mirror of
https://github.com/coursera-dl/coursera-dl.git
synced 2026-01-23 18:55:24 +00:00
08b8ad44c2
1. enable downloading resources blocks. 1. enable file to store parameters. 1. added unit-test.
835 lines
34 KiB
Python
835 lines
34 KiB
Python
# -*- coding: utf-8 -*-
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"""
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This module defines the global constants.
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"""
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import os
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import getpass
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import tempfile
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HTTP_FORBIDDEN = 403
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COURSERA_URL = 'https://www.coursera.org'
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AUTH_URL = 'https://accounts.coursera.org/api/v1/login'
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AUTH_URL_V3 = 'https://www.coursera.org/api/login/v3'
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CLASS_URL = 'https://class.coursera.org/{class_name}'
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# The following link is left just for illustative purposes:
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# https://www.coursera.org/api/courses.v1?fields=display%2CpartnerIds%2CphotoUrl%2CstartDate%2Cpartners.v1(homeLink%2Cname)&includes=partnerIds&q=watchlist&start=0
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# Reply is as follows:
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# {
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# "elements": [
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# {
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# "courseType": "v1.session",
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# "name": "Computational Photography",
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# "id": "v1-87",
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# "slug": "compphoto"
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# }
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# ],
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# "paging": {
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# "next": "100",
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# "total": 154
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# },
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# "linked": {}
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# }
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OPENCOURSE_LIST_COURSES = 'https://www.coursera.org/api/courses.v1?q=watchlist&start={start}'
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# The following link is left just for illustative purposes:
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# https://www.coursera.org/api/memberships.v1?fields=courseId,enrolledTimestamp,grade,id,lastAccessedTimestamp,onDemandSessionMembershipIds,onDemandSessionMemberships,role,v1SessionId,vc,vcMembershipId,courses.v1(courseStatus,display,partnerIds,photoUrl,specializations,startDate,v1Details,v2Details),partners.v1(homeLink,name),v1Details.v1(sessionIds),v1Sessions.v1(active,certificatesReleased,dbEndDate,durationString,hasSigTrack,startDay,startMonth,startYear),v2Details.v1(onDemandSessions,plannedLaunchDate,sessionsEnabledAt),specializations.v1(logo,name,partnerIds,shortName)&includes=courseId,onDemandSessionMemberships,vcMembershipId,courses.v1(partnerIds,specializations,v1Details,v2Details),v1Details.v1(sessionIds),v2Details.v1(onDemandSessions),specializations.v1(partnerIds)&q=me&showHidden=true&filter=current,preEnrolled
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# Sample reply:
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# {
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# "elements": [
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# {
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# id: "4958~bVgqTevEEeWvGQrWsIkLlw",
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# userId: 4958,
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# courseId: "bVgqTevEEeWvGQrWsIkLlw",
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# role: "LEARNER"
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# },
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# ],
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# "paging": null,
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# "linked": {
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# "courses.v1": [
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# {
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# "id": "0w0JAG9JEeSp0iIAC12Jpw",
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# "slug": "computational-neurosciencecompneuro",
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# "courseType": "v2.ondemand",
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# "name": "Computational Neuroscience"
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# }
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# ]
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# }
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# }
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OPENCOURSE_MEMBERSHIPS = 'https://www.coursera.org/api/memberships.v1?includes=courseId,courses.v1&q=me&showHidden=true&filter=current,preEnrolled'
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OPENCOURSE_CONTENT_URL = 'https://www.coursera.org/api/opencourse.v1/course/{class_name}?showLockedItems=true'
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OPENCOURSE_VIDEO_URL = 'https://www.coursera.org/api/opencourse.v1/video/{video_id}'
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OPENCOURSE_SUPPLEMENT_URL = 'https://www.coursera.org/api/onDemandSupplements.v1/'\
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'{course_id}~{element_id}?includes=asset&fields=openCourseAssets.v1%28typeName%29,openCourseAssets.v1%28definition%29'
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OPENCOURSE_PROGRAMMING_ASSIGNMENTS_URL = \
