From 04155fc0b84df3330d46c75f6c1c71b40a0c115b Mon Sep 17 00:00:00 2001 From: lhenry15 Date: Thu, 4 Feb 2021 02:46:36 -0600 Subject: [PATCH] recover KPI example dataset --- datasets/NAB/README.md | 68 - .../labeled_art_daily_no_noise.csv | 4033 -------- .../labeled_art_daily_perfect_square_wave.csv | 4033 -------- .../labeled_art_daily_small_noise.csv | 4033 -------- .../artificialNoAnomaly/labeled_art_flatline.csv | 4033 -------- .../NAB/artificialNoAnomaly/labeled_art_noisy.csv | 4033 -------- .../labeled_art_daily_flatmiddle.csv | 4033 -------- .../labeled_art_daily_jumpsdown.csv | 4033 -------- .../labeled_art_daily_jumpsup.csv | 4033 -------- .../labeled_art_daily_nojump.csv | 4033 -------- .../labeled_art_increase_spike_density.csv | 4033 -------- .../labeled_art_load_balancer_spikes.csv | 4033 -------- datasets/NAB/combined_labels.json | 232 - .../labeled_ec2_cpu_utilization_24ae8d.csv | 4033 -------- .../labeled_ec2_cpu_utilization_53ea38.csv | 4033 -------- 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a/datasets/NAB/README.md +++ /dev/null @@ -1,68 +0,0 @@ -NAB Data Corpus ---- - -Data are ordered, timestamped, single-valued metrics. All data files contain anomalies, unless otherwise noted. - - -### Real data -- realAWSCloudwatch/ - - AWS server metrics as collected by the AmazonCloudwatch service. Example metrics include CPU Utilization, Network Bytes In, and Disk Read Bytes. - -- realAdExchange/ - - Online advertisement clicking rates, where the metrics are cost-per-click (CPC) and cost per thousand impressions (CPM). One of the files is normal, without anomalies. - -- realKnownCause/ - - This is data for which we know the anomaly causes; no hand labeling. - - - ambient_temperature_system_failure.csv: The ambient temperature in an office - setting. - - cpu_utilization_asg_misconfiguration.csv: From Amazon Web Services (AWS) - monitoring CPU usage – i.e. average CPU usage across a given cluster. When - usage is high, AWS spins up a new machine, and uses fewer machines when usage - is low. - - ec2_request_latency_system_failure.csv: CPU usage data from a server in - Amazon's East Coast datacenter. The dataset ends with complete system failure - resulting from a documented failure of AWS API servers. There's an interesting - story behind this data in the [Numenta - blog](http://numenta.com/blog/anomaly-of-the-week.html). - - machine_temperature_system_failure.csv: Temperature sensor data of an - internal component of a large, industrial mahcine. The first anomaly is a - planned shutdown of the machine. The second anomaly is difficult to detect and - directly led to the third anomaly, a catastrophic failure of the machine. - - nyc_taxi.csv: Number of NYC taxi passengers, where the five anomalies occur - during the NYC marathon, Thanksgiving, Christmas, New Years day, and a snow - storm. The raw data is from the [NYC Taxi and Limousine Commission](http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml). - The data file included here consists of aggregating the total number of - taxi passengers into 30 minute buckets. - - rogue_agent_key_hold.csv: Timing the key holds for several users of a - computer, where the anomalies represent a change in the user. - - rogue_agent_key_updown.csv: Timing the key strokes for several users of a - computer, where the anomalies represent a change in the user. - -- realTraffic/ - - Real time traffic data from the Twin Cities Metro area in Minnesota, collected - by the - [Minnesota Department of Transportation](http://www.dot.state.mn.us/tmc/trafficinfo/developers.html). - Included metrics include occupancy, speed, and travel time from specific - sensors. - -- realTweets/ - - A collection of Twitter mentions of large publicly-traded companies - such as Google and IBM. The metric value represents the number of mentions - for a given ticker symbol every 5 minutes. - - -### Artificial data - -- artificialNoAnomaly/ - - Artificially-generated data without any anomalies. - -- artificialWithAnomaly/ - - Artificially-generated data with varying types of anomalies. diff --git a/datasets/NAB/artificialNoAnomaly/labeled_art_daily_no_noise.csv b/datasets/NAB/artificialNoAnomaly/labeled_art_daily_no_noise.csv deleted file mode 100644 index 718d638..0000000 --- a/datasets/NAB/artificialNoAnomaly/labeled_art_daily_no_noise.csv +++ /dev/null @@ -1,4033 +0,0 @@ -timestamp,value,label -1396328400.0,20.0,0 -1396328700.0,20.0,0 -1396329000.0,20.0,0 -1396329300.0,20.0,0 -1396329600.0,20.0,0 -1396329900.0,20.0,0 -1396330200.0,20.0,0 -1396330500.0,20.0,0 -1396330800.0,20.0,0 -1396331100.0,20.0,0 -1396331400.0,20.0,0 -1396331700.0,20.0,0 -1396332000.0,20.0,0 -1396332300.0,20.0,0 -1396332600.0,20.0,0 -1396332900.0,20.0,0 -1396333200.0,20.0,0 -1396333500.0,20.0,0 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-1397537700.0,45.0,0 diff --git a/datasets/NAB/artificialNoAnomaly/labeled_art_noisy.csv b/datasets/NAB/artificialNoAnomaly/labeled_art_noisy.csv deleted file mode 100644 index 4a7c859..0000000 --- a/datasets/NAB/artificialNoAnomaly/labeled_art_noisy.csv +++ /dev/null @@ -1,4033 +0,0 @@ -timestamp,value,label -1396328400.0,18.6221849224,0 -1396328700.0,8.1634173476,0 -1396329000.0,13.29238261,0 -1396329300.0,11.667046219300001,0 -1396329600.0,12.9403575532,0 -1396329900.0,14.2773103688,0 -1396330200.0,12.791480738099999,0 -1396330500.0,12.563315353299998,0 -1396330800.0,16.0599575695,0 -1396331100.0,15.544639025999999,0 -1396331400.0,17.1659841024,0 -1396331700.0,10.749548793599999,0 -1396332000.0,14.7048136073,0 -1396332300.0,14.437142639300001,0 -1396332600.0,12.0671159196,0 -1396332900.0,13.3466329609,0 -1396333200.0,11.451603576400002,0 -1396333500.0,9.5602883738,0 -1396333800.0,10.3518133173,0 -1396334100.0,9.160650110399999,0 -1396334400.0,15.5137653271,0 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index 60fc56d..0000000 --- a/datasets/NAB/realAWSCloudwatch/labeled_ec2_cpu_utilization_ac20cd.csv +++ /dev/null @@ -1,4033 +0,0 @@ -timestamp,value,label -1396466940.0,42.652,0 -1396467240.0,41.362,0 -1396467540.0,43.408,0 -1396467840.0,40.262,0 -1396468140.0,40.328,0 -1396468440.0,42.652,0 -1396468740.0,39.836,0 -1396469040.0,42.57,0 -1396469340.0,45.212,0 -1396469640.0,41.15,0 -1396469940.0,42.91,0 -1396470240.0,43.756,0 -1396470540.0,38.522,0 -1396470840.0,42.488,0 -1396471140.0,41.268,0 -1396471440.0,42.136,0 -1396471740.0,42.066,0 -1396472040.0,43.544,0 -1396472340.0,38.404,0 -1396472640.0,42.018,0 -1396472940.0,44.872,0 -1396473240.0,40.658,0 -1396473540.0,42.276,0 -1396473840.0,43.052,0 -1396474140.0,43.56800000000001,0 -1396474440.0,38.356,0 -1396474740.0,39.788,0 -1396475040.0,41.83,0 -1396475340.0,39.038,0 -1396475640.0,42.184,0 -1396475940.0,41.76,0 -1396476240.0,43.99,0 -1396476540.0,41.476000000000006,0 -1396476840.0,39.236,0 -1396477140.0,41.798,0 -1396477440.0,39.722,0 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index f3dc571..0000000 --- a/datasets/NAB/realAWSCloudwatch/labeled_ec2_cpu_utilization_c6585a.csv +++ /dev/null @@ -1,4033 +0,0 @@ -timestamp,value,label -1396466940.0,0.066,0 -1396467240.0,0.066,0 -1396467540.0,0.068,0 -1396467840.0,0.134,0 -1396468140.0,0.066,0 -1396468440.0,0.066,0 -1396468740.0,0.134,0 -1396469040.0,0.066,0 -1396469340.0,0.068,0 -1396469640.0,0.066,0 -1396469940.0,0.066,0 -1396470240.0,0.134,0 -1396470540.0,0.066,0 -1396470840.0,0.068,0 -1396471140.0,0.066,0 -1396471440.0,0.132,0 -1396471740.0,0.066,0 -1396472040.0,0.068,0 -1396472340.0,0.132,0 -1396472640.0,0.066,0 -1396472940.0,0.066,0 -1396473240.0,0.066,0 -1396473540.0,0.066,0 -1396473840.0,0.134,0 -1396474140.0,0.066,0 -1396474440.0,0.066,0 -1396474740.0,0.066,0 -1396475040.0,0.066,0 -1396475340.0,0.132,0 -1396475640.0,0.068,0 -1396475940.0,0.134,0 -1396476240.0,0.066,0 -1396476540.0,0.066,0 -1396476840.0,0.068,0 -1396477140.0,0.066,0 -1396477440.0,0.134,0 -1396477740.0,0.066,0 -1396478040.0,0.068,0 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c520967..0000000 --- a/datasets/NAB/realAWSCloudwatch/labeled_ec2_cpu_utilization_fe7f93.csv +++ /dev/null @@ -1,4033 +0,0 @@ -timestamp,value,label -1392409620.0,2.296,0 -1392409920.0,2.144,0 -1392410220.0,2.274,0 -1392410520.0,2.066,0 -1392410820.0,2.35,0 -1392411120.0,2.136,0 -1392411420.0,2.366,0 -1392411720.0,2.252,0 -1392412020.0,2.4,0 -1392412320.0,2.18,0 -1392412620.0,2.352,0 -1392412920.0,3.434,0 -1392413220.0,2.43,0 -1392413520.0,2.0340000000000003,0 -1392413820.0,2.296,0 -1392414120.0,2.052,0 -1392414420.0,2.314,0 -1392414720.0,2.07,0 -1392415020.0,2.4,0 -1392415320.0,2.07,0 -1392415620.0,3.588,0 -1392415920.0,2.192,0 -1392416220.0,2.3,0 -1392416520.0,2.134,0 -1392416820.0,2.2840000000000003,0 -1392417120.0,2.12,0 -1392417420.0,2.35,0 -1392417720.0,2.166,0 -1392418020.0,2.314,0 -1392418320.0,2.186,0 -1392418620.0,2.33,0 -1392418920.0,2.108,0 -1392419220.0,2.3280000000000003,0 -1392419520.0,3.434,0 -1392419820.0,2.424,0 -1392420120.0,2.158,0 -1392420420.0,2.342,0 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diff --git a/datasets/NAB/realAWSCloudwatch/labeled_ec2_network_in_257a54.csv b/datasets/NAB/realAWSCloudwatch/labeled_ec2_network_in_257a54.csv deleted file mode 100644 index 48b7a5b..0000000 --- a/datasets/NAB/realAWSCloudwatch/labeled_ec2_network_in_257a54.csv +++ /dev/null @@ -1,4033 +0,0 @@ -timestamp,value,label -1397106240.0,251643.0,0 -1397106540.0,3203510.0,0 -1397106840.0,287397.0,0 -1397107140.0,238944.0,0 -1397107440.0,245880.0,0 -1397107740.0,234170.0,0 -1397108040.0,255797.0,0 -1397108340.0,244002.0,0 -1397108640.0,514385.0,0 -1397108940.0,270883.0,0 -1397109240.0,249887.0,0 -1397109540.0,3201940.0,0 -1397109840.0,280638.0,0 -1397110140.0,3231740.0,0 -1397110440.0,223158.0,0 -1397110740.0,242568.0,0 -1397111040.0,221907.0,0 -1397111340.0,248537.0,0 -1397111640.0,223433.0,0 -1397111940.0,247310.0,0 -1397112240.0,224771.0,0 -1397112540.0,223668.0,0 -1397112840.0,240044.0,0 -1397113140.0,3221290.0,0 -1397113440.0,235606.0,0 -1397113740.0,3202800.0,0 -1397114040.0,244192.0,0 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diff --git a/datasets/NAB/realAdExchange/labeled_exchange-2_cpc_results.csv b/datasets/NAB/realAdExchange/labeled_exchange-2_cpc_results.csv deleted file mode 100644 index 1e5bd01..0000000 --- a/datasets/NAB/realAdExchange/labeled_exchange-2_cpc_results.csv +++ /dev/null @@ -1,1625 +0,0 @@ -timestamp,value,label -1309496401.0,0.08196473551639999,0 -1309500001.0,0.0989722357526,0 -1309503601.0,0.0653139485883,0 -1309507201.0,0.0706628339533,0 -1309510801.0,0.102490196078,0 -1309514401.0,0.12339464882899999,0 -1309518001.0,0.153045112782,0 -1309521601.0,0.148321513002,0 -1309525201.0,0.218257756563,0 -1309528801.0,0.22659793814399998,0 -1309532401.0,0.174045801527,0 -1309536001.0,0.159901960784,0 -1309539601.0,0.145626477541,0 -1309543201.0,0.143828571429,0 -1309546801.0,0.136604651163,0 -1309550401.0,0.134952494062,0 -1309554001.0,0.112707774799,0 -1309557601.0,0.103763277693,0 -1309561201.0,0.0987147766323,0 -1309564801.0,0.0916247582205,0 -1309568401.0,0.08049516908210001,0 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b/datasets/NAB/realKnownCause/labeled_ambient_temperature_system_failure.csv deleted file mode 100644 index a00308b..0000000 --- a/datasets/NAB/realKnownCause/labeled_ambient_temperature_system_failure.csv +++ /dev/null @@ -1,7268 +0,0 @@ -timestamp,value,label -1372914000.0,69.88083514,0 -1372917600.0,71.22022706,0 -1372921200.0,70.87780496,0 -1372924800.0,68.95939994,0 -1372928400.0,69.28355102,0 -1372932000.0,70.06096581,0 -1372935600.0,69.27976479,0 -1372939200.0,69.36960846,0 -1372942800.0,69.16671394,0 -1372946400.0,68.98608257,0 -1372950000.0,69.96506224,0 -1372953600.0,70.55619466,0 -1372957200.0,70.30750511,0 -1372960800.0,70.24625215,0 -1372964400.0,69.85490839,0 -1372968000.0,71.64329118,0 -1372971600.0,71.24565942,0 -1372975200.0,70.74509976,0 -1372978800.0,71.37329829,0 -1372982400.0,71.7957509,0 -1372986000.0,72.09160609999998,0 -1372989600.0,71.55307612,0 -1372993200.0,72.18769545,0 -1372996800.0,70.64995744,0 -1373000400.0,71.34274211,0 -1373004000.0,71.5867281,0 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a/datasets/NAB/realKnownCause/labeled_cpu_utilization_asg_misconfiguration.csv.REMOVED.git-id b/datasets/NAB/realKnownCause/labeled_cpu_utilization_asg_misconfiguration.csv.REMOVED.git-id deleted file mode 100644 index 428f835..0000000 --- a/datasets/NAB/realKnownCause/labeled_cpu_utilization_asg_misconfiguration.csv.REMOVED.git-id +++ /dev/null @@ -1 +0,0 @@ -948611b07519538ef036e0ec1c948f6bf97009cf \ No newline at end of file diff --git a/datasets/NAB/realKnownCause/labeled_ec2_request_latency_system_failure.csv b/datasets/NAB/realKnownCause/labeled_ec2_request_latency_system_failure.csv deleted file mode 100644 index 2eafe7f..0000000 --- a/datasets/NAB/realKnownCause/labeled_ec2_request_latency_system_failure.csv +++ /dev/null @@ -1,4033 +0,0 @@ -timestamp,value,label -1394185260.0,45.868,0 -1394185560.0,47.606,0 -1394185860.0,42.58,0 -1394186160.0,46.03,0 -1394186460.0,44.992,0 -1394186760.0,45.238,0 -1394187060.0,45.752,0 -1394187360.0,46.476000000000006,0 -1394187660.0,42.752,0 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100644 index 8fe94b9..0000000 --- a/datasets/NAB/realKnownCause/labeled_machine_temperature_system_failure.csv.REMOVED.git-id +++ /dev/null @@ -1 +0,0 @@ -428229640a5466e68014f74649a24f00abb1150b \ No newline at end of file diff --git a/datasets/NAB/realKnownCause/labeled_nyc_taxi.csv b/datasets/NAB/realKnownCause/labeled_nyc_taxi.csv deleted file mode 100644 index aae44ca..0000000 --- a/datasets/NAB/realKnownCause/labeled_nyc_taxi.csv +++ /dev/null @@ -1,10321 +0,0 @@ -timestamp,value,label -1404190800.0,10844,0 -1404192600.0,8127,0 -1404194400.0,6210,0 -1404196200.0,4656,0 -1404198000.0,3820,0 -1404199800.0,2873,0 -1404201600.0,2369,0 -1404203400.0,2064,0 -1404205200.0,2221,0 -1404207000.0,2158,0 -1404208800.0,2515,0 -1404210600.0,4364,0 -1404212400.0,6526,0 -1404214200.0,11039,0 -1404216000.0,13857,0 -1404217800.0,15865,0 -1404219600.0,17920,0 -1404221400.0,20346,0 -1404223200.0,19539,0 -1404225000.0,20107,0 -1404226800.0,18984,0 -1404228600.0,17720,0 -1404230400.0,17249,0 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@@ -1 +0,0 @@ -68dd1084ed091fb9affe45b4e0894250c6c62c07 \ No newline at end of file diff --git a/datasets/NAB/realTweets/labeled_Twitter_volume_AMZN.csv.REMOVED.git-id b/datasets/NAB/realTweets/labeled_Twitter_volume_AMZN.csv.REMOVED.git-id deleted file mode 100644 index 8b4529f..0000000 --- a/datasets/NAB/realTweets/labeled_Twitter_volume_AMZN.csv.REMOVED.git-id +++ /dev/null @@ -1 +0,0 @@ -7bebf0fe077dda56f789d644090faf1d2484913c \ No newline at end of file diff --git a/datasets/NAB/realTweets/labeled_Twitter_volume_CRM.csv.REMOVED.git-id b/datasets/NAB/realTweets/labeled_Twitter_volume_CRM.csv.REMOVED.git-id deleted file mode 100644 index 27a9468..0000000 --- a/datasets/NAB/realTweets/labeled_Twitter_volume_CRM.csv.REMOVED.git-id +++ /dev/null @@ -1 +0,0 @@ -59fdf3c2b8d171704e3de1e10d8ccfca72c8ab9a \ No newline at end of file diff --git a/datasets/NAB/realTweets/labeled_Twitter_volume_CVS.csv.REMOVED.git-id b/datasets/NAB/realTweets/labeled_Twitter_volume_CVS.csv.REMOVED.git-id 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diff --git a/datasets/anomaly_reserve/kpi/TRAIN/dataset_TRAIN/datasetDoc.json b/datasets/anomaly_reserve/kpi/TRAIN/dataset_TRAIN/datasetDoc.json deleted file mode 100644 index a196e7d..0000000 --- a/datasets/anomaly_reserve/kpi/TRAIN/dataset_TRAIN/datasetDoc.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "about": { - "datasetID": "kpi_dataset_TRAIN", - "datasetName": "NULL", - "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'", - "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ", - "license": " CC Public Domain Mark 1.0 ", - "source": "OpenML", - "sourceURI": 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b/datasets/anomaly_reserve/kpi/TRAIN/problem_TRAIN/problemDoc.json deleted file mode 100644 index 1fd55ad..0000000 --- a/datasets/anomaly_reserve/kpi/TRAIN/problem_TRAIN/problemDoc.json +++ /dev/null @@ -1,65 +0,0 @@ -{ - "about": { - "problemID": "kpi_problem", - "problemName": "kpi_problem", - "problemDescription": "Anomaly detection", - "problemVersion": "4.0.0", - "problemSchemaVersion": "4.0.0", - "taskKeywords": [ - "classification", - "binary", - "tabular" - ] - }, - "inputs": { - "data": [ - { - "datasetID": "kpi_dataset", - "targets": [ - { - "targetIndex": 0, - "resID": "learningData", - "colIndex": 3, - "colName": "ground_truth" - } - ] - } - ], - "dataSplits": { - "method": "holdOut", - "testSize": 0.2, - "stratified": true, - "numRepeats": 0, - "randomSeed": 42, - "splitsFile": "dataSplits.csv", - "datasetViewMaps": { - "train": [ - { - "from": "kpi_dataset", - "to": "kpi_dataset_TRAIN" - } - ], - "test": [ - { - "from": "kpi_dataset", - "to": "kpi_dataset_TEST" - } - ], - "score": [ - { - "from": "kpi_dataset", - "to": "kpi_dataset_SCORE" - } - ] - } - }, - "performanceMetrics": [ - { - "metric": "f1Macro" - } - ] - }, - "expectedOutputs": { - "predictionsFile": "predictions.csv" - } -} \ No newline at end of file diff --git a/datasets/anomaly_reserve/kpi/kpi_dataset/datasetDoc.json b/datasets/anomaly_reserve/kpi/kpi_dataset/datasetDoc.json deleted file mode 100644 index c062016..0000000 --- a/datasets/anomaly_reserve/kpi/kpi_dataset/datasetDoc.