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  • 人工智能/数据科学比赛汇总 2019.7

    万次阅读 2019-07-13 10:02:36
    内容来自 DataSciComp,人工智能/数据科学比赛整理平台。 Github:iphysresearch/DataSciComp 本项目由 ApacheCN 强力支持。 微博 | 知乎 | CSDN | 简书 | OSChina | 博客园 Data Science for Good: CareerVillage....

    内容来自 DataSciComp,人工智能/数据科学比赛整理平台。

    Github:iphysresearch/DataSciComp

    本项目由 ApacheCN 强力支持。

    微博 | 知乎 | CSDN | 简书 | OSChina | 博客园

    Data Science for Good: CareerVillage.org

    https://www.kaggle.com/c/data-science-for-good-careervillage

    Feb 26th - April 30th, 2019 // Host by Kaggle // Prize: $15,000

    Note: Match career advice questions with professionals in the field

    Entry Deadline:


    AI Drug Discovery: Pharmacokinetic Parameter Prediction

    https://signate.jp/competitions/168

    6月27日 - 11月2019年 // Host by SIGNATE // Prize: ¥2,000,000

    Note: Predict pharmacokinetic parameters from compound information and experimental data in the screening test.

    Entry Deadline:


    CCKS 2019 医疗命名实体识别

    https://www.biendata.com/competition/ccks_2019_1/

    04/01 - 08/15 2019 // Host by Biendata // Prize: ¥15,000

    Note: 本任务是CCKS围绕中文电子病历语义化开展的系列评测的一个延续,在CCKS 2017,2018医疗命名实体识别评测任务的基础上进行了延伸和拓展。
    包括两个子任务:1)医疗命名实体识别:由于国内没有公开可获得的面向中文电子病历医疗实体识别数据集,本年度保留了医疗命名实体识别任务,对2018年度数据集做了修订,并随任务一同发布。2)医疗实体及属性抽取(跨院迁移):在医疗实体识别的基础上,对预定义实体属性进行抽取。

    Entry Deadline:


    CCKS 2019 中文短文本的实体链指

    https://www.biendata.com/competition/ccks_2019_el/

    04/20 - 07/30 2019 // Host by Biendata // Prize: ¥10,000

    Note: 输入文件包括若干行中文短文本。输出文本每一行包括此中文短文本的实体识别与链指结果,需识别出文本中所有mention(包括实体与概念),每个mention包含信息如下:mention在给定知识库中的ID,mention名和在中文短文本中的位置偏移。

    Entry Deadline:


    KDD Cup 2019

    https://www.4paradigm.com/competition/kddcup2019

    Apr 1st - Jul 16th, 2019 // Host by 4Paradigm & ChaLearn & CodaLab // Prize: $65,000

    Note: The 5th AutoML Challenge: AutoML for Temporal Relational Data
    In this challenge, participants are invited to develop AutoML solutions to binary classification problems for temporal relational data.

    Entry Deadline:


    SMP - ECISA “拓尔思杯”中文隐式情感分析评测 2019

    https://biendata.com/competition/smpecisa2019/

    04/24 - 08/18 2019 // Host by Biendata // Prize: 30000元

    Note: 欢迎来到SMP2019“拓尔思杯”中文隐式情感分析评测(The Evaluation of Chinese Implicit Sentiment Analysis,SMP-ECISA 2019)。
    我们将隐式情感定义为:“不含有显式情感词,但表达了主观情感的语言片段”,并将其划分为事实型隐式情感和修辞型隐式情感。其中,修辞型隐式情感又可细分为隐喻/比喻型、反问型以及反讽型。本次评测任务中,仅针对隐式情感的识别与情感倾向性分类。

    Entry Deadline:


    7th Emotion Recognition in the Wild Challenge (EmotiW)

    https://sites.google.com/view/emotiw2019

    February - July, 2019 // Host by ICMI 2019 // Prize: NaN

    Note: The goal of this challenge is to extend and carry forward the new common platform for evaluation of emotion recognition methods in real-world conditions defined in EmotiW2018 Grand Challenge held at the ACM International Conference on Multimodal Interaction 2018. This year there will be three sub-challenges:
    Audio-video based emotion recognition sub-challenge (AV)
    Group-level Cohesion sub-challenge (GC)
    Engagement prediction in the Wild (EW)

    Entry Deadline:


    Shared Task on Hierarchical Classification of Blurbs

    https://competitions.codalab.org/competitions/21226

    Feb. 1 - Aug, 2019 // Host by CodaLab & KONVENS 2019 // Prize: NaN

    Note: This task is focusing on classifying German books into their respective hierarchically structured writing genres using short advertisement texts (Blurbs) and further meta information such as author, page number, release date, etc.

    Entry Deadline:


    eBay SIGIR 2019 eCommerce Search Challenge: High Accuracy Recall Task

    https://sigir-ecom.github.io/data-task.html

    May 17 - July 25, 2019 // Host by EvalAI & The 2019 SIGIR Workshop On eCommerce // Prize: NaN

    Note: The challenge data consists of a set of popular search queries and a fair size set of candidate documents. Challenge participants make a boolean relevant-or-not decision for each query-document pair. Human judgments are used to create labeled training and evaluation data for a subset of the query-document pairs. Evaluation of submissions will be based on the traditional F1 metric, incorporating components of both recall and precision.

    Entry Deadline:


    2019 年县域农业大脑AI挑战赛

    https://tianchi.aliyun.com/competition/entrance/231717/introduction

    6月05 - 9月25, 2019 // Host by 天池 // Prize: ¥300000

    Note: 本次大赛,我们选择了具有独特的地理环境、气候条件以及人文特色的贵州省兴仁市作为研究区域,聚焦当地的特色优势产业和支柱产业——薏仁米产业, 以薏仁米作物识别以及产量预测为比赛命题,要求选手开发算法模型,通过无人机航拍的地面影像,探索作物分类的精准算法,识别薏仁米、玉米、烤烟三大作物类型,提升作物识别的准确度,降低对人工实地勘察的依赖,提升农业资产盘点效率,并结合产量标注数据预测当年的薏仁米产量,提升农业精准管理能力。

    Entry Deadline:


    第五届中间件性能挑战赛

    https://tianchi.aliyun.com/competition/entrance/231714/

    6月10 - 7月22, 2019 // Host by 天池 // Prize: $200,000

    Note:
    初赛:《自适应负载均衡的设计实现》
    复赛:《实现一个进程内基于队列的消息持久化存储引擎》

    Entry Deadline:


    CCKS 2019 中文知识图谱问答

    https://www.biendata.com/competition/ccks_2019_6/

    04/20 - 07/30 2019 // Host by Biendata // Prize: ¥15,000

    Note: 本评测任务为基于中文知识图谱的自然语言问答,简称CKBQA (Chinese Knowledge Base Question Answering)。即输入一句中文问题,问答系统从给定知识库中选择若干实体或属性值作为该问题的答案。问题均为客观事实型,不包含主观因素。理解并回答问题的过程中可能需要进行实体识别、关系抽取、语义解析等子任务。这些子任务的训练可以使用额外的资源,但是最终的答案必须来自给定的知识库。

    Entry Deadline:


    MLCAS 2019 - Sorghum head detection

    https://www.register.extension.iastate.edu/mlcas2019/competition

    June 10 - July 21, 2019 // Host by CodaLab & MLCAS 2019 // Prize: $37,000

    Note: This challenge uses RGB image frames of Sorghum heads collected from an UAV. The dataset consists of 1300 images wth sorghum head annotations. You can find further information about the datasets for Training phase and Final test phase in the “Participate” tab under the heading “Get Data”. The datasets could be downloaded from the “Participate” tab under the heading “Files”. The training dataset contains both labelled images and unlabelled images. Participants are encouraged to use algorithms from active learning, semi-supervised learning or unsupervised learning for utilizing the unlabelled images during training.

    Entry Deadline:


    Recursion Cellular Image Classification

    https://www.kaggle.com/c/recursion-cellular-image-classification

    Now - Sept 26, 2019 // Host by Kaggle & NeurIPS 2019 // Prize: $13,000

    Note: CellSignal: Disentangling biological signal from experimental noise in cellular images

    Entry Deadline:


    全球数据智能大赛(2019)——“数字人体”赛场一:肺部CT多病种智能诊断

    https://tianchi.aliyun.com/competition/entrance/231724/

    6月24 - 9月09, 2019 // Host by 天池 // Prize: $900,000

    Note: 赛场一“数字人体”挑战赛以肺部CT多病种智能诊断为课题,开放高质量CT标注数据,要求选手提出并综合运用目标检测、深度学习等人工智能算法,识别肺结节、索条(条索状影)、动脉硬化或钙化、淋巴结钙化等多个病种,避免同一部位单病种的反复筛查,提高检测的速度和精度,辅助医生进行诊断。

    Entry Deadline:


    Online Challenge: Build A Recommendation Engine (Powered by IBM Cloud)

    https://datahack.analyticsvidhya.com/contest/build-a-recommendation-engine-powered-by-ibm-cloud/

    24 Jan - 25 July, 2019 // Host by Analytics Vidhya // Prize: INR 50,000

    Note: You are expected to build a high performing recommendation engine using any framework of your choice. You are encouraged to use IBM Watson Studio Apache spark based Jupyter notebook.

    Entry Deadline:


    WIDER Face & Person Challenge 2019

    http://wider-challenge.org/2019.html

    May 8 - July 25, 2019 // Host by CodaLab & ICCV 2019 & 商汤 & Amazon // Prize: cash prize and AWS credits

    Note: Following the success of the First WIDER Challenge Workshop, we organize a new round of challenge in conjunction with ICCV 2019. The challenge centers around the problem of precise localization of human faces and bodies, and accurate association of identities. It comprises of four tracks:
    WIDER Face Detection, aims at soliciting new approaches to advance the state-of- the-art in face detection.
    WIDER Pedestrian Detection, has the goal of gathering effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments.
    WIDER Cast Search by Portrait, presents an exciting challenge of searching cast across hundreds of movies.
    WIDER Person Search by Language, aims to seek new approaches to search person by natural language.

