 'pred_val_KNN_IMPUTE.csv': 'runs/alzheimer_study/preds/pred_val_KNN_IMPUTE.csv',
 'pred_test_KNN_IMPUTE.csv': 'runs/alzheimer_study/preds/pred_test_KNN_IMPUTE.csv',
 'pred_real_na_KNN_IMPUTE.csv': 'runs/alzheimer_study/preds/pred_real_na_KNN_IMPUTE.csv',
 'pred_val_LLS.csv': 'runs/alzheimer_study/preds/pred_val_LLS.csv',
 'pred_test_LLS.csv': 'runs/alzheimer_study/preds/pred_test_LLS.csv',
 'pred_real_na_LLS.csv': 'runs/alzheimer_study/preds/pred_real_na_LLS.csv',
 'pred_val_MINDET.csv': 'runs/alzheimer_study/preds/pred_val_MINDET.csv',
 'pred_test_MINDET.csv': 'runs/alzheimer_study/preds/pred_test_MINDET.csv',
 'pred_real_na_MINDET.csv': 'runs/alzheimer_study/preds/pred_real_na_MINDET.csv',
 'pred_val_MINIMUM.csv': 'runs/alzheimer_study/preds/pred_val_MINIMUM.csv',
 'pred_test_MINIMUM.csv': 'runs/alzheimer_study/preds/pred_test_MINIMUM.csv',
 'pred_real_na_MINIMUM.csv': 'runs/alzheimer_study/preds/pred_real_na_MINIMUM.csv',
 'pred_val_MINPROB.csv': 'runs/alzheimer_study/preds/pred_val_MINPROB.csv',
 'pred_test_MINPROB.csv': 'runs/alzheimer_study/preds/pred_test_MINPROB.csv',
 'pred_real_na_MINPROB.csv': 'runs/alzheimer_study/preds/pred_real_na_MINPROB.csv',
 'pred_val_MLE.csv': 'runs/alzheimer_study/preds/pred_val_MLE.csv',
 'pred_test_MLE.csv': 'runs/alzheimer_study/preds/pred_test_MLE.csv',
 'pred_real_na_MLE.csv': 'runs/alzheimer_study/preds/pred_real_na_MLE.csv',
 'pred_val_PI.csv': 'runs/alzheimer_study/preds/pred_val_PI.csv',
 'pred_test_PI.csv': 'runs/alzheimer_study/preds/pred_test_PI.csv',
 'pred_real_na_PI.csv': 'runs/alzheimer_study/preds/pred_real_na_PI.csv',
 'pred_val_QRILC.csv': 'runs/alzheimer_study/preds/pred_val_QRILC.csv',
 'pred_test_QRILC.csv': 'runs/alzheimer_study/preds/pred_test_QRILC.csv',
 'pred_real_na_QRILC.csv': 'runs/alzheimer_study/preds/pred_real_na_QRILC.csv',
 'pred_val_RF.csv': 'runs/alzheimer_study/preds/pred_val_RF.csv',
 'pred_test_RF.csv': 'runs/alzheimer_study/preds/pred_test_RF.csv',
 'pred_real_na_RF.csv': 'runs/alzheimer_study/preds/pred_real_na_RF.csv',
 'pred_val_ROWMEDIAN.csv': 'runs/alzheimer_study/preds/pred_val_ROWMEDIAN.csv',
 'pred_test_ROWMEDIAN.csv': 'runs/alzheimer_study/preds/pred_test_ROWMEDIAN.csv',
 'pred_real_na_ROWMEDIAN.csv': 'runs/alzheimer_study/preds/pred_real_na_ROWMEDIAN.csv',
 'pred_val_SVDMETHOD.csv': 'runs/alzheimer_study/preds/pred_val_SVDMETHOD.csv',
 'pred_test_SVDMETHOD.csv': 'runs/alzheimer_study/preds/pred_test_SVDMETHOD.csv',
 'pred_real_na_SVDMETHOD.csv': 'runs/alzheimer_study/preds/pred_real_na_SVDMETHOD.csv',
 'pred_val_TRKNN.csv': 'runs/alzheimer_study/preds/pred_val_TRKNN.csv',
 'pred_test_TRKNN.csv': 'runs/alzheimer_study/preds/pred_test_TRKNN.csv',
 'pred_real_na_TRKNN.csv': 'runs/alzheimer_study/preds/pred_real_na_TRKNN.csv',
 'pred_val_ZERO.csv': 'runs/alzheimer_study/preds/pred_val_ZERO.csv',
 'pred_test_ZERO.csv': 'runs/alzheimer_study/preds/pred_test_ZERO.csv',
 'pred_real_na_ZERO.csv': 'runs/alzheimer_study/preds/pred_real_na_ZERO.csv',
 'NAGuideR_errors_per_bin_val.png': 'runs/alzheimer_study/figures/NAGuideR_errors_per_bin_val.png'}
Contents