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "about": { - "datasetID": "kpi_dataset", - "datasetName": "kpi", - "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'", - "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ", - "license": " CC Public Domain Mark 1.0 ", - "source": "OpenML", - "sourceURI": "http://www.openml.org/d/185", - "approximateSize": "", - "datasetSchemaVersion": "4.0.0", - "redacted": false, - "datasetVersion": "4.0.0" - }, - "dataResources": [ - { - "resID": "learningData", - "resPath": "tables/learningData.csv", - "resType": "table", - "resFormat": { - "text/csv": [ - "csv" - ] - }, - "isCollection": false, - "columns": [ - { - "colIndex": 0, - "colName": "d3mIndex", - "colType": "integer", - "role": [ - "index" - ] - }, - { - "colIndex": 1, - "colName": "timestamp", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 2, - "colName": "value", - "colType": "real", - "role": [ - "attribute" - ] - }, - { - "colIndex": 3, - "colName": "ground_truth", - "colType": "integer", - "role": [ - "suggestedTarget" - ] - } - ], - "columnsCount": 4 - } - ] -} \ No newline at end of file diff --git a/datasets/anomaly_reserve/kpi/kpi_dataset/tables/learningData.csv.REMOVED.git-id b/datasets/anomaly_reserve/kpi/kpi_dataset/tables/learningData.csv.REMOVED.git-id deleted file mode 100644 index 3a466ae..0000000 --- a/datasets/anomaly_reserve/kpi/kpi_dataset/tables/learningData.csv.REMOVED.git-id +++ /dev/null @@ -1 +0,0 @@ -d80846dc46c173472f646a52005a1fb3670ccd09 \ No newline at end of file diff --git a/datasets/anomaly_reserve/kpi/kpi_problem/dataSplits.csv.REMOVED.git-id b/datasets/anomaly_reserve/kpi/kpi_problem/dataSplits.csv.REMOVED.git-id deleted file mode 100644 index 7376921..0000000 --- a/datasets/anomaly_reserve/kpi/kpi_problem/dataSplits.csv.REMOVED.git-id +++ /dev/null @@ -1 +0,0 @@ -44db328c252a8156434142a37ef65765869e7548 \ No newline at end of file diff --git a/datasets/anomaly_reserve/kpi/kpi_problem/problemDoc.json b/datasets/anomaly_reserve/kpi/kpi_problem/problemDoc.json deleted file mode 100644 index 1fd55ad..0000000 --- a/datasets/anomaly_reserve/kpi/kpi_problem/problemDoc.json +++ /dev/null @@ -1,65 +0,0 @@ -{ - "about": { - "problemID": "kpi_problem", - "problemName": "kpi_problem", - "problemDescription": "Anomaly detection", - "problemVersion": "4.0.0", - "problemSchemaVersion": "4.0.0", - "taskKeywords": [ - "classification", - "binary", - "tabular" - ] - }, - "inputs": { - "data": [ - { - "datasetID": "kpi_dataset", - "targets": [ - { - "targetIndex": 0, - "resID": "learningData", - "colIndex": 3, - "colName": "ground_truth" - } - ] - } - ], - "dataSplits": { - "method": "holdOut", - "testSize": 0.2, - "stratified": true, - "numRepeats": 0, - "randomSeed": 42, - "splitsFile": "dataSplits.csv", - "datasetViewMaps": { - "train": [ - { - "from": "kpi_dataset", - "to": "kpi_dataset_TRAIN" - } - ], - "test": [ - { - "from": "kpi_dataset", - "to": "kpi_dataset_TEST" - } - ], - "score": [ - { - "from": "kpi_dataset", - "to": "kpi_dataset_SCORE" - } - ] - } - }, - "performanceMetrics": [ - { - "metric": "f1Macro" - } - ] - }, - "expectedOutputs": { - "predictionsFile": "predictions.csv" - } -} \ No newline at end of file diff --git a/datasets/anomaly_reserve/raw_data/kpi.csv.REMOVED.git-id b/datasets/anomaly_reserve/raw_data/kpi.csv.REMOVED.git-id deleted file mode 100644 index 9049ed1..0000000 --- a/datasets/anomaly_reserve/raw_data/kpi.csv.REMOVED.git-id +++ /dev/null @@ -1 +0,0 @@ -bea5d1c052730eaba76b84ff5df854477cdfa80b \ No newline at end of file diff --git a/datasets/anomaly_reserve/template/datasetDoc.json b/datasets/anomaly_reserve/template/datasetDoc.json deleted file mode 100644 index 0494777..0000000 --- a/datasets/anomaly_reserve/template/datasetDoc.json +++ /dev/null @@ -1,183 +0,0 @@ -{ - "about": { - "datasetID": "template", - "datasetName": "baseball", - "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'", - "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ", - "license": " CC Public Domain Mark 1.0 ", - "source": "OpenML", - "sourceURI": "http://www.openml.org/d/185", - "approximateSize": "", - "datasetSchemaVersion": "4.0.0", - "redacted": false, - "datasetVersion": "4.0.0" - }, - "dataResources": [ - { - "resID": "learningData", - "resPath": "tables/learningData.csv", - "resType": "table", - "resFormat": { - "text/csv": [ - "csv" - ] - }, - "isCollection": false, - "columns": [ - { - "colIndex": 0, - "colName": "d3mIndex", - "colType": "integer", - "role": [ - "index" - ] - }, - { - "colIndex": 1, - "colName": "Player", - "colType": "categorical", - "role": [ - "attribute" - ] - }, - { - "colIndex": 2, - "colName": "Number_seasons", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 3, - "colName": "Games_played", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 4, - "colName": "At_bats", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 5, - "colName": "Runs", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 6, - "colName": "Hits", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 7, - "colName": "Doubles", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 8, - "colName": "Triples", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 9, - "colName": "Home_runs", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 10, - "colName": "RBIs", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 11, - "colName": "Walks", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 12, - "colName": "Strikeouts", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 13, - "colName": "Batting_average", - "colType": "real", - "role": [ - "attribute" - ] - }, - { - "colIndex": 14, - "colName": "On_base_pct", - "colType": "real", - "role": [ - "attribute" - ] - }, - { - "colIndex": 15, - "colName": "Slugging_pct", - "colType": "real", - "role": [ - "attribute" - ] - }, - { - "colIndex": 16, - "colName": "Fielding_ave", - "colType": "real", - "role": [ - "attribute" - ] - }, - { - "colIndex": 17, - "colName": "Position", - "colType": "categorical", - "role": [ - "attribute" - ] - }, - { - "colIndex": 18, - "colName": "Hall_of_Fame", - "colType": "categorical", - "role": [ - "suggestedTarget" - ] - } - ], - "columnsCount": 19 - } - ] -} diff --git a/datasets/anomaly_reserve/template/problemDoc.json b/datasets/anomaly_reserve/template/problemDoc.json deleted file mode 100644 index 514a80a..0000000 --- a/datasets/anomaly_reserve/template/problemDoc.json +++ /dev/null @@ -1,65 +0,0 @@ -{ - "about": { - "problemID": "template", - "problemName": "baseball_problem", - "problemDescription": "**Author**: Jeffrey S. Simonoff \n**Source**: [AnalCatData](http://www.stern.nyu.edu/~jsimonof/AnalCatData) - 2003 \n**Please cite**: Jeffrey S. Simonoff, Analyzing Categorical Data, Springer-Verlag, New York, 2003 \n \nDatabase of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave' \n\nNotes: \n* Quotes, Single-Quotes and Backslashes were removed, Blanks replaced with Underscores\n* Player is an identifier that should be ignored when modelling the data", - "problemVersion": "4.0.0", - "problemSchemaVersion": "4.0.0", - "taskKeywords": [ - "classification", - "multiClass", - "tabular" - ] - }, - "inputs": { - "data": [ - { - "datasetID": "185_baseball_dataset", - "targets": [ - { - "targetIndex": 0, - "resID": "learningData", - "colIndex": 18, - "colName": "Hall_of_Fame" - } - ] - } - ], - "dataSplits": { - "method": "holdOut", - "testSize": 0.2, - "stratified": true, - "numRepeats": 0, - "randomSeed": 42, - "splitsFile": "dataSplits.csv", - "datasetViewMaps": { - "train": [ - { - "from": "185_baseball_dataset", - "to": "185_baseball_dataset_TRAIN" - } - ], - "test": [ - { - "from": "185_baseball_dataset", - "to": "185_baseball_dataset_TEST" - } - ], - "score": [ - { - "from": "185_baseball_dataset", - "to": "185_baseball_dataset_SCORE" - } - ] - } - }, - "performanceMetrics": [ - { - "metric": "f1Macro" - } - ] - }, - "expectedOutputs": { - "predictionsFile": "predictions.csv" - } -} diff --git a/datasets/anomaly_reserve/transform.py b/datasets/anomaly_reserve/transform.py deleted file mode 100644 index e8bd319..0000000 --- a/datasets/anomaly_reserve/transform.py +++ /dev/null @@ -1,160 +0,0 @@ -# TODO: Wrap it as a class and connect it to GUI -# A script to transform anomaly data to d3m format -import pandas as pd -import numpy as np -import os -import json - -############################## -# Some information for the dataset to be transformed -# Designed for time series data -name = 'kpi' -src_path = './raw_data/kpi.csv' -label_name = 'label' -timestamp_name = 'timestamp' -value_names = ['value'] -ratio = 0.8 # Ratio of training data, the rest is for testing - -############################### - - - -dst_root = './' + name -dirs = ['./', 'SCORE', 'TEST', 'TRAIN'] -maps = {'./': None, 'SCORE': 'TEST', 'TEST': 'TEST', 'TRAIN': 'TRAIN'} - -# Create the corresponding directories -for d in dirs: - if maps[d] is not None: - dataset_name = 'dataset_' + maps[d] - problem_name = 'problem_' + maps[d] - else: - dataset_name = name + '_dataset' - problem_name = name + '_problem' - tables_dir = os.path.join(dst_root, d, dataset_name, 'tables') - if not os.path.exists(tables_dir): - os.makedirs(tables_dir) - problem_dir = os.path.join(dst_root, d, problem_name) - if not os.path.exists(problem_dir): - os.makedirs(problem_dir) - -# Process data -_df = pd.DataFrame() -df = pd.read_csv(src_path) -_df['d3mIndex'] = df.index -_df['timestamp'] = df[timestamp_name] -for value_name in value_names: - _df[value_name] = df[value_name] -_df['ground_truth'] = df[label_name] -df = _df -cols = df.columns.tolist() - -# Save all the data -df.to_csv(os.path.join(dst_root, name+'_dataset', 'tables', 'learningData.csv'), index=False) - -# Save training and testing data -train_df, test_df = df[:int(df.shape[0]*ratio)], df[int(df.shape[0]*ratio):] - -train_df.to_csv(os.path.join(dst_root, 'TRAIN', 'dataset_TRAIN', 'tables', 'learningData.csv'), index=False) -test_df.to_csv(os.path.join(dst_root, 'TEST', 'dataset_TEST', 'tables', 'learningData.csv'), index=False) -test_df.to_csv(os.path.join(dst_root, 'SCORE', 'dataset_TEST', 'tables', 'learningData.csv'), index=False) - -# Data splits -row_0 = train_df.shape[0] -row_1 = train_df.shape[0] -row = row_0 + row_1 -df = pd.DataFrame(np.array([[i for i in range(row)], ['TRAIN' for _ in range(row_0)] + ['TEST' for _ in range(row_1)], [0 for _ in range(row)], [0 for _ in range(row)]]).transpose(), columns = ['d3mIndex', 'type', 'repeat', 'fold']) - -# Save data splits for all data -train_df.to_csv(os.path.join(dst_root, name+'_problem', 'dataSplits.csv'), index=False) - -# Save training and testing splits -train_df, test_df = df[:row_0], df[row_0:] -train_df.to_csv(os.path.join(dst_root, 'TRAIN', 'problem_TRAIN', 'dataSplits.csv'), index=False) -test_df.to_csv(os.path.join(dst_root, 'TEST', 'problem_TEST', 'dataSplits.csv'), index=False) -test_df.to_csv(os.path.join(dst_root, 'SCORE', 'problem_TEST', 'dataSplits.csv'), index=False) - - -# Dataset JSON files -# Load template -with open('template/datasetDoc.json') as json_file: - data = json.load(json_file) -columns = [] -for i in range(len(cols)): - c = {} - c['colIndex'] = i - c['colName'] = cols[i] - if i == 0: - c['colType'] = 'integer' - c['role'] = ['index'] - elif i == 1: - c['colType'] = 'integer' - c['role'] = ['attribute'] - elif i == len(cols)-1: - c['colType'] = 'integer' - c['role'] = ['suggestedTarget'] - else: - c['colType'] = 'real' - c['role'] = ['attribute'] - columns.append(c) -data['dataResources'][0]['columns'] = columns -data['dataResources'][0]['columnsCount'] = len(cols) - -data['about']['datasetID'] = name + '_dataset' -data['about']['datasetName'] = name -with open(os.path.join(dst_root, name+'_dataset', 'datasetDoc.json'), 'w') as outfile: - json.dump(data, outfile, indent=4) - -data['about']['datasetID'] = name +'_dataset_TRAIN' -data['about']['datasetName'] = "NULL" -with open(os.path.join(dst_root, 'TRAIN', 'dataset_TRAIN', 'datasetDoc.json'), 'w') as outfile: - json.dump(data, outfile, indent=4) - -data['about']['datasetID'] = name + '_dataset_TEST' -data['about']['datasetName'] = 'NULL' -with open(os.path.join(dst_root, 'TEST', 'dataset_TEST', 'datasetDoc.json'), 'w') as outfile: - json.dump(data, outfile, indent=4) - -data['about']['datasetID'] = name + '_dataset_TEST' -data['about']['datasetName'] = 'NULL' -with open(os.path.join(dst_root, 'SCORE', 'dataset_TEST', 'datasetDoc.json'), 'w') as outfile: - json.dump(data, outfile, indent=4) - -# Problem JSON files -# Load template -with open('template/problemDoc.json') as json_file: - data = json.load(json_file) - -data['about']['problemID'] = name+'_problem' -data['about']['problemName'] = name+'_problem' -data['about']['problemDescription'] = 'Anomaly detection' -data['about']['taskKeywords'] = ['classification', 'binary', 'tabular'] -data['inputs']['data'][0]['datasetID'] = name + '_dataset' -data['inputs']['data'][0]['targets'][0]['colIndex'] = len(cols)-1 -data['inputs']['data'][0]['targets'][0]['colName'] = cols[-1] -data['inputs']['dataSplits']['datasetViewMaps']['train'][0]['from'] = name+'_dataset' -data['inputs']['dataSplits']['datasetViewMaps']['test'][0]['from'] = name+'_dataset' -data['inputs']['dataSplits']['datasetViewMaps']['score'][0]['from'] = name+'_dataset' -data['inputs']['dataSplits']['datasetViewMaps']['train'][0]['to'] = name+'_dataset_TRAIN' -data['inputs']['dataSplits']['datasetViewMaps']['test'][0]['to'] = name+'_dataset_TEST' -data['inputs']['dataSplits']['datasetViewMaps']['score'][0]['to'] = name+'_dataset_SCORE' - -with open(os.path.join(dst_root, name+'_problem', 'problemDoc.json'), 'w') as outfile: - json.dump(data, outfile, indent=4) - -with open(os.path.join(dst_root, 'TRAIN', 'problem_TRAIN', 'problemDoc.json'), 'w') as outfile: - json.dump(data, outfile, indent=4) - -with open(os.path.join(dst_root, 'TEST', 'problem_TEST', 'problemDoc.json'), 'w') as outfile: - json.dump(data, outfile, indent=4) - -with open(os.path.join(dst_root, 'SCORE', 'problem_TEST', 'problemDoc.json'), 'w') as outfile: - json.dump(data, outfile, indent=4) - -# Make an empty targets.csv -with open(os.path.join(dst_root, 'SCORE', 'targets.csv'), 'w') as outfile: - outfile.write('') - - - - diff --git a/datasets/anomaly_reserve/yahoo_sub_5/SCORE/dataset_TEST/datasetDoc.json b/datasets/anomaly_reserve/yahoo_sub_5/SCORE/dataset_TEST/datasetDoc.json deleted file mode 100644 index ff5dec4..0000000 --- a/datasets/anomaly_reserve/yahoo_sub_5/SCORE/dataset_TEST/datasetDoc.json +++ /dev/null @@ -1,95 +0,0 @@ -{ - "about": { - "datasetID": "yahoo_sub_5_dataset_TEST", - "datasetName": "NULL", - "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'", - "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ", - "license": " CC Public Domain Mark 1.0 ", - "source": "OpenML", - "sourceURI": "http://www.openml.org/d/185", - "approximateSize": "", - "datasetSchemaVersion": "4.0.0", - "redacted": false, - "datasetVersion": "4.0.0" - }, - "dataResources": [ - { - "resID": "learningData", - "resPath": "tables/learningData.csv", - "resType": "table", - "resFormat": { - "text/csv": [ - "csv" - ] - }, - "isCollection": false, - "columns": [ - { - "colIndex": 0, - "colName": "d3mIndex", - "colType": "integer", - "role": [ - "index" - ] - }, - { - "colIndex": 1, - "colName": "timestamp", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 2, - "colName": "value_0", - "colType": "real", - "role": [ - "attribute" - ] - }, - { - "colIndex": 3, - "colName": "value_1", - "colType": "real", - "role": [ - "attribute" - ] - }, - { - "colIndex": 4, - "colName": "value_2", - "colType": "real", - "role": [ - "attribute" - ] - }, - { - "colIndex": 5, - "colName": "value_3", - "colType": "real", - "role": [ - "attribute" - ] - }, - { - "colIndex": 6, - "colName": "value_4", - "colType": "real", - "role": [ - "attribute" - ] - }, - { - "colIndex": 7, - "colName": "ground_truth", - "colType": "integer", - "role": [ - "suggestedTarget" - ] - } - ], - "columnsCount": 8 - } - ] -} \ No newline at end of file diff --git a/datasets/anomaly_reserve/yahoo_sub_5/SCORE/dataset_TEST/tables/learningData.csv b/datasets/anomaly_reserve/yahoo_sub_5/SCORE/dataset_TEST/tables/learningData.csv deleted file mode 100644 index e3d5131..0000000 --- a/datasets/anomaly_reserve/yahoo_sub_5/SCORE/dataset_TEST/tables/learningData.csv +++ /dev/null @@ -1,141 +0,0 @@ -d3mIndex,timestamp,value_0,value_1,value_2,value_3,value_4,ground_truth -1260,1261,7782,0.034280386319742985,2.5072222222222003,104,3119,0 -1261,1262,7829,0.039360296791109,2.5927777777778,82,3590,0 -1262,1263,7902,0.0,2.6894444444444,208,3893,0 -1263,1264,8039,0.038944065994356014,2.6291666666667,92,3264,0 -1264,1265,8350,0.18176011684739,2.6469444444444,53,3963,0 -1265,1266,8142,0.18521047165852,2.7461111111111003,65,2757,0 -1266,1267,7886,0.13079770999921,2.9363888888889,62,2306,0 -1267,1268,7743,0.13310058077443,3.2797222222222,73,2549,0 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index e69de29..0000000 diff --git a/datasets/anomaly_reserve/yahoo_sub_5/TEST/dataset_TEST/datasetDoc.json b/datasets/anomaly_reserve/yahoo_sub_5/TEST/dataset_TEST/datasetDoc.json deleted file mode 100644 index ff5dec4..0000000 --- a/datasets/anomaly_reserve/yahoo_sub_5/TEST/dataset_TEST/datasetDoc.json +++ /dev/null @@ -1,95 +0,0 @@ -{ - "about": { - "datasetID": "yahoo_sub_5_dataset_TEST", - "datasetName": "NULL", - "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'", - "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ", - "license": " CC Public Domain Mark 1.0 ", - 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b/datasets/anomaly_reserve/yahoo_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json deleted file mode 100644 index be6f5c0..0000000 --- a/datasets/anomaly_reserve/yahoo_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json +++ /dev/null @@ -1,95 +0,0 @@ -{ - "about": { - "datasetID": "yahoo_sub_5_dataset_TRAIN", - "datasetName": "NULL", - "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'", - "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ", - "license": " CC Public Domain Mark 1.0 ", - "source": "OpenML", - "sourceURI": "http://www.openml.org/d/185", - "approximateSize": "", - 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-1232,1233,6122,0.3688420983862,2.1338888888889,58,1896,0 -1233,1234,6034,0.15672074166098002,2.2247222222222005,70,2083,0 -1234,1235,6079,0.099476236793782,2.3308333333333,67,1792,0 -1235,1236,5998,0.18394691317126,2.3902777777778,70,3258,0 -1236,1237,6004,0.076264605227629,2.5819444444444,95,2265,0 -1237,1238,5908,0.058100747891124,2.6661111111111,100,2775,0 -1238,1239,6022,0.18015967729618,2.8258333333333,116,1545,0 -1239,1240,5981,0.059431847203259,2.7502777777778,123,1818,0 -1240,1241,6399,0.14870829462531002,2.6730555555556004,71,1481,0 -1241,1242,6119,0.09565694822541,2.7536111111111,65,1677,0 -1242,1243,6114,0.16022629962173002,2.9677777777778,73,1858,0 -1243,1244,5915,0.4140256163498,3.37,53,1643,0 -1244,1245,6192,0.32447726333369004,3.5958333333333,79,1582,0 -1245,1246,6021,0.15394421357627,3.8144444444444,77,1611,0 -1246,1247,6060,0.060070368432038,3.8283333333333,59,1803,0 -1247,1248,7510,0.14236976564388,3.7030555555556,66,2121,0 -1248,1249,7560,0.12741851179236,3.5802777777778,54,2375,0 -1249,1250,7525,0.093634078744746,3.4197222222222,54,1866,0 -1250,1251,7483,0.13709947889982,3.4438888888889,89,2398,0 -1251,1252,7452,0.06298116794216299,3.3425,85,2577,0 -1252,1253,7512,0.13125017838571,3.1608333333333,96,1801,0 -1253,1254,7572,0.21161148728916,2.7413888888888995,149,1840,0 -1254,1255,7629,0.06783428261124,2.3808333333333,139,1985,0 -1255,1256,7529,0.20877561051189,2.12,90,2041,0 -1256,1257,7623,0.10394294206935002,2.1533333333333,68,2075,0 -1257,1258,7637,0.0,2.2569444444444,445,2564,0 -1258,1259,7921,0.076424293095548,2.3183333333333,100,2734,0 -1259,1260,7790,0.08809461878011901,2.3583333333333,138,3143,0 diff --git a/datasets/anomaly_reserve/yahoo_sub_5/yahoo_sub_5_problem/problemDoc.json b/datasets/anomaly_reserve/yahoo_sub_5/yahoo_sub_5_problem/problemDoc.json deleted file mode 100644 index 417cb6b..0000000 --- a/datasets/anomaly_reserve/yahoo_sub_5/yahoo_sub_5_problem/problemDoc.json +++ /dev/null @@ -1,65 +0,0 @@ -{ - "about": { - "problemID": "yahoo_sub_5_problem", - "problemName": "yahoo_sub_5_problem", - "problemDescription": "Anomaly detection", - "problemVersion": "4.0.0", - "problemSchemaVersion": "4.0.0", - "taskKeywords": [ - "classification", - "binary", - "tabular" - ] - }, - "inputs": { - "data": [ - { - "datasetID": "yahoo_sub_5_dataset", - "targets": [ - { - "targetIndex": 0, - "resID": "learningData", - "colIndex": 7, - "colName": "ground_truth" - } - ] - } - ], - "dataSplits": { - "method": "holdOut", - "testSize": 0.2, - "stratified": true, - "numRepeats": 0, - "randomSeed": 42, - "splitsFile": "dataSplits.csv", - "datasetViewMaps": { - "train": [ - { - "from": "yahoo_sub_5_dataset", - "to": "yahoo_sub_5_dataset_TRAIN" - } - ], - "test": [ - { - "from": "yahoo_sub_5_dataset", - "to": "yahoo_sub_5_dataset_TEST" - } - ], - "score": [ - { - "from": "yahoo_sub_5_dataset", - "to": "yahoo_sub_5_dataset_SCORE" - } - ] - } - }, - "performanceMetrics": [ - { - "metric": "f1Macro" - } - ] - }, - "expectedOutputs": { - "predictionsFile": "predictions.csv" - } -} \ No newline at end of file diff --git a/datasets/data-supply/README.md b/datasets/data-supply/README.md deleted file mode 100644 index 74d7cd4..0000000 --- a/datasets/data-supply/README.md +++ /dev/null @@ -1,4 +0,0 @@ -This repository contains D3M datasets schemas and related documentation from the DARPA [Data-Driven Discovery of Models (D3M)](https://www.darpa.mil/program/data-driven-discovery-of-models) program. These were developed researchers at MIT Lincoln Lab in collaboration with D3M performers. - -__________________ -This work was sponsored by the Defense Advanced Research Projects Agency (DARPA) under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. diff --git a/datasets/data-supply/documentation/README.md b/datasets/data-supply/documentation/README.md deleted file mode 100644 index 92292b4..0000000 --- a/datasets/data-supply/documentation/README.md +++ /dev/null @@ -1,6 +0,0 @@ -The documentation for the data supply infrastructure, including the dataset schema and problem schema, is provided here. - -# [Overview](overview.md) -# [Dataset Schema](datasetSchema.md) -# [Problem Schema](problemSchema.md) -# [Minimal Metadata](minimalMetadata.md) \ No newline at end of file diff --git a/datasets/data-supply/documentation/code/consolidated-new-metrics.ipynb b/datasets/data-supply/documentation/code/consolidated-new-metrics.ipynb deleted file mode 100644 index 818f7f2..0000000 --- a/datasets/data-supply/documentation/code/consolidated-new-metrics.ipynb +++ /dev/null @@ -1,906 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hamming loss" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from io import StringIO\n", - "from sklearn.metrics import hamming_loss" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def hammingLoss(y_true, y_pred):\n", - " \"\"\"\n", - " Computes the hamming loss. Used for multiclass and multilabel classification.