    Entry Deadline:


    "华为云杯"2019深圳开放数据应用创新大赛

    https://opendata.sz.gov.cn/sodic2019/

    2019-06-19 至 2019-09-07 // Host by 深圳市政府数据开放平台 & 华为 HUAWEI// Prize: 1400000元 + 300000元华为云资源

    Note: 赛题数据:
    交通数据 室内停车 公租房轮候 卫星遥感 文体公益活动 游客预约 道路积水 深圳图书馆进馆人次统计 龙岗区坂田街道交通流量 企业信用目录 坪山区民生诉求数据 坪山区河流域和易积水道路视频 光明区政府服务办事大厅预约

    Entry Deadline:


    第四届魔镜杯大赛

    https://ai.ppdai.com/mirror/goToMirrorDetail?mirrorId=17

    2019-05-28 至 2019-07-27 // Host by 拍拍贷·AI & DC // Prize: ¥370000

    Note: 本次比赛以互联网金融信贷业务为背景,参赛选手需要利用提供的数据,预测资产组合在未来一段时间内每日的回款金额。赛题涵盖了信贷违约预测、现金流预测等金融领域常见问题,同时又是复杂的时序问题和多目标预测问题。

    Entry Deadline:


    CCKS 2019 面向金融领域的事件主体抽取

    https://www.biendata.com/competition/ccks_2019_4/

    05/01 - 07/30 2019 // Host by Biendata // Prize: ¥15,000

    Note: 本次评测任务的主要目标是从真实的新闻语料中,抽取特定事件类型的主体。即给定一段文本T,和文本所属的事件类型S,从文本T中抽取指定事件类型S的事件主体。

    Entry Deadline:


    全国高校大数据应用创新大赛

    https://ai.futurelab.tv/contest_detail/4

    6月8日 - 9月, 2019 // Host by 睡前FUTURE.AI // Prize: 20,000元 x 2

    Note: 全国高校大数据应用创新大赛”(以下简称大赛)是由教育部高等学校计算机类专业教学指导委员会、中国工程院中国工程科技知识中心和联合国教科文组织国际工程科技知识中心联合主办,复旦大学计算机学院承办,面向全国高校在校学生的,年度性大数据学科竞赛。 通用赛道:
    大数据技术技能赛: 大赛提供的数据和自选数据建立并训练模型,使之能够预测给定地区、日期和前置气象条件下,未来7天的部分气象要素的变化情况;
    大数据与人工智能创意赛: 本次大赛气象大数据开放式命题赛道,提供过去5年若干城市的气象数据,参赛选手可自主运用和扩充数据,设计一个基于气象大数据的跨行业跨领域的应用解决方案。

    Entry Deadline:


    DeepFashion2 Challenge 2019

    https://sites.google.com/view/cvcreative/deepfashion2?authuser=0

    May 27 - July 30, 2019 // Host by CodaLab // Prize: NaN

    Note: DeepFashion2 (github) is a comprehensive fashion dataset. It contains 491K diverse images of 13 popular clothing categories from both commercial shopping stores and consumers. It totally has 801K clothing clothing items, where each item in an image is labeled with scale, occlusion, zoom-in, viewpoint, category, style, bounding box, dense landmarks and per-pixel mask.There are also 873K Commercial-Consumer clothes pairs.
    The dataset is split into a training set (391K images), a validation set (34k images), and a test set (67k images).
    Track 1 Clothes Landmark Estimation
    Track 2 Clothes Retrieval

    Entry Deadline:


    莱斯杯:全国第二届“军事智能机器阅读"挑战赛"

    https://www.kesci.com/home/competition/5d142d8cbb14e6002c04e14a

    2019-09-03 至 2019-10-28 // Host by Kesci // Prize: 50万元人民币

    Note: 本次竞赛提供的大规模中文阅读理解数据集,共包含15万余篇的专业文章,7万个军事类复杂问题,每个问题对应五篇文章

    Entry Deadline:


    第三届"长风杯"大数据分析与挖掘竞赛

    http://contest.cfdsj.cn/index/care

    2019-05-15 至 2019-10-31 // Host by 长风大数据平台 // Prize: ¥5万

    Note: 第三届“长风杯”大数据分析与挖掘竞赛是一场面向全国普通高等院校经济与管理类、信息技术类等专业在校大学生的全国性赛事。
    长风大数据平台将向本次竞赛的参赛者免费开放物流、电商、交通、公共、贸易等多行业的海量数据资源;其他生产型/服务型企业所提供真实数据。

    Entry Deadline:


    “添翼杯”人工智能创新应用大赛

    https://tianyicup.kesci.com/

    2019-06-14 至 2019-09-20 // Host by 上海电信 // Prize: 40,000 元 x 2

    Note:
    智慧环保-垃圾分类图像检测问题: 请参赛选手利用训练集图片,建立算法模型,对测试集给定的物品图片,判断其属于可回收垃圾的概率。
    智慧教育-成绩预测问题:请参赛选手利用脱敏后的初中学生过往考试情况与考试考点信息,建立算法模型,预测学生初中最后一次期末考试的成绩。

    Entry Deadline:


    Northeastern SMILE Lab - Recognizing Faces in the Wild

    https://www.kaggle.com/c/recognizing-faces-in-the-wild/overview/description

    Now - August 8, 2019 // Host by Kaggle // Prize: NaN

    Note: Can you determine if two individuals are related?

    Entry Deadline:


    首届中文NL2SQL挑战赛

    https://tianchi.aliyun.com/competition/entrance/231716/introduction

    6月24 - 9月, 2019 // Host by 天池 // Prize: ¥十五万

    Note: 首届中文NL2SQL挑战赛,使用金融以及通用领域的表格数据作为数据源,提供在此基础上标注的自然语言与SQL语句的匹配对,希望选手可以利用数据训练出可以准确转换自然语言到SQL的模型。

    Entry Deadline:


    Segmentation of THoracic Organs at Risk in CT images (SegTHOR)

    https://competitions.codalab.org/competitions/21012

    Jan. 5 - Aug 8, 2019 // Host by CodaLab & ISBI 2019 // Prize: NaN

    Note: The goal of the SegTHOR challenge is to automatically segment 4 OAR: heart, aorta, trachea, esophagus. Participants will be provided with a training set 40 CT scans with manual segmentation. The test set will include 20 CT scans.

    Entry Deadline:


    安泰杯 —— 跨境电商智能算法大赛

    https://tianchi.aliyun.com/competition/entrance/231718/introduction?spm=5176.12281949.1003.1.3a994c2axCiMpB

    7月16 - 9月16, 2019 // Host by 天池 // Prize: ¥100000

    Note: 本次比赛给出若干日内来自成熟国家的部分用户的行为数据,以及来自待成熟国家的A部分用户的行为数据,以及待成熟国家的B部分用户的行为数据去除每个用户的最后一条购买数据,让参赛人预测B部分用户的最后一条行为数据。

    Entry Deadline:


    Generative Dog Images

    https://www.kaggle.com/c/generative-dog-images

    Now - August 9, 2019 // Host by Kaggle // Prize: $10,000

    Note: Experiment with creating puppy pics

    Entry Deadline:


    Challenges and Opportunities in Automated Coding of COntentious Political Events (Cope 2019) @Euro CSS 2019

    https://emw.ku.edu.tr/?event=challenges-and-opportunities-in-automated-coding-of-contentious-political-events

    6/7 - 9/2, 2019 // Host by CodaLab & Euro CSS 2019 // Prize: NaN

    Note: We use English online news archives from India and China as data sources to create the training and test corpora. India and China are the source and the target countries respectively in our setting.

    Entry Deadline:


    遥感图像稀疏表征与智能分析竞赛

    http://rscup.bjxintong.com.cn/

    2019-06-01 至 2019-09-20 // Host by 中国科学院空间应用工程与技术中心 // Prize: ¥160000

    Note: 本次大赛设置遥感图像场景分类遥感图像目标检测遥感图像语义分割遥感图像变化检测遥感卫星视频目标跟踪五个竞赛单元,并在决赛中设置 基于华为昇腾AI处理器的遥感图像解译加分赛。 组织方将提供面向各竞赛单元的大规模遥感图像精确标注数据集与标准规范的测试数据, 制定可量化的算法评测标准,通过初赛、决赛和复审答辩等多个阶段的评比, 遴选出优秀的遥感图像解译算法,决胜出优胜团队。

    Entry Deadline:


    CIKM 2019 EComm AI

    https://tianchi.aliyun.com/competition/entrance/231719/

    7月05 - 9月25, 2019 // Host by 天池 // Prize: $25000 + $25000

    Note:
    Predicting User Behavior Diversities in A Dynamic Interactive Environment
    Efficient and Novel Item Retrieval for Large-scale Online Shopping Recommendation

    Entry Deadline:


    ARIEL Data Challenge Series 2019

    https://ariel-datachallenge.azurewebsites.net/
    15th of August 2019 // Host by ECML-PKDD 2019 // Prize: Eternal gratitude … or a bottle of wine.

    Note: ARIEL, a mission to make the first large-scale survey of exoplanet atmospheres, has launched a global competition series to find innovative solutions for the interpretation and analysis of exoplanet data. You can find our press release here.
    The first ARIEL Data Challenge invites professional and amateur data scientists around the world to use Machine Learning (ML) to remove noise from exoplanet observations caused by starspots and by instrumentation.
    A second ARIEL Data Challenge that focuses on the retrieval of spectra from simulations of cloudy and cloud-free super-Earth and hot-Jupiter data was also launched today.
    A further data analysis challenge to create pipelines for faster, more effective processing of the raw data gathered by the mission will be launched in June.