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'https://www.coursera.org/api/onDemandProgrammingLearnerAssignments.v1/{course_id}~{element_id}?fields=submissionLearnerSchema'
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OPENCOURSE_PROGRAMMING_IMMEDIATE_INSTRUCTIOINS_URL = \
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'https://www.coursera.org/api/onDemandProgrammingImmediateInstructions.v1/{course_id}~{element_id}'
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OPENCOURSE_REFERENCES_POLL_URL = \
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"https://www.coursera.org/api/onDemandReferences.v1/?courseId={course_id}&q=courseListed&fields=name%2CshortId%2Cslug%2Ccontent&includes=assets"
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OPENCOURSE_REFERENCE_ITEM_URL = \
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"https://www.coursera.org/api/onDemandReferences.v1/?courseId={course_id}&q=shortId&shortId={short_id}&fields=name%2CshortId%2Cslug%2Ccontent&includes=assets"
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# These are ids that are present in <asset> tag in assignment text:
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#
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# <asset id=\"yeJ7Q8VAEeWPRQ4YsSEORQ\"
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# name=\"statement-pca\"
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# extension=\"pdf\"
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# assetType=\"generic\"/>
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#
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# Sample response:
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#
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# {
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# "elements": [
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# {
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# "id": "yeJ7Q8VAEeWPRQ4YsSEORQ",
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# "url": "<some url>",
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# "expires": 1454371200000
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# }
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# ],
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# "paging": null,
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# "linked": null
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# }
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OPENCOURSE_ASSET_URL = \
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'https://www.coursera.org/api/assetUrls.v1?ids={ids}'
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# These ids are provided in lecture json:
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#
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# {
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# "id": "6ydIh",
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# "name": "Введение в теорию игр",
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# "elements": [
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# {
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# "id": "ujNfj",
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# "name": "Что изучает теория игр?",
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# "content": {
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# "typeName": "lecture",
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# "definition": {
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# "duration": 536000,
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# "videoId": "pGNiQYo-EeWNvA632PIn3w",
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# "optional": false,
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# "assets": [
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# "giAxucdaEeWJTQ5WTi8YJQ@1"
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# ]
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# }
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# },
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# "slug": "chto-izuchaiet-tieoriia-ighr",
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# "timeCommitment": 536000
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# }
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# ],
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# "slug": "vviedieniie-v-tieoriiu-ighr",
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# "timeCommitment": 536000,
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# "optional": false
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# }
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#
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# Sample response:
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#
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# {
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# "elements": [
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# {
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# "id": "giAxucdaEeWJTQ5WTi8YJQ",
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# "typeName": "asset",
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# "definition": {
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# "name": "",
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# "assetId": "Vq8hwsdaEeWGlA7xclFASw"
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# }
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# }
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# ],
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# "paging": null,
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# "linked": null
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# }
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OPENCOURSE_ASSETS_URL = \
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'https://www.coursera.org/api/openCourseAssets.v1/{id}'
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# These asset ids are ids returned from OPENCOURSE_ASSETS_URL request:
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# See example above.