\n", - " \"\"\"\n", - " from sklearn import preprocessing\n", - " from sklearn.metrics import hamming_loss\n", - " import numpy as np\n", - " from itertools import chain\n", - "\n", - " def to_2d_array(df):\n", - " MULTI_CLASS_CONDITION=True\n", - " _dict = {}\n", - " for [index,value] in df.as_matrix():\n", - " if index in _dict.keys():\n", - " _dict[index].append(value)\n", - " MULTI_CLASS_CONDITION = False\n", - " else:\n", - " _dict[index]=[value]\n", - " return list(_dict.keys()), list(_dict.values()), MULTI_CLASS_CONDITION\n", - " \n", - " (y_true_keys, y_true_mat, b_multiclass) = to_2d_array(y_true)\n", - " (y_pred_keys, y_pred_mat, b_multiclass) = to_2d_array(y_pred)\n", - " \n", - " assert y_true_keys==y_pred_keys\n", - " \n", - " if b_multiclass:\n", - "# print('this is a multiclass case')\n", - " y_true_label_encoded = np.array(y_true_mat).ravel()\n", - " y_pred_label_encoded = np.array(y_pred_mat).ravel()\n", - " else: # MULTI_LABEL_CONDITION\n", - "# print('this is a multilabel case')\n", - " y_true_classes=(set(list(chain.from_iterable(y_true_mat))))\n", - " y_pred_classes=(set(list(chain.from_iterable(y_pred_mat))))\n", - " all_classes = list(y_true_classes.union(y_pred_classes))\n", - " lb = preprocessing.MultiLabelBinarizer(classes=all_classes)\n", - " y_true_label_encoded = lb.fit_transform(y_true_mat)\n", - " y_pred_label_encoded = lb.transform(y_pred_mat)\n", - " return hamming_loss(y_true_label_encoded, y_pred_label_encoded)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svattam/miniconda3/envs/automl/lib/python3.6/site-packages/ipykernel_launcher.py:13: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", - " del sys.path[0]\n" - ] - }, - { - "data": { - "text/plain": [ - "0.26666666666666666" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Testcase 1: MultiLabel, typical\n", - "y_true = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,class_label\n", - "3,happy-pleased\n", - "3,relaxing-calm\n", - "7,amazed-suprised\n", - "7,happy-pleased\n", - "13,quiet-still\n", - "13,sad-lonely\n", - "\"\"\"))\n", - "\n", - "y_pred = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,class_label\n", - "3,happy-pleased\n", - "3,sad-lonely\n", - "7,amazed-suprised\n", - "7,happy-pleased\n", - "13,quiet-still\n", - "13,happy-pleased\n", - "\"\"\"))\n", - "\n", - "hammingLoss(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svattam/miniconda3/envs/automl/lib/python3.6/site-packages/ipykernel_launcher.py:13: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", - " del sys.path[0]\n" - ] - }, - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Testcase 2: MultiLabel, Zero loss\n", - "y_true = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,class_label\n", - "3,happy-pleased\n", - "3,relaxing-calm\n", - "7,amazed-suprised\n", - "7,happy-pleased\n", - "13,quiet-still\n", - "13,sad-lonely\n", - "\"\"\"))\n", - "\n", - "y_pred = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,class_label\n", - "3,happy-pleased\n", - "3,relaxing-calm\n", - "7,amazed-suprised\n", - "7,happy-pleased\n", - "13,quiet-still\n", - "13,sad-lonely\n", - "\"\"\"))\n", - "\n", - "hammingLoss(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svattam/miniconda3/envs/automl/lib/python3.6/site-packages/ipykernel_launcher.py:13: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", - " del sys.path[0]\n" - ] - }, - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Testcase 3: MultiLabel, Complete loss\n", - "y_true = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,class_label\n", - "3,happy-pleased\n", - "3,relaxing-calm\n", - "7,amazed-suprised\n", - "7,happy-pleased\n", - "13,quiet-still\n", - "13,sad-lonely\n", - "\"\"\"))\n", - "\n", - "y_pred = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,class_label\n", - "3,ecstatic\n", - "3,sad-lonely\n", - "3,quiet-still\n", - "3,amazed-suprised\n", - "7,ecstatic\n", - "7,sad-lonely\n", - "7,relaxing-calm\n", - "7,quiet-still\n", - "13,ecstatic\n", - "13,happy-pleased\n", - "13,relaxing-calm\n", - "13,amazed-suprised\n", - "\"\"\"))\n", - "\n", - "hammingLoss(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svattam/miniconda3/envs/automl/lib/python3.6/site-packages/ipykernel_launcher.py:13: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", - " del sys.path[0]\n" - ] - }, - { - "data": { - "text/plain": [ - "0.2" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Testcase 4: Multiclass, Typical\n", - "y_true = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,species\n", - "1,versicolor\n", - "2,versicolor\n", - "16,virginica\n", - "17,setosa\n", - "22,versicolor\n", - "26,versicolor\n", - "30,versicolor\n", - "31,virginica\n", - "33,versicolor\n", - "37,virginica\n", - "\"\"\"))\n", - "\n", - "y_pred = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,species\n", - "1,setosa\n", - "2,versicolor\n", - "16,virginica\n", - "17,setosa\n", - "22,versicolor\n", - "26,virginica\n", - "30,versicolor\n", - "31,virginica\n", - "33,versicolor\n", - "37,virginica\n", - "\"\"\"))\n", - "\n", - "hammingLoss(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svattam/miniconda3/envs/automl/lib/python3.6/site-packages/ipykernel_launcher.py:13: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", - " del sys.path[0]\n" - ] - }, - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Testcase 5: Multiclass, Zero loss\n", - "y_true = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,species\n", - "1,versicolor\n", - "2,versicolor\n", - "16,virginica\n", - "17,setosa\n", - "22,versicolor\n", - "26,versicolor\n", - "30,versicolor\n", - "31,virginica\n", - "33,versicolor\n", - "37,virginica\n", - "\"\"\"))\n", - "\n", - "y_pred = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,species\n", - "1,versicolor\n", - "2,versicolor\n", - "16,virginica\n", - "17,setosa\n", - "22,versicolor\n", - "26,versicolor\n", - "30,versicolor\n", - "31,virginica\n", - "33,versicolor\n", - "37,virginica\n", - "\"\"\"))\n", - "\n", - "hammingLoss(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svattam/miniconda3/envs/automl/lib/python3.6/site-packages/ipykernel_launcher.py:13: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", - " del sys.path[0]\n" - ] - }, - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Testcase 6: Multiclass, Complete loss\n", - "y_true = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,species\n", - "1,versicolor\n", - "2,versicolor\n", - "16,versicolor\n", - "17,virginica\n", - "22,versicolor\n", - "26,versicolor\n", - "30,versicolor\n", - "31,virginica\n", - "33,versicolor\n", - "37,virginica\n", - "\"\"\"))\n", - "\n", - "y_pred = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,species\n", - "1,setosa\n", - "2,setosa\n", - "16,setosa\n", - "17,setosa\n", - "22,setosa\n", - "26,setosa\n", - "30,setosa\n", - "31,setosa\n", - "33,setosa\n", - "37,setosa\n", - "\"\"\"))\n", - "\n", - "hammingLoss(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RMSE" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def rootMeanSquaredError(y_true, y_pred):\n", - " \"\"\"\n", - " Computes the root mean squared error, for both univariate and multivariate case\n", - " \"\"\"\n", - " import numpy as np\n", - " from sklearn.metrics import mean_squared_error\n", - " from math import sqrt\n", - " \n", - " rmse = None\n", - " \n", - " # perform some checks\n", - " assert 'd3mIndex' in y_true.columns\n", - " assert 'd3mIndex' in y_pred.columns\n", - " assert y_true.shape == y_pred.shape\n", - " \n", - " # preprocessing\n", - " y_true.set_index('d3mIndex', inplace=True)\n", - " y_pred.set_index('d3mIndex', inplace=True)\n", - " \n", - " # determine the dimension\n", - " y_true_dim=y_true.shape[1]\n", - " \n", - " # univariate case\n", - " if y_true_dim == 1: \n", - " y_true_array = y_true.as_matrix().ravel()\n", - " y_pred_array = y_pred.as_matrix().ravel()\n", - " mse = mean_squared_error(y_true, y_pred)\n", - " rmse = sqrt(mse)\n", - " \n", - " # multivariate case\n", - " elif y_true_dim > 1:\n", - " y_true_array = y_true.as_matrix()\n", - " y_pred_array = y_pred.as_matrix()\n", - " mse = mean_squared_error(y_true_array, y_pred_array, multioutput='uniform_average')\n", - " rmse = sqrt(mse)\n", - " \n", - " return rmse" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svattam/miniconda3/envs/automl/lib/python3.6/site-packages/ipykernel_launcher.py:25: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", - "/home/svattam/miniconda3/envs/automl/lib/python3.6/site-packages/ipykernel_launcher.py:26: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n" - ] - }, - { - "data": { - "text/plain": [ - "0.8381527307120105" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# test case 1\n", - "# y_true_uni=[3, -1., 2, 7]\n", - "# y_pred_uni=[2.1, 0.0, 2, 8]\n", - "# expected rmse = 0.8381527307120105\n", - "\n", - "y_true = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,value\n", - "1,3\n", - "2,-1.0\n", - "16,2\n", - "17,7\n", - "\"\"\"))\n", - "y_pred = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,value\n", - "1,2.1\n", - "2,0.0\n", - "16,2\n", - "17,8\n", - "\"\"\"))\n", - "rootMeanSquaredError(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svattam/miniconda3/envs/automl/lib/python3.6/site-packages/ipykernel_launcher.py:32: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", - "/home/svattam/miniconda3/envs/automl/lib/python3.6/site-packages/ipykernel_launcher.py:33: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n" - ] - }, - { - "data": { - "text/plain": [ - "0.8416254115301732" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# test case 2\n", - "# y_true_multi=[[0.5, 1],[-1, 1],[7, -6]]\n", - "# y_pred_multi=[[0, 2],[-1, 2],[8, -5]]\n", - "# expected rmse = 0.8416254115301732\n", - "\n", - "y_true = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,value1, value2\n", - "1,0.5,1\n", - "2,-1,1\n", - "16,7,-6\n", - "\"\"\"))\n", - "y_pred = pd.read_csv(StringIO(\"\"\"\n", - "d3mIndex,value1,value2\n", - "1,0,2\n", - "2,-1,2\n", - "16,8,-5\n", - "\"\"\"))\n", - "rootMeanSquaredError(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Object detection average precision" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TEST CASE 1 --- AP: 0.6666666666666666\n", - "TEST CASE 2 --- AP: 0.125\n", - "TEST CASE 3 --- AP: 0.4444444444444444\n", - "TEST CASE 4 --- AP: 0.4444444444444444\n" - ] - } - ], - "source": [ - "def group_gt_boxes_by_image_name(gt_boxes):\n", - " gt_dict: typing.Dict = {}\n", - "\n", - " for box in gt_boxes:\n", - " image_name = box[0]\n", - " bounding_polygon = box[1:]\n", - " bbox = convert_bouding_polygon_to_box_coords(bounding_polygon)\n", - "\n", - " if image_name not in gt_dict.keys():\n", - " gt_dict[image_name] = []\n", - "\n", - " gt_dict[image_name].append({'bbox': bbox})\n", - "\n", - " return gt_dict\n", - "\n", - "\n", - "def convert_bouding_polygon_to_box_coords(bounding_polygon):\n", - " # box_coords = [x_min, y_min, x_max, y_max]\n", - " box_coords = [bounding_polygon[0], bounding_polygon[1],\n", - " bounding_polygon[4], bounding_polygon[5]]\n", - " return box_coords\n", - "\n", - "\n", - "def voc_ap(rec, prec):\n", - " import numpy\n", - "\n", - " # First append sentinel values at the end.\n", - " mrec = numpy.concatenate(([0.], rec, [1.]))\n", - " mpre = numpy.concatenate(([0.], prec, [0.]))\n", - "\n", - " # Compute the precision envelope.\n", - " for i in range(mpre.size - 1, 0, -1):\n", - " mpre[i - 1] = numpy.maximum(mpre[i - 1], mpre[i])\n", - "\n", - " # To calculate area under PR curve, look for points\n", - " # where X axis (recall) changes value.\n", - " i = numpy.where(mrec[1:] != mrec[:-1])[0]\n", - "\n", - " # And sum (\\Delta recall) * prec.\n", - " ap = numpy.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])\n", - "\n", - " return float(ap)\n", - "\n", - "\n", - "def object_detection_average_precision(y_true, y_pred):\n", - " \"\"\"\n", - " This function takes a list of ground truth bounding polygons (rectangles in this case)\n", - " and a list of detected bounding polygons (also rectangles) for a given class and\n", - " computes the average precision of the detections with respect to the ground truth polygons.\n", - " Parameters:\n", - " -----------\n", - " y_true: list\n", - " List of ground truth polygons. Each polygon is represented as a list of\n", - " vertices, starting in the upper-left corner going counter-clockwise.\n", - " Since in this case, the polygons are rectangles, they will have the\n", - " following format:\n", - " [image_name, x_min, y_min, x_min, y_max, x_max, y_max, x_max, y_min].\n", - " y_pred: list\n", - " List of bounding box polygons with their corresponding confidence scores. Each\n", - " polygon is represented as a list of vertices, starting in the upper-left corner\n", - " going counter-clockwise. Since in this case, the polygons are rectangles, they\n", - " will have the following format:\n", - " [image_name, x_min, y_min, x_min, y_max, x_max, y_max, x_max, y_min, confidence_score].\n", - " Returns:\n", - " --------\n", - " ap: float\n", - " Average precision between detected polygons (rectangles) and the ground truth polylgons (rectangles).\n", - " (it is also the area under the precision-recall curve).\n", - " Example 1:\n", - " >> predictions_list_1 = [['img_00001.png', 110, 110, 110, 210, 210, 210, 210, 110, 0.6],\n", - " ['img_00002.png', 5, 10, 5, 20, 20, 20, 20, 10, 0.9],\n", - " ['img_00002.png', 120, 130, 120, 200, 200, 200, 200, 130, 0.6]]\n", - " >> ground_truth_list_1 = [['img_00001.png', 100, 100, 100, 200, 200, 200, 200, 100],\n", - " ['img_00002.png', 10, 10, 10, 20, 20, 20, 20, 10],\n", - " ['img_00002.png', 70, 80, 70, 150, 140, 150, 140, 80]]\n", - " >> ap_1 = object_detection_average_precision(ground_truth_list_1, predictions_list_1)\n", - " >> print(ap_1)\n", - " 0.667\n", - " Example 2:\n", - " >> predictions_list_2 = [['img_00285.png', 330, 463, 330, 505, 387, 505, 387, 463, 0.0739],\n", - " ['img_00285.png', 420, 433, 420, 498, 451, 498, 451, 433, 0.0910],\n", - " ['img_00285.png', 328, 465, 328, 540, 403, 540, 403, 465, 0.1008],\n", - " ['img_00285.png', 480, 477, 480, 522, 508, 522, 508, 477, 0.1012],\n", - " ['img_00285.png', 357, 460, 357, 537, 417, 537, 417, 460, 0.1058],\n", - " ['img_00285.png', 356, 456, 356, 521, 391, 521, 391, 456, 0.0843],\n", - " ['img_00225.png', 345, 460, 345, 547, 415, 547, 415, 460, 0.0539],\n", - " ['img_00225.png', 381, 362, 381, 513, 455, 513, 455, 362, 0.0542],\n", - " ['img_00225.png', 382, 366, 382, 422, 416, 422, 416, 366, 0.0559],\n", - " ['img_00225.png', 730, 463, 730, 583, 763, 583, 763, 463, 0.0588]]\n", - " >> ground_truth_list_2 = [['img_00285.png', 480, 457, 480, 529, 515, 529, 515, 457],\n", - " ['img_00285.png', 480, 457, 480, 529, 515, 529, 515, 457],\n", - " ['img_00225.png', 522, 540, 522, 660, 576, 660, 576, 540],\n", - " ['img_00225.png', 739, 460, 739, 545, 768, 545, 768, 460]]\n", - " >> ap_2 = object_detection_average_precision(ground_truth_list_2, predictions_list_2)\n", - " >> print(ap_2)\n", - " 0.125\n", - " Example 3:\n", - " >> predictions_list_3 = [['img_00001.png', 110, 110, 110, 210, 210, 210, 210, 110, 0.6],\n", - " ['img_00002.png', 120, 130, 120, 200, 200, 200, 200, 130, 0.6],\n", - " ['img_00002.png', 5, 8, 5, 16, 15, 16, 15, 8, 0.9],\n", - " ['img_00002.png', 11, 12, 11, 18, 21, 18, 21, 12, 0.9]]\n", - " >> ground_truth_list_3 = [['img_00001.png', 100, 100, 100, 200, 200, 200, 200, 100],\n", - " ['img_00002.png', 10, 10, 10, 20, 20, 20, 20, 10],\n", - " ['img_00002.png', 70, 80, 70, 150, 140, 150, 140, 80]]\n", - " >> ap_3 = object_detection_average_precision(ground_truth_list_3, predictions_list_3)\n", - " >> print(ap_3)\n", - " 0.444\n", - " Example 4:\n", - " (Same as example 3 except the last two box predictions in img_00002.png are switched)\n", - " >> predictions_list_4 = [['img_00001.png', 110, 110, 110, 210, 210, 210, 210, 110, 0.6],\n", - " ['img_00002.png', 120, 130, 120, 200, 200, 200, 200, 130, 0.6],\n", - " ['img_00002.png', 11, 12, 11, 18, 21, 18, 21, 12, 0.9],\n", - " ['img_00002.png', 5, 8, 5, 16, 15, 16, 15, 8, 0.9]]\n", - " >> ground_truth_list_4 = [['img_00001.png', 100, 100, 100, 200, 200, 200, 200, 100],\n", - " ['img_00002.png', 10, 10, 10, 20, 20, 20, 20, 10],\n", - " ['img_00002.png', 70, 80, 70, 150, 140, 150, 140, 80]]\n", - " >> ap_4 = object_detection_average_precision(ground_truth_list_4, predictions_list_4)\n", - " >> print(ap_4)\n", - " 0.444\n", - " \"\"\"\n", - "\n", - " \"\"\"\n", - " This function is different from others because ``y_true`` and ``y_pred`` are not vectors but arrays.\n", - " \"\"\"\n", - " import numpy\n", - " ovthresh = 0.5\n", - "\n", - " # y_true = typing.cast(Truth, unvectorize(y_true))\n", - " # y_pred = typing.cast(Predictions, unvectorize(y_pred))\n", - "\n", - " # Load ground truth.