    Entry Deadline:


    2nd 3D Face Alignment in the Wild Challenge - Dense Reconstruction from Video

    https://competitions.codalab.org/competitions/23626

    July 4 - Aug 15 2019 // Host by CodaLab // Prize: NaN

    Note: The 2nd 3DFAW Challenge evaluates 3D face reconstruction methods on a new large corpora of profile-to-profile face videos annotated with corresponding high-resolution 3D ground truth meshes. The corpora includes profile-to-profile videos obtained under a range of conditions:
    high-definition in-the-lab video,
    unconstrained video from an iPhone device

    Entry Deadline:


    飯田産業 土地の販売価格の推定

    https://signate.jp/competitions/162

    6月10日 - 8月2019年 // Host by SIGNATE // Prize: ¥2,300,000

    Note: 「日本語をネイティブに話せる方」

    Entry Deadline:


    AI开发者大赛

    https://www.dcjingsai.com/

    5月21日-9月21日, 2019 // Host by DC 竞赛 & 科大讯飞 // Prize: 1000000 x 8

    Note:
    AI开发者大赛-工程机械核心部件寿命预测挑战赛
    AI开发者大赛-大数据应用分类标注挑战赛
    AI开发者大赛-广告营销反作弊算法挑战赛
    AI开发者大赛-阿尔茨海默综合征预测挑战赛

    Entry Deadline:


    AutoCV2: Image and video Classification

    https://autodl.lri.fr/competitions/146

    July 2 - Aug 20, 2019 // Host by AutoDL & NeurIPS 2019 // Prize: 4000 USD

    Note: This is round 2 of AutoCV: Image + Video! This is a 2-phase challenge, see the challenge rules for details. This is the FEED-BACK PHASE. The second phase (final blind-test phase) will be run from a separate submission site, to be announced after the end of the feed-back phase.

    Entry Deadline:


    iFLYTEK AI 开发者大赛

    http://challenge.xfyun.cn/2019/

    5月21日 - 10月14日, 2019 // Host by 讯飞开放平台 // Prize: 100万 RMB

    Note: "iFLYTEK AI 开发者大赛"是由科大讯飞发起的顶尖人工智能竞赛平台,汇聚产学研各界力量,面向全球开发者发起数据算法及创新应用类挑战,推动人工智能前沿科学研究和创新成果转化,培育人工智能产业人才,助力人工智能生态建设。 2019 年,第二届 iFLYTEK AI 开发者大赛将继续开放科大讯飞优质大数据资源及人工智能核心技术,面向全球开发者发起数据算法及创新应用类挑战。
    阿尔茨海默综合症预测挑战赛: 基于老年人在特定图片描述任务中产生的语音,给定语音数据中提取出的声学特征、主被试对话的切分信息、人工文本转写结果以及对应的认知标签,建立2分类模型预测认知标签(正常或认知障碍)。
    移动广告反欺诈算法挑战赛: 移动广告反欺诈需要强大的数据作为支撑,本次大赛提供了讯飞AI营销云海量的现网流量数据作为训练样本,参赛选手需基于提供的样本构建模型,预测流量作弊与否。
    大数据应用分类标注挑战赛: 选手基于提供的应用二级分类标签以及若干随机应用标注样本,实现应用分类标注算法(每个应用一个标签,以应用最主要属性对应的标签为该应用的标签)。
    工程机械核心部件寿命预测挑战赛: 由中科云谷科技有限公司提供某类工程机械设备的核心耗损性部件的工作数据,包括部件工作时长、转速、温度、电压、电流等多类工况数据。希望参赛者利用大数据分析、机器学习、深度学习等方法,提取合适的特征、建立合适的寿命预测模型,预测核心耗损性部件的剩余寿命。

    Entry Deadline:


    SIIM-ACR Pneumothorax Segmentation

    https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation

    Now - Sept 22, 2019 // Host by Kaggle & C-MIMI 2019 // Prize: $30,000

    Note: Identify Pneumothorax disease in chest x-rays

    Entry Deadline:


    Predicting Molecular Properties

    https://www.kaggle.com/c/champs-scalar-coupling

    Now - August 28, 2019 // Host by Kaggle // Prize: $30,000

    Note: Can you measure the magnetic interactions between a pair of atoms?

    Entry Deadline:


    APTOS 2019 Blindness Detection

    https://www.kaggle.com/c/aptos2019-blindness-detection

    Now - Sept 5, 2019 // Host by Kaggle & 4th APTOS Symposium // Prize: $50,000

    Note: Detect diabetic retinopathy to stop blindness before it’s too late

    Entry Deadline:


    QMUL Surveillance Face Recognition Challenge @ ICCV2019 workshop RLQ

    https://qmul-survface.github.io/

    27 June - 30 Aug, 2019 // Host by EvalAI & ICCV 2019 // Prize: NaN

    Note: The challenge data consists of a set of popular search queries and a fair size set of candidate documents. Challenge participants make a boolean relevant-or-not decision for each query-document pair. Human judgments are used to create labeled training and evaluation data for a subset of the query-document pairs. Evaluation of submissions will be based on the traditional F1 metric, incorporating components of both recall and precision.

    Entry Deadline:


    “达观杯”文本智能信息抽取挑战赛

    https://www.biendata.com/competition/datagrand/

    06/28 - 08/31 2019 // Host by Biendata // Prize: 七万七千元

    Note: 本次大赛的任务是给定一定数量的标注语料以及海量的未标注语料,在3个字段上做信息抽取任务。

    Entry Deadline:


    Challenge on Deep Learning based Loop Filter for Video Coding

    http://challenge.ai.iqiyi.com/detail?raceId=5b112a742a360316a898ff50

    May, 25th - May, 31st, 2018 // Host by 爱奇艺|iQIYI & AVS Workgroup // Prize: NaN

    Note: The participants are encouraged to investigate neural network based methods (especially convolutional neural networks) with different network structures, in a hope of achieving the best quality with lightest network configuration for a good tradeoff of efficiency and complexity.

    Entry Deadline:


    CoNLL 2019 Shard Task on Cross-Framework Meaning Representation Parsing

    http://mrp.nlpl.eu/

    March 6 - November 3, 2019 // Host by CodaLab // Prize: NaN

    Note: The 2019 Conference on Computational Language Learning (CoNLL) hosts a shared task (or ‘system bake-off’) on Cross-Framework Meaning Representation Parsing (MRP 2019).
    The goal of the task is to advance data-driven parsing into graph-structured representations of sentence meaning.

    Entry Deadline:


    The 2nd Large-scale Video Object Segmentation Challenge

    https://youtube-vos.org/challenge/2019/

    May. 20 - Sep. 5 2019 // Host by CodaLab & ICCV 2019 // Prize: NaN

    Note: As a continuous effort to push forward the research on video object segmentation tasks, we plan to host a second workshop with a challenge based on the YouTube-VOS dataset, targeting at more diversified problem settings, i.e., we plan to provide two challenge tracks in this workshop.
    Track 1: Video Object Segmentation
    Track 2: Video Instance Segmentation

    Entry Deadline:


    The 3rd YouTube-8M Video Understanding Challenge

    https://www.kaggle.com/c/youtube8m-2019

    Now - October 28, 2019 // Host by Kaggle & ICCV 2019 // Prize: $25,000

    Note: Temporal localization of topics within video

    Entry Deadline:


    Automatic Structure Segmentation for Radiotherapy Planning Challenge 2019

    https://structseg2019.grand-challenge.org/

    June 15 - Oct 1, 2019 // Host by Grand Challenges & MICCAI 2019 // Prize: NaN

    Note: The goal of the challenge is to set up tasks for evaluating automatic algorithms on segmentation of organs-at-risk (OAR) and gross target volume (GTV) of tumors of two types of cancers, nasopharynx cancer and lung cancer, for radiation therapy planning. There are four tasks for evaluating the performance of the algorithms. Participants can choose to join all or either tasks according to their interests.
    Task 1: Organ-at-risk segmentation from head & neck CT scans.
    Task 2: Gross Target Volume segmentation of nasopharynx cancer.
    Task 3: Organ-at-risk segmentation from chest CT scans.
    Task 4: Gross Target Volume segmentation of lung cancer.

    Entry Deadline:


    OpenEDS Challenge

    https://research.fb.com/programs/openeds-challenge

    May 3 - Sep 16, 2019 // Host by EvalAI & Facebook // Prize: $13,000 USD x2

    Note: In the absence of accurate gaze labels, we propose to advance the state of the art by carefully designing two challenges that combine human annotation of eye features with unlabeled data. These challenges focus on deeper understanding of the distribution underlying human eye state. We invite ML and CV researchers for participation.
    Track-1 Semantic Segmentation challenge
    Track-2 Synthetic Eye Generation challenge

    Entry Deadline:


    Exoplanet imaging data challenge

    https://exoplanet-imaging-challenge.github.io/

    May 16th - Sep 16th, 2019 // Host by CodaLab // Prize: NaN

    Note: This competition is composed of two sub-challenges focusing on the two most widely used observing techniques: pupil tracking (angular differential imaging, ADI) and multi-spectral imaging combined with pupil tracking (multi-channel spectral differential imaging, ADI+mSDI).

    Entry Deadline:


    成语阅读理解大赛

    https://www.biendata.com/competition/idiom/

    06/25 - 09/25 2019 // Host by Biendata // Prize: ¥24,000元

    Note: 本次竞赛将基于选词填空的任务形式,提供大规模的成语填空训练语料。在给定若干段文本下,选手需要在提供的候选项中,依次选出填入文本中的空格处最恰当的成语。

    Entry Deadline:


    Peking University International Competition on Ocular Disease Intelligent Recognition (ODIR-2019)

    https://odir2019.grand-challenge.org/

    May 18 - Sep 25, 2019 // Host by Grand Challenges & 北京大学 // Prize: 10,00,000 RMB (140,000+ USD)

    Note: 北京大学’智慧之眼’国际眼科疾病智能识别竞赛
    The SG will provide participants with 5,000 structured desensitized ophthalmologic image set of patient’s age, sex, binocular color fundus photos and doctors’ diagnostic report.
    上工医信将为参赛者提供5000组包含患者的性别、年龄、双眼彩色眼底照片和医生印象报告等的结构化脱敏后眼科的数据集。
    The purpose of this challenge is to compare approaches of ophthalmic disease classification in color fundus images. Participant will have to submit classification results of eight categories for all the testing data. For every category, a classification probability (value from 0.0 to 1.0) denotes risk of a patient diagnosed with corresponding category.
    该竞赛的目的是比较基于彩色眼底图像进行眼科疾病分类的不同方法。 参与者必须提交所有测试数据集的八个类别的分类结果。 对于每个类别,分类概率(值从0.0到1.0)表示患者被诊断为具有相应类别的可能性/风险。

    Entry Deadline:


    Digestive-System Pathological Detection and Segmentation Challenge 2019

    https://digestpath2019.grand-challenge.org/

    June 14 - Oct 1, 2019 // Host by Grand Challenges & MICCAI 2019 // Prize: NaN

    Note: The goal of the challenge is to set up tasks for evaluating automatic algorithms on signet ring cell detection and colonoscopy tissue screening from digestive system pathological images. This will be the first challenge and first public dataset on signet ring cell detection and colonoscopy tissue screening. Releasing the large quantity of expert-level annotations on digestive-system pathological images will substantially advance the research on automatic pathological object detection and lesion segmentation.
    Task 1: Signet ring cell detection.
    Task 2: Colonoscopy tissue segmentation and classification.