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#
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# Sample response:
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#
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# {
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# "elements": [
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# {
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# "id": "Vq8hwsdaEeWGlA7xclFASw",
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# "name": "1_Strategic_Interactions.pdf",
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# "typeName": "generic",
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# "url": {
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# "url": "<some url>",
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# "expires": 1454371200000
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# }
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# }
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# ],
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# "paging": null,
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# "linked": null
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# }
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OPENCOURSE_API_ASSETS_V1_URL = \
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'https://www.coursera.org/api/assets.v1?ids={id}'
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OPENCOURSE_ONDEMAND_COURSE_MATERIALS = \
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'https://www.coursera.org/api/onDemandCourseMaterials.v1/?'\
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'q=slug&slug={class_name}&includes=moduleIds%2ClessonIds%2CpassableItemGroups%2CpassableItemGroupChoices%2CpassableLessonElements%2CitemIds%2Ctracks'\
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'&fields=moduleIds%2ConDemandCourseMaterialModules.v1(name%2Cslug%2Cdescription%2CtimeCommitment%2ClessonIds%2Coptional)%2ConDemandCourseMaterialLessons.v1(name%2Cslug%2CtimeCommitment%2CelementIds%2Coptional%2CtrackId)%2ConDemandCourseMaterialPassableItemGroups.v1(requiredPassedCount%2CpassableItemGroupChoiceIds%2CtrackId)%2ConDemandCourseMaterialPassableItemGroupChoices.v1(name%2Cdescription%2CitemIds)%2ConDemandCourseMaterialPassableLessonElements.v1(gradingWeight)%2ConDemandCourseMaterialItems.v1(name%2Cslug%2CtimeCommitment%2Ccontent%2CisLocked%2ClockableByItem%2CitemLockedReasonCode%2CtrackId)%2ConDemandCourseMaterialTracks.v1(passablesCount)'\
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'&showLockedItems=true'
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ABOUT_URL = ('https://api.coursera.org/api/catalog.v1/courses?'
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'fields=largeIcon,photo,previewLink,shortDescription,smallIcon,'
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'smallIconHover,universityLogo,universityLogoSt,video,videoId,'
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'aboutTheCourse,targetAudience,faq,courseSyllabus,courseFormat,'
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'suggestedReadings,instructor,estimatedClassWorkload,'
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'aboutTheInstructor,recommendedBackground,subtitleLanguagesCsv&'
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'q=search&query={class_name}')
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AUTH_REDIRECT_URL = ('https://class.coursera.org/{class_name}'
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'/auth/auth_redirector?type=login&subtype=normal')
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#POST_OPENCOURSE_API_QUIZ_SESSION = 'https://www.coursera.org/api/opencourse.v1/user/4958/course/text-mining/item/7OQHc/quiz/session'
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# Sample response:
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#
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# {
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# "contentResponseBody": {
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# "session": {
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# "id": "opencourse~bVgqTevEEeWvGQrWsIkLlw:4958:BiNDdOvPEeWAkwqbKEEh3w@13:1468773901987@1",
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# "open": true
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# }
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# },
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# "itemProgress": {
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# "contentVersionedId": "BiNDdOvPEeWAkwqbKEEh3w@13",
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# "timestamp": 1468774458435,
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# "progressState": "Started"
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# }
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# }
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POST_OPENCOURSE_API_QUIZ_SESSION = 'https://www.coursera.org/api/opencourse.v1/user/{user_id}/course/{class_name}/item/{quiz_id}/quiz/session'
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#POST_OPENCOURSE_API_QUIZ_SESSION_GET_STATE = 'https://www.coursera.org/api/opencourse.v1/user/4958/course/text-mining/item/7OQHc/quiz/session/opencourse~bVgqTevEEeWvGQrWsIkLlw:4958:BiNDdOvPEeWAkwqbKEEh3w@13:1468773901987@1/action/getState?autoEnroll=false'
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# Sample response:
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||
#
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# {
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# "contentResponseBody": {
