diff --git a/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_002/Gender_Recognition_by_Voice_B4_002_current.json b/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_002/Gender_Recognition_by_Voice_B4_002_current.json new file mode 100644 index 0000000000000000000000000000000000000000..cbdb33d755538d08ebe54aca5edbecc5d113eddd --- /dev/null +++ b/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_002/Gender_Recognition_by_Voice_B4_002_current.json @@ -0,0 +1,5243 @@ +{ + "id": "002", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Gender Recognition by Voice", + "table_path": "output/daily/classification/Gender Recognition by Voice_B4/Gender Recognition by Voice_B4_002/test.csv", + "query": "Our team is building a voice-controlled system and needs to calibrate it for male vocal ranges. I'm providing our archive of past voice sample analyses. 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0000000000000000000000000000000000000000..58ba53ce1ca43336a90db2236c9e13c14684dac8 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_002/info.json @@ -0,0 +1,127 @@ +{ + "name": "Gender Recognition by Voice", + "source": "https://www.kaggle.com/datasets/primaryobjects/voicegender/data", + "data_intro": "This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz ([human vocal range][2]).", + "is_splited": false, + "overall_size": 3168, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 20, + "cat_feature_intro": { + "label": "- **label**: male or female" + }, + "num_feature_intro": { + "meanfreq": "- **meanfreq**: mean frequency (in kHz)", + "sd": "- **sd**: standard deviation of frequency", + "median": "- **median**: median frequency (in kHz)", + "Q25": "- **Q25**: first quantile (in kHz)", + "Q75": "- **Q75**: third quantile (in kHz)", + "IQR": "- **IQR**: interquantile range (in kHz)", + "skew": "- **skew**: skewness (see note in specprop description)", + "kurt": "- **kurt**: kurtosis (see note in specprop description)", + "sp.ent": "- **sp.ent**: spectral entropy", + "sfm": "- **sfm**: spectral flatness", + "mode": "![CART model][4]", + "centroid": "- **centroid**: frequency centroid (see specprop)", + "meanfun": "- **meanfun**: average of fundamental frequency measured across acoustic signal", + "minfun": "- **minfun**: minimum fundamental frequency measured across acoustic signal", + "maxfun": "- **maxfun**: maximum fundamental frequency measured across acoustic signal", + "meandom": "- **meandom**: average of dominant frequency measured across acoustic signal", + "mindom": "- **mindom**: minimum of dominant frequency measured across acoustic signal", + "maxdom": "- **maxdom**: maximum of dominant frequency measured across acoustic signal", + "dfrange": "- **dfrange**: range of dominant frequency measured across acoustic signal", + "modindx": "- **modindx**: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "label", + "num_feature_value": { + "IQR": [ + 0.0145577312626604, + 0.252225201072386 + ], + "Q25": [ + 0.0002287581699346, + 0.24734693877551 + ], + "Q75": [ + 0.042946273830156, + 0.273469387755102 + ], + "centroid": [ + 0.0393633425835608, + 0.251123758720282 + ], + "dfrange": [ + 0.0, + 21.84375 + ], + "kurt": [ + 2.06845549084691, + 1309.61288737064 + ], + "maxdom": [ + 0.0078125, + 21.8671875 + ], + "maxfun": [ + 0.103092783505155, + 0.279113924050633 + ], + "meandom": [ + 0.0078125, + 2.95768229166667 + ], + "meanfreq": [ + 0.0393633425835608, + 0.251123758720282 + ], + "meanfun": [ + 0.0555653493134555, + 0.237636387269209 + ], + "median": [ + 0.0109745762711864, + 0.261224489795918 + ], + "mindom": [ + 0.0048828125, + 0.458984375 + ], + "minfun": [ + 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mode 100644 index 0000000000000000000000000000000000000000..499d51cd5286bd55670388c38179baf7a4f23fec --- /dev/null +++ b/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_004/Gender_Recognition_by_Voice_B4_004_current.json @@ -0,0 +1,5243 @@ +{ + "id": "004", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Gender Recognition by Voice", + "table_path": "output/daily/classification/Gender Recognition by Voice_B4/Gender Recognition by Voice_B4_004/test.csv", + "query": "We're developing a new voice modulation feature and need to test it on a specific vocal profile. Based on the comprehensive analysis file I just uploaded, which contains our past voice assessments, can you list all the new candidate voice samples that exhibit the acoustic characteristics typical of male speakers?", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "002", + "features": { + "meanfreq": 0.182312372171057, + "sd": 0.039541110874359, + "median": 