    Entry Deadline:


    The 2nd China (Hengqin) International University Quantitative Finance Competition

    http://qfc-c.com/

    2019-04-19 至 2020-03-21 // Host by 珠海市横琴新区金融服务中心 // Prize: ¥140万

    Note: 第二届中国(横琴)国际高校量化金融大赛
    参赛要求 参赛者应根据题目要求,完成一篇包括量化金融策略原理、模型的假设、建立和求解、计算方法的设计、分析和检验、模型的改进等方面的书面报告(即答卷);并在规定竞赛期间内,将参赛策略的市场运行进行模拟仿真竞赛。根据参赛策略的测试结果(包括样本内和样本外)的收益水平及市场风险防范的效果等统一指标打分评比,以市场的标准来决定优劣,评价策略的回测和实盘模拟表现,同时考虑策略逻辑的稳健性和创新性。竞赛评奖以策略的合理性、建模的创新性、测试策略的市场适应性及收益风险水平等结果为主要标准。
    Requirements Participants should write a report covering quantitative financial strategy theories 1) Model theoretical hypothesis and description of quantitive model 2) Data analysis 3) Strategy back testing results and performance analysis. According to the requirements of the competition, participants’ strategies will be back tested and paper traded during the required period. Evaluation and scoring will base on unified measurements including return, volatility, max drawdown of the strategies and so on. The determination of merits and evaluation of strategy back test and paper trading performance will be made according to market standards, while the robustness and innovation of the strategic logic will also be taken into consideration. Key criteria will include the rationality of the strategy, the creativeness of the model, the market adaptability of the testing strategy and the level of return and risk.

    Entry Deadline:


    Open Images 2019

    https://storage.googleapis.com/openimages/web/challenge2019.html

    Now - Oct 27, 2019 // Host by Kaggle & ICCV 2019 // Prize: $25,000

    Note: This year’s Open Images V5 release enabled the second Open Images Challenge to include the following 3 tracks:
    Object detection track for detecting bounding boxes around object instances, relaunched from 2018.
    Visual relationship detection track for detecting pairs of objects in particular relations, also relaunched from 2018.
    Instance segmentation track [Link to be provided when launched on July 1], brand new for 2019.

    Entry Deadline:


    Visual Domain Adaptation Challenge (VisDA-2019)

    http://ai.bu.edu/visda-2019/

    April 9 - Sept. 27, 2019 // Host by CodaLab & ICCV 2019 // Prize: NaN

    Note: We are pleased to announce the 2019 Visual Domain Adaptation (VisDA2019) Challenge! It is well known that the success of machine learning methods on visual recognition tasks is highly dependent on access to large labeled datasets. Unfortunately, performance often drops significantly when the model is presented with data from a new deployment domain which it did not see in training, a problem known as dataset shift. The VisDA challenge aims to test domain adaptation methods’ ability to transfer source knowledge and adapt it to novel target domains.
    This challenge includes two tracks:
    Multi-Source Domain Adaptation Challenge
    Semi-Supervised Domain Adaptation

    Entry Deadline:


    AI in RTC-超分辨率图像质量比较挑战赛

    https://www.dcjingsai.com/common/cmpt/AI in RTC-超分辨率图像质量比较挑战赛_竞赛信息.html

    7月1日-10月23日, 2019 // Host by DC 竞赛 // Prize: 100000

    Note: 单帧图像超分辨率近年来备受关注。同样的图像,在经过不同超分辨率算法处理后,获得的图像质量也有所不同。在这个挑战中,参赛者需要对100张图片进行4倍超分辨率处理。比赛最终以PI (perceptual index)指标作为评判标准,PI值越小,表明图像质量越高,得分越高,分值高的团队获得优胜。

    Entry Deadline:


    AI in RTC-超分辨率算法性能比较挑战赛

    https://www.dcjingsai.com/common/cmpt/AI in RTC-超分辨率算法性能比较挑战赛_竞赛信息.html

    7月1日-10月23日, 2019 // Host by DC 竞赛 // Prize: 100000

    Note: 将超分辨算法用于处理实时视频流时,模型的处理表现与运算性能,是一个两难的选择。为了追求较低复杂度,可能需要牺牲图像质量;为了追求较高质量的输出,导致设备资源占用过高,产生设备发烫、视频模糊卡顿等现象。该挑战主要考察算法模型的性能,参赛者需要对图像做2倍的超分辨率处理,算法复杂度控制在1GFLOPs之内,我们以SRCNN模型为baseline, 并采用PSNR、SSIM及运行时间来综合评估算法的性能,分值高者即获胜。

    Entry Deadline:


    Alchemy Contest

    https://alchemy.tencent.com/

    5/22 - 9/30, 2019 // Host by CodaLab & Tencent Quantum Lab 腾讯量子实验室// Prize: total ¥100,000 RMB

    Note: The Tencent Quantum Lab has recently introduced a new molecular dataset, called Alchemy, to facilitate the development of new machine learning models useful for chemistry and materials science.
    The dataset lists 12 quantum mechanical properties of 130,000+ organic molecules comprising up to 12 heavy atoms (C, N, O, S, F and Cl), sampled from the GDBMedChem database. These properties have been calculated using the open-source computational chemistry program Python-based Simulation of Chemistry Framework (PySCF).

    Entry Deadline:


    Fashion IQ Challenge

    https://competitions.codalab.org/competitions/23391

    June 1 - Sept 30, 2019 // Host by CodaLab & ICCV 2019 & Github // Prize: NaN

    Note: Fashion IQ is a new dataset we contribute to the research community to facilitate research on natural language based interactive image retrieval

    Entry Deadline:


    MicroNet Challenge @NeurIPS 2019

    https://micronet-challenge.github.io/

    June 1, 2018 - Dec 13, 2019 // Host by NeurIPS 2019 // Prize: NaN

    Note: The competition consists of three different tasks. Contestants are free to submit entries for one, two, or all three tasks. Contestants are allowed to enter up to three models for each task, but will be ranked according to their top entry in each task. Entries can only be trained on the training data for the task they are entered in. No pre-training, or use of auxiliary data is allowed.
    ImageNet Classification: The de facto standard dataset for image classification. The dataset is composed of 1,281,167 training images and 50,000 development images. Entries are required to achieve 75% top-1 accuracy on the public test set.
    CIFAR-100 Classification: A widely popular image classification dataset of small images. The dataset is composed of 50,000 training images and 10,000 development images. Entries are required to achieve 80% top-1 accuracy on the test set.
    WikiText-103 Language Modeling: A language modeling dataset that emphasizes long-term dependencies. Entries will perform the standard language modeling task, predicting the next token from the current one. The dataset is composed of 103 million training words, 217 thousand development words, and 245 thousand testing words. Entries should use the standard word-level vocabulary of 267,735 tokens. Entries are required to achieve a word-level perplexity below 35 on the test set.

    Entry Deadline:


    Multi-domain Task-Completion Dialog Challenge [DSTC 8]

    https://www.microsoft.com/en-us/research/project/multi-domain-task-completion-dialog-challenge/

    June 17 - Oct 6, 2019 // Host by CodaLab & DSTC8 // Prize: NaN

    Note: As part of the Eighth Dialog System Technology Challenge (DSTC8), Microsoft Research and Tsinghua University are hosting a track intended to foster progress in two important aspects of dialog systems: dialog complexity and scaling to new domains. For this DSTC8 track, there are two tasks you can compete in (see below). The challenge runs from June 17, 2019 – October 6, 2019.
    Participants will build an end-to-end multi-domain dialog system for tourist information desk settings.
    Participants will develop fast adaptation methods for building a conversation model that generates appropriate domain-specific user responses to an incomplete dialog history.

    Entry Deadline:


    Endoscopic Vision Challenge 2019

    https://endovis.grand-challenge.org/Endoscopic_Vision_Challenge/

    June 5 - Oct 13, 2019 // Host by Grand Challenges // Prize: NaN

    Note: As a vision CAI challenge at MICCAI, our aim is to provide a formal framework for evaluating the current state of the art, gather researchers in the field and provide high quality data with protocols for validating endoscopic vision algorithms.
    EndoVis 2019 Sub-challenges:
    Surgical Workflow and Skill Analysis
    Stereo Correspondence and Reconstruction of Endoscopic Data

    Entry Deadline:


    Graph Golf: The Order/degree Problem Competition

    http://research.nii.ac.jp/graphgolf/

    05-13 ~ 11-26, 2019 // Host by CodaLab & CANDAR 2019 // Prize: NaN

    Note: Find a graph that has smallest diameter & average shortest path length given an order and a degree.
    Graph Golf is an international competition of the order/degree problem since 2015. It is conducted with the goal of making a catalog of smallest-diameter graphs for every order/degree pair. Anyone in the world can take part in the competition by submitting a graph. Outstanding authors are awarded in CANDAR 2019, an international conference held in Nagasaki, Japan, in November 2019.

    Entry Deadline:


    The Animal-AI Olympics

    http://animalaiolympics.com/

    April - December 2019 // Host by NeurIPS 2019 // Prize: $10,000+

    Note: 基于Unity ML Agents Toolkit的动物认知-AI 挑战
    This competition pits our best AI approaches against the animal kingdom to determine if the great successes of AI are now ready to compete with the great successes of evolution at their own game.