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||
# "return": {
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# "questions": [
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||
# {
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# "id": "89424f6873744b5c0b92da2936327bb4",
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# "question": {
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||
# "type": "mcq"
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||
# },
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||
# "variant": {
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||
# "definition": {
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||
# "prompt": {
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||
# "typeName": "cml",
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# "definition": {
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# "dtdId": "assess/1",
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# "value": "<co-content><text hasMath=\"true\">You are given a unigram language model $$\\theta$$ distributed over a vocabulary set $$V$$ composed of <strong>only</strong> 4 words: “the”, “machine”, “learning”, and “data”. The distribution of $$\\theta$$ is given in the table below:</text><table rows=\"5\" columns=\"2\"><tr><th><text>$$w$$</text></th><th><text>$$P(w|\\theta)$$</text></th></tr><tr><td><text>machine</text></td><td><text>0.1</text></td></tr><tr><td><text>learning</text></td><td><text>0.2</text></td></tr><tr><td><text>data</text></td><td><text>0.3</text></td></tr><tr><td><text>the</text></td><td><text>0.4</text></td></tr></table><text hasMath=\"true\"> $$P(\\text{“machine learning”}|\\theta) = $$</text></co-content>"
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# }
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||
# },
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||
# "options": [
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||
# {
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||
# "id": "717bd78dec2b817bed4b2d6096cbc9fc",
|
||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
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||
# "value": "<co-content><text>0.004</text></co-content>"
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||
# }
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||
# }
|
||
# },
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||
# {
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||
# "id": "a06c614cbb15b4e54212296b16fc4e62",
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||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
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||
# "value": "<co-content><text>0.2</text></co-content>"
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# }
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||
# }
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||
# },
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||
# {
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||
# "id": "029fe0fee932d6ad260f292dd05dc5c9",
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||
# "display": {
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||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
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||
# "value": "<co-content><text>0.3</text></co-content>"
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||
# }
|
||
# }
|
||
# },
|
||
# {
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||
# "id": "b6af6403d4ddde3b1e58599c12b6397a",
|
||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
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||
# "value": "<co-content><text>0.02</text></co-content>"
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||
# }
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||
# }
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||
# }
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||
# ]
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||
# },
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||
# "detailLevel": "Full"
|
||
# },
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||
# "weightedScoring": {
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||
# "maxScore": 1
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||
# },
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||
# "isSubmitAllowed": true
|
||
# }
|
||
# ],
|
||
# "evaluation": null
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||
# }
|
||
# },
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||
# "itemProgress": {
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||
# "contentVersionedId": "BiNDdOvPEeWAkwqbKEEh3w@13",
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||
# "timestamp": 1468774458894,
|
||
# "progressState": "Started"
|
||
# }
|
||
# }
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||
#
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POST_OPENCOURSE_API_QUIZ_SESSION_GET_STATE = 'https://www.coursera.org/api/opencourse.v1/user/{user_id}/course/{class_name}/item/{quiz_id}/quiz/session/{session_id}/action/getState?autoEnroll=false'
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#POST_OPENCOURSE_ONDEMAND_EXAM_SESSIONS = 'https://www.coursera.org/api/onDemandExamSessions.v1/-N44X0IJEeWpogr5ZO8qxQ~YV0W4~10!~1467462079068/actions?includes=gradingAttempts'
|
||
# Sample response:
|
||
#
|
||
# {
|
||
# "elements": [
|
||
# {
|
||
# "id": 0,
|
||
# "result": {
|
||
# "questions": [
|
||
# {
|
||
# "id": "8uUpMzm_EeaetxLgjw7H8Q@0",
|
||
# "question": {
|
||
# "type": "mcq"
|
||
# },
|
||
# "variant": {
|
||
# "definition": {
|
||