0.180569514237856, + "Q25": 0.161809045226131, + "Q75": 0.200268006700168, + "IQR": 0.0384589614740369, + "skew": 1.83017542350835, + "kurt": 5.49071465837439, + "sp.ent": 0.868161013716811, + "sfm": 0.299374942041647, + "mode": 0.182914572864322, + "centroid": 0.182312372171057, + "meanfun": 0.134339122383445, + "minfun": 0.0476663356504469, + "maxfun": 0.262295081967213, + "meandom": 1.42205668604651, + "mindom": 0.0234375, + "maxdom": 8.7890625, + "dfrange": 8.765625, + "modindx": 0.097626190165645, + "label": "female" + } + 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b/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_005/Gender_Recognition_by_Voice_B4_005_current.json new file mode 100644 index 0000000000000000000000000000000000000000..de1a5b95d3110816714ae8df4082f67509de47a8 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_005/Gender_Recognition_by_Voice_B4_005_current.json @@ -0,0 +1,5249 @@ +{ + "id": "005", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Gender Recognition by Voice", + "table_path": "output/daily/classification/Gender Recognition by Voice_B4/Gender Recognition by Voice_B4_005/test.csv", + "query": "I’ve shared our historical voice analysis records with you. I want to focus our outreach on male speakers whose vocal profiles are most similar to our previously successful female conversion candidates. Out of the new batch, please shortlist the male speakers with a mean frequency (meanfreq) below 0.1 kHz who you believe are the best candidates for our campaign.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "002", + "features": { + "meanfreq": 0.186074594765626, + "sd": 0.065966266367623, + "median": 0.192868217054264, + "Q25": 0.129612403100775, + "Q75": 0.247441860465116, + "IQR": 0.117829457364341, + "skew": 1.49691111487071, + "kurt": 5.10434986403132, + "sp.ent": 0.92448385741424, + "sfm": 0.46318658741138, + "mode": 0.136434108527132, + "centroid": 0.186074594765626, + "meanfun": 0.131363441153192, + "minfun": 0.0471050049067713, + "maxfun": 0.277456647398844, + "meandom": 0.675707547169811, + "mindom": 0.0234375, + "maxdom": 4.2890625, + "dfrange": 4.265625, + "modindx": 0.0809434467971053, + "label": "male" + } + }, + { + "scenario_id": "005", + "features": { + "meanfreq": 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diff --git a/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_005/info.json b/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_005/info.json new file mode 100644 index 0000000000000000000000000000000000000000..58ba53ce1ca43336a90db2236c9e13c14684dac8 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_005/info.json @@ -0,0 +1,127 @@ +{ + "name": "Gender Recognition by Voice", + "source": "https://www.kaggle.com/datasets/primaryobjects/voicegender/data", + "data_intro": "This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz ([human vocal range][2]).", + "is_splited": false, + "overall_size": 3168, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 20, + "cat_feature_intro": { + "label": "- **label**: male or female" + }, + "num_feature_intro": { + "meanfreq": "- **meanfreq**: mean frequency (in kHz)", + "sd": "- **sd**: standard deviation of frequency", + "median": "- **median**: median frequency (in kHz)", + "Q25": "- **Q25**: first quantile (in kHz)", + "Q75": "- **Q75**: third quantile (in kHz)", + "IQR": "- **IQR**: interquantile range (in kHz)", + "skew": "- **skew**: skewness (see note in specprop description)", + "kurt": "- **kurt**: kurtosis (see note in specprop description)", + "sp.ent": "- **sp.ent**: spectral entropy", + "sfm": "- **sfm**: spectral flatness", + "mode": "![CART model][4]", + "centroid": "- **centroid**: frequency centroid (see specprop)", + "meanfun": "- **meanfun**: average of fundamental frequency measured across acoustic signal", + "minfun": "- **minfun**: minimum fundamental frequency measured across acoustic signal", + "maxfun": "- **maxfun**: maximum fundamental frequency measured across acoustic signal", + "meandom": "- **meandom**: average of dominant frequency measured across acoustic signal", + "mindom": "- **mindom**: minimum of dominant frequency measured across acoustic signal", + "maxdom": "- **maxdom**: maximum of dominant frequency measured across acoustic signal", + "dfrange": "- **dfrange**: range of dominant frequency measured across acoustic signal", + "modindx": "- **modindx**: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "label", + "num_feature_value": { + "IQR": [ + 0.0145577312626604, + 0.252225201072386 + ], + "Q25": [ + 0.0002287581699346, + 0.24734693877551 + ], + "Q75": [ + 0.042946273830156, + 0.273469387755102 + ], + "centroid": [ + 0.0393633425835608, + 0.251123758720282 + ], + "dfrange": [ + 0.0, + 21.84375 + ], + "kurt": [ + 2.06845549084691, + 1309.61288737064 + ], + "maxdom": [ + 0.0078125, + 21.8671875 + ], + "maxfun": [ + 0.103092783505155, + 0.279113924050633 + ], + "meandom": [ + 0.0078125, + 2.95768229166667 + ], + "meanfreq": [ + 0.0393633425835608, + 0.251123758720282 + ], + "meanfun": [ + 0.0555653493134555, + 0.237636387269209 + ], + "median": [ + 0.0109745762711864, + 0.261224489795918 + ], + "mindom": [ + 0.0048828125, + 0.458984375 + ], + "minfun": [ + 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diff --git a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/Mobile_Price_Classification_B4_001_current.json b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/Mobile_Price_Classification_B4_001_current.json new file mode 100644 index 0000000000000000000000000000000000000000..88fd8469df34a6c81f01f56da7c46e2ccac80107 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/Mobile_Price_Classification_B4_001_current.json @@ -0,0 +1,5243 @@ +{ + "id": "001", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Mobile Price Classification", + "table_path": "output/Mobile Price Classification_B4/Mobile Price Classification_B4_001/test.csv", + "query": "I'm reviewing our latest batch of mobile device inventory and need to flag all models that historically fall into the lowest price tier. I've shared our archive of past product specs and their final price categories—can you sift through the new arrivals and list every phone that fits the profile of our most budget-friendly range which is 0?", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "battery_power": 1172, + "blue": 1, + "clock_speed": 2.1, + "dual_sim": 1, + "fc": 4, + "four_g": 0, + "int_memory": 41, + "m_dep": 0.1, + "mobile_wt": 95, + "n_cores": 3, + "pc": 7, + "px_height": 1314, + "px_width": 1884, + "ram": 1107, + "sc_h": 7, + "sc_w": 3, + "talk_time": 5, + "three_g": 0, + "touch_screen": 0, + "wifi": 0, + "price_range": 1 + } + }, + { + "scenario_id": "002", + "features": { + "battery_power": 834, + "blue": 0, + "clock_speed": 0.5, + "dual_sim": 0, + "fc": 2, + "four_g": 1, + "int_memory": 27, + "m_dep": 0.2, + "mobile_wt": 123, + "n_cores": 1, + "pc": 16, + "px_height": 1421, + 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b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/info.json new file mode 100644 index 0000000000000000000000000000000000000000..be3bbddec36292e76378cfc1e0a5057e7d16d99e --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/info.json @@ -0,0 +1,130 @@ +{ + "name": "Mobile Price Classification", + "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data", + "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.", + "is_splited": true, + "overall_size": 3000, + "train_size": 2000, + "test_size": 1000, + "c_classes": 0, + "n_classes": 22, + "cat_feature_intro": {}, + "num_feature_intro": { + "id": "Unique identifier for each mobile phone", + "battery_power": "Total energy capacity of the battery in mAh", + "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)", + "clock_speed": "Speed at which microprocessor executes instructions (in GHz)", + "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)", + "fc": "Front Camera megapixels", + "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)", + "int_memory": "Internal memory in Gigabytes", + "m_dep": "Mobile depth in cm", + "mobile_wt": "Weight of the mobile phone in grams", + "n_cores": "Number of cores in the processor", + "pc": "Primary Camera megapixels", + "px_height": "Pixel resolution height", + "px_width": "Pixel resolution width", + "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.", + "sc_h": "Screen height of the mobile in cm", + "sc_w": "Screen width of the mobile in cm", + "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours", + "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)", + "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)", + "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)", + "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "price_range", + "num_feature_value": { + "battery_power": [ + 500.0, + 1999.0 + ], + "blue": [ + 0.0, + 1.0 + ], + "clock_speed": [ + 0.5, + 3.0 + ], + "dual_sim": [ + 0.0, + 1.0 + ], + "fc": [ + 0.0, + 19.0 + ], + "four_g": [ + 0.0, + 1.0 + ], + "id": [ + 1.0, + 1000.0 + ], + "int_memory": [ + 2.0, + 64.0 + ], + "m_dep": [ + 0.1, + 1.0 + ], + "mobile_wt": [ + 80.0, + 200.0 + ], + "n_cores": [ + 1.0, + 8.0 + ], + "pc": [ + 0.0, + 20.0 + ], + "price_range": [ + 0.0, + 3.0 + ], + "px_height": [ + 0.0, + 1960.0 + ], + "px_width": [ + 500.0, + 