\n", - " gt_dict = group_gt_boxes_by_image_name(y_true)\n", - "\n", - " # Extract gt objects for this class.\n", - " recs = {}\n", - " npos = 0\n", - "\n", - " imagenames = sorted(gt_dict.keys())\n", - " for imagename in imagenames:\n", - " Rlist = [obj for obj in gt_dict[imagename]]\n", - " bbox = numpy.array([x['bbox'] for x in Rlist])\n", - " det = [False] * len(Rlist)\n", - " npos = npos + len(Rlist)\n", - " recs[imagename] = {'bbox': bbox, 'det': det}\n", - "\n", - " # Load detections.\n", - " det_length = len(y_pred[0])\n", - "\n", - " # Check that all boxes are the same size.\n", - " for det in y_pred:\n", - " assert len(det) == det_length, 'Not all boxes have the same dimensions.'\n", - "\n", - " image_ids = [x[0] for x in y_pred]\n", - " BP = numpy.array([[float(z) for z in x[1:-1]] for x in y_pred])\n", - " BB = numpy.array([convert_bouding_polygon_to_box_coords(x) for x in BP])\n", - "\n", - " confidence = numpy.array([float(x[-1]) for x in y_pred])\n", - " boxes_w_confidences_list = numpy.hstack((BB, -1 * confidence[:, None]))\n", - " boxes_w_confidences = numpy.empty((boxes_w_confidences_list.shape[0],),\n", - " dtype=[('x_min', float), ('y_min', float),\n", - " ('x_max', float), ('y_max', float),\n", - " ('confidence', float)])\n", - " boxes_w_confidences[:] = [tuple(i) for i in boxes_w_confidences_list]\n", - "\n", - " # Sort by confidence.\n", - " #sorted_ind = numpy.argsort(-confidence)\n", - " sorted_ind = numpy.argsort(\n", - " boxes_w_confidences, kind='mergesort',\n", - " order=('confidence', 'x_min', 'y_min'))\n", - " BB = BB[sorted_ind, :]\n", - " image_ids = [image_ids[x] for x in sorted_ind]\n", - "\n", - " # Go down y_pred and mark TPs and FPs.\n", - " nd = len(image_ids)\n", - " tp = numpy.zeros(nd)\n", - " fp = numpy.zeros(nd)\n", - " for d in range(nd):\n", - " R = recs[image_ids[d]]\n", - " bb = BB[d, :].astype(float)\n", - " ovmax = -numpy.inf\n", - " BBGT = R['bbox'].astype(float)\n", - "\n", - " if BBGT.size > 0:\n", - " # Compute overlaps.\n", - " # Intersection.\n", - " ixmin = numpy.maximum(BBGT[:, 0], bb[0])\n", - " iymin = numpy.maximum(BBGT[:, 1], bb[1])\n", - " ixmax = numpy.minimum(BBGT[:, 2], bb[2])\n", - " iymax = numpy.minimum(BBGT[:, 3], bb[3])\n", - " iw = numpy.maximum(ixmax - ixmin + 1., 0.)\n", - " ih = numpy.maximum(iymax - iymin + 1., 0.)\n", - " inters = iw * ih\n", - "\n", - " # Union.\n", - " uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +\n", - " (BBGT[:, 2] - BBGT[:, 0] + 1.) *\n", - " (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)\n", - "\n", - " overlaps = inters / uni\n", - " ovmax = numpy.max(overlaps)\n", - " jmax = numpy.argmax(overlaps)\n", - "\n", - " if ovmax > ovthresh:\n", - " if not R['det'][jmax]:\n", - " tp[d] = 1.\n", - " R['det'][jmax] = 1\n", - " else:\n", - " fp[d] = 1.\n", - " else:\n", - " fp[d] = 1.\n", - "\n", - " # Compute precision recall.\n", - " fp = numpy.cumsum(fp)\n", - " tp = numpy.cumsum(tp)\n", - " rec = tp / float(npos)\n", - " # Avoid divide by zero in case the first detection matches a difficult ground truth.\n", - " prec = tp / numpy.maximum(tp + fp, numpy.finfo(numpy.float64).eps)\n", - " ap = voc_ap(rec, prec)\n", - "\n", - " return ap\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " predictions_list_1 = [\n", - " ['img_00001.png', 110, 110, 110, 210, 210, 210, 210, 110, 0.6],\n", - " ['img_00002.png', 5, 10, 5, 20, 20, 20, 20, 10, 0.9],\n", - " ['img_00002.png', 120, 130, 120, 200, 200, 200, 200, 130, 0.6]\n", - " ]\n", - " ground_truth_list_1 = [\n", - " ['img_00001.png', 100, 100, 100, 200, 200, 200, 200, 100],\n", - " ['img_00002.png', 10, 10, 10, 20, 20, 20, 20, 10],\n", - " ['img_00002.png', 70, 80, 70, 150, 140, 150, 140, 80]\n", - " ]\n", - " ap_1 = object_detection_average_precision(\n", - " ground_truth_list_1, predictions_list_1)\n", - " print('TEST CASE 1 --- AP: ', ap_1)\n", - "\n", - " predictions_list_2 = [\n", - " ['img_00285.png', 330, 463, 330, 505, 387, 505, 387, 463, 0.0739],\n", - " ['img_00285.png', 420, 433, 420, 498, 451, 498, 451, 433, 0.0910],\n", - " ['img_00285.png', 328, 465, 328, 540, 403, 540, 403, 465, 0.1008],\n", - " ['img_00285.png', 480, 477, 480, 522, 508, 522, 508, 477, 0.1012],\n", - " ['img_00285.png', 357, 460, 357, 537, 417, 537, 417, 460, 0.1058],\n", - " ['img_00285.png', 356, 456, 356, 521, 391, 521, 391, 456, 0.0843],\n", - " ['img_00225.png', 345, 460, 345, 547, 415, 547, 415, 460, 0.0539],\n", - " ['img_00225.png', 381, 362, 381, 513, 455, 513, 455, 362, 0.0542],\n", - " ['img_00225.png', 382, 366, 382, 422, 416, 422, 416, 366, 0.0559],\n", - " ['img_00225.png', 730, 463, 730, 583, 763, 583, 763, 463, 0.0588],\n", - " ]\n", - " ground_truth_list_2 = [\n", - " ['img_00285.png', 480, 457, 480, 529, 515, 529, 515, 457],\n", - " ['img_00285.png', 480, 457, 480, 529, 515, 529, 515, 457],\n", - " ['img_00225.png', 522, 540, 522, 660, 576, 660, 576, 540],\n", - " ['img_00225.png', 739, 460, 739, 545, 768, 545, 768, 460],\n", - " ]\n", - " ap_2 = object_detection_average_precision(\n", - " ground_truth_list_2, predictions_list_2)\n", - " print('TEST CASE 2 --- AP: ', ap_2)\n", - "\n", - " predictions_list_3 = [\n", - " ['img_00001.png', 110, 110, 110, 210, 210, 210, 210, 110, 0.6],\n", - " ['img_00002.png', 120, 130, 120, 200, 200, 200, 200, 130, 0.6],\n", - " ['img_00002.png', 5, 8, 5, 16, 15, 16, 15, 8, 0.9],\n", - " ['img_00002.png', 11, 12, 11, 18, 21, 18, 21, 12, 0.9]\n", - " ]\n", - " ground_truth_list_3 = [\n", - " ['img_00001.png', 100, 100, 100, 200, 200, 200, 200, 100],\n", - " ['img_00002.png', 10, 10, 10, 20, 20, 20, 20, 10],\n", - " ['img_00002.png', 70, 80, 70, 150, 140, 150, 140, 80]\n", - " ]\n", - " ap_3 = object_detection_average_precision(\n", - " ground_truth_list_3, predictions_list_3)\n", - " print('TEST CASE 3 --- AP: ', ap_3)\n", - "\n", - " predictions_list_4 = [\n", - " ['img_00001.png', 110, 110, 110, 210, 210, 210, 210, 110, 0.6],\n", - " ['img_00002.png', 120, 130, 120, 200, 200, 200, 200, 130, 0.6],\n", - " ['img_00002.png', 11, 12, 11, 18, 21, 18, 21, 12, 0.9],\n", - " ['img_00002.png', 5, 8, 5, 16, 15, 16, 15, 8, 0.9]\n", - " ]\n", - " ground_truth_list_4 = [\n", - " ['img_00001.png', 100, 100, 100, 200, 200, 200, 200, 100],\n", - " ['img_00002.png', 10, 10, 10, 20, 20, 20, 20, 10],\n", - " ['img_00002.png', 70, 80, 70, 150, 140, 150, 140, 80]\n", - " ]\n", - " ap_4 = object_detection_average_precision(\n", - " ground_truth_list_4, predictions_list_4)\n", - " print('TEST CASE 4 --- AP: ', ap_4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/datasets/data-supply/documentation/code/d3m_eval.py b/datasets/data-supply/documentation/code/d3m_eval.py deleted file mode 100644 index e6ba03a..0000000 --- a/datasets/data-supply/documentation/code/d3m_eval.py +++ /dev/null @@ -1,246 +0,0 @@ -import numpy as np - - -def group_gt_boxes_by_image_name(gt_boxes): - gt_dict = {} - - for box in gt_boxes: - #x = box.split() - #image_name = x[0] - #bbox = [float(z) for z in x[1:]] - image_name = box[0] - bbox = box[1:] - #print(image_name, bbox) - - if image_name not in gt_dict.keys(): - gt_dict[image_name] = [] - - gt_dict[image_name].append({'bbox': bbox}) - - return gt_dict - - -def voc_ap(rec, prec, use_07_metric=False): - """ ap = voc_ap(rec, prec, [use_07_metric]) - Compute VOC AP given precision and recall. - If use_07_metric is true, uses the - VOC 07 11 point method (default:False). - """ - if use_07_metric: - # 11 point metric - ap = 0. - for t in np.arange(0., 1.1, 0.1): - if np.sum(rec >= t) == 0: - p = 0 - else: - p = np.max(prec[rec >= t]) - ap = ap + p / 11. - else: - # correct AP calculation - # first append sentinel values at the end - mrec = np.concatenate(([0.], rec, [1.])) - mpre = np.concatenate(([0.], prec, [0.])) - - # compute the precision envelope - for i in range(mpre.size - 1, 0, -1): - mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) - - # to calculate area under PR curve, look for points - # where X axis (recall) changes value - i = np.where(mrec[1:] != mrec[:-1])[0] - - # and sum (\Delta recall) * prec - ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) - return ap - - -def objectDetectionAP(dets, - gts, - ovthresh=0.5, - use_07_metric=False): - """ - This function takes a list of ground truth boxes and a list of detected bounding boxes - for a given class and computes the average precision of the detections with respect to - the ground truth boxes. - - Parameters: - ----------- - dets: list - List of bounding box detections. Each box is represented as a list - with format: - Case 1 (confidence provided): - ['image_name', 'x_min', 'y_min', 'x_max', 'y_max', 'confidence'] - Case 2 (confidence not provided): - ['image_name', 'x_min', 'y_min', 'x_max', 'y_max'] - - gts: list - List of ground truth boxes. Each box is represented as a list with the - following format: [image_name, x_min, y_min, x_max, y_max]. - - [ovthresh]: float - Overlap threshold (default = 0.5) - - [use_07_metric]: boolean - Whether to use VOC07's 11 point AP computation (default False) - - Returns: - -------- - rec: 1d array-like - Array where each element (rec[i]) is the recall when considering i+1 detections - - prec: 1d array-like - Array where each element (rec[i]) is the precision when considering i+1 detections - - ap: float - Average precision between detected boxes and the ground truth boxes. - (it is also the area under the precision-recall curve). - - Example: - - With confidence scores: - >> predictions_list = [['img_00285.png',330,463,387,505,0.0739], - ['img_00285.png',420,433,451,498,0.0910], - ['img_00285.png',328,465,403,540,0.1008], - ['img_00285.png',480,477,508,522,0.1012], - ['img_00285.png',357,460,417,537,0.1058], - ['img_00285.png',356,456,391,521,0.0843], - ['img_00225.png',345,460,415,547,0.0539], - ['img_00225.png',381,362,455,513,0.0542], - ['img_00225.png',382,366,416,422,0.0559], - ['img_00225.png',730,463,763,583,0.0588]] - >> ground_truth_list = [['img_00285.png',480,457,515,529], - ['img_00285.png',480,457,515,529], - ['img_00225.png',522,540,576,660], - ['img_00225.png',739,460,768,545]] - - >> rec, prec, ap = objectDetectionAP(predictions_list, ground_truth_list) - >> print(ap) - 0.125 - - Without confidence scores: - >> predictions_list = [['img_00285.png',330,463,387,505], - ['img_00285.png',420,433,451,498], - ['img_00285.png',328,465,403,540], - ['img_00285.png',480,477,508,522], - ['img_00285.png',357,460,417,537], - ['img_00285.png',356,456,391,521], - ['img_00225.png',345,460,415,547], - ['img_00225.png',381,362,455,513], - ['img_00225.png',382,366,416,422], - ['img_00225.png',730,463,763,583]] - >> ground_truth_list = [['img_00285.png',480,457,515,529], - ['img_00285.png',480,457,515,529], - ['img_00225.png',522,540,576,660], - ['img_00225.png',739,460,768,545]] - - >> rec, prec, ap = objectDetectionAP(predictions_list, ground_truth_list) - >> print(ap) - 0.0625 - - """ - - # Load ground truth - gt_dict = group_gt_boxes_by_image_name(gts) - - # extract gt objects for this class - recs = {} - npos = 0 - - imagenames = sorted(gt_dict.keys()) - for imagename in imagenames: - R = [obj for obj in gt_dict[imagename]] - bbox = np.array([x['bbox'] for x in R]) - det = [False] * len(R) - npos = npos + len(R) - recs[imagename] = {'bbox': bbox, - 'det': det} - - # Load detections - det_length = len(dets[0]) - - # Check that all boxes are the same size - for det in dets: - assert len(det) == det_length, 'Not all boxes have the same dimensions.' - - image_ids = [x[0] for x in dets] - BB = np.array([[float(z) for z in x[1:5]] for x in dets]) - - if det_length == 6: - print('confidence scores are present') - confidence = np.array([float(x[-1]) for x in dets]) - # sort by confidence - sorted_ind = np.argsort(-confidence) - sorted_scores = np.sort(-confidence) - - else: - print('confidence scores are not present') - num_dets = len(dets) - sorted_ind = np.arange(num_dets) - sorted_scores = np.ones(num_dets) - - BB = BB[sorted_ind, :] - image_ids = [image_ids[x] for x in sorted_ind] - - # print('sorted_ind: ', sorted_ind) - # print('sorted_scores: ', sorted_scores) - # print('BB: ', BB) - # print('image_ids: ', image_ids) - - # go down dets and mark TPs and FPs - nd = len(image_ids) - tp = np.zeros(nd) - fp = np.zeros(nd) - for d in range(nd): - R = recs[image_ids[d]] - bb = BB[d, :].astype(float) - ovmax = -np.inf - BBGT = R['bbox'].astype(float) - # print('det %d: ' % d) - # print('bb: ', bb) - - if BBGT.size > 0: - # compute overlaps - # intersection - ixmin = np.maximum(BBGT[:, 0], bb[0]) - iymin = np.maximum(BBGT[:, 1], bb[1]) - ixmax = np.minimum(BBGT[:, 2], bb[2]) - iymax = np.minimum(BBGT[:, 3], bb[3]) - iw = np.maximum(ixmax - ixmin + 1., 0.) - ih = np.maximum(iymax - iymin + 1., 0.) - inters = iw * ih - - # union - uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + - (BBGT[:, 2] - BBGT[:, 0] + 1.) * - (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) - - overlaps = inters / uni - ovmax = np.max(overlaps) - jmax = np.argmax(overlaps) - # print('overlaps: ', overlaps) - - if ovmax > ovthresh: - if not R['det'][jmax]: - # print('Box matched!') - tp[d] = 1. - R['det'][jmax] = 1 - else: - # print('Box was already taken!') - fp[d] = 1. - else: - # print('No match with sufficient overlap!') - fp[d] = 1. - - # print('tp: ', tp) - # print('fp: ', fp) - - # compute precision recall - fp = np.cumsum(fp) - tp = np.cumsum(tp) - rec = tp / float(npos) - # avoid divide by zero in case the first detection matches a difficult - # ground truth - prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) - ap = voc_ap(rec, prec, use_07_metric) - - return rec, prec, ap diff --git a/datasets/data-supply/documentation/datasetSchema.md b/datasets/data-supply/documentation/datasetSchema.md deleted file mode 100644 index 208b909..0000000 --- a/datasets/data-supply/documentation/datasetSchema.md +++ /dev/null @@ -1,590 +0,0 @@ -# Dataset Schema (version 4.0.0) - -Dataset schema provides a specification of an abstract dataset. It is contained in the [datasetSchema.json](../schemas/datasetSchema.json) file. An instance of this dataset schema is included with every dataset in the datasetDoc.json file. The semantics of datasetSchema.json and datasetDoc.json is [here](FAQ.md#semantics). - -Dataset schema specifies a dataset in three sections: about, dataResources, and qualities. Each of these sections are described below. - -## About - -The "about" section contains of some general information about the dataset and consists of the following fields. - -| Field | Description | -|-----------------------|---------------------------------------------------------------------------------------------------| -| datasetID | a unique ID assigned to a dataset | -| datasetName | the name of a dataset | -| datasetURI | the location of the dataset | -| description | a brief description of the dataset | -| citation | citation of the source of the dataset | -| humanSubjectsResearch | indicates if the dataset contains human subjects data or not | -| license | license of the source of the dataset | -| source | source of the dataset | -| sourceURI | location of the source of the dataset | -| approximateSize | size of the dataset | -| applicationDomain | application domain of the dataset (e.g., medical, environment, transportation, agriculture, etc.) | -| datasetVersion | version of the current dataset | -| datasetSchemaVersion | version of the datasetSchema | -| redacted | a field indicating if the dataset has been redacted or not | -| publicationDate | publication date of the dataset, if available | - -## Data Resources - -The "datasetResources" section annotates all the data resources in a dataset. A dataset is considered as a set of data resources. Therefore the "datasetResources" field is a list of dictionaries, one item per resource. Each dictionary item contains the following information about a data resource. - -| Field | Description | -|-----------------------|-------------------------------------------------------------------------------------------------------------------| -| resID | a unique ID assigned to a resource | -| resPath | path of this resource relative to the dataset root | -| resType | the type of this resource+ | -| resFormat | a dictionary structure containing the occurring media types in this dataset as the keys and their corresponding list of file formats as the values ++| -| isCollection | a boolean flag indicating if this resource if a single item/file or a collection of items/files +++. | - -+ __resType__ can be one of the following: __["image","video","audio","speech","text","graph","edgeList","table","timeseries","raw"]__ - -If `resType` is `edgeList`, the resource is basically a graph, but it is represented using an edge list. Each item in this list refers to an edge given by its source node and its target node (in that order, if it is a directed graph). For example, refer to [Case 5](#case-5) below. The edge list can also have optional edge attributes: [edgeID, source_node, target_node, edge_attr1, edge_attr2, ....] - - -++ Here is an example of `resFormat` -``` -"resFormat":{ - "image/jpeg":["jpeg", "jpg"], - "image/png":["png"] -} -``` - -The [supportedResourceTypesFormats.json](supportedResourceTypesFormats.json) file contains the current list of all the resTypes, resFormats and the file types used in the datasets in a machine-readable way. - - -+++A **collection** of resources/files can be organized into directories and sub-directories. - - -If `resType` is "table" or "timeseries" (which is also a table basically), columns in the table can be annotated with the following information. -Not all columns are necessary annotated (see [minimal metadata datasets](./minimalMetadata.md) for an example). -There is an optional `columnsCount` field of the resource which can convey how many columns there are, -which can be used to determine if all columns are annotated. - -| Field | Description | -|-----------------------|-------------------------------------------------------------------------------| -| colIndex | index of the column | -| colName | name of the column | -| colDescription | description of a column | -| colType | the data type of this column | -| role | the role this column plays | -| refersTo | an optional reference to another resource if applicable | -| timeGranularity | if a particular column represents time axis, this field captures the time sampling granularity as a numeric `value` and `unit` (e.g., 15-second granularity). | - -A column's type can be __one of__ the following: - -| Type | Description | -|----------------------|-------------------------------------------------------------------------------| -| boolean || -| integer || -| real || -| string || -| categorical || -| dateTime || -| realVector | This is stored as a string in CSV files. It's represented as "val1,val2,vl3,val4,...". For example, in the objectDetection task, the bounding boundary is a rectangle, which is represented as: "0,160,156,182,276,302,18,431". | -| json || -| geojson | See here: http://geojson.org/ | -| unknown | Column type is unknown. | - - -A column's role can be __one or more__ of the following: - -| Role | Description | -|-------------------------|-----------------| -| index | Primary index of the table. Should have unique values without missing values | -| multiIndex | Primary index of the table. Values in this primary index are not necessary unique to allow the same row to be repeated multiple times (useful for certain task types like multiLabel and objectDetection) | -| key | Any column that satisfies the uniqueness constraint can splay the role of a key. A table can have many keys. Values can be missing in such column. A key can be referenced in other tables (foreign key)| -| attribute | The column contains an input feature or attribute that should be used for analysis | -| suggestedTarget | The column is a potential target variable for a problem. If a problem chooses not to employ it as a target, by default, it will also have an attribute role | -| timeIndicator | Entries in this column are time entries | -| locationIndicator | Entries in this column correspond to physical locations | -| boundaryIndicator | Entries in this column indicated boundary (e.g., start/stop in audio files, bounding-boxes in an image files, etc.) | -| interval | Values in this column represents an interval and will contain a "realVector" of two values| -| instanceWeight | This is a weight assigned to the row during training and testing. It is used when a single row or pattern should be weighted more or less than others.