# "prompt": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
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||
# "value": "<co-content><text>\n\nSuppose you’d like to perform nearest neighbor search from the following set of houses:</text><table rows=\"5\" columns=\"4\"><tr><td><text>\n\n\n\n\n\n</text></td><td><text>\n\n\nPrice (USD)</text></td><td><text>\n\n\nNumber of rooms</text></td><td><text>\n\n\nLot size (sq. ft.)</text></td></tr><tr><td><text>\n\n\nHouse 1</text></td><td><text>\n\n\n500000</text></td><td><text>\n\n\n3</text></td><td><text>\n\n\n1840</text></td></tr><tr><td><text>\n\n\nHouse 2</text></td><td><text>\n\n\n350000</text></td><td><text>\n\n\n2</text></td><td><text>\n\n\n1600</text></td></tr><tr><td><text>House 3</text></td><td><text>\n\n600000</text></td><td><text>\n\n4</text></td><td><text>\n\n2000</text></td></tr><tr><td><text>House 4</text></td><td><text>\n400000</text></td><td><text>\n2</text></td><td><text>\n1900</text></td></tr></table><text>\n\nSince the features come in wildly different scales, you decide to use scaled Euclidean distances. Choose the set of weights a_i (as presented in the video lecture) that properly incorporates the relative amount of variation of the feature.</text><text>Note: </text><code language=\"plain_text\">a_price = weight assigned to price (USD)\na_room = weight assigned to number of rooms\na_lot = weight assigned to lot size (sq.ft.)</code></co-content>"
|
||
# }
|
||
# },
|
||
# "options": [
|
||
# {
|
||
# "id": "0.9109180361318947",
|
||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>a_price = 1, a_room = 1, a_lot = 1</text></co-content>"
|
||
# }
|
||
# }
|
||
# },
|
||
# {
|
||
# "id": "0.11974743029080992",
|
||
# "display": {
|
||
# "typeName": "cml",
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||
# "definition": {
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||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>a_price = 1, a_room = 1, a_lot = 1e-6</text></co-content>"
|
||
# }
|
||
# }
|
||
# },
|
||
# {
|
||
# "id": "0.8214165539451299",
|
||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>a_price = 1e-10, a_room = 1, a_lot = 1e-6</text></co-content>"
|
||
# }
|
||
# }
|
||
# },
|
||
# {
|
||
# "id": "0.6784789645868041",
|
||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>a_price = 1e-5, a_room = 1, a_lot = 1e-3</text></co-content>"
|
||
# }
|
||
# }
|
||
# },
|
||
# {
|
||
# "id": "0.9664001374497642",
|
||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>a_price = 1e5, a_room = 1, a_lot = 1e3</text></co-content>"
|
||
# }
|
||
# }
|
||
# }
|
||
# ]
|
||
# },
|
||
# "detailLevel": "Full"
|
||
# },
|
||
# "weightedScoring": {
|
||
# "maxScore": 1
|
||
# },
|
||
# "isSubmitAllowed": true
|
||
# },
|
||
# {
|
||
# "id": "jeVDBjnNEeaetxLgjw7H8Q@0",
|
||
# "question": {
|
||
# "type": "singleNumeric"
|
||
# },
|
||
# "variant": {
|
||
# "definition": {
|
||
# "prompt": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>\n\nConsider the following two sentences.\n</text><list bulletType=\"bullets\"><li><text>Sentence 1: The quick brown fox jumps over the lazy dog.\n</text></li><li><text>Sentence 2: A quick brown dog outpaces a quick fox.\n</text></li></list><text>\n\nCompute the Euclidean distance using word counts. Round your answer to 3 decimal places.</text><text>Note. To compute word counts, turn all words into lower case and strip all punctuation, so that \"The\" and \"the\" are counted as the same token.</text></co-content>"
|
||
# }
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# }
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# },
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# "definition": {
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# "dtdId": "assess/1",
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# "value": "<co-content><text>Refer back to the two sentences given in Question 2 to answer the following:</text><text>Recall that we can use cosine similarity to define a distance. We call that distance cosine distance. </text><text>Compute the <strong>cosine distance</strong> using word counts. Round your answer to 3 decimal places.\n</text><text>Note: To compute word counts, turn all words into lower case and strip all punctuation, so that \"The\" and \"the\" are counted as the same token.</text><text>Hint. Recall that we can use cosine similarity to define a distance. We call that distance cosine distance.</text></co-content>"
|
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# }
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# }
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# },
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# "detailLevel": "Full"
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# },
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# "weightedScoring": {
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# "isSubmitAllowed": true
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# }
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# "evaluation": null
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# }
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# }
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# ],
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# "paging": null,
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# "linked": {
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||
# "gradingAttempts.v1": []
|
||
# }
|
||
# }
|
||
#
|
||
# Request payload:
|
||
# {"courseId":"-N44X0IJEeWpogr5ZO8qxQ","itemId":"YV0W4"}
|
||
#
|
||
#POST_OPENCOURSE_ONDEMAND_EXAM_SESSIONS = 'https://www.coursera.org/api/onDemandExamSessions.v1/-N44X0IJEeWpogr5ZO8qxQ~YV0W4~10!~1467462079068/actions?includes=gradingAttempts'