    Entry Deadline:


    EPIC-Kitchens Action Anticipation

    https://competitions.codalab.org/competitions/20115

    July 3, 2018 - Nov. 22 2019 // Host by CodaLab & EPIC-KITCHENS 2018 // Prize: NaN

    Note: The largest dataset in first-person (egocentric) vision; multi-faceted non-scripted recordings in native environments - i.e. the wearers’ homes, capturing all daily activities in the kitchen over multiple days.
    Action-Recognition Challenge
    Action-Anticipation Challenge
    Object-Detection Challenge

    Entry Deadline:


    Geopolitical Forecasting [GF] Challenge 2

    https://www.herox.com/IARPAGFChallenge2

    April 4, 2018 - Feb. 1, 2020 // Host by Herox // Prize: $250,000

    Note: Solvers, whether individuals or teams, will create innovative solutions and methods to produce forecasts to a set of more than 300 questions referred to as Individual Forecasting Problems (IFPs), released regularly over the course of the nine-month Challenge.

    Entry Deadline:


    ModaNet Fashion Understanding Challenge

    https://evalai.cloudcv.org/web/challenges/challenge-page/151

    Oct 1, 2018 - Dec 11, 2019 // Host by EvalAI // Prize: NaN

    Note: In this challenge, we evaluate model performance for three tasks, object detection, semantic segmentation and instance segmentation. You can participate all tasks or any one of them by choosing which results to be included in your submission.

    Entry Deadline:


    「二分类算法」提供银行精准营销解决方案 | 练习赛

    https://www.kesci.com/home/competition/5c234c6626ba91002bfdfdd3

    2018年12月29日 - 2019年12月29日 // Host by Kesci // Prize: NaN

    Note: 本练习赛的数据,选自UCI机器学习库中的「银行营销数据集(Bank Marketing Data Set)」

    Entry Deadline:


    SPIE-AAPM-NCI BreastPathQ: Cancer Cellularity Challenge 2019

    http://spiechallenges.cloudapp.net/competitions/14

    Oct. 15, 2018 - Dec. 31, 2019 // Host by ISBI 2019 & Grand Challenges &cloudapp.net // Prize: NaN

    Note: Participants will be tasked to develop an automated method for analyzing histology patches extracted from whole slide images and assign a score reflecting cancer cellularity in each.

    Entry Deadline:


    Optimizing well-being at work

    https://challengedata.ens.fr/challenges/15

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & Oze Energies // Prize: NaN

    Note: This challenge proposes to develop machine learning based approaches so as to predict individuals’ comfort model using several time series of environmental data obtained from sensors in a large building. The objective is to learn a classifier that uses these time series as inputs to predict the associated comfort class computed as an average of the comfort classes of all individuals in the building, assumed to experience the same environmental conditions.

    Entry Deadline:


    Drug-related questions classification

    https://challengedata.ens.fr/challenges/17

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & Posos // Prize: NaN

    Note: The goal of Posos challenge is to predict for each question the associated intent.

    Entry Deadline:


    Detecting breast cancer metastases

    https://challengedata.ens.fr/challenges/18

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & OWKIN // Prize: NaN

    Note: The challenge proposed by Owkin is a weakly-supervised binary classification problem : predict whether a patient has any metastase in its lymph node or not, given its slide.

    Entry Deadline:


    Building Claim Prediction

    https://challengedata.ens.fr/challenges/19

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & Generali // Prize: NaN

    Note: The goal of the challenge is to predict if a building will have an insurance claim during a certain period. You will have to predict a probability of having at least one claim over the insured period of a building.

    Entry Deadline:


    Crack the neural code of the brain

    https://challengedata.ens.fr/challenges/14

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & GNT ENS // Prize: NaN

    Note: The challenge goal is to classify the brain activity state of an animal based on spiking activity patterns of its individual neurons.

    Entry Deadline:


    Prediction of Sharpe ratio for blends of quantitative strategies

    https://challengedata.ens.fr/challenges/13

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & Napoleon X // Prize: NaN

    Note: The problem is a prediction challenge that aims at helping the Company to build an optimal blend of quantitative strategies, given a set of such strategies.

    Entry Deadline:


    Historical consumption regression for electricity supply pricing

    https://challengedata.ens.fr/challenges/12

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & BCM Energy // Prize: NaN

    Note: The goal of the challenge is to predict, based on the analysis of the correlation of a year of consumption and weather training data, the electricity consumption of two given sites for a test year.

    Entry Deadline:


    Predict brain deep sleep slow oscillation

    https://challengedata.ens.fr/challenges/10

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & Dreem // Prize: NaN

    Note: In this dataset, we try to predict whether or not a slow oscillation will be followed by another one in sham condition, i.e. without any stimulation.

    Entry Deadline:


    Spatiotemporal PM10 concentration prediction

    https://challengedata.ens.fr/challenges/7

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & Plume Labs // Prize: NaN

    Note: In order to provide air quality forecasts, Plume Labs has built a unique database with readings collected by monitoring stations all over the world. The problem we submit consists in predicting the PM10 readings of some air quality monitoring stations using the readings provided by the monitoring stations nearby as well as urban features.

    Entry Deadline:


    Dynamic Profile Forecasting

    https://challengedata.ens.fr/challenges/6

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & Enedis // Prize: NaN

    Note: This challenge is about forecasting dynamic profiles values from their past values and all the components of Enedis’ Half hourly Electrical Balancing.

    Entry Deadline:


    Solve 2x2x2 Rubik’s cube

    https://challengedata.ens.fr/challenges/20

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & LumenAI // Prize: NaN

    Note: The goal is to design an automatic Rubik’s analyzer that estimates the current length of the shortest path to the solution.

    Entry Deadline:


    Exotic pricing with multidimensional non-linear interpolation

    https://challengedata.ens.fr/challenges/9

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & Natixis // Prize: NaN

    Note: The purpose of the challenge is to use a training set of 1 million prices to learn how to price a specific type of instruments described by 23 parameters by nonlinear interpolation on these prices.

    Entry Deadline:


    Screening and Diagnosis of esophageal cancer from in-vivo microscopy images

    https://challengedata.ens.fr/challenges/11

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & Mauna Kea Technologies // Prize: NaN

    Note: The goal of this challenge is to build an image classifier to assist physicians in the screening and diagnosis of esophageal cancer.

    Entry Deadline:


    Prediction of daily stock movements on the US market

    https://challengedata.ens.fr/challenges/16

    Jan. 1, 2019 - Jan. 1, 2020 // Host by Challenge data & CFM // Prize: NaN

    Note: The goal of this challenge is to predict the sign of the returns (= price change over some time interval) at the end of about 700 days for about 700 stocks.

    Entry Deadline:


    MEMENTO: MRI White Matter Reconstruction

    https://my.vanderbilt.edu/memento/

    March 7, 2019 - March 4, 2020 // Host by ISBI 2019 // Prize: NaN

    Note: This will be a 2-year challenge.
    We aim to host 3 sub-challenges evaluating our current ability to:
    (1) predict unseen signal (signal representation; sub-challenge #1)
    (2) estimate microstructural measures (signal modeling; sub-challenge #2)
    (3) evaluate sensitivity and specificity of potential biomarkers (biomarker evaluation; sub-challenge #3).

    Entry Deadline:


    Propensity to Fund Mortgages

    https://www.crowdanalytix.com/contests/propensity-to-fund-mortgages

    25 APR 2019 - 6 JUN 2019 // Host by CrowdANALYTIX // Prize: $10000

    Note: Develop a model to predict, given mortgage application information, whether the mortgage will be funded or not.
    To predict whether a mortgage will be funded using only this application data, certain leading factors driving the loan’s ultimate status will be identified. Solvers will discover the specific aspects of the dataset that have the greatest impact, and build a model based on this information.

    Entry Deadline:


    Identify Characters from Product Images

    https://www.crowdanalytix.com/contests/identify-characters-from-product-images

    12 MAY 2019 - 9 JUL 2019 // Host by CrowdANALYTIX // Prize: NaN

    Note: Identify the characters from product image from a list of 42 possible values.
    While using machine learning to perform image recognition is currently one of the most popular use cases, in some cases, the existing large-scale models are too broad to be effective for specific business use cases. In this contest we will use a data driven approach to identify the “characters” in an image (product images).

    Entry Deadline:


    KiTS19 Challenge

    https://kits19.grand-challenge.org/

    March 15 - August 2, 2019 // Host by Grand Challenges // Prize: NaN

    Note: The goal of this challenge is to accelerate the development of reliable kidney and kidney tumor semantic segmentation methodologies.

    Entry Deadline:


    PAIP 2019 Challenge

    https://paip2019.grand-challenge.org/

    April 15 - September 2, 2019 // Host by Grand Challenges & MICCAI 2019 // Prize: NaN

    Note: The goal of the challenge is to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). There are two tasks and therefore two leaderboards for evaluating the performance of the algorithms. Participants can choose to join both or either tasks according to their interests.
    Task 1: Liver Cancer Segmentation
    Task 2: Viable Tumor Burden Estimation

    Entry Deadline:


    ImageNet Object Localization Challenge

    https://www.kaggle.com/c/imagenet-object-localization-challenge

    Now - December 31 2029 // Host by Kaggle // Prize: NaN

    Note: Identify the objects in images

    Entry Deadline:


    nocaps

    https://nocaps.org/

    Feb 8, 2019 - Apr 26, 2099 // Host by EvalAI // Prize: NaN

    Note: Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed nocaps, for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets. The associated training data consists of COCO image-caption pairs, plus Open Images imagelevel labels and object bounding boxes. Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work on this task.

    Entry Deadline:


    nuScenes detection challenge

    https://www.nuscenes.org/

    Apr 1, 2019 - Jan 1, 2099 // Host by EvalAI & CVPR 2019 // Prize: NaN

    Note: The nuScenes dataset is a large-scale autonomous driving dataset.

    Entry Deadline:


    Predict Future Sales

    https://www.kaggle.com/c/competitive-data-science-predict-future-sales

    No deadline // Host by Kaggle // Prize: NaN

    Note: Final project for “How to win a data science competition” Coursera course

    No Deadline.


    Evaluating grammatical error corrections

    https://competitions.codalab.org/competitions/15475

    Nov. 23, 2016 - Never // Host by CodaLab // Prize: NaN

    No Deadline.