| -| boundingPolygon | This is a type of boundary indicator representing geometric shapes. It's type is "realVector". It contains two coordinates for each polygon vertex - always even number of elements in string. The vertices are ordered counter-clockwise. For e.g., a bounding box in objectDetection consisting of 4 vertices and 8 coordinates: "0,160,156,182,276,302,18,431" | -| suggestedPrivilegedData | The column is potentially unavailable variable during testing for a problem. If a problem chooses not to employ it as a privileged data, by default, it will also have an attribute role | -| suggestedGroupingKey | A column with this role is a potential candidate for group_by operation (rows having the same values in this column should be grouped togehter). Multiple columns with this role can be grouped to obtain a hierarchical multi-index data. | -| edgeSource | Entries in this column are source nodes of edges in a graph | -| directedEdgeSource | Entries in this column are source nodes of directed edges in a graph | -| undirectedEdgeSource | Entries in this column are source nodes of undirected edges in a graph | -| multiEdgeSource | Entries in this column are source nodes of edges in a multi graph | -| simpleEdgeSource | Entries in this column are source nodes of edges in a simple graph | -| edgeTarget | Entries in this column are target nodes of edges in a graph | -| directedEdgeTarget | Entries in this column are target nodes of directed edges in a graph | -| undirectedEdgeTarget | Entries in this column are target nodes of undirected edges in a graph | -| multiEdgeTarget | Entries in this column are target nodes of edges in a multi graph | -| simpleEdgeTarget | Entries in this column are target nodes of edges in a simple graph | - -A time granularity unit can be __one of__ the following: - -| Unit | Description | -|----------------------|-------------------------------------------------------------------------------| -| seconds || -| minutes || -| days || -| weeks || -| months || -| years || -| unspecified || - -__Notes regarding index, multi-index roles__ -A table can have only one `index` or `multiIndex` column. - -For a table with `multiIndex` column, for a particular index value there can be multiple rows. Those rows have exactly the same values in all columns except columns with `suggestedTarget` role. - -Of special note is the "refersTo" field, which allows linking of tables to other entities. A typical "refersTo" entry looks like the following. -``` -"colIndex":1, - "colName":"customerID", - "colType":"integer", - "role":["attribute"], - "refersTo":{ - "resID":"0", - "resObject":{ - "columnName":"custID"}} -``` -Here, the customerID column in one table is linking to another resource whose "resID"="0", which is presumably a different table. Further, the object of reference, "resObject", is a column whose "columnName"="custID". - -A table entry can also refer to other kinds of resources besides another tables, including, a raw file in a collection of raw files (e.g., img1.png in a collection of images), a node in a graph, an edge in a graph. The below table lists the different resource objects that can be referenced. - -| resObject | resource referenced | Description | -|-----------|---------------------|-------------| -| columnIndex, columnName | table | Entries in this column refer to entries in another table (foreign key). See [example](#case-3) | -| item | a collection of raw files | Each entry in this column refers to an item in a collection of raw files. See [example](#case-2) | -| nodeAttribute | graph | Entries in this column refer to attribute values of a node in a graph. This can include a reference to the nodeID| -| edgeAttribute | graph | Entries in this column refer to attribute values of an edge in a graph. This can include a reference to the edgeID| - -__Notes regarding graph node/edge roles__ -The parent roles of egdeSource/edgeTarget can be combined with derived roles such as directedEdgeSource/directedEdgeTarget and undirectedEdgeSource/undirectedEdgeTarget to indicate directed or undirected edges in a graph. Similarly, the metadata can capture information about simple/multi-edges using multiEdgeSource/multiEdgeTarget and simpleEdgeSource/simpleEdgeTarget. - - -Next, we will see how this approach of treating everything as a resource and using references to link resources can handle a range of cases that can arise. - -## Case 1: Single table - -In many openml and other tabular cases, all the learning data is contained in a single tabular file. In this case, an example dataset will look like the following. -``` -/ -|-- tables/ - |-- learningData.csv - d3mIndex,sepalLength,sepalWidth,petalLength,petalWidth,species - 0,5.2,3.5,1.4,0.2,I.setosa - 1,4.9,3.0,1.4,0.2,I.setosa - 2,4.7,3.2,1.3,0.2,I.setosa - 3,4.6,3.1,1.5,0.2,I.setosa - 4,5.0,3.6,1.4,0.3,I.setosa - 5,5.4,3.5,1.7,0.4,I.setosa - ... -|-- datasetDoc.json -``` -The [datasetDoc.json](examples/iris.datasetDoc.json) for this example shows how this case is handled. Note that in this datasetDoc.json, the "role" of the column "species" is "suggestedTarget". The reason for this is given [here](FAQ.md#suggested-targets). - -## Case 2: Single table, multiple raw files - -Consider the following sample image classification dataset. It has one learningData.csv file, whose "image" column has pointers to image files. -``` -/ -|-- media/ - |-- img1.png - |-- img2.png - |-- img3.png - |-- img4.png -|-- tables/ - |-- learningData.csv - d3mIndex,image,label - 0,img1.png,cat - 1,img2.png,dog - 2,img3.png,dog - 3,img4.png,cat -|-- datasetDoc.json -``` -The [datasetDoc.json](examples/image.datasetDoc.json) for this example shows how this case is handled. Two things to note in this datasetDoc.json are: -- We define all the images in media/ as a single resource, albeit a collective resource (notice "isCollection"=true). -``` -{ - "resID": "0", - "resPath": "media/", - "resType": "image", - "resFormat": ["img/png"], - "isCollection": true, -} -``` -- We reference this image-collection resource ("resID"="1") in the "image" column of the "learningData.csv" resource: -``` -{ - "colIndex": 1, - "colName": "image", - "colType": "string", - "role": "attribute", - "refersTo":{ - "resID": "0", - "resObject": "item" - } -} -``` -The semantics here is as follows: The entries in the "image" column refers to an item ("resObject"="item") in a resource whose "resID"="0". Therefore, an entry 'img1.png' in this column refers to an item of the same name in the image collection. Since we know the relative path of this image-collection resource ("resPath"="media/"), we can locate "img1.png" at /media/img1.png. - -## Case 3: Multiple tables referencing each other -Consider the following dataset containing multiple relational tables referencing each other. -``` -/ -|-- tables/ - |-- customers.csv - custID,country,first_invoices_time,facebookHandle - 0,USA,12161998,"johnDoe" - 1,USA,06272014,"janeSmith" - 2,USA,12042014,"fooBar" - 3,AUS,02022006,"johnyAppleseed" - ... - |-- items.csv - stockCode,first_item_purchases_time,Description - FIL36,02162005,waterfilter - MAN42,06272014,userManual - GHDWR2,01112012,generalHardware - ... - |-- invoices.csv - invoiceNo,customerID,first_item_purchases_time - 0,734,12161998 - 1,474,11222010 - 2,647,10182011 - ... - |-- learningData.csv - item_purchase_ID,invoiceNo,invoiceDate,stockCode,unitPrice,quantity,customerSatisfied,profitMargin - 0,36,03142009,GYXR15,35.99,2,1,0.1, - 1,156,02022006,TEYT746,141.36,2,0,0.36 - 2,8383,11162010,IUYU132,57.25,2,0,0.22 - 3,3663,12042014,FURY145,1338.00,1,1,0.11 - 4,6625,07072013,DSDV97,762.00,1,1,0.05 - ... -``` -Here, "learningData" references both "invoice" and "items" tables. "invoice" table in turn references "customers" table. The [datasetDoc.json](examples/multitable.datasetDoc.json) for this example shows how this case is handled. - -There are a number of important things to notice in this example: - -- The "isColleciton" flag is False for all the resources, indicating that there are no collection-of-files resource as was the case in the image example above. - -- As shown in the snippet below, columns in one resource are referencing columns in another resource. -``` - -- In "resID"="1" --- -- In "resID"="3" --- -- In "resID"="3" --- - "colIndex":1, "colIndex":1, "colIndex":3, - "colName":"customerID", "colName":"invoiceNo", "colName":"stockCode", - "colType":"integer", "colType":"integer", "colType":"string", - "role":["attribute"], "role":["attribute"], "role":["attribute"], - "refersTo":{ "refersTo":{ "refersTo":{ - "resID":"0", "resID":"1", "resID":"2", - "resObject":{ "resObject":{ "resObject":{ - "columnName":"custID"}} "columnIndex":0}} "columnName":"stockCode"}} -``` -- A column can have multiple roles. For instance in "customer.csv", the column "facebookHandle" has two roles ("role":["attribute", "key"]). It is a key because it satisfies the uniqueness constraint. (see [here](FAQ.md#index-key) for distinction between index and key roles.) -``` -{ - "colIndex":3, - "colName":"facebookHandle", - "colType":"string", - "role":["attribute", "key"] -} -``` -- Finally, a column can be referenced using its columnIndex or columnName. See "resID"=3 entries above. Ideally we want column names participating in a reference for readability reasons, however, occasionally we have come across datasets with tables that do not have unique column names. In such cases, columnIndex is used. - -## Case 4: Referencing Graph Elements -Assume a sample graph resource below, G2.gml, whose 'resID' = 1 -``` -graph [ - node [ - id 0 - label "1" - nodeID 1 - ] - node [ - id 1 - label "88160" - nodeID 88160 - ] -... -``` - -A corresponding learningData.csv - -| d3mIndex | G2.nodeID | class | -|----------|-----------|-------| -|0 |88160 |1 | -|... |... |... | - - -A corresponding datasetDoc.json -``` -{ - "colIndex": 2, - "colName": "G2.nodeID", - "colType": "integer", - "role": [ - "attribute" - ], - "refersTo": { - "resID": "1", - "resObject": "node" - } -}, -``` - -- "resID": "1" - this points to the graph resource G2.gml -- "resObject": "node" - this points to a node in that graph. **All nodes have a 'nodeID' in the GML file and all nodes are uniquely identified and referenced by its nodeID.** -- So, in the learningData table above, row 0 points to a node in G2.gml whose nodeID = 88160: - -``` - node [ - id 1 - label "88160" - nodeID 88160 - ] -``` - -**Note:** nodeID's can also be strings as shown in the example below: -``` -graph.gml (resID=1) learningData.csv datasetDoc.json -=================== ================= ================ -graph [ | d3mIndex | child.nodeID | parent.nodeID | bond | ... - node [ |----------|--------------|---------------|------| { - id 0 | 0 | BSP | OPSIN | C | "colIndex": 1, - label "Protein Acyl Transferases" | 1 | RHOD | OPSIN | NC | "colName": "child.nodeID", - nodeID "PATS" | 2 | RXN | GPCR | NC | "colType": "string", - ] | ... | ... | ..... | ... | "role": [ - node [ "attribute" - id 1 ], - label "Blue Sensitive Opsins" "refersTo": { - nodeID "BSoP" "resID": "1", - ] "resObject": "node" -... }}, - ... -``` - -## Case 5: Graph resources represented as edge lists - -In v3.1.1, we introduced a new "resType" called "edgeList". This is how a dataset migth look for a case where the graphs are represented using edge lists. - -``` -/ - |-- tables/ - |-- learningData.csv - d3mIndex,user_ID,user_location,gender,age,high_valued_user - 0,2048151474,46614,M,37,"johnDoe",1 - 1,6451671537,08105,F,55,"janeSmith",1 - 2,6445265804,31021,F,23,"fooBar",1 - 3,0789614390,11554,58,"johnyAppleseed",1 - ... - |-- graphs - |-- mentions.edgelist.csv - edgeID,user_ID,mentioned_userID - 0,2048151474,0688363262 - 1,2048151474,1184169266 - 2,2048151474,3949024994 - 3,2048151474,7156662506 - ... -``` - -It's corresponding datasetDoc.json will look like the following: -``` - ... - { - "resID": "0", - "resPath": "graphs/mentions.edgelist.csv", - "resType": "edgeList", - "resFormat": ["text/csv"], - "isCollection": false, - "columns": [ - { - "colIndex": 0, - "colName": "edgeID", - "colType": "integer", - "role": [ - "index" - ], - }, - { - "colIndex": 1, - "colName": "user_ID", - "colType": "integer", - "role": [ - "attribute" - ], - "refersTo": { - "resID": "1", - "resObject":{ - "columnName":"user_ID"} - } - }, - { - "colIndex": 2, - "colName": "mentioned_userID", - "colType": "integer", - "role": [ - "attribute" - ], - "refersTo": { - "resID": "1", - "resObject":{ - "columnName":"user_ID"} - } - } - ] - }, - { - "resID": "learningData", - "resPath": "tables/learningData.csv", - "resType": "table", - "resFormat": ["text/csv"], - "isCollection": false, - "columns": [ - { - "colIndex": 0, - "colName": "d3mIndex", - "colType": "integer", - "role": [ - "index" - ], - }, - { - "colIndex": 1, - "colName": "user_ID", - "colType": "integer", - "role": [ - "attribute" - ] - }, - { - "colIndex": 2, - "colName": "user_location", - "colType": "categorical", - "role": [ - "attribute" - ], - }, - ... - }, -... -``` - -# Qualities - -A dataset can be annotated with its unique properties or __qualities__. The "qualities" section of the datasetSchema specifies the fields that can be used to used to capture those qualities. - -| Field | Description | -|-------|-------------| -| qualName | name of the quality variable | -| qualValue | value of the quality variable | -| qualValueType | data type of the quality variable; can be one of ["boolean","integer","real","string","dict"]| -| qualValueUnits | units of the quality variable | -| restrictedTo | if a quality is restricted to specific resource and not the entire dataset, this field captures that constraint | - -__Note:__ We are not providing a taxonomy of qualities but, through this section in the dataset schema, are providing a provision for capturing the qualities of a dataset in its datasetDoc.json file. - -Some qualities apply to an entire dataset. For instance, if one wants to annotate a dataset with (1) information about the presence of multi relational tables or not, (2) requires LUPI (Learning Using Privileged Information) or not, and/or (3) its approximate size: -``` -"qualities":[ - { - "qualName":"multiRelationalTables", - "qualValue":true, - "qualValueType":"boolean" - }, - { - "qualityName":"LUPI", - "qualityValue":true, - "qualValueType":"boolean" - }, - { - "qualName":"approximateSize", - "qualValue":300, - "qualValueType":"integer" - "qualValueUnits":"MB" -}] -``` - -Some qualities are applicable only to certain resources within a dataset. For instance, "maxSkewOfNumericAttributes" is table specific and not dataset wide. In such cases, we include the "restrictedTo" field: -``` -{ - "qualName":"maxSkewOfNumericAttributes", - "qualValue":6.57, - "qualValueType":"real", - "restrictedTo":{ - "resID":"3" // redID 3 is a table - } -}, -``` - -Further, some qualities are restricted to not only a specific resource but to a specific component within a resource. For instance, "classEntropy" and "numMissingValues" are only applicable to a column within a table within a dataset. In such cases, the "restrictedTo" field will have additional "resComponent" qualifier: -``` -{ - "qualName":"classEntropy", - "qualValue":3.63 - "qualValueType":"real", - "restrictedTo":{ - "resID":"3", - "resComponent":{"columnName":"customerSatisfied"} - } -}, -{ - "qualName":"numMissingValues", - "qualValue":12, - "qualValueType":"integer", - "restrictedTo":{ - "resID":"3", - "resComponent":{"columnName":"customerSatisfied"} - } -} -``` -Similarly a "privilegedFeature" called "facebookHandle" will be restricted to its column within a table (resID=0, customer table) within a dataset as shown below: -``` -{ - "qualName":"privilegedFeature", - "qualValue":true, - "qualValueType":"boolean", - "restrictedTo":{ - "resID":"0", - "resComponent":{"colName":"facebookHandle"} - } -} -``` - -For a full example see the [datasetDoc.json](examples/multitable.datasetDoc.json) file for the above Case 3. - -"resComponent" can also be strings "nodes" and "edges". - -# FAQ - -#### Why do we have distinct "index" and "key" roles - -An field which has unique values is a key. However, a key that is used to uniquely identify a row in a table is an index. There can be multiple keys in a table, but a single index. Both index and key columns can participate in the foreign key relationship with other tables. - -#### Why is "role" filed a list? - -A column can play multiple roles. For instance, it can be a "locationIndicator" and an "attribute", a "key" and an "attribute", and so on. - -#### How are LUPI datasets represented - -LUPI datasets contain columns for variables which are not available during testing (testing dataset split does not contain data for those columns). - -Such dataset can have some columns marked with role "suggestedPrivilegedData" to suggest that those columns might be used in the problem description as LUPI columns. The problem description has a list of such columns specified through "privilegedData" list of columns. - -#### Why are we using "suggestedTarget" role instead of "target" in datasetSchema? - -Defining targets is inherently in the domain of a problem. The best we can do in a dataset is to make a suggestion to the problem that these are potential targets. Therefore we use "target" in the problemSchema vocabulary and "suggestedTarget" in the datasetSchema vocabulary. - -#### What is the use for the role 'boundaryIndicator' -In some datasets, there are columns that denote boundaries and is best not treated as attributes for learning. In such cases, this tag comes in handy. Example: - -``` -d3mIndex,audio_file,startTime,endTime,event -0,a_001.wav,0.32,0.56,car_horn -1,a_002.wav,0.11,1.34,engine_start -2,a_003.wav,0.23,1.44,dog_bark -3,a_004.wav,0.34,2.56,dog_bark -4,a_005.wav,0.56,1.22,car_horn -... -``` -Here, 'startTime' and 'endTime' indicate the start and end of an event of interest in the audio file. These columns are good candidates for the role of boundary indicators. - -A boundary column can contain `refersTo` reference to point to the column for which it is a boundary. -If `refersTo` is not provided it means that a boundary column is a boundary for the first preceding non-boundary column. -E.g., in the example above, `startTime` and `endTime` columns are boundary columns for `audio_file` column -(or more precisely, for files pointed to by the `audio_file` column). diff --git a/datasets/data-supply/documentation/examples/image.datasetDoc.json b/datasets/data-supply/documentation/examples/image.datasetDoc.json deleted file mode 100644 index 42e417b..0000000 --- a/datasets/data-supply/documentation/examples/image.datasetDoc.json +++ /dev/null @@ -1,52 +0,0 @@ -{ -"about": - { - "datasetID": "image_dataset_1", - "datasetName":"Sample Image Dataset", - "humanSubjectsResearch": false, - "license":"CC", - "datasetSchemaVersion":"3.0", - "redacted":false - }, -"dataResources": - [ - { - "resID": "0", - "resPath": "media/", - "resType": "image", - "resFormat": ["img/png"], - "isCollection": true, - }, - { - "resID": "1", - "resPath": "tables/learningDoc.csv", - "resType": "table", - "resFormat": ["text/csv"], - "isCollection": false, - "columns":[ - { - "colIndex": 0, - "colName": "d3mIndex", - "colType": "integer", - "role": ["index"] - }, - { - "colIndex": 1, - "colName": "image", - "colType": "string", - "role": ["attribute"], - "refersTo":{ - "resID": "0", - "resObject": "item" - } - }, - { - "colIndex": 2, - "colName": "label", - "colType": "categorical", - "role": ["suggestedTarget"] - } - ] - } - ] -} \ No newline at end of file diff --git a/datasets/data-supply/documentation/examples/iris.datasetDoc.json b/datasets/data-supply/documentation/examples/iris.datasetDoc.json deleted file mode 100644 index 32e5b0c..0000000 --- a/datasets/data-supply/documentation/examples/iris.datasetDoc.json +++ /dev/null @@ -1,59 +0,0 @@ -{ -"about": - { - "datasetID": "iris_dataset_1", - "datasetName":"Iris Dataset", - "humanSubjectsResearch": false, - "license":"CC", - "datasetSchemaVersion":"3.0", - "redacted":false - }, -"dataResources": - [ - { - "resID": "0", - "resPath": "tables/learningDoc.csv", - "resType": "table", - "resFormat": ["text/csv"], - "isCollection": false, - "columns":[ - { - "colIndex": 0, - "colName": "d3mIndex", - "colType": "integer", - "role": ["index"] - }, - { - "colIndex": 1, - "colName": "sepalLength", - "colType": "real", - "role": ["attribute"] - }, - { - "colIndex": 2, - "colName": "sepalWidth", - "colType": "real", - "role": ["attribute"] - }, - { - "colIndex": 3, - "colName": "petalLength", - "colType": "real", - "role": ["attribute"] - }, - { - "colIndex": 4, - "colName": "petalWidth", - "colType": "real", - "role": ["attribute"] - }, - { - "colIndex": 5, - "colName": "species", - "colType": "categorical", - "role": ["suggestedTarget"] - } - ] - } - ] -} \ No newline at end of file diff --git a/datasets/data-supply/documentation/examples/multitable.datasetDoc.json b/datasets/data-supply/documentation/examples/multitable.datasetDoc.json deleted file mode 100644 index 6981d59..0000000 --- a/datasets/data-supply/documentation/examples/multitable.datasetDoc.json +++ /dev/null @@ -1,232 +0,0 @@ -{ - "about": { - "datasetID":"mt_mtc_1", - "datasetName":"Sample multitable relational dataset", - "humanSubjectsResearch": false, - "license":"CC", - "datasetSchemaVersion":"3.0", - "redacted":false - }, - "dataResources":[ - { - "resID":"0", - "resPath":"tables/customers.csv", - "resType":"table", - "resFormat":["text/csv"], - "isCollection":false, - "columns":[ - { - "colIndex":0, - "colName":"custID", - "colType":"integer", - "role":["index"] - }, - { - "colIndex":1, - "colName":"country", - "colType":"categorical", - "role":["attribute"] - }, - { - "colIndex":2, - "colName":"first_invoices_time", - "colType":"dateTime", - "role":["attribute"] - }, - { - "colIndex":3, - "colName":"facebookHandle", - "colType":"string", - "role":["attribute", "key"] - } - ] - }, - { - "resID":"1", - "resPath":"tables/invoices.csv", - "resType":"table", - "resFormat":["text/csv"], - "isCollection":false, - "columns":[ - { - "colIndex":0, - "colName":"invoiceNo", - "colType":"integer", - "role":["index"] - }, - { - "colIndex":1, - "colName":"customerID", - "colType":"integer", - "role":["attribute"], - "refersTo":{ - "resID":"0", - "resObject":{ - "columnName":"custID"}} - }, - { - "colIndex":2, - "colName":"first_item_purchases_time", - "colType":"dateTime", - "role":["attribute"] - } - ] - }, - { - "resID":"2", - "resPath":"tables/items.csv", - "resType":"table", - "resFormat":["text/csv"], - "isCollection":false, - "columns":[ - { - "colIndex":0, - "colName":"stockCode", - "colType":"string", - "role":["index"] - }, - { - "colIndex":1, - "colName":"first_item_purchases_time", - "colType":"dateTime", - "role":["attribute"] - }, - { - "colIndex":2, - "colName":"Description", - "colType":"string", - "role":["attribute"] - } - ] - }, - { - "resID":"learningData", - "resPath":"tables/learningData.csv", - "resType":"table", - "resFormat":["text/csv"], - "isCollection":false, - "columns":[ - { - "colIndex":0, - "colName":"item_purchase_ID", - "colType":"integer", - "role":["index"] - }, - { - "colIndex":1, - "colName":"invoiceNo", - "colType":"integer", - "role":["attribute"], - "refersTo":{ - "resID":"1", - "resObject":{ - "columnIndex":0}} - }, - { - "colIndex":2, - "colName":"invoiceDate", - "colType":"dateTime", - "role":["attribute"] - }, - { - "colIndex":3, - "colName":"stockCode", - "colType":"string", - "role":["attribute"], - "refersTo":{ - "resID":"2", - "resObject":{ - "columnName":"stockCode" - } - } - }, - { - "colIndex":4, - "colName":"unitPrice", - "colType":"real", - "role":["attribute"] - }, - { - "colIndex":5, - "colName":"quantity", - "colType":"integer", - "role":["attribute"] - }, - { - "colIndex":6, - "colName":"customerSatisfied", - "colType":"categorical", - "role":["suggestedTarget"] - }, - { - "colIndex":7, - "colName":"profitMargin", - "colType":"real", - "role":["suggestedTarget"] - } - ] - } - ], - "qualities":[ - { - "qualName":"multiRelationalTables", - "qualValue":true, - "qualValueType":"boolean" - }, - { - "qualityName":"LUPI", - "qualityValue":true, - "qualValueType":"boolean" - }, - { - "qualName":"privilegedFeature", - "qualValue":true, - "qualValueType":"boolean", - "restrictedTo":{ - "resID":"0", - "resComponent":{"colName":"facebookHandle"} - } - }, - { - "qualName":"approximateSize", - "qualValue":300, - "qualValueType":"integer", - "qualValueUnits":"MB" - }, - { - "qualName":"maxSkewOfNumericAttributes", - "qualValue":6.57, - "qualValueType":"real", - "restrictedTo":{ - "resID":"3" - } - }, - { - "qualName":"classEntropy", - "qualValue":3.63, - "qualValueType":"real", - "restrictedTo":{ - "resID":"3", - "resComponent":{"colName":"customerSatisfied"} - } - }, - { - "qualName":"numUniqueValues", - "qualValue":3667, - "qualValueType":"integer", - "restrictedTo":{ - "resID":"3", - "resComponent":{"columnIndex":1} - } - }, - { - "qualName":"numMissingValues", - "qualValue":12, - "qualValueType":"integer", - "restrictedTo":{ - "resID":"3", - "resComponent":{"columnName":"customerSatisfied"} - } - } - ] -} diff --git a/datasets/data-supply/documentation/minimalMetadata.md b/datasets/data-supply/documentation/minimalMetadata.md deleted file mode 100644 index 099fe29..0000000 --- a/datasets/data-supply/documentation/minimalMetadata.md +++ /dev/null @@ -1,53 +0,0 @@ -# Minimal Metadata Datasets (version 4.0.0) - -Begining with D3M Winter Workshop 2020, there is a push in the program to reduce the amount of manually curated metadata information that is provided in the datasets. The motivation is that as the D3M systems are transitioned to partner environments, it's unrealistic to expect that the datasets will be fully curated in D3M format. Therefore, the systems need to reduce their reliance on manually hand-coded metadata. This is a step in that direction. - -D3M core package clearly requires some metadata to work. Some of that metadata information can be inferred and others will be difficult to infer automatically and will have to be provided manually. For those that are provided manually, this page lists what metadata elements can be reliably expected in minimal metadata datasets, and the rest of it will be optional (i.e., not provided in most cases). - -**Note 1:** The structure of the D3M datasets will remain intact. - -**Note 2:** There will be not changes to the problem metadata that is provided, i.e., the problemDoc.json files will not be affected by this change. - -**Note 3:** All the changes in minimal metadata datasets (for now) will be w.r.t to the data schema, i.e, only datasetDoc.json files will be affected. For now, only the column metadata like column types and roles (with a few exceptions) have been removed when transitioning to minimal metadata format. The exceptions are listed in the table below. The resourse metadata will remain intact. - -**Note4** The master branch (seed datasets) moving forward will by default contain datasets in min metadata format - -**Note 5:** The original full metadata counterparts of the min metadata seeds are archived in a separate directory: training_datasets/seed_datasets_archive/ - -The following table lists the column metadata that can be extected per problem type. - - -| Problem Type | Column information retained in minimal metadata | -|------------------------------------------------------------|-------------------------------------------------| -| (classification, binary, tabular) | (index) | -| (classification, multiClass, tabular) | (index) | -| (classification, multiLabel, tabular) | (index, multiIndex) | -| (classification, binary, lupi, tabular) | (index) | -| (classification, binary, semiSupervised, tabular) | (index) | -| (classification, multiClass, semiSupervised, tabular) | (index) | -| (regression, univariate, tabular) | (index) | -| (regression, multivariate, tabular) | (index) | -| (classification, binary, tabular, relational) | (index, refersTo) | -| (classification, multiClass, tabular, relational) | (index, refersTo) | -| (regression, univariate, tabular, relational) | (index, refersTo) | -| (regression, multivariate, tabular, relational) | (index, refersTo) | -| (classification, binary, text) | (index, refersTo) | -| (classifciation, multiClass, text) | (index, refersTo) | -| (classification, binary, text, relational) | (index, refersTo) | -| ('classification', 'multiClass', 'video') | (index, refersTo) | -| ('classification', 'multiClass', 'image') | (index, refersTo) | -| (regression, univariate, image) | (index, refersTo) | -| (regression, multivariate, image) | (index, refersTo) | -| ('classification', 'multiClass', 'audio') | (index, refersTo) | -| (objectDetection, image) | (index, refersTo, multiIndex) | -| (graph, vertexClassification, multiClass) | (index, refersTo) | -| (graph, linkPrediction) | (index, refersTo) | -| (graph, graphMatching) | (index, refersTo) | -| (graph, communityDetection, nonOverlapping) | (index, refersTo) | -| (graph, linkPrediction, timeSeries) | (index, refersTo, timeIndicator) | -| (timeseries, forecasting, tabular) | (index, timeIndicator) | -| (timeseries, forecasting, tabular, grouped) | (index, timeIndicator, suggestedGroupingKey) | -| (classification, multiClass, timeseries) | (index, refersTo) | -| (classification, multiClass, timeseries) | (index, refersTo) | -| (classification, binary, timeseries, tabular, grouped) | (index, refersTo, suggestedGroupingKey) | -| (classification, multiClass, timeseries, tabular, grouped) | (index, refersTo, suggestedGroupingKey) | \ No newline at end of file diff --git a/datasets/data-supply/documentation/overview.md b/datasets/data-supply/documentation/overview.md deleted file mode 100644 index ebc0c5a..0000000 --- a/datasets/data-supply/documentation/overview.md +++ /dev/null @@ -1,119 +0,0 @@ -# D3MDS - -D3M Data Supply (D3MDS) refers to the data infrastructure provided by MIT Lincoln Lab for the D3M program. The basic components of D3MDS includes the following: Datasets, Problems and Baseline solutions. The guiding principles for this design are as follows: -1. Keep the three entities, Dataset, Problem, and Baseline solutions, decoupled. -2. Keep a dataset self-contained. -2. Provide a uniform and consistent way of dealing with datasets, which could be full datasets, a train view of the dataset (during blind evaluation), or a test view of the dataset (during blind evaluation). -3. Allow the possibility of annotating a dataset with metadata information. -4. Dataset schema should be able to handle multiple relational tables. -3. Datasets and Problems should be decoupled. - -## Dataset - -One of the core components of D3MDS are datasets. Each dataset is a self-contained set of data resources. These data resources can come in many types and formats. Some of the types of data resources that one can expect to see in D3MDS include: image, video, audio, speech, text, table, graph, timeseries, etc. Each type can be supplied in one or more formats. For example, image resources can come in PNG, JPEG, etc. formats. Our file structure convention for organizing a dataset is as follows: -``` -. / -|-- media/ -|-- text/ -|-- tables/ -|-- graphs/ -|-- datasetDoc.json -``` -Convention: The name of the root directory of a dataset is its dataset_id - -Suggested sub-directory names and structure: - -| | | -|-----------------|-------------------------------------------------------------------------------------------------------------------| -| / | the root directory | -| media/ | (optional) directory containing media files, if any (e.g. images, video, audio, etc.) | -| text/ | (optional) directory containing text documents, if any | -| tables/ | (required) directory containing tabular data. This is not optional as a dataset will contain at least one table | -| graphs/ | (optional) directory containing graphs, if any | -| datasetDoc.json | (required) JSON document that describes all the data resources in the dataset (an instance of the dataset schema) | - -The datasetDoc.json file provides a description of all the elements of a dataset as a JSON document. This document is specified according to a predefined dataset schema. In other words, datasetDoc is an instance of datasetSchema. - -A small sample Dataset is shown in the figure below - -![A sample dataset](static/sampleDataset.PNG) - - -__Special "learningData" file__: All datasets will have a main data file, a dataset entry point, which can be considered as an entry point into the dataset. Its resource name is always 'learningData' and for backwards compatibility this file is always named 'learningData'. It's format is typically CSV (learningData.csv), but not necessarily the case. This file is treated as just another tabular resource and will be placed in the tables/ directory as shown below. It's columns and format will be annotated in the datasetDoc.json similar to any other table that may be part of the dataset. An example of learningDoc file can be seen in the sample dataset figure above. -``` -. / -|-- tables/ - |-- learningData.csv -|-- datasetDoc.json -``` - -### datasetSchema - -* The datasetSchema is version controlled and can be found in [datasetSchema.json](../schemas/datasetSchema.json) -* Full documentation for datasetSchema can be found in [datasetSchema.md](datasetSchema.md) - -## Problems - -A dataset alone does not constitute a data science problem. A problem is developed over a dataset by defining a task, inputs for the task (which includes a dataset), and expected outputs for the task. Multiple problems can be developed for a single dataset. Our convention for organizing a problem is as follows: - -``` -. / -|-- dataSplits.csv -|-- problemDoc.json -``` -Convention: The name of the root directory of a problem is its problem_id - -| | | -|-----------------|--------------------------------------------------------------------------------------------------------------------------------| -| / | the root directory | -|dataSplits.csv | (optional) data splits file which specifies the train/test split of the data when the split is defined manually | -|problemDoc.json | (required) JSON document that describes the task, inputs and expected output of the problem (an instance of the problem schema)| - -A small sample Problem is shown in the figure below. - -![A sample problem](static/sampleProblem.PNG) - -__Special "dataSplits" file__: When evaluation split has been done manually and not algorithmically, the dataSplits file contains information about which rows in learningData.csv are 'TRAIN' rows and which ones are 'TEST' rows. Normally (outside the context of the blind evaluation), performer systems can use this file to infer the trainData and testData splits. In the context of the blind evaluation, this file will be used to create two separate views of the dataset, the train view and test view, as described in the Dataset Views section below. An example of dataSplits file can be seen in the sample problem figure above. - -The dataSplits file also contains the repeat number and fold number if multiple repeats or folds are used in the problem definition. Some training datasets have different evaluation procedures(e.g., 10-fold CV, 3 repeats of 20% holdout, etc.). This approach of including repeat and fold information in dataSplits will handle all the different cases. __However, during blind evaluation, only holdout method is used without any repeats. Therefore, for evaluation problems, repeat and fold will always contain 0 values__ - -### problemSchema -* The problemSchema is version controlled and can be found in [problemSchema.json](../schemas/problemSchema.json) -* Full documentation for problemSchema can be found in [problemSchema.md](problemSchema.md) - -## Baseline solutions -A baseline solution is a solution to a problem. In its current conception, it consists of runnable code that takes as input a problem and produces an output (as specified in the problem schema). There can be multiple solution for a given problem. Our convention for organizing a python solution is as follows: -``` -. / -|-- src/ -|-- run.py -|-- predictions.csv -``` -| | | -|-----------------|--------------------------------------------------------------------------------------------------------------------------------| -| / | the root directory | -|src/ | source code for the pipeline | -|run.py | runner script for running the solution | -|predictions.csv | a file containing the predictions of the solution for the give problem | -|scores.json | a file containing the performance scores of the solution | - - -# Dataset Views - -For all the datasets that are released to the performers (e.g., training