Cudf: [BUG] A column with `inf` values is being treated as "object" dtype instead of "float" in cudf.read_csv

Created on 14 Sep 2020  路  2Comments  路  Source: rapidsai/cudf

Describe the bug
When there are inf values in a column(float dtype), cudf.read_csv is inferring the dtype as "object" instead of float. Attached is the csv file generated in fuzz test: temp_csv.zip

Steps/Code to reproduce bug

In[2]: import cudf
In[3]: import pandas as pd
In[4]: pdf = pd.read_csv("temp_csv.csv")
In[5]: gdf = cudf.read_csv("temp_csv.csv")
In[6]: pdf['14']
Out[6]: 
0      inf
1      inf
2      inf
3      NaN
4      inf
      ... 
836    inf
837    inf
838    inf
839    inf
840    inf
Name: 14, Length: 841, dtype: float64
In[7]: gdf['14']
Out[7]: 
0       inf
1       inf
2       inf
3      <NA>
4       inf
       ... 
836     inf
837     inf
838     inf
839     inf
840     inf
Name: 14, Length: 841, dtype: object
In[8]: cudf.from_pandas(pdf['14'])
Out[8]: 
0       Inf
1       Inf
2       Inf
3      <NA>
4       Inf
       ... 
836     Inf
837     Inf
838     Inf
839     Inf
840     Inf
Name: 14, Length: 841, dtype: float64

Expected behavior
Expected behavior is column 14 should be inferred as float64 dtype.

Environment overview (please complete the following information)

  • Environment location: Bare-metal
  • Method of cuDF install: from source

Environment details
Please run and paste the output of the cudf/print_env.sh script here, to gather any other relevant environment details

Click here to see environment details



 **git***
 commit 49558bdfa8f493ffc26d435a2f644bc2c092cc21 (HEAD -> 6001, origin/6001)
 Author: galipremsagar <[email protected]>
 Date:   Mon Sep 14 09:18:36 2020 -0500

 replace prints with logging
 **git submodules***

 ***OS Information***
 DISTRIB_ID=Ubuntu
 DISTRIB_RELEASE=20.04
 DISTRIB_CODENAME=focal
 DISTRIB_DESCRIPTION="Ubuntu 20.04.1 LTS"
 NAME="Ubuntu"
 VERSION="20.04.1 LTS (Focal Fossa)"
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 VERSION_ID="20.04"
 HOME_URL="https://www.ubuntu.com/"
 SUPPORT_URL="https://help.ubuntu.com/"
 BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
 PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
 VERSION_CODENAME=focal
 UBUNTU_CODENAME=focal
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 ***GPU Information***
 Mon Sep 14 10:00:17 2020
 +-----------------------------------------------------------------------------+
 | NVIDIA-SMI 450.66       Driver Version: 450.66       CUDA Version: 11.0     |
 |-------------------------------+----------------------+----------------------+
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 | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
 |                               |                      |               MIG M. |
 |===============================+======================+======================|
 |   0  Quadro RTX 8000     Off  | 00000000:22:00.0 Off |                  Off |
 | 33%   32C    P8     5W / 260W |    589MiB / 48601MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   1  Quadro RTX 8000     Off  | 00000000:2D:00.0  On |                  Off |
 | 33%   35C    P8    19W / 260W |    340MiB / 48592MiB |      6%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+

 +-----------------------------------------------------------------------------+
 | Processes:                                                                  |
 |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
 |        ID   ID                                                   Usage      |
 |=============================================================================|
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 +-----------------------------------------------------------------------------+

 ***CPU***
 Architecture:                    x86_64
 CPU op-mode(s):                  32-bit, 64-bit
 Byte Order:                      Little Endian
 Address sizes:                   46 bits physical, 48 bits virtual
 CPU(s):                          12
 On-line CPU(s) list:             0-11
 Thread(s) per core:              2
 Core(s) per socket:              6
 Socket(s):                       1
 NUMA node(s):                    1
 Vendor ID:                       GenuineIntel
 CPU family:                      6
 Model:                           85
 Model name:                      Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz
 Stepping:                        4
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 L1i cache:                       192 KiB
 L2 cache:                        6 MiB
 L3 cache:                        19.3 MiB
 NUMA node0 CPU(s):               0-11
 Vulnerability Itlb multihit:     KVM: Vulnerable
 Vulnerability L1tf:              Mitigation; PTE Inversion
 Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable
 Vulnerability Meltdown:          Mitigation; PTI
 Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
 Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
 Vulnerability Spectre v2:        Mitigation; Full generic retpoline, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling
 Vulnerability Srbds:             Not affected
 Vulnerability Tsx async abort:   Mitigation; Clear CPU buffers; SMT vulnerable
 Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke md_clear flush_l1d

 ***CMake***
 /home/pgali/anaconda3/envs/cudf_dev/bin/cmake
 cmake version 3.18.2

 CMake suite maintained and supported by Kitware (kitware.com/cmake).

 ***g++***
 /usr/bin/g++
 g++ (Ubuntu 9.3.0-10ubuntu2) 9.3.0
 Copyright (C) 2019 Free Software Foundation, Inc.
 This is free software; see the source for copying conditions.  There is NO
 warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.


 ***nvcc***
 /usr/local/cuda/bin/nvcc
 nvcc: NVIDIA (R) Cuda compiler driver
 Copyright (c) 2005-2020 NVIDIA Corporation
 Built on Wed_Jul_22_19:09:09_PDT_2020
 Cuda compilation tools, release 11.0, V11.0.221
 Build cuda_11.0_bu.TC445_37.28845127_0

 ***Python***
 /home/pgali/anaconda3/envs/cudf_dev/bin/python
 Python 3.7.8

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 urllib3                   1.25.10                    py_0    conda-forge
 virtualenv                20.0.20          py37hc8dfbb8_1    conda-forge
 wcwidth                   0.2.5              pyh9f0ad1d_1    conda-forge
 webencodings              0.5.1                      py_1    conda-forge
 wheel                     0.35.1             pyh9f0ad1d_0    conda-forge
 xz                        5.2.5                h516909a_1    conda-forge
 yaml                      0.2.5                h516909a_0    conda-forge
 zeromq                    4.3.2                he1b5a44_3    conda-forge
 zict                      2.0.0                      py_0    conda-forge
 zipp                      3.1.0                      py_0    conda-forge
 zlib                      1.2.11            h516909a_1009    conda-forge
 zstd                      1.4.5                h6597ccf_2    conda-forge

Additional context
Surfaced while running fuzz tests #6001

bug cuIO
Source
picture galipremsagar

Most helpful comment

With PR #6234 , 

In [4]: pdf = pd.read_csv("temp_csv.csv")
In [5]: gdf = cudf.read_csv("temp_csv.csv")
In [7]: assert_eq(pdf, gdf)
Out[7]: True
picture karthikeyann on 15 Sep 2020
❤1 🎉1 👍1

All 2 comments

Quite surprising. csv reader parsing didn't have infinity support until now. (even with datatypes passed).

karthikeyann picture karthikeyann on 15 Sep 2020

With PR #6234 , 

In [4]: pdf = pd.read_csv("temp_csv.csv")
In [5]: gdf = cudf.read_csv("temp_csv.csv")
In [7]: assert_eq(pdf, gdf)
Out[7]: True
karthikeyann picture karthikeyann on 15 Sep 2020
❤1 🎉1 👍1
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