    Lexical Semantic Change Detection in German

    https://competitions.codalab.org/competitions/23563

    July 1 - Never // Host by CodaLab // Prize: NaN

    Note: Given two corpora Ca and Cb, rank all target words according to their degree of lexical semantic change between Ca and Cb as annotated by human judges. (Higher rank means higher change.)

    No Deadline.


    YouCook2-BoundingBoxes Video Object Grounding Task

    http://youcook2.eecs.umich.edu/

    June 24, 2019 - Never // Host by CodaLab & Github // Prize: NaN

    Note: YouCook2 is the largest task-oriented, instructional video dataset in the vision community. It contains 2000 long untrimmed videos from 89 cooking recipes; on average, each distinct recipe has 22 videos. The procedure steps for each video are annotated with temporal boundaries and described by imperative English sentences (see the example below).

    No Deadline.


    Oil Radish Semantic Segmentation and Yield Estimation Challenges

    https://competitions.codalab.org/competitions/23386

    June 1, 2019 - Never // Host by CodaLab & CVPPP 2019 & CVPR 2019 // Prize: NaN

    Note: The challenges associated with the dataset are the Semantic Segmentation challenge and the Yield Estimation challenge. In the Semantic Segmentation challenge, participants must perform pixel-wise classifiction on a subset of the labelled images. In the Yield Estimation challenge, participants must estimate the oil radish yield of same subset of labelled images.

    No Deadline.


    Challenge: Learning To Drive (L2D)

    https://competitions.codalab.org/competitions/23245

    June 1, 2019 - Never // Host by CodaLab & ICCV 2019 // Prize: NaN

    Note: Challenge participants need to develop driving models that can drive most similar to the human driver that recorded the dataset.

    No Deadline.


    Mobile age group classification

    https://competitions.codalab.org/competitions/22946

    May. 17, 2019 - Never // Host by CodaLab // Prize: NaN

    Note: This is an EE331 competition leaderboard for Mobile age group classification. It consists of 157K datasamples with 85 various features and age group label (ranging from 1 to 6). The data is splitted into train : validation : test sset with 70 : 20 : 10 ratio.

    No Deadline.


    Perfect Pitching Simulator

    https://fastballs.wordpress.com/category/pitchfx-glossary/

    May. 17, 2019 - Never // Host by CodaLab // Prize: NaN

    Note: Perfect Pitching Simulator!

    No Deadline.


    ActivityNet-Entities Object Localization Task

    https://github.com/facebookresearch/ActivityNet-Entities

    May 7, 2019 - Never // Host by CodaLab & CVPR 2019 // Prize: NaN

    Note: ActivityNet-Entities, is based on the video description dataset ActivityNet Captions and augments it with 158k bounding box annotations, each grounding a noun phrase (NP). Here we release the complete set of NP-based annotations as well as the pre-processed object-based annotations.
    please see our dataset repo, code repo, and CVPR 2019 oral paper.

    No Deadline.


    YouCook2 Dense Video Captioning

    http://youcook2.eecs.umich.edu/

    May 6, 2019 - Never // Host by CodaLab // Prize: NaN

    Note: YouCook2 is currently suitable for video-language research, weakly-supervised activity and object recognition in video, common object and action discovery across videos and procedure learning.

    No Deadline.


    The First Australian Centre for Robotic Vision (ACRV) Challenge

    https://competitions.codalab.org/competitions/20940

    Dec. 1, 2018 - Never // Host by CodaLab // Prize: NaN

    Note: The challenge consists in building an AI agent that can play efficiently and win simplified text-based games using TextWorld.

    No Deadline.


    TVQA Test Public Evaluation (w/timestamp) Beta

    https://competitions.codalab.org/competitions/20686

    Nov. 16, 2018 - Never // Host by CodaLab & TVQA // Prize: NaN

    Note: This portal is only used for models that used ‘ts’ (timestamp annotations)
    TVQA is a large-scale video QA dataset based on 6 popular TV shows (Friends, The Big Bang Theory, How I Met Your Mother, House M.D., Grey’s Anatomy, Castle).

    No Deadline.


    IWCS-2019 shared task: DRS Parsing

    https://competitions.codalab.org/competitions/20220

    Feb. 25, 2018 - Never // Host by CodaLab & IWCS-2019 // Prize: NaN

    Note: The shared task on DRS parsing will be co-located with IWCS-2019 held in Gothenburg, Sweden on 23-27 May.

    No Deadline.


    Intuitive Physics Challenge 2019

    https://competitions.codalab.org/competitions/20574

    Oct. 1, 2018 - Never // Host by CodaLab & IntPhys // Prize: NaN

    No Deadline.


    SemEval-2019

    http://alt.qcri.org/semeval2019/index.php?id=tasks

    Now - Never // Host by CodaLab & SemEval-2019 // Prize: NaN

    Note:
    Frame semantics and semantic parsing:
    Task 1: Cross-lingual Semantic Parsing with UCCA
    Task 2: Unsupervised Lexical Semantic Frame Induction
    Opinion, emotion and abusive language detection
    Task 3: EmoContext: Contextual Emotion Detection in Text
    Task 4: Hyperpartisan News Detection
    Task 5: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter
    Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media<\a>
    Fact vs fiction
    Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours
    Task 8: Fact Checking in Community Question Answering Forums
    Information extraction and question answering
    Task 9: Suggestion Mining from Online Reviews and Forums
    Task 10: Math Question Answering
    NLP for scientific applications
    Task 12: Toponym Resolution in Scientific Papers

    No Deadline.


    WiC_competition

    https://competitions.codalab.org/competitions/20010

    Aug. 18, 2018 - Never // Host by CodaLab // Prize: NaN

    Note: You can get all the information and data at https://pilehvar.github.io/wic

    No Deadline.


    OxUvA Long-Term Tracking Challenge

    https://competitions.codalab.org/competitions/19529

    July 1, 2018 - Never // Host by CodaLab & ECCV 2018 // Prize: NaN

    Note: “We introduce a new video dataset and benchmark to assess single-object tracking algorithms.”

    No Deadline.


    Evergreen: Automatically detect drill core tray outlines in core photography

    https://unearthed.solutions/u/competitions/evergreen/get-2-the-core

    June 27, 2019 - Never // Host by Unearthed // Prize: NaN

    Note: This global online competition invites innovators from around the world to build an algorithm that can determine and map the spatial extents of the core tray and then the individual rows contained within.

    No Deadline.


    Evergreen: Identify depth measurements in core images

    https://unearthed.solutions/u/competitions/evergreen/get-2-core-ii-revenge-depths

    June 27, 2019 - Never // Host by Unearthed // Prize: NaN

    Note: This is an online competition inviting companies and individuals from around the world to provide a solution that can correctly identify recorded depths within a core photograph.

    No Deadline.


    Evergreen: Reduce water usage in gold processing through tailings density prediction

    https://unearthed.solutions/u/competitions/evergreen/hydrosaver

    June 27, 2019 - Never // Host by Unearthed // Prize: NaN

    Note: This global online competition invites data scientists and innovators from around the world to develop a prediction model for tailings density (and therefore water consumption) in Newcrest’s gold processing operations.

    No Deadline.


    Lymphocyte Detection

    https://lyon19.grand-challenge.org/

    under construction // Host by Grand Challenges // Prize: NaN

    Note: Dataset contains manual annotations as a ground truth data.

    No Deadline.


    DRIVE: Digital Retinal Images for Vessel Extraction

    https://drive.grand-challenge.org/

    No deadline // Host by Grand Challenges // Prize: NaN

    Note: The DRIVE database has been established to enable comparative studies on segmentation of blood vessels in retinal images. Develop a system to automatically segment vessels in human retina fundus images.

    No Deadline.


    PatchCamelyon

    https://github.com/basveeling/pcam

    No deadline // Host by Grand Challenges // Prize: NaN

    Note: The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU.

    No Deadline.


    The Large Scale Vertebrae Segmentation Challenge (VerSe2019)

    https://verse2019.grand-challenge.org/

    May 16 - TBA // Host by Grand Challenges // Prize: NaN

    Note: Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. With the advent of deep learning, for such a task on computed tomography (CT) scans, a big and varied data is a primary sought-after resource.
    Task 1: Vertebra Labelling
    Task 2: Vertebra Segmentation

    No Deadline.


    Vision and Language Navigation

    https://evalai.cloudcv.org/web/challenges/challenge-page/97

    Mar 13, 2018 - Dec 31, 2099 // Host by EvalAI // Prize: NaN

    Note: The challenge requires an autonomous agent to follow a natural language navigation instruction to navigate to a goal location in a previously unseen real-world building.

    No Deadline.


    VizWiz Challenge 2018

    http://vizwiz.org/data/#challenge

    Jun 20, 2018 - Jun 22, 2100 // Host by EvalAI // Prize: NaN

    Note: Our proposed challenge addresses the following two tasks for this dataset: (1) predict the answer to a visual question and (2) predict whether a visual question cannot be answered.

    No Deadline.


    SQuAD2.0: The Stanford Question Answering Dataset

    https://rajpurkar.github.io/SQuAD-explorer/

    No deadline // Host by Stanford NLP Group // Prize: NaN

    Note: Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.

    No Deadline.


    CoQA: A Conversational Question Answering Challenge

    https://stanfordnlp.github.io/coqa/

    No deadline // Host by Stanford NLP Group // Prize: NaN

    Note: CoQA is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation. CoQA is pronounced as coca.

    No Deadline.


    Unrestricted Adversarial Examples Challenge

    https://github.com/google/unrestricted-adversarial-examples

    No deadline // Host by Google AI // Prize: NaN

    Note: A community-based challenge to incentivize and measure progress towards the goal of zero confident classification errors in machine learning models.
    (不受限对抗样本挑战) The project on Github

    No Deadline.


    The Natural Language Decathlon: A Multitask Challenge for NLP

    http://decanlp.com/

    No deadline // Host by salesforce // Prize: NaN

    Note: The Natural Language Decathlon is a multitask challenge that spans ten tasks: question answering (SQuAD), machine translation (IWSLT), summarization (CNN/DM), natural language inference (MNLI), sentiment analysis (SST), semantic role labeling(QA‑SRL), zero-shot relation extraction (QA‑ZRE), goal-oriented dialogue (WOZ), semantic parsing (WikiSQL), and commonsense reasoning (MWSC).

