ARTS version v1.1 Running preprocessing for subject: XXXXXX Preprocessing started without reverse_polarity "j" Found vertical phase direction in dcm2niix json The folder ~/DIFF_PREP_WORK was created for you.. DIFFPREP -i TORTOISE_Pipeline_no_rev/Import_up_proc/Import_up.list --do_QC 0 Denoising DWIs for use in registration.. Done denoising the DWIs..... Gibbs ringing correction of volume: 59, 12, 33, 35, 28, 6, 19, 58, 44, 13, 30, 64, 31, 45, 22, 0, 37, 53, 47, 65, 27, 4, 50, 10, 61, 16, 39, 43, 63, 38, 67, 55, 9, 14, 52, 18, 23, 51, 62, 15, 42, 17, 24, 48, 29, 34, 46, 21, 11, 20, 60, 2, 68, 54, 66, 5, 57, 36, 32, 3, 40, 7, 41, 8, 56, 25, 49, 1, 26,  Gibbs ringing correction of volume: 13, 4, 18, 41, 27, 25, 35, 37, 38, 8, 58, 14, 7, 39, 31, 36, 52, 1, 24, 42, 17, 23, 20, 48, 11, 45, 32, 33, 34, 21, 12, 47, 29, 9, 66, 59, 6, 60, 62, 15, 56, 68, 54, 5, 65, 28, 2, 57, 19, 44, 49, 16, 61, 55, 50, 43, 46, 30, 67, 51, 22, 64, 63, 3, 10, 40, 26, 53, 0,  Registering DWIs to b=0 image... Done registering vol 0 Done registering vol 3 Done registering vol 4 Done registering vol 22 Done registering vol 9 Done registering vol 21 Done registering vol 1 Done registering vol 18 Done registering vol 8 Done registering vol 26 Done registering vol 13 Done registering vol 10 Done registering vol 2 Done registering vol 29 Done registering vol 12 Done registering vol 19 Done registering vol 15 Done registering vol 11 Done registering vol 20 Done registering vol 28 Done registering vol 6 Done registering vol 25 Done registering vol 16 Done registering vol 7 Done registering vol 32 Done registering vol 5 Done registering vol 31 Done registering vol 43 Done registering vol 47 Done registering vol 23 Done registering vol 17 Done registering vol 14 Done registering vol 30 Done registering vol 24 Done registering vol 33 Done registering vol 49 Done registering vol 56 Done registering vol 45 Done registering vol 27 Done registering vol 38 Done registering vol 59 Done registering vol 39 Done registering vol 52 Done registering vol 41 Done registering vol 51 Done registering vol 61 Done registering vol 53 Done registering vol 34 Done registering vol 36 Done registering vol 48 Done registering vol 55 Done registering vol 67 Done registering vol 63 Done registering vol 50 Done registering vol 60 Done registering vol 40 Done registering vol 37 Done registering vol 44 Done registering vol 66 Done registering vol 65 Done registering vol 57 Done registering vol 58 Done registering vol 68 Done registering vol 46 Done registering vol 42 Done registering vol 35 Done registering vol 62 Done registering vol 54 Done registering vol 64 Done registering DWIs ... No structural image was provided... Skipping concomitant field and EPI distortion correction... Starting final steps... Please hold on Writing the final output... 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Congratulations... Estimating the L0 diffusion tensors... Computing Tensors... Computing N1 Tensors... Estimating the L0 diffusion tensors... Computing Tensors... Computing N1 Tensors... Computing R1 RESTORE Tensors... Computing FA Map... Computing TR Map... Computing AD Map... Computing DEC Map... Computing WLDEC Map... Computing WPDEC Map... Computing Eigenvector image... Computing LI Map... Computing Mode Map... Computing NSDEC Map... Computing RD Map... Computing WL Map... Computing WP Map... Computing WS Map... Chris Rorden's dcm2niiX version v1.0.20211006 GCC5.4.0 x86-64 (64-bit Linux) Found 192 DICOM file(s) Convert 192 DICOM as /output/T1/input_T1_ADRC-DV26-32ch-2017-11-2_20180627110649_3 (256x256x192x1) Conversion required 1.455763 seconds (0.875072 for core code). input_T1_ADRC-DV26-32ch-2017-11-2_20180627110649_3.json input_T1_ADRC-DV26-32ch-2017-11-2_20180627110649_3.nii T1.nii.gz Chris Rorden's dcm2niiX version v1.0.20211006 GCC5.4.0 x86-64 (64-bit Linux) Found 192 DICOM file(s) Resolved discrepancy between tags (0020,1002; 0021,104F; 0054,0081) Convert 192 DICOM as /output/FLAIR/input_FLAIR_ADRC-DV26-32ch-2017-11-2_20180627110649_9 (512x512x192x1) Conversion required 3.548382 seconds (2.521214 for core code). FLAIR.nii.gz input_FLAIR_ADRC-DV26-32ch-2017-11-2_20180627110649_9.json input_FLAIR_ADRC-DV26-32ch-2017-11-2_20180627110649_9.nii ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/opt/matlabmcr-2018b/v95//runtime/glnxa64:/opt/matlabmcr-2018b/v95//bin/glnxa64:/opt/matlabmcr-2018b/v95//sys/os/glnxa64:/opt/matlabmcr-2018b/v95//sys/opengl/lib/glnxa64 SPM12, version 7771 (standalone) MATLAB, version 9.5.0.944444 (R2018b) ___ ____ __ __ / __)( _ \( \/ ) \__ \ )___/ ) ( Statistical Parametric Mapping (___/(__) (_/\/\_) SPM12 - https://www.fil.ion.ucl.ac.uk/spm/ ------------------------------------------------------------------------ 04-Nov-2026 06:45:40 - Running job #1 ------------------------------------------------------------------------ 04-Nov-2026 06:45:40 - Running 'CAT12: Segmentation' ------------------------------------------------------------------------ CAT12.6-rc1 r1434: 1/1: /output/T1/T1_n4_reorient.nii ------------------------------------------------------------------------ SANLM denoising (medium): 27s APP: Rough bias correction: Initialize 6s Estimate background 8s Initial correction 6s Refine background 3s Final correction 5s Final scaling 6s 40s Affine registration 5s Affine registration 9s SPM preprocessing 1 (estimate 1): 49s SPM preprocessing 1 (estimate 2): 11s SPM preprocessing 2 (write): Write Segmentation 14s Update Segmentation 37s Update Skull-Stripping 14s Update probability maps 7s 72s Global intensity correction: 16s SANLM denoising after intensity normalization (medium): 8s Fast Shooting registration 29s Local adaptive segmentation (LASstr=0.50): Prepare maps 3s Prepare partitions 3s Prepare segments (LASmod = 1.00) 13s Estimate local tissue thresholds (WM) 14s Estimate local tissue thresholds (GM) 16s Estimate local tissue thresholds (CSF/BG) 4s Intensity transformation 57s SANLM denoising after LAS (medium) 23s 23s ROI segmentation (partitioning): Atlas -> subject space 6s Major structures 4s Ventricle detection 17s WMH detection (WMHCstr=0.50 > WMHCstr'=0.50) 17s Manual stroke lesion detection 0s Closing of deep structures 1s Side alignment 3s Final corrections 4s 52s Skull-stripping using graph-cut (gcutstr=0.50): WM initialisation 3s GM region growing 1s GM-CSF region growing 3s CSF region growing 3s Ventricle filling 3s 15s Amap using initial SPM12 segmentations (MRF filter strength 0.07): 18s AMAP peaks: [CSF,GM,WM] = [0.45±0.09,0.71±0.07,0.96±0.05] Final cleanup (gcutstr=0.12): Level 1 cleanup (ROI estimation) 3s Level 1 cleanup (brain masking) 2s Level 2 cleanup (CSF correction) 1s Level 3 cleanup (CSF/WM PVE) 2s 7s Shooting registration is not required. Write result maps: 5s Quality check: Warning: CAT-ERROR: CAT XML-im/export requires JVM! Read/Write only MAT file. > In cat_io_xml (line 51) In cat_vol_qa (line 840) In cat_main (line 707) In cat_run_job (line 748) In cat_run_newcatch (line 15) In cat_run>run_job (line 450) In cat_run (line 161) In cfg_run_cm (line 29) In cfg_util>local_runcj (line 1717) In cfg_util (line 972) In spm_jobman>fill_run_job (line 469) In spm_jobman (line 247) In spm_standalone (line 101) 6s ------------------------------------------------------------------------ CAT preprocessing takes 7 minute(s) and 59 second(s). Image Quality Rating (IQR): 82.45% (B-) Segmentations are saved in /output/T1/mri Reports are saved in /output/T1/report ------------------------------------------------------------------------ 04-Nov-2026 06:53:42 - Done 'CAT12: Segmentation' 04-Nov-2026 06:53:42 - Done Bye for now... /usr/local/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_converters Using TensorFlow