|
||
|
||
# Response for this request is empty. Result (session_id) should be taken
|
||
# either from Location header or from X-Coursera-Id header.
|
||
#
|
||
# Request payload:
|
||
# {"courseId":"-N44X0IJEeWpogr5ZO8qxQ","itemId":"YV0W4"}
|
||
POST_OPENCOURSE_ONDEMAND_EXAM_SESSIONS = 'https://www.coursera.org/api/onDemandExamSessions.v1'
|
||
|
||
# Sample response:
|
||
# {
|
||
# "elements": [
|
||
# {
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||
# "id": 0,
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# "result": {
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# "question": {
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# },
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# "variant": {
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# "typeName": "cml",
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# "definition": {
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# "dtdId": "assess/1",
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# "value": "<co-content><text>\n\nSuppose you’d like to perform nearest neighbor search from the following set of houses:</text><table rows=\"5\" columns=\"4\"><tr><td><text>\n\n\n\n\n\n</text></td><td><text>\n\n\nPrice (USD)</text></td><td><text>\n\n\nNumber of rooms</text></td><td><text>\n\n\nLot size (sq. ft.)</text></td></tr><tr><td><text>\n\n\nHouse 1</text></td><td><text>\n\n\n500000</text></td><td><text>\n\n\n3</text></td><td><text>\n\n\n1840</text></td></tr><tr><td><text>\n\n\nHouse 2</text></td><td><text>\n\n\n350000</text></td><td><text>\n\n\n2</text></td><td><text>\n\n\n1600</text></td></tr><tr><td><text>House 3</text></td><td><text>\n\n600000</text></td><td><text>\n\n4</text></td><td><text>\n\n2000</text></td></tr><tr><td><text>House 4</text></td><td><text>\n400000</text></td><td><text>\n2</text></td><td><text>\n1900</text></td></tr></table><text>\n\nSince the features come in wildly different scales, you decide to use scaled Euclidean distances. Choose the set of weights a_i (as presented in the video lecture) that properly incorporates the relative amount of variation of the feature.</text><text>Note: </text><code language=\"plain_text\">a_price = weight assigned to price (USD)\na_room = weight assigned to number of rooms\na_lot = weight assigned to lot size (sq.ft.)</code></co-content>"
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# "value": "<co-content><text>a_price = 1e-5, a_room = 1, a_lot = 1e-3</text></co-content>"
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# }
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# {
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# "value": "<co-content><text>a_price = 1e5, a_room = 1, a_lot = 1e3</text></co-content>"
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# "typeName": "cml",
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# "dtdId": "assess/1",
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# "value": "<co-content><text>\n\nConsider the following two sentences.\n</text><list bulletType=\"bullets\"><li><text>Sentence 1: The quick brown fox jumps over the lazy dog.\n</text></li><li><text>Sentence 2: A quick brown dog outpaces a quick fox.\n</text></li></list><text>\n\nCompute the Euclidean distance using word counts. Round your answer to 3 decimal places.</text><text>Note. To compute word counts, turn all words into lower case and strip all punctuation, so that \"The\" and \"the\" are counted as the same token.</text></co-content>"
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# "typeName": "cml",
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# "definition": {
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# "dtdId": "assess/1",
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||
# "value": "<co-content><text>Refer back to the two sentences given in Question 2 to answer the following:</text><text>Recall that we can use cosine similarity to define a distance. We call that distance cosine distance. </text><text>Compute the <strong>cosine distance</strong> using word counts. Round your answer to 3 decimal places.\n</text><text>Note: To compute word counts, turn all words into lower case and strip all punctuation, so that \"The\" and \"the\" are counted as the same token.</text><text>Hint. Recall that we can use cosine similarity to define a distance. We call that distance cosine distance.</text></co-content>"
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# },
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# "variant": {