datasets), performers get a full view of the dataset as shown below: - -![](static/sampleSupply.PNG) - -However, for the blind evaluation purposes, three separate views of the dataset will be created. These are no just logical views, but two separate physical views of the dataset. For each dataset and problem combination, dataset_TRAIJN, dataset_TEST, dataset_SCORE variants will be created. By convention they always have those suffixes. -TEST view is the same as SCORE view, only that TEST view has all target values redacted. - -![](static/allViews.PNG) - -Only the pertinent data resources from the full dataset will be replicated in the train and test views. For instance, the train view will be created by making a copy of a full dataset and deleting 'TEST' rows in learningData.csv and any other data resources that are referenced by those rows. The same is true for train view of the problem. - -![](static/trainView.PNG) - -During blind evaluation, at test time, test view will be provided as shown below. Note that the labels in the learningDoc.csv have been removed for the test view. - -![](static/testView.PNG) - diff --git a/datasets/data-supply/documentation/problemSchema.md b/datasets/data-supply/documentation/problemSchema.md deleted file mode 100644 index acda70e..0000000 --- a/datasets/data-supply/documentation/problemSchema.md +++ /dev/null @@ -1,595 +0,0 @@ -# Problem Schema (version 4.0.0) - -Dataset schema provides a specification of an abstract data science problem. It is contained in the [problemSchema.json](../schemas/problemSchema.json) file. An instance of this schema is included with every problem in the problemDoc.json file. - -Problem schema specifies a dataset in three sections: about, inputs, and expectedOutputs. Each of these sections are described below. - -# About - -The "about" section contains of some general information about the problem and consists of the following fields. - -| Field | Description | -|-----------------------|---------------------------------------------------------------------------------------------------| -| problemID | a unique ID assigned to a problem | -| problemName | the name of a problem | -| problemDescription | a brief description of the problem | -| problemURI | the location of the problem | -| taskKeywords | a list of keywords that capture the nature of the machine learning task | -| problemVersion | the version of the current problem | -| problemSchemaVersion | the version of the problem schema | - -Currently, the keywords that can be combined to describe the task are the following: - -| taskKeywords |Notes| -|--------------|-----| -|classification|supervised learning task - learn from a labeled dataset to assign a class labels to prediction samples| -|regression|supervised learning task - learn from a labeled dataset to assign a numeric values to prediction samples| -|clustering|unsupervised learning task - no labeled dataset, cluster samples and assign a cluster label to all samples| -|linkPrediction|[linkPrediction](#link-prediction) task| -|vertexNomination|[vertexNomination](#vertex-nomination) task| -|vertexClassification|[vertexClassification](#vertex-classification) task| -|communityDetection|[communityDetection](#community-detection) task| -|graphMatching|[graphMatching](#graph-matching) task| -|forecasting|data is indexed by time dimension and the task is to predict future values based on previously observed values| -|collaborativeFiltering|task of filling blank cells in a utility matrix where each cell in the matrix holds association between two entities (e.g., users and products)| -|objectDetection|[objectDetection](#object-detection-task) task| -|semiSupervised|[semiSupervised](#semi-supervised) learning task| -|unsupervised|unsupervised learning task - no labeled dataset| -|binary|binary classification task| -|multiClass|multi-class classification task| -|multiLabel|multi-label classification task| -|univariate|applied to "regression" task with a single response variable| -|multivariate|applied to "regression" task with more than one response variables| -|overlapping|applied to "communityDetection" problems to indicate overlapping communites: multiple community memberships for nodes| -|nonOverlapping|applied to "communityDetection" problems to indicate disjoint communites: single community memberships for nodes| -|tabular|indicates data is tabular| -|relational|indicates data is a relational database| -|nested |indicates that a table consists of nested tables. For example, a column entry can point to an entire table stored in a separate CSV file (similar to pointing to an image or other media files)| -|image|indicates data consists of raw images| -|audio|indicates data consists of raw audio| -|video|indicates data consists of raw video| -|speech|indicates human speech data| -|text|indicates data consists of raw text| -|graph|indicates data consists of graphs| -|multiGraph|indicates data consists of multigraphs| -|timeSeries|indicates data consists of time series| -|grouped|applied to time series data (or tabular data in general) to indicate that some columns should be [grouped](#grouped-time-series)| -|geospatial|indicates data contains geospatial information| -|remoteSensing|indicates data contains remote-sensing data| -|lupi|indicates the presence of privileged features: [lupi](#LUPI)| -|missingMetadata|indicates that the metadata for dataset is not complete| - -# Inputs - -This section specifies the three inputs that are required to understand and solve a problem in the context of D3M. They include: data, data splits, and performance metrics. -``` -"inputs":{ - "data":[ - ... - ], - "dataSplits":{ - ... - }, - "performanceMetrics":{ - ... - } -} -``` - -## Data - -Data refers to the dataset(s) over which a problem is defined. A problem can refer to multiple datasets. That is captured by the datasetID field, which is a list. -``` -"data":[ - { - "datasetID":"sample_dataset_ID", - ... - } -] -``` - -In addition to the datasetID, one has to also specify target variable(s) on that dataset as part of the problem specification. Each target variable is specified by referring to a table using its "resID" and specifying its target column index and column name. For a correct specification, the target column names and indexes in problemDoc should match with the corresponding column names and indexes in datasetDoc. The redundancy of specifying a target by its column name and index is by design. -``` -"data": [ - { - "datasetID":"sample_dataset_ID" - "targets": - [ - { - "targetIndex":0, - "resID":"0", // reference to a table in the dataset - "colIndex":18, - "colName":"classLabel" - } - ] - } -] -``` -For more information about "resID", "colIndex", and "colName", refer to the [datasetSchema.json](../schemas/datasetSchema.json) - -"targets" also has an optional "numClusters" field. This field is applicable to clustering problems. It is used to specify the number of clusters to be generated by the solution algorithm (if this information is known apriori). - - -If the task is time series forecasting, then the problem specification can contain additional information about the horizon of forecast. It will contain a number, which indicates the max number of time steps in future the predictions will need to be made. The horizon is in the units of `timeGranularity` in the column metadata. In the following example, assuming that the `timeGranularity` 5 days, the prediction horizon is 10 future steps, that is 50 days: -``` -"forecastingHorizon":{ - "resID": "learningData", - "colIndex": 8, - "colName": "time", - "horizonValue": 10.0 - } -``` - -Data can also contains "privilegedData" list of columns related to unavailable variables during testing. Those columns do not have data available in the test split of a dataset. -``` -"data": [ - { - "datasetID":"sample_dataset_ID" - "privilegedData": [ - { - "privilegedDataIndex": 0, - "resID": "learningData", - "colIndex": 20, - "colName": "HISTOLOGY" - } - ] - } -] -``` - - -## Data Splits - -Every problem has a special __dataSplits__ file. This file contains information about which rows in the learning data are 'TRAIN' rows and which ones are 'TEST' rows. It has the following columns: [d3mIndex, type, repeat, fold] as shown in the problem sample below. - -![](static/sampleProblem.PNG) - -This split file indirectly reflects the evaluation procedure that was used to define the problem. The "dataSplits" section in the problem schema contains information that can be used to infer the evaluation procedure and interpret the dataSplits file. It contains the following fields: - -| Field | Description | -|-------|-------------| -| method | refers to the evaluation method reflected in the data splits. Currently, it can be one of "holdOut" or "kFold". Can be extended in future to accommodate others. It is always 'holdOut' by default | -| testSize | applicable to "holdOut" method, it specifies the size of the test split in the range 0.0 to 1.0. | -| numFolds | applicable to "kFold" method, it specifies the number of folds | -| stratified | specifies if the split is stratified or not, default value being True | -| numRepeats | specifies how many repeats, e.g., 3 repeats of 50% holdout | -| randomSeed | the random number generator seed that was used to generate the splits | -| splitsFile | the relative path to the splits file from the problem root, which is "dataSplits.csv" directly under the problem root by default| -| splitScript | the relative path from the problem root to the script that was used to create the data split (optional) | - -## Performance metrics - -Another important part of a problem specification is set of metrics that will be used to evaluate the performance of a solution. The "performanceMetrics" section of the problem schema is a **list** of metrics. The provided metrics are the following. - -| Metric | Notes | -|--------|-------| -| accuracy | sklearn.metrics.accuracy_score | -| precision | sklearn.metrics.precision_score | -| recall | sklearn.metrics.recall_score | -| f1 | sklearn.metrics.f1_score (pos_label=1) | -| f1Micro | sklearn.metrics.f1_score(average='micro') | -| f1Macro | sklearn.metrics.f1_score(average='macro') | -| rocAuc | Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) - only works with binary data, requires confidence | -| rocAucMacro | Compute rocAuc metric for each label and compute unweighted mean - only works for multi-class and multi-label data, requires confidence | -| rocAucMicro | Compute rocAuc metric globally by considering each element of the label indicator matrix as a binary prediction - only works for multi-class and multi-label data, requires confidence | -| meanSquaredError | sklearn.metrics.mean_squared_error, average computed over multiple target columns | -| rootMeanSquaredError | sqrt(sklearn.metrics.mean_squared_error), average computed over multiple target columns | -| meanAbsoluteError | sklearn.metrics.mean_absolute_error, average computed over multiple target columns | -| rSquared | sklearn.metrics.r2_score | -| normalizedMutualInformation | sklearn.metrics.normalized_mutual_info_score | -| jaccardSimilarityScore | sklearn.metrics.jaccard_similarity_score | -| precisionAtTopK | number of values shared between first K entries of ground truth and predicted labels and normalized by K | -| objectDetectionAP | an implementation of mean average precision for object detection problems, mean is computed accross classes (when there are multiple classes), average accross bounding polygons of one class, requires confidence | -| hammingLoss | wraps sklearn.metrics.hamming_loss function - used for multilabel classification problems. | -| meanReciprocalRank | computes the mean of the reciprocal of elements of a vector of rankings - used for linkPrediction problems.| -| hitsAtK | computes how many elements of a vector of ranks make it to the top 'k' positions - used for linkPrediction problems.| - - -**Note**: There can be multiple performance metrics included for a single problem. For example: -``` -"performanceMetrics": [ - { - "metric": "accuracy" - }, - { - "metric": "f1" - } - ] -``` -### Optional parameters for metrics -Notice that there are optional metric parameters in the problemSchema: -``` -... - "K":{"type":"integer","required":false,"dependencies": {"metric":["precisionAtTopK","hitsAtK"]}}, - "posLabel":{"type":"string","required":false, "dependencies": {"metric":["f1","precision","recall"]}} -... -``` - -| parameter | Notes | -|-----------------------|--------------------------------------------------------------------------------------------------------------------------------| -| K | This is applicable to the metrics ['precisionAtTopK', 'hitsAtK'] provided the value of K | -| posLabel | This is applicable to 'f1', 'precision', and 'recall' metrics, and indicates which class should be treated as "positive" class | - -# Expected outputs - -The "expectedOutputs" in the problem schema directs the solution systems to produce output(s) in a standardized manner. -``` -"expectedOutputs":{"type":"dict","required":true,"schema":{ - "predictionsFile": {"type":"string", "required":true, "default":"predictions.csv"}, - "scoresFile": {"type":"string", "required":false, "default":"scores.csv"} -}} -``` -## Predictions file - -Currently, there is only one required output, which is the predictions file - all solution systems should output this file. By default the name of this file is "predictions.csv", but can be changed in a problem instance using the "predictionsFile" field in the problem schema. - -The structure of predictions file depends on the metric used in the problem description. - -### `objectDetectionAP` metric - -`objectDetectionAP` metric requires the following structure: - * `d3mIndex` column - * target columns (names depend on problem target columns) - * `confidence` column - -Generally target columns are: a column for class/label and a column for object boundary. -For same input sample (image) multiple output rows can be made (matching in `d3mIndex` value), -but **only** those rows for objects which are determined to be found in the image. - -|d3mIndex|class|bounding_box |confidence| -|--------|-----|---------------|----------| -|0 |dog |"..." |... | -|0 |cat |"..." |... | -|0 |cat |"..." |... | -|0 |cat |"..." |... | -|1 |cat |"..." |... | -|1 |cat |"..." |... | -|1 |cat |"..." |... | -|1 |pig |"..." |... | -|1 |pig |"..." |... | -|2 |man |"..." |... | -|2 |man |"..." |... | -|2 |man |"..." |... | -|2 |bird |"..." |... | -|2 |car |"..." |... | - -### `rocAuc`, `rocAucMacro`, `rocAucMicro` metrics - -`rocAuc`, `rocAucMacro`, `rocAucMicro` metrics require the following structure: - * `d3mIndex` column - * target column (name depends on the problem target column) - * `confidence` column - -Target column represents class/label. For one input sample multiple output rows should be made (matching in `d3mIndex` value), -for **all** possible class/label values, each one row. The `confidence` value should then be confidence of classification -into that class/label. - -| d3mIndex | label | confidence | -|----------|-------|------------| -| 640 | 0 | 0.612 | -| 640 | 1 | 0.188 | -| 640 | 2 | 0.2 | -| 641 | 0 | 0.4 | -| 641 | 1 | 0.25 | -| 641 | 2 | 0.35 | -| 642 | 0 | 1.0 | -| 642 | 1 | 0.0 | -| 642 | 2 | 0.0 | -| 643 | 0 | 0.52 | -| 643 | 1 | 0.38 | -| 643 | 2 | 0.1 | -| 644 | 0 | 0.3 | -| 644 | 1 | 0.2 | -| 644 | 2 | 0.5 | -| 645 | 0 | 0.1 | -| 645 | 1 | 0.2 | -| 645 | 2 | 0.7 | -| 646 | 0 | 1.0 | -| 646 | 1 | 0.0 | -| 646 | 2 | 0.0 | - -### `hitsAtK`, `meanReciprocalRank` -`hitsAtK` and `meanReciprocalRank`metrics require the following structure: -* `d3mIndex` column -* target column (name depends on the problem target column) -* `rank` column - -`rank` is a reserved keyword. Target column represents class/label. For one input sample multiple output rows should be made (matching in `d3mIndex` value). Unlike `rocAuc` this need not have all possible class/label values. Example: - -learningData.csv: - -|d3mIndex |subject |object |relationship (target)| -|------------|--------|------------|---------------------| -|0 |James |John |father | -|1 |John |Patricia |sister | -|2 |Robert |Thomas |brother | -|... |... |... |... | -|... |... |... |... | - -ground truth (coming from learningData.csv): - -|d3mIndex |relationship | -|------------|-------------| -|0 |father | -|1 |sister | -|2 |brother | - -predictions.csv: - -|d3mIndex |relationships |rank | -|------------|----------------|-----| -|0 |brother |1 | -|0 |cousin |2 | -|0 |mother |3 | -|0 |father |4* | -|0 |grandfather |5 | -|1 |sister |1* | -|1 |mother |2 | -|1 |aunt |3 | -|2 |father |1 | -|2 |brother |2* | -|2 |sister |3 | -|2 |grandfather |4 | -|2 |aunt |5 | - -Note that the rank vector = [4,1,2] - -MRR = sum(1/ranks)/len(ranks) = 0.58333 - -Hits@3 = 2/3 = 0.666666; Hits@1 = 1/3 = 0.3333333; Hits@5 = 3/3 = 1.0 - - -### Other metrics, multi-label - -Output has for each input sample multiple output rows (matching in `d3mIndex` value), with `d3mIndex` column and a column for predicted labels. -Only predicted labels should be listed in output rows. - -| d3mIndex | label | -|----------|-------| -| 640 | 0 | -| 640 | 1 | -| 641 | 0 | -| 642 | 0 | -| 642 | 1 | -| 642 | 2 | -| 643 | 1 | -| 644 | 1 | -| 645 | 0 | -| 645 | 2 | -| 646 | 2 | - -### Other metrics - -For each input sample one output row should be provided (matching in `d3mIndex` value), with `d3mIndex` column and a target column for the prediction, -or multiple target columns in `multivariate` case. -Target column names depend on problem target columns. - -| d3mIndex | class | -|----------|-------| -| 640 | 0 | -| 641 | 0 | -| 642 | 2 | -| 643 | 1 | -| 644 | 1 | -| 645 | 0 | -| 646 | 2 | - -__Notes__ -- A target column is represented in the predictions file by its **colName**, which is specified in problemDoc.json under **inputs**->**data**->**targets**. -- We are interested in predictions for 'TEST' data points only. -- Typically, a predictions file will contain only one target column. If a given problem has multiple independent targets, it will be presented as separate problems, each with a single target. A simple test if multiple targets can be listed in one problem: is there only one confidence computed for those targets. -- If a dataset has multiple targets that are related, then the predictions file will have additional target columns. For example, see [object detection](#object-detection-task). -- Predictions can optionally have one or more of the below listed special columns which are required to compute certain metrics (e.g., `rocAuc` requires `confidence` and `meanReciprocalRank` requires `rank`). Besides `d3mIndex`, the following are reserved column names in predictions: - - `confidence` - - `rank` - -## Scores file - -A scores file can be another output from a solution pipeline when the ground truth for test data is known. Hence, this output is optional. Problem schema already specifies the given metrics under `performanceMetrics` under `inputs` section. -The standard scores.csv file can contain the following columns: `[metric, value, normalized, randomSeed, fold]`, with `normalized` (a normalized score into the range [0, 1] where higher is better), -`randomSeed` (random seed value used to run the solution pipeline), and `fold` (a 0-based index of the fold) being optional. `randomSeed` is the main seed used by a solution pipeline. -`metric` is the name of the metric as defined in `performanceMetrics` or equivalent (like d3m core package enumeration). - -The baselines systems produced by MIT-LL will produce both predictions file and scores file. - -Old scores file format with columns `[index, metric, value]` has been deprecated, but you might still see it around. - -# Data Augmentation -The "dataAugmentation" field provides information about external sources of data that can be used to address the challenge of data augmentation. It is a list of dictionaries, each item corresponding to one external source. Each dictionary for an external source contains two fields: domain and keywords. "domain" captures the application domain(s) of an external dataset (e.g., government, census, economics) and "keywords" capture additional tags that help narrow the search (e.g., housing, household income). - -# Appendix - - -## Link Prediction -Given an input graph/multiGraph, link prediction involves predicting future possible links in the network or predicting missing links due to incomplete data in the network. - - -## Vertex Nomination -Given an input graph, vertex nomination involves learning a model that, to each observed node in a graph, assigns a prioritized nomination list. - - -## Vertex Classification -Given an input (possibly node-attributed) graph, vertex classification involves assigning labels/classes to unknown nodes in the network based on the classes (and attributes) of known nodes and the network structure. Similar to traditional classification tasktype, vertex classification can be of subtype binary, multiclass, or multilabel. - - -## Community Detection -Given an input graph/network possibly with a community structure, community detection involves grouping nodes into sets of (overlapping or non-overlapping) groups/clusters that reflect the community structure. - - -## Graph Matching -Given two input graphs, the task here is to find an approximate matching between these two graphs based on their structural properties. We assume that a partial correspondence between graphs is given. Given two other nodes not specified in the partial correspondence, the model has to predict if they match/correspond or not. - - -## Semi-supervised Classification -This task requires learning classification models from both labeled and unlabeled data. The structure of this task is the same as the standard classification task, except that the target column will only have a small number of the labels provided. The rest of the values in the target column will be empty. Similar to supervised classification, semi-supervised classification can be of subtype binary, multiclass, or multilabel. -## Semi-supervised Regression -This task requires learning regression models from both labeled and unlabeled data. The structure of this task is the same as the standard regression task, except that the target column will only have a small number of the labels provided. The rest of the values in the target column will be empty. Similar to supervised regression, semi-supervised regression can be of subtype univariate, or multivariate. - - -## Learning Using Privileged Information -This is a special case of classification and regression problems where the training set has certain extra features called privileged features and the test set does not have those features. The models have to deal with feature mismatch across train/test split and take advantage of extra features available at train time. - - -## Grouped time series forecasting -Adding "grouped" qualifier to a time-series forecasting task implies that the data is grouped hierarchically. For example, the total monthly sales data of a bike manufacturer might organized per "bike-model" per "region". In other words, we get individual time series of monthly sales if the whole table is grouped buy two both ("region","bike-model"). The metadata of the dataset in datasetDoc.json will have "suggestedGroupingKey" role set on those columns that would serve as a grouping key. - - -## Object Detection Task -The typical structure of an objectDetection task is as follows: -``` -LL1_some_object_detection_task / - |-- LL1_some_object_detection_task_dataset / - |-- tables / - |-- learningData.csv - |-- media / - |-- img01.jpg - |-- img02.jpg - |-- img03.jpg - |-- img04.jpg - ... - |-- datasetDoc.json - |-- LL1_some_object_detection_task_problem / - |-- problemDoc.json - |-- dataSplits.csv - |-- LL1_some_object_detection_task_solution / - |-- src / - |-- predictions.csv - |-- scores.csv - -``` -### learningData.csv - -An example learningData: - -| d3mIndex | image | someOtherCol1 | someOtherCol2 | ...| class |bounding_box| -|-----------|--------|---------------|---------------|----|-------|------------| -|0|img01.jpg|someVal|someVal|...| dog |"10,10,60,11,60,100,10,100"| -|0|img01.jpg|someVal|someVal|...| dog |"10,20,60,31,60,110,10,110"| -|0|img01.jpg|someVal|someVal|...| tree| "20,10,70,11,70,100,20,100"| -|...|...|...|...|...|...|...|...|...|...| -|2|img03.jpg|someVal|someVal|...| cat |"50,6,100,13,100,131,50,54"| -|2|img03.jpg|someVal|someVal|...| car |"70,6,120,13,120,131,70,54"| -|...|...|...|...|...|...|...|...|...|...| - -__Notes__ - -- Each row corresponds to a bounding box in an image -- There is a one-to-one correspondence between d3mIndex and an image. Therefore, d3mIndex is a multiIndex as it can have non-unique values as shown above. -- For this type of the datasets, the target column is a bounding polygon (named "bounding_box" in the above example). -- The type of the bounding_box column is "realVector" and captures 4 vertices using 8 coordinate values. -- The role of the bounding_box column is "boundingPolygon". -- Object detection dataset and problem can have a "class" target column. This column identifies which object is present inside the bounding polygon, when there are multiple classes of objects can be detected. -- The role of the class cloumn is suggestedTarget, indicating that model should output a class associated with a bounding box. Again if this is a case of a single class, all the output values will be the same. -- Typically, the metric that is used for this problem type requires that a confidence value be output along with the prediction. - -### datasetDoc.json - -An example datasetDoc: - -``` -... - - "dataResources": [ - { - "resID": "0", - "resPath": "media/", - "resType": "image", - "resFormat": [ - "image/png" - ], - "isCollection": true - }, - { - "resID": "learningData", - "resPath": "tables/learningData.csv", - "resType": "table", - "resFormat": [ - "text/csv" - ], - "isCollection": false, - "columns": [ - { - "colIndex": 0, - "colName": "d3mIndex", - "colType": "integer", - "role": [ - "multiIndex" - ] - }, - { - "colIndex": 1, - "colName": "image", - "colType": "string", - "role": [ - "index" - ], - "refersTo": { - "resID": "0", - "resObject": "item" - } - }, - { - "colIndex": 2, - "colName": "class", - "colType": "string", - "role": [ - "suggestedTarget" - ] - }, - { - "colIndex": 3, - "colName": "bounding_box", - "colType": "realVector", - "role": [ - "suggestedTarget", - "boundingPolygon" - ], - "refersTo": { - "resID": "learningData", - "resObject": { - "columnName": "image" - } - } - } - ] - } -... -``` - -# FAQ - -#### Why did we include repeat and fold columns in dataSplits? - -A number of datasets come with hold-out CV and k-fold CV splits provided. To leverage these splits and not create our own, we have included fold and repeat columns. - -#### How is a problem with multiple target variables handled? - -If the targets are independent, then we will create multiple problems, one for each target. On the other hand, if the targets are joint, he "targets" field in problemSchema will contain a list of dictionaries, one for each target description in the problem. An example of joint multi-target is the object detection problem where the output is both a class (e.g., dog) and a bounding polygon. - -``` -"targets": [ - { - "targetIndex": 0, - "resID": "1", - "colIndex": 2, - "colName": "class" - }, - { - "targetIndex": 1, - "resID": "1", - "colIndex": 3, - "colName": "bounding_box" - } - ] -``` - -#### How to submit predictions when there are multiple targets? - -The target columns are carried over in the predictions file as shown: - -|d3mIndex|class|bounding_box |confidence| -|--------|-----|---------------|----------| -|0 |dog |"..." |... | -|0 |cat |"..." |... | -|0 |cat |"..." |... | -|0 |cat |"..." |... | diff --git a/datasets/data-supply/documentation/standardValues.json b/datasets/data-supply/documentation/standardValues.json deleted file mode 100644 index 9f7a4cd..0000000 --- a/datasets/data-supply/documentation/standardValues.json +++ /dev/null @@ -1,137 +0,0 @@ -{ - "unit": [ - "seconds", - "minutes", - "days", - "weeks", - "months", - "years", - "unspecified" - ], - "qualValueType": [ - "boolean", - "integer", - "real", - "string", - "dict" - ], - "resType": [ - "image", - "video", - "audio", - "speech", - "text", - "graph", - "edgeList", - "table", - "timeseries", - "raw" - ], - "colType": [ - "boolean", - "integer", - "real", - "string", - "categorical", - "dateTime", - "realVector", - "json", - "geojson", - "unknown" - ], - "role": [ - "index", - "multiIndex", - "key", - "attribute", - "suggestedTarget", - "timeIndicator", - "locationIndicator", - "boundaryIndicator", - "interval", - "instanceWeight", - "boundingPolygon", - "suggestedPrivilegedData", - "suggestedGroupingKey", - "edgeSource", - "directedEdgeSource", - "undirectedEdgeSource", - "multiEdgeSource", - "simpleEdgeSource", - "edgeTarget", - "directedEdgeTarget", - "undirectedEdgeTarget", - "multiEdgeTarget", - "simpleEdgeTarget" - ], - "resObject": [ - "item" - ], - "resComponent": [ - "nodes", - "edges" - ], - "taskKeywords": [ - "classification", - "regression", - "clustering", - "linkPrediction", - "vertexNomination", - "vertexClassification", - "communityDetection", - "graphMatching", - "forecasting", - "collaborativeFiltering", - "objectDetection", - "semiSupervised", - "binary", - "multiClass", - "multiLabel", - "univariate", - "multivariate", - "overlapping", - "nonOverlapping", - "tabular", - "relational", - "nested", - "image", - "audio", - "video", - "speech", - "text", - "graph", - "multiGraph", - "timeSeries", - "grouped", - "geospatial", - "remoteSensing", - "lupi", - "missingMetadata" - ], - "method": [ - "holdOut", - "kFold" - ], - "metric": [ - "accuracy", - "precision", - "recall", - "f1", - "f1Micro", - "f1Macro", - "rocAuc", - "rocAucMacro", - "rocAucMicro", - "meanSquaredError", - "rootMeanSquaredError", - "meanAbsoluteError", - "rSquared", - "normalizedMutualInformation", - "jaccardSimilarityScore", - "precisionAtTopK", - "objectDetectionAP", - "hammingLoss", - "hitsAtK", - "meanReciprocalRank" - ] -} diff 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zgb&j|)JXKZQmIrbl}e>jsZ=VJO64{3f-Z3hep~n$Y(S}0DwRs5QmIrbl}e@Zrg$0t zNcd3kL#b3Ml}e>jsZ=VJO65({XcU*=_l1uLQ3Fb)QmIrbl}e>jsZ=VJ|C&F+p9>!f zg+ifFC=?2XLZMJ76e9{1J`@UtLZMJ76bgkxp-|jesPLgsC=?2XLZMJ76bgmnjso}} cLv+%p$d)`z00000NkvXXu0mjf0RRC1|AUAnjsO4v diff --git a/datasets/data-supply/documentation/supportedResourceTypesFormats.json b/datasets/data-supply/documentation/supportedResourceTypesFormats.json deleted file mode 100644 index 5a8b9ae..0000000 --- a/datasets/data-supply/documentation/supportedResourceTypesFormats.json +++ /dev/null @@ -1,60 +0,0 @@ -{ - "datasets_release":"3.0", - "supported_resource_types_and_formats":[ - { - "resType":"audio", - "resFormat":{ - "audio/aiff":["aif", "aiff"], - "audio/flac":["flac"], - "audio/ogg":["ogg"], - "audio/wav":["wav"], - "audio/mpeg":["mp3"] - } - }, - { - "resType":"image", - "resFormat":{ - "image/jpeg":["jpeg", "jpg"], - "image/png":["png"] - } - }, - { - "resType":"video", - "resFormat":{ - "video/mp4": ["mp4"], - "video/avi": ["avi"] - } - }, - { - "resType":"table", - "resFormat":{ - "text/csv": ["csv"], - "text/csv+gzip": ["csv.gz"] - } - }, - { - "resType":"text", - "resFormat":{ - "text/plain": ["txt"] - } - }, - { - "resType":"graph", - "resFormat":{ - "text/vnd.gml": ["gml"] - } - }, - { - "resType":"edgeList", - "resFormat":{ - "text/csv": ["csv"] - } - }, - { - "resType":"timeseries", - "resFormat":{ - "text/csv": ["csv"] - } - } - ] -} \ No newline at end of file diff --git a/datasets/data-supply/schemas/README.md b/datasets/data-supply/schemas/README.md deleted file mode 100644 index 99cbc10..0000000 --- a/datasets/data-supply/schemas/README.md +++ /dev/null @@ -1,5 +0,0 @@ -# D3M Schemas - -Please see the contents of [documentation/](../documentation/) for details. - -Schemas are described using [Cerberus](https://docs.python-cerberus.org/en/stable/). diff --git a/datasets/data-supply/schemas/datasetSchema.json b/datasets/data-supply/schemas/datasetSchema.json deleted file mode 100644 index 711e359..0000000 --- a/datasets/data-supply/schemas/datasetSchema.json +++ /dev/null @@ -1,74 +0,0 @@ -{ - "about": {"type":"dict", "required":true, "allow_unknown":true, "schema": { - "datasetID":{"type":"string", "required":true, "empty": false}, - "datasetName":{"type":"string", "required":true, "empty": false}, - "datasetURI":{"type":"string","required":false}, - "description":{"type":"string","required":false}, - "citation":{"type":"string","required":false}, - "publicationDate":{"type":"string","required":false}, - "humanSubjectsResearch": {"type":"boolean", "required":false}, - "license":{"type":"string", "required":false}, - "source":{"type":"string", "required":false}, - "sourceURI":{"type":"string", "required":false}, - "approximateSize":{"type":"string", "required":false}, - "applicationDomain":{"type":"string","required":false}, - "datasetVersion":{"type":"string", "required":true}, - "datasetSchemaVersion":{"type":"string", "required":true}, - "redacted":{"type":"boolean", "required":false}, - "digest":{"type":"string", "required":false} - }}, - - "dataResources":{"type":"list","required":true,"schema":{"type":"dict","required": true,"allow_unknown":true,"schema":{ - "resID":{"type":"string","required":true}, - "resPath":{"type":"string","required":true}, - "resType":{"type":"string","required":true}, - "resFormat":{"type":"dict", "required":true, "keysrules": {"type":"string"}, "valuesrules": {"type":"list"}}, - "isCollection":{"type":"boolean","required":false,"default":false}, - "columnsCount": {"type":"integer","required":false}, - "columns": { - "type":"list", - "required": false, - "schema": {"type":"dict", "required": true, "allow_unknown":true, "schema": { - "colIndex":{"type":"integer", "required":true}, - "colName":{"type":"string", "required":true, "empty":false}, - "colDescription":{"type":"string", "required":false}, - "colType":{"required":true, "type":"string"}, - "role":{"type":"list","required":true,"schema":{"type":"string"}}, - "refersTo":{"type": "dict", "required":false, "allow_unknown":true, "schema":{ - "resID":{"type":"string","required":true}, - "resObject":{"required":true, "oneof":[ - {"type":"string"}, - {"type":"dict", "allow_unknown":true, "schema":{ - "nodeAttribute":{"type":"string","excludes":["edgeAttribute","columnIndex","columnName"]}, - "edgeAttribute":{"type":"string","excludes":["nodeAttribute","columnIndex","columnName"]}, - "columnIndex":{"type":"integer","excludes":["nodeAttribute","edgeAttribute","columnName"]}, - "columnName":{"type":"string","excludes":["nodeAttribute","edgeAttribute","columnIndex"]}}} - ]} - }}, - "timeGranularity":{"type":"dict", "required":false, "allow_unknown":true, "schema":{ - "value":{"type":"number", "required":true}, - "unit":{"type":"string", "required":true} - }} - }} - }}}}, - - "qualities":{"type":"list","required":false,"schema":{"type":"dict","required":true,"allow_unknown":true,"schema":{ - "qualName":{"type":"string","required":true}, - "qualValue":{"required":true}, - "qualValueType":{"type":"string","required":true}, - "qualValueUnits":{"type":"string","required":false}, - "restrictedTo":{"type":"dict","required":false,"allow_unknown":true,"schema":{ - "resID":{"type":"string","required":true}, - "resComponent":{"oneof":[ - {"type":"dict","required":false,"allow_unknown":true,"schema":{ - "columnIndex":{"type":"integer","excludes":["columnName","nodeAttribute","edgeAttribute","selector"]}, - "columnName":{"type":"string","excludes":["columnIndex","nodeAttribute","edgeAttribute","selector"]}, - "nodeAttribute":{"type":"string","excludes":["columnIndex","columnName","edgeAttribute","selector"]}, - "edgeAttribute":{"type":"string","excludes":["columnIndex","columnName","nodeAttribute","selector"]}, - "selector":{"type":"list","excludes":["columnIndex","columnName","nodeAttribute","edgeAttribute"]} - }}, - {"type":"string","required":false} - ]} - }} - }}} -} diff --git a/datasets/data-supply/schemas/problemSchema.json b/datasets/data-supply/schemas/problemSchema.json deleted file mode 100644 index 3c65329..0000000 --- a/datasets/data-supply/schemas/problemSchema.json +++ /dev/null @@ -1,72 +0,0 @@ -{ - "about":{"type":"dict", "required":true, "allow_unknown":true, "schema": { - "problemID":{"type":"string", "required":true, "empty":false}, - "problemName":{"type":"string", "required":true, "empty":false}, - "problemDescription":{"type":"string", "required":false}, - "problemURI":{"type":"string","required":false}, - "taskKeywords":{"type":"list", "required":true, "schema":{"type":"string"}}, - "problemVersion":{"type":"string", "required":true}, - "problemSchemaVersion":{"type":"string", "required":true} - }}, - "inputs":{"type":"dict","required":true,"allow_unknown":true,"schema":{ - "data":{"type":"list","required":true,"schema":{"type":"dict", "required": true, "allow_unknown":true, "schema": { - "datasetID":{"type":"string","required":true}, - "targets":{"type":"list", "required":true,"schema":{"type":"dict","required":true,"allow_unknown":true,"schema":{ - "targetIndex":{"type":"integer","required":true}, - "resID":{"type":"string","required":true}, - "colIndex":{"type":"integer", "required":true}, - "colName":{"type":"string","required":true}, - "numClusters":{"type":"integer","required":false} - }}}, - "forecastingHorizon":{"type":"dict", "required":false, "allow_unknown":true, "schema":{ - "resID":{"type":"string","required":true}, - "colIndex":{"type":"integer", "required":true}, - "colName":{"type":"string","required":true}, - "horizonValue":{"type":"number", "required":true} - }}, - "privilegedData":{"type":"list", "required":false,"schema":{"type":"dict","required":true,"allow_unknown":true,"schema":{ - "privilegedDataIndex":{"type":"integer","required":true}, - "resID":{"type":"string","required":true}, - "colIndex":{"type":"integer", "required":true}, - "colName":{"type":"string","required":true} - }}} - }}}, - 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