    展开全文
  • The service-oriented architecture (SOA) has become today's reference architecture for modern distributed systems. As SOA concepts and technologies become more and more widespread and the number of ...
    Author(s): Fan Liqing; Kumar, A.S.; Jagdish, B.N.; Anbuselvan, S.; Bok Shung-Hwee
    Source: 2008 IEEE International Conference on Automation Science and Engineering (CASE 2008) Pages: 656-61  Published: 2008  
    Conference Information: 2008 IEEE International Conference on Automation Science and Engineering (CASE 2008)
    Arlington, VA, USA, 23-26 August 2008
    Abstract: Distributed collaborative design and manufacture enables manufacturing organizations in maintaining competitiveness in the fiercely competitive global industry. A collaborative design system should be able to transfer information with the various other systems in order to realize a seamless product design and manufacturing environment. This paper addresses the development of an integrated fixture design and analysis system. Web services and service-oriented architecture (SOA) are employed in this system in order to facilitate the advantages of interoperability, platform -independence and language-neutrality. The use of XML as a file format provides a means for the transfer of information and knowledge between design and analysis.

     

     

     

    A model for semantic service matching with leftover and missing information

     

    Author(s): Sanchez, C.; Sheremetov, L.
    Source: 2008 8th International Conference on Hybrid Intelligent Systems (HIS) Pages: 198-203  Published: 2008  
    Conference Information: 2008 8th International Conference on Hybrid Intelligent Systems (HIS)
    Barcelona, Spain, 10-12 September 2008

    Abstract: One of the challenges of SOA is to deal with service matching which is uncertain and ambiguous. A service requester must be prepared to cope with situations where no required services are found or, on the other hand, multiple matching services are found. The paper proposes a formal model of service matching with incomplete information. The model is defined using set theory and description logics. Fuzzy logic is used to calculate the degrees of semantic matching of services and their ranking. This matching is defined by its type (exact, leftover and missing information) and service functional properties specified by OWL-S. The model is illustrated by example from a case study.

     

     

    Service-oriented architectures and software product lines: putting both together
    Author(s): Krut, R.; Cohen, S.
    Source: 2008 12th International Software Product Line Conference (SPLC) Pages: 383  Published: 2008  
    Conference Information: 2008 12th International Software Product Line Conference (SPLC)
    Limerick, Ireland, 8-12 September 2008

    Abstract: Service-oriented architecture (SOA) and software product line (SPL) approaches to software development share a common goal. They both encourage an organization to reuse existing assets and capabilities, rather than repeatedly redevelop them for new systems. Their distinct goals may be stated as: 1) SOA: enable assembly, orchestration, and maintenance of enterprise solutions to quickly react to changing business requirements. 2) SPL: systematically capture and exploit commonality among a set of related systems, while managing variations for specific customers or market segments.

     

     

    Toward Web service dependency discovery for SOA management
    Author(s): Basu, S.; Casati, F.; Daniel, F.
    Source: 2008 IEEE International Conference on Services Computing (SCC) Pages: (vol.2) 422-9  Published: 2008  
    Conference Information: 2008 IEEE International Conference on Services Computing (SCC)
    Honolulu, HI, USA, 7-11 July 2008
    Abstract: The service-oriented architecture (SOA) has become today's reference architecture for modern distributed systems. As SOA concepts and technologies become more and more widespread and the number of services in operation within enterprises increases, the problem of managing these services becomes manifest. One of the most pressing needs we hear from customers is the ability to "discover", within a maze of services each offering functionality to (and in turn using functionality offered by) other services, which are the actual dependencies between such services. Understanding dependencies is essential to performing two functions: impact analysis (understanding which other services are affected when a service becomes unavailable) and service-level root-cause analysis (which is the opposite problem: under-standing the causes of a service failure by looking at the other services it relies on). Discovering dependencies is essential as the hope that the enterprise maintains documentation that describe these dependencies (on top of a complex maze and evolving implementations) is vane. Hence, we have to look for dependencies by observing and analyzing the interactions among services. In this paper we identify the importance of the problem of discovering dynamic dependencies among Web services and we propose a solution for the automatic identification of traces of dependent messages, based on the correlation of messages exchanged among services. We also discuss our lessons learned and results from applying the techniques to data related to HP processes and services.

    展开全文
  • 数据科学家的Pytest

    2020-10-22 23:26:38
    在范围[- 1,1]内''' text = TextBlob(text) return text.sentiment.polarity def test_extract_sentiment(): text = "I think today will be a great day" sentiment = extract_sentiment(text) assert sentiment >...

    作者|Khuyen Tran 编译|VK 来源|Towards Datas Science

    动机

    应用不同的python代码来处理notebook中的数据是很有趣的,但是为了使代码具有可复制性,你需要将它们放入函数和类中。将代码放入脚本时,代码可能会因某些函数而中断。那么,如何检查你的功能是否如你所期望的那样工作呢?

    例如,我们使用TextBlob创建一个函数来提取文本的情感,TextBlob是一个用于处理文本数据的Python库。我们希望确保它像我们预期的那样工作:如果测试为积极,函数返回一个大于0的值;如果文本为消极,则返回一个小于0的值。

    from textblob import TextBlob
    
    def extract_sentiment(text: str):
            '''使用textblob提取情绪。
                在范围[- 1,1]内'''
    
            text = TextBlob(text)
    
            return text.sentiment.polarity

    要知道函数是否每次都会返回正确的值,最好的方法是将这些函数应用于不同的示例,看看它是否会产生我们想要的结果。这就是测试的重要性。

    一般来说,你应该在数据科学项目中使用测试,因为它允许你:

    • 确保代码按预期工作

    • 检测边缘情况

    • 有信心用改进的代码交换现有代码,而不必担心破坏整个管道

    有许多Python工具可用于测试,但最简单的工具是Pytest。

    Pytest入门

    Pytest是一个框架,它使得用Python编写小测试变得容易。我喜欢pytest,因为它可以帮助我用最少的代码编写测试。如果你不熟悉测试,那么pytest是一个很好的入门工具。

    要安装pytest,请运行

    pip install -U pytest

    要测试上面所示的函数,我们可以简单地创建一个函数,该函数以test_开头,后面跟着我们要测试的函数的名称,即extract_sentiment

    #sentiment.py
    def extract_sentiment(text: str):
            '''使用textblob提取情绪。
                在范围[- 1,1]内'''
    
            text = TextBlob(text)
    
            return text.sentiment.polarity
    
    def test_extract_sentiment():
    
        text = "I think today will be a great day"
    
        sentiment = extract_sentiment(text)
    
        assert sentiment > 0

    在测试函数中,我们将函数extract_sentiment应用于示例文本:“I think today will be a great day”。我们使用assert sentiment > 0来确保情绪是积极的。

    就这样!现在我们准备好运行测试了。

    如果我们的脚本名是sentiment.py,我们可以运行

    pytest sentiment.py

    Pytest将遍历我们的脚本并运行以test开头的函数。上面的测试输出如下所示

    ========================================= test session starts ==========================================
    platform linux -- Python 3.8.3, pytest-5.4.2, py-1.8.1, pluggy-0.13.1
    
    collected 1 item
    process.py .                                                                                     [100%]
    
    ========================================== 1 passed in 0.68s ===========================================

    很酷!我们不需要指定要测试哪个函数。只要函数名以test开头,pytest就会检测并执行该函数!我们甚至不需要导入pytest就可以运行pytest

    如果测试失败,pytest会产生什么输出?

    #sentiment.py
    
    def test_extract_sentiment():
    
        text = "I think today will be a great day"
    
        sentiment = extract_sentiment(text)
    
        assert sentiment < 0
    >>> pytest sentiment.py
    
    ========================================= test session starts ==========================================
    platform linux -- Python 3.8.3, pytest-5.4.2, py-1.8.1, pluggy-0.13.1
    collected 1 item
    
    process.py F                                                                                     [100%]
    =============================================== FAILURES ===============================================
    ________________________________________ test_extract_sentiment ________________________________________
    
    def test_extract_sentiment():
    
            text = "I think today will be a great day"
    
            sentiment = extract_sentiment(text)
    
    >       assert sentiment < 0
    E       assert 0.8 < 0
    
    process.py:17: AssertionError
    ======================================= short test summary info ========================================
    FAILED process.py::test_extract_sentiment - assert 0.8 < 0
    ========================================== 1 failed in 0.84s ===========================================

    从输出可以看出,测试失败是因为函数的情感值为0.8,并且不小于0!我们不仅可以知道我们的函数是否如预期的那样工作,而且还可以知道为什么它不起作用。从这个角度来看,我们知道在哪里修复我们的函数,以实现我们想要的功能。

    同一函数的多次测试

    我们可以用其他例子来测试我们的函数。新测试函数的名称是什么?