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# "prompt": {
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# "typeName": "cml",
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# "definition": {
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# "dtdId": "assess/1",
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||
# "value": "<co-content><text>(True/False) For positive features, cosine similarity is always between 0 and 1.</text></co-content>"
|
||
# }
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||
# },
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||
# "options": [
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||
# {
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# "id": "0.838238929639803",
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# "display": {
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# "typeName": "cml",
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# "definition": {
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# "value": "<co-content><text>True</text></co-content>"
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# }
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# }
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# },
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# {
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# "id": "0.9654190569725087",
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# "typeName": "cml",
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# "definition": {
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# "dtdId": "assess/1",
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# "value": "<co-content><text>False</text></co-content>"
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||
# }
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||
# }
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||
# }
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# ]
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# },
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# "detailLevel": "Full"
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# "weightedScoring": {
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# "maxScore": 1
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# "isSubmitAllowed": true
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# },
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||
# {
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||
# "id": "N62eSDnOEea5PAq35BZMoQ@0",
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||
# "question": {
|
||
# "type": "mcq"
|
||
# },
|
||
# "variant": {
|
||
# "definition": {
|
||
# "prompt": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>\n\nUsing the formula for TF-IDF presented in the lecture, complete the following sentence:</text><text>A word is assigned a zero TF-IDF weight when it appears in ____ documents. (N: number of documents in the corpus)</text></co-content>"
|
||
# }
|
||
# },
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||
# "options": [
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||
# {
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||
# "id": "0.10877084920366831",
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||
# "display": {
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||
# "typeName": "cml",
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||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>N - 1</text></co-content>"
|
||
# }
|
||
# }
|
||
# },
|
||
# {
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||
# "id": "0.29922629273211787",
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||
# "display": {
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||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>N/2</text></co-content>"
|
||
# }
|
||
# }
|
||
# },
|
||
# {
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||
# "id": "0.69796593807345",
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||
# "display": {
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# "typeName": "cml",
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||
# "definition": {
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||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>N</text></co-content>"
|
||
# }
|
||
# }
|
||
# },
|
||
# {
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||
# "id": "0.6731572688278926",
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||
# "display": {
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||
# "typeName": "cml",
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||