backend. /wmhseg_example/example_nothresh_v2_combined_sysu.py:59: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding="same", activation="relu", name="conv1_1", data_format="channels_last")` conv1 = Convolution2D(64, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering, name='conv1_1')( /wmhseg_example/example_nothresh_v2_combined_sysu.py:61: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv1 = Convolution2D(64, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv1) /wmhseg_example/example_nothresh_v2_combined_sysu.py:62: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv1 = Convolution2D(64, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv1) /wmhseg_example/example_nothresh_v2_combined_sysu.py:63: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv1 = Convolution2D(64, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv1) /wmhseg_example/example_nothresh_v2_combined_sysu.py:64: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv1 = Convolution2D(64, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv1) /wmhseg_example/example_nothresh_v2_combined_sysu.py:65: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(data_format="channels_last", pool_size=(2, 2))` pool1 = MaxPooling2D(pool_size=(2, 2), dim_ordering=dim_ordering)(conv1) /wmhseg_example/example_nothresh_v2_combined_sysu.py:66: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(96, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv2 = Convolution2D(96, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(pool1) /wmhseg_example/example_nothresh_v2_combined_sysu.py:67: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(96, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv2 = Convolution2D(96, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv2) /wmhseg_example/example_nothresh_v2_combined_sysu.py:68: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(data_format="channels_last", pool_size=(2, 2))` pool2 = MaxPooling2D(pool_size=(2, 2), dim_ordering=dim_ordering)(conv2) /wmhseg_example/example_nothresh_v2_combined_sysu.py:70: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(pool2) /wmhseg_example/example_nothresh_v2_combined_sysu.py:71: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv3) /wmhseg_example/example_nothresh_v2_combined_sysu.py:72: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(data_format="channels_last", pool_size=(2, 2))` pool3 = MaxPooling2D(pool_size=(2, 2), dim_ordering=dim_ordering)(conv3) /wmhseg_example/example_nothresh_v2_combined_sysu.py:74: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(pool3) /wmhseg_example/example_nothresh_v2_combined_sysu.py:75: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (4, 4), padding="same", activation="relu", data_format="channels_last")` conv4 = Convolution2D(256, 4, 4, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv4) /wmhseg_example/example_nothresh_v2_combined_sysu.py:76: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(data_format="channels_last", pool_size=(2, 2))` pool4 = MaxPooling2D(pool_size=(2, 2), dim_ordering=dim_ordering)(conv4) /wmhseg_example/example_nothresh_v2_combined_sysu.py:78: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(416, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv5 = Convolution2D(416, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(pool4) /wmhseg_example/example_nothresh_v2_combined_sysu.py:79: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(416, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv5 = Convolution2D(416, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv5) /wmhseg_example/example_nothresh_v2_combined_sysu.py:81: UserWarning: Update your `UpSampling2D` call to the Keras 2 API: `UpSampling2D(data_format="channels_last", size=(2, 2))` up_conv5 = UpSampling2D(size=(2, 2), dim_ordering=dim_ordering)(conv5) /wmhseg_example/example_nothresh_v2_combined_sysu.py:83: UserWarning: Update your `Cropping2D` call to the Keras 2 API: `Cropping2D(cropping=((0, 0), (..., data_format="channels_last")` crop_conv4 = Cropping2D(cropping=(ch, cw), dim_ordering=dim_ordering)(conv4) /wmhseg_example/example_nothresh_v2_combined_sysu.py:84: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc. up6 = merge([up_conv5, crop_conv4], mode='concat', concat_axis=concat_axis) /usr/local/lib/python2.7/site-packages/keras/legacy/layers.py:464: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc. name=name) /wmhseg_example/example_nothresh_v2_combined_sysu.py:85: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(up6) /wmhseg_example/example_nothresh_v2_combined_sysu.py:86: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv6) /wmhseg_example/example_nothresh_v2_combined_sysu.py:88: UserWarning: Update your `UpSampling2D` call to the Keras 2 API: `UpSampling2D(data_format="channels_last", size=(2, 2))` up_conv6 = UpSampling2D(size=(2, 2), dim_ordering=dim_ordering)(conv6) /wmhseg_example/example_nothresh_v2_combined_sysu.py:90: UserWarning: Update your `Cropping2D` call to the Keras 2 API: `Cropping2D(cropping=((0, 0), (..., data_format="channels_last")` crop_conv3 = Cropping2D(cropping=(ch, cw), dim_ordering=dim_ordering)(conv3) /wmhseg_example/example_nothresh_v2_combined_sysu.py:91: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc. up7 = merge([up_conv6, crop_conv3], mode='concat', concat_axis=concat_axis) /wmhseg_example/example_nothresh_v2_combined_sysu.py:92: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(up7) /wmhseg_example/example_nothresh_v2_combined_sysu.py:93: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv7) /wmhseg_example/example_nothresh_v2_combined_sysu.py:95: UserWarning: Update your `UpSampling2D` call to the Keras 2 API: `UpSampling2D(data_format="channels_last", size=(2, 2))` up_conv7 = UpSampling2D(size=(2, 2), dim_ordering=dim_ordering)(conv7) /wmhseg_example/example_nothresh_v2_combined_sysu.py:97: UserWarning: Update your `Cropping2D` call to the Keras 2 API: `Cropping2D(cropping=((0, 0), (..., data_format="channels_last")` crop_conv2 = Cropping2D(cropping=(ch, cw), dim_ordering=dim_ordering)(conv2) /wmhseg_example/example_nothresh_v2_combined_sysu.py:98: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc. up8 = merge([up_conv7, crop_conv2], mode='concat', concat_axis=concat_axis) /wmhseg_example/example_nothresh_v2_combined_sysu.py:99: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(96, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv8 = Convolution2D(96, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(up8) /wmhseg_example/example_nothresh_v2_combined_sysu.py:100: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(96, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv8 = Convolution2D(96, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv8) /wmhseg_example/example_nothresh_v2_combined_sysu.py:102: UserWarning: Update your `UpSampling2D` call to the Keras 2 API: `UpSampling2D(data_format="channels_last", size=(2, 2))` up_conv8 = UpSampling2D(size=(2, 2), dim_ordering=dim_ordering)(conv8) /wmhseg_example/example_nothresh_v2_combined_sysu.py:104: UserWarning: Update your `Cropping2D` call to the Keras 2 API: `Cropping2D(cropping=((0, 0), (..., data_format="channels_last")` crop_conv1 = Cropping2D(cropping=(ch, cw), dim_ordering=dim_ordering)(conv1) /wmhseg_example/example_nothresh_v2_combined_sysu.py:105: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc. up9 = merge([up_conv8, crop_conv1], mode='concat', concat_axis=concat_axis) /wmhseg_example/example_nothresh_v2_combined_sysu.py:106: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv9 = Convolution2D(64, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(up9) /wmhseg_example/example_nothresh_v2_combined_sysu.py:107: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding="same", activation="relu", data_format="channels_last")` conv9 = Convolution2D(64, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv9) /wmhseg_example/example_nothresh_v2_combined_sysu.py:110: UserWarning: Update your `ZeroPadding2D` call to the Keras 2 API: `ZeroPadding2D(padding=((0, 0), (..., data_format="channels_last")` conv9 = ZeroPadding2D(padding=(ch, cw), dim_ordering=dim_ordering)(conv9) /wmhseg_example/example_nothresh_v2_combined_sysu.py:111: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1, (1, 1), activation="sigmoid", data_format="channels_last")` conv10 = Convolution2D(1, 1, 1, activation='sigmoid', dim_ordering=dim_ordering)(conv9) /wmhseg_example/example_nothresh_v2_combined_sysu.py:112: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor("co..., inputs=Tensor("in...)` model = Model(input=inputs, output=conv10) 2026-11-04 07:13:07.774130: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA 5/512 [..............................] - 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Orientation check passed Performing registration (can take an hour or more to finish) Registration completed WMH feature calculated successfully! *** Calculating diffusion anisotropy features... Registering subject to IIT template (may take a few minutes)... All_Command_lines_OK Using double precision for computations. Exception Object caught: itk::ExceptionObject (0x4209230) Location: "unknown" File: /src/ants/build/ITKv5-install/include/ITK-5.0/itkImageMomentsCalculator.hxx Line: 156 Description: itk::ERROR: ImageMomentsCalculator(0x4228d10): Compute(): Total Mass of the image was zero. Aborting here to prevent division by zero later on. WARNING:: Inconsistent orientations for individual images in pipeline! Will use voxel-based orientation which is probably incorrect - *PLEASE CHECK*! Image Exception : #3 :: Attempted to multiply images/ROIs of different sizes terminate called after throwing an instance of 'RBD_COMMON::BaseException' /opt/biokit/scripts/feature_extraction/calculateFeatures_FA.sh: line 92: 862 Aborted (core dumped) fslmaths stats/all_FA -mas stats/mean_FA_mask stats/all_FA Finished registration Calculating mean FA values from 4 predefined ROIs /opt/biokit/scripts/feature_extraction/calculateFeatures_FA.sh: line 108: 877 Segmentation fault (core dumped) ${path_biomarker}/scripts/feature_extraction/calculate_DTI_ROIs ${path_skeleton_data::-3} DTI anisotropy features calculated successfully! *** Generating QC files... QC files created successfully! Feature Extraction Completed for ID: XXXXXX in 1167 seconds ------------------------------------------ ************* Running classifier... Traceback (most recent call last): File "/opt/biokit/scripts/classifier/final_classifier.py", line 44, in y_pp = model.decision_function(df) File "/opt/miniconda-latest/envs/main/lib/python3.7/site-packages/sklearn/utils/metaestimators.py", line 116, in out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) File "/opt/miniconda-latest/envs/main/lib/python3.7/site-packages/sklearn/model_selection/_search.py", line 508, in decision_function return self.best_estimator_.decision_function(X) File "/opt/miniconda-latest/envs/main/lib/python3.7/site-packages/sklearn/utils/metaestimators.py", line 116, in out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) File "/opt/miniconda-latest/envs/main/lib/python3.7/site-packages/sklearn/pipeline.py", line 493, in decision_function return self.steps[-1][-1].decision_function(Xt) File "/opt/miniconda-latest/envs/main/lib/python3.7/site-packages/sklearn/linear_model/base.py", line 265, in decision_function X = check_array(X, accept_sparse='csr') File "/opt/miniconda-latest/envs/main/lib/python3.7/site-packages/sklearn/utils/validation.py", line 542, in check_array allow_nan=force_all_finite == 'allow-nan') File "/opt/miniconda-latest/envs/main/lib/python3.7/site-packages/sklearn/utils/validation.py", line 56, in _assert_all_finite raise ValueError(msg_err.format(type_err, X.dtype)) ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). Analysis failed Check log for errors