    第二个函数的名称可以是test_extract_sentiment_2,如果我们想在带有负面情绪的文本上测试函数,那么它的名称可以是test_extract_sentiment_negative。任何函数名只要以test开头就可以工作

    #sentiment.py
    
    def test_extract_sentiment_positive():
    
        text = "I think today will be a great day"
    
        sentiment = extract_sentiment(text)
    
        assert sentiment > 0
    
    def test_extract_sentiment_negative():
    
        text = "I do not think this will turn out well"
    
        sentiment = extract_sentiment(text)
    
        assert sentiment < 0
    >>> pytest sentiment.py
    
    ========================================= test session starts ==========================================
    platform linux -- Python 3.8.3, pytest-5.4.2, py-1.8.1, pluggy-0.13.1
    collected 2 items
    
    process.py .F                                                                                    [100%]
    =============================================== FAILURES ===============================================
    ___________________________________ test_extract_sentiment_negative ____________________________________
    
    def test_extract_sentiment_negative():
    
            text = "I do not think this will turn out well"
    
            sentiment = extract_sentiment(text)
    
    >       assert sentiment < 0
    E       assert 0.0 < 0
    
    process.py:25: AssertionError
    ======================================= short test summary info ========================================
    FAILED process.py::test_extract_sentiment_negative - assert 0.0 < 0
    ===================================== 1 failed, 1 passed in 0.80s ======================================

    从输出中,我们知道一个测试通过,一个测试失败,以及测试失败的原因。我们希望“I do not think this will turn out well”这句话是消极的,但结果却是0。

    这有助于我们理解,函数可能不会100%准确;因此,在使用此函数提取文本情感时,我们应该谨慎。

    参数化:组合测试

    以上2个测试功能用于测试同一功能。有没有办法把两个例子合并成一个测试函数?这时参数化就派上用场了

    用样本列表参数化

    使用pytest.mark.parametrize(),通过在参数中提供示例列表,我们可以使用不同的示例执行测试。

    # sentiment.py
    
    from textblob import TextBlob
    import pytest
    
    def extract_sentiment(text: str):
            '''使用textblob提取情绪。
                在范围[- 1,1]内'''
    
            text = TextBlob(text)
    
            return text.sentiment.polarity
    
    testdata = ["I think today will be a great day","I do not think this will turn out well"]
    
    @pytest.mark.parametrize('sample', testdata)
    def test_extract_sentiment(sample):
    
        sentiment = extract_sentiment(sample)
    
        assert sentiment > 0

    在上面的代码中,我们将变量sample分配给一个示例列表,然后将该变量添加到测试函数的参数中。现在每个例子将一次测试一次。

    ========================== test session starts ===========================
    platform linux -- Python 3.8.3, pytest-5.4.2, py-1.8.1, pluggy-0.13.1
    collected 2 items
    
    sentiment.py .F                                                    [100%]
    
    ================================ FAILURES ================================
    _____ test_extract_sentiment[I do not think this will turn out well] _____
    
    sample = 'I do not think this will turn out well'
    
    @pytest.mark.parametrize('sample', testdata)
        def test_extract_sentiment(sample):
    
            sentiment = extract_sentiment(sample)
    
    >       assert sentiment > 0
    E       assert 0.0 > 0
    
    sentiment.py:19: AssertionError
    ======================== short test summary info =========================
    FAILED sentiment.py::test_extract_sentiment[I do not think this will turn out well]
    ====================== 1 failed, 1 passed in 0.80s ===================

    使用parametrize(),我们可以在once函数中测试两个不同的示例!

    使用示例列表和预期输出进行参数化

    如果我们期望不同的例子有不同的输出呢?Pytest还允许我们向测试函数的参数添加示例和预期输出!

    例如,下面的函数检查文本是否包含特定的单词。

    def text_contain_word(word: str, text: str):
        '''检查文本是否包含特定的单词'''
    
        return word in text

    如果文本包含单词,则返回True。

    如果单词是“duck”,而文本是“There is a duck in this text”,我们期望返回True。

    如果单词是‘duck’,而文本是‘There is nothing here’,我们期望返回False。

    我们将使用parametrize()而不使用元组列表。

    # process.py
    import pytest
    def text_contain_word(word: str, text: str):
        '''查找文本是否包含特定的单词'''
    
        return word in text
    
    testdata = [
        ('There is a duck in this text',True),
        ('There is nothing here', False)
        ]
    
    @pytest.mark.parametrize('sample, expected_output', testdata)
    def test_text_contain_word(sample, expected_output):
    
        word = 'duck'
    
        assert text_contain_word(word, sample) == expected_output

    函数的参数结构为parametrize('sample,expected_out','testdata),testdata=[( , ),( , )

    >>> pytest process.py
    
    ========================================= test session starts ==========================================
    platform linux -- Python 3.8.3, pytest-5.4.2, py-1.8.1, pluggy-0.13.1
    plugins: hydra-core-1.0.0, Faker-4.1.1
    collected 2 items
    
    process.py ..                                                                                    [100%]
    
    ========================================== 2 passed in 0.04s ===========================================

    我们的两个测试都通过了!

    一次测试一个函数

    当脚本中测试函数的数量越来越大时,你可能希望一次测试一个函数而不是多个函数。用pytest很容易,pytest file.py::function_name

    testdata = ["I think today will be a great day","I do not think this will turn out well"]
    
    @pytest.mark.parametrize('sample', testdata)
    def test_extract_sentiment(sample):
    
        sentiment = extract_sentiment(sample)
    
        assert sentiment > 0
    
    
    testdata = [
        ('There is a duck in this text',True),
        ('There is nothing here', False)
        ]
    
    @pytest.mark.parametrize('sample, expected_output', testdata)
    def test_text_contain_word(sample, expected_output):
    
        word = 'duck'
    
        assert text_contain_word(word, sample) == expected_output

    例如,如果你只想运行test_text_contain_word,请运行

    pytest process.py::test_text_contain_word

    而pytest只执行我们指定的一个测试!

    fixture:使用相同的数据来测试不同的函数

    如果我们想用相同的数据来测试不同的函数呢?例如,我们想测试“今Today I found a duck and I am happy”这句话是否包含“duck ”这个词,它的情绪是否是积极的。这是fixture派上用场的时候。

    pytest fixture是一种向不同的测试函数提供数据的方法

    @pytest.fixture
    def example_data():
        return 'Today I found a duck and I am happy'
    
    
    def test_extract_sentiment(example_data):
    
        sentiment = extract_sentiment(example_data)
    
        assert sentiment > 0
    
    def test_text_contain_word(example_data):
    
        word = 'duck'
    
        assert text_contain_word(word, example_data) == True

    在上面的示例中,我们使用decorator创建了一个示例数据@pytest.fixture在函数example_data的上方。这将把example_data转换成一个值为“Today I found a duck and I am happy”的变量

    现在,我们可以使用示例数据作为任何测试的参数!

    组织你的项目

    最后但并非最不重要的是,当代码变大时,我们可能需要将数据科学函数和测试函数放在两个不同的文件夹中。这将使我们更容易找到每个函数的位置。

    test_<name>.py<name>_test.py命名我们的测试函数. Pytest将搜索名称以“test”结尾或以“test”开头的文件,并在该文件中执行名称以“test”开头的函数。这很方便!

    有不同的方法来组织你的文件。你可以将我们的数据科学文件和测试文件组织在同一个目录中,也可以在两个不同的目录中组织,一个用于源代码,一个用于测试

    方法1:

    test_structure_example/
    ├── process.py
    └── test_process.py

    方法2:

    test_structure_example/
    ├── src
    │   └── process.py
    └── tests
        └── test_process.py

    由于数据科学函数很可能有多个文件,测试函数有多个文件,所以你可能需要将它们放在不同的目录中,如方法2。

    这是2个文件的样子

    from textblob import TextBlob
    
    def extract_sentiment(text: str):
            '''使用textblob提取情绪。
                在范围[- 1,1]内'''
    
            text = TextBlob(text)
    
            return text.sentiment.polarity
    import sys
    import os.path
    sys.path.append(
        os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
    from src.process import extract_sentiment
    import pytest
    
    
    def test_extract_sentiment():
    
        text = 'Today I found a duck and I am happy'
    
        sentiment = extract_sentiment(text)
    
        assert sentiment > 0

    简单地说添加sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))可以从父目录导入函数。

    在根目录(test_structure_example/)下,运行pytest tests/test_process.py或者运行在test_structure_example/tests目录下的pytest test_process.py

    ========================== test session starts ===========================
    platform linux -- Python 3.8.3, pytest-5.4.2, py-1.8.1, pluggy-0.13.1
    collected 1 item
    
    tests/test_process.py .                                            [100%]
    
    =========================== 1 passed in 0.69s ============================

    很酷!

    结论

    你刚刚了解了pytest。我希望本文能很好地概述为什么测试很重要,以及如何将测试与pytest结合到数据科学项目中。通过测试,你不仅可以知道你的函数是否按预期工作,而且还可以自信地使用不同的工具或不同的代码结构来切换现有代码。

    本文的源代码可以在这里找到:

    https://github.com/khuyentran1401/Data-science/tree/master/data_science_tools/pytest

    我喜欢写一些基本的数据科学概念,玩不同的算法和数据科学工具。

    原文链接:https://towardsdatascience.com/pytest-for-data-scientists-2990319e55e6

    欢迎关注磐创AI博客站: http://panchuang.net/

    sklearn机器学习中文官方文档: http://sklearn123.com/

    欢迎关注磐创博客资源汇总站: http://docs.panchuang.net/

    展开全文
  • Rails的fixture文件在传递给YAML解析之前先用ERB解析,这样一来我们就可以使用Ruby代码动态生成测试数据,而不用一条数据一条数据的写了: [code] child_post_: id: title: This is auto-generated reply ...
    Rails的fixture文件在传递给YAML解析之前先用ERB解析,这样一来我们就可以使用Ruby代码动态生成测试数据,而不用一条数据一条数据的写了:
    
    [code]
    <% 1.upto(50) do |number| %>
    child_post_<%= number %>:
    id: <%= number + 3 %>
    title: This is auto-generated reply number <%= number %>
    body: We're on number <%= number %>
    created_at: 2006-01-30 08:03:56
    updated_at: 2006-01-30 08:03:56
    <%# Randomly choose a parent from a post we've already generated -%>
    parent_id: <%= rand(number - 1) + 1 %>
    user_id: <%= rand(5) + 1 %>
    <% end %>
    [/code]
    我们还可以定义一些helper方法:
    [code]
    <%
    def today
    Time.now.to_s(:db)
    end
    def next_week
    1.week.from_now.to_s(:db)
    end
    def last_week
    1.week.ago.to_s(:db)
    end
    post_from_last_week:
    id: 60
    title: pizza
    body: Last night I had pizza. I readlly liked that story from AWDWR.
    created_at: <%= last_week %>
    updated_at: <%= last_week %>
    user_id: 1
    post_created_in_future_should_not_display:
    id: 61
    title: Prognostication
    body: I predict that this post will show up next week.
    created_at: <%= next_week %>
    updated_at: <%= next_week %>
    updated_post_displays_based_on_updated_time:
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    title: This should show up as posted today.
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    created_at: <%= last_week %>
    updated_at: <%= today %>
    user_id: 2
    [/code]
    展开全文
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