# "definition": {
|
||
# "dtdId": "assess/1",
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||
# "value": "<co-content><text>0.1*N</text></co-content>"
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||
# }
|
||
# }
|
||
# },
|
||
# {
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||
# "id": "0.8467992755507772",
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||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>100</text></co-content>"
|
||
# }
|
||
# }
|
||
# }
|
||
# ]
|
||
# },
|
||
# "detailLevel": "Full"
|
||
# },
|
||
# "weightedScoring": {
|
||
# "maxScore": 1
|
||
# },
|
||
# "isSubmitAllowed": true
|
||
# },
|
||
# {
|
||
# "id": "TuHdkjnOEeaPCw5NUSdt1w@0",
|
||
# "question": {
|
||
# "type": "mcq"
|
||
# },
|
||
# "variant": {
|
||
# "definition": {
|
||
# "prompt": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>\n\nWhich of the following does <strong>not </strong>describe the word count document representation?</text></co-content>"
|
||
# }
|
||
# },
|
||
# "options": [
|
||
# {
|
||
# "id": "0.3821039264467949",
|
||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>Ignores the order of the words</text></co-content>"
|
||
# }
|
||
# }
|
||
# },
|
||
# {
|
||
# "id": "0.3470767421220087",
|
||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>Assigns a high score to a frequently occurring word</text></co-content>"
|
||
# }
|
||
# }
|
||
# },
|
||
# {
|
||
# "id": "0.3341840649172314",
|
||
# "display": {
|
||
# "typeName": "cml",
|
||
# "definition": {
|
||
# "dtdId": "assess/1",
|
||
# "value": "<co-content><text>Penalizes words that appear in every document</text></co-content>"
|
||
# }
|
||
# }
|
||
# }
|
||
# ]
|
||
# },
|
||
# "detailLevel": "Full"
|
||
# },
|
||
# "weightedScoring": {
|
||
# "maxScore": 1
|
||
# },
|
||
# "isSubmitAllowed": true
|
||
# }
|
||
# ],
|
||
# "evaluation": null
|
||
# }
|
||
# }
|
||
# ],
|
||
# "paging": null,
|
||
# "linked": {
|
||
# "gradingAttempts.v1": []
|
||
# }
|
||
# }
|
||
#
|
||
# Request payload:
|
||
# {"name":"getState","argument":[]}
|
||
POST_OPENCOURSE_ONDEMAND_EXAM_SESSIONS_GET_STATE = 'https://www.coursera.org/api/onDemandExamSessions.v1/{session_id}/actions?includes=gradingAttempts'
|
||
|
||
ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
|
||
# define a per-user cache folder
|
||
if os.name == "posix": # pragma: no cover
|
||
import pwd
|
||
_USER = pwd.getpwuid(os.getuid())[0]
|
||
else:
|
||
_USER = getpass.getuser()
|
||
|
||
PATH_CACHE = os.path.join(tempfile.gettempdir(), _USER + "_coursera_dl_cache")
|
||
PATH_COOKIES = os.path.join(PATH_CACHE, 'cookies')
|
||
|
||
WINDOWS_UNC_PREFIX = u'\\\\?\\'
|
||
|
||
#: This extension is used to save contents of supplementary instructions.
|
||
IN_MEMORY_EXTENSION = 'html'
|
||
|
||
#: This marker is added in front of a URL when supplementary instructions
|
||
#: are passed from parser to downloader. URL field fill contain the data
|
||
#: that will be stored to a file. The marker should be removed from URL
|
||
#: field first.
|
||
IN_MEMORY_MARKER = '#inmemory#'
|
||
|
||
#: These are hard limits for format (file extension) and
|
||
#: title (file name) lengths to avoid too long file names
|
||
#: (longer than 255 characters)
|
||
FORMAT_MAX_LENGTH = 20
|
||
TITLE_MAX_LENGTH = 200
|
||
|
||
#: CSS that is usen to prettify instructions
|
||
INSTRUCTIONS_HTML_INJECTION_PRE = '''
|
||
<style>
|
||
body {
|
||
padding: 50px 85px 50px 85px;
|
||
}
|
||
|
||
table th, table td {
|
||
border: 1px solid #e0e0e0;
|
||
padding: 5px 20px;
|
||
text-align: left;
|
||
}
|
||
input {
|
||
margin: 10px;
|
||
}
|
||
}
|
||
th {
|
||
font-weight: bold;
|
||
}
|
||
td, th {
|
||
display: table-cell;
|
||
vertical-align: inherit;
|
||
}
|
||
img {
|
||
height: auto;
|
||
max-width: 100%;
|
||
}
|
||
pre {
|
||
display: block;
|
||
margin: 20px;
|
||
background: #424242;
|
||
color: #fff;
|
||
font-size: 13px;
|
||
white-space: pre-wrap;
|
||
padding: 9.5px;
|
||
margin: 0 0 10px;
|
||
border: 1px solid #ccc;
|
||
}
|
||
</style>
|
||
|
||
<script type="text/javascript" async
|
||
src="'''
|
||
INSTRUCTIONS_HTML_MATHJAX_URL = 'https://cdn.mathjax.org/mathjax/latest/MathJax.js'
|
||
INSTRUCTIONS_HTML_INJECTION_AFTER ='''?config=TeX-AMS-MML_HTMLorMML">
|
||
</script>
|
||
|
||
<script type="text/x-mathjax-config">
|
||
MathJax.Hub.Config({
|
||
tex2jax: {
|
||
inlineMath: [ ['$$','$$'], ['$','$'] ],
|
||
displayMath: [ ["\\\\[","\\\\]"] ],
|
||
processEscapes: true
|
||
}
|
||
});
|
||
</script>
|
||
'''
|