1998.0 + ], + "ram": [ + 256.0, + 3998.0 + ], + "sc_h": [ + 5.0, + 19.0 + ], + "sc_w": [ + 0.0, + 18.0 + ], + "talk_time": [ + 2.0, + 20.0 + ], + "three_g": [ + 0.0, + 1.0 + ], + "touch_screen": [ + 0.0, + 1.0 + ], + "wifi": [ + 0.0, + 1.0 + ] + }, + "cat_feature_value": {} +} \ No newline at end of file diff --git a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/test.csv b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..ebf65c5e68edd03852daf2062afb70bf25da9667 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/test.csv @@ -0,0 +1,201 @@ 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b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_002/Mobile_Price_Classification_B4_002_current.json @@ -0,0 +1,5243 @@ +{ + "id": "002", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Mobile Price Classification", + "table_path": "output/Mobile Price Classification_B4/Mobile Price Classification_B4_002/test.csv", + "query": "Our mid-range segment is performing well, and we want to identify similar candidates in the new procurement list. Using the historical data I just uploaded, which maps device characteristics to their market positioning, can you filter the new records to show all phones that are likely to be medium-priced which means price_range equals 1?", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "battery_power": 742, + "blue": 1, + "clock_speed": 2.3, + "dual_sim": 0, + "fc": 0, + "four_g": 0, + "int_memory": 21, + "m_dep": 0.7, + "mobile_wt": 104, + "n_cores": 8, + "pc": 1, + "px_height": 875, + "px_width": 925, + "ram": 331, + "sc_h": 11, + "sc_w": 8, + "talk_time": 7, + "three_g": 1, + "touch_screen": 0, + "wifi": 0, + "price_range": 0 + } + }, + { + "scenario_id": "002", + "features": { + "battery_power": 1965, + "blue": 1, + "clock_speed": 0.5, + "dual_sim": 0, + "fc": 4, + "four_g": 1, + "int_memory": 29, + "m_dep": 0.5, + "mobile_wt": 149, + "n_cores": 5, + "pc": 13, + 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+510,1,2.0,1,5,1,45,0.9,168,6,16,483,754,3919,19,4,2,1,1,1,3 diff --git a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_002/info.json b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_002/info.json new file mode 100644 index 0000000000000000000000000000000000000000..be3bbddec36292e76378cfc1e0a5057e7d16d99e --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_002/info.json @@ -0,0 +1,130 @@ +{ + "name": "Mobile Price Classification", + "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data", + "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.", + "is_splited": true, + "overall_size": 3000, + "train_size": 2000, + "test_size": 1000, + "c_classes": 0, + "n_classes": 22, + "cat_feature_intro": {}, + "num_feature_intro": { + "id": "Unique identifier for each mobile phone", + "battery_power": "Total energy capacity of the battery in mAh", + "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)", + "clock_speed": "Speed at which microprocessor executes instructions (in GHz)", + "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)", + "fc": "Front Camera megapixels", + "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)", + "int_memory": "Internal memory in Gigabytes", + "m_dep": "Mobile depth in cm", + "mobile_wt": "Weight of the mobile phone in grams", + "n_cores": "Number of cores in the processor", + "pc": "Primary Camera megapixels", + "px_height": "Pixel resolution height", + "px_width": "Pixel resolution width", + "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.", + "sc_h": "Screen height of the mobile in cm", + "sc_w": "Screen width of the mobile in cm", + "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours", + "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)", + "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)", + "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)", + "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "price_range", + "num_feature_value": { + "battery_power": [ + 500.0, + 1999.0 + ], + "blue": [ + 0.0, + 1.0 + ], + "clock_speed": [ + 0.5, + 3.0 + ], + "dual_sim": [ + 0.0, + 1.0 + ], + "fc": [ + 0.0, + 19.0 + ], + "four_g": [ + 0.0, + 1.0 + ], + "id": [ + 1.0, + 1000.0 + ], + "int_memory": [ + 2.0, + 64.0 + ], + "m_dep": [ + 0.1, + 1.0 + ], + "mobile_wt": [ + 80.0, + 200.0 + ], + "n_cores": [ + 1.0, + 8.0 + ], + "pc": [ + 0.0, + 20.0 + ], + "price_range": [ + 0.0, + 3.0 + ], + "px_height": [ + 0.0, + 1960.0 + ], + "px_width": [ + 500.0, + 1998.0 + ], + "ram": [ + 256.0, + 3998.0 + ], + "sc_h": [ + 5.0, + 19.0 + ], + "sc_w": [ + 0.0, + 18.0 + ], + "talk_time": [ + 2.0, + 20.0 + ], + "three_g": [ + 0.0, + 1.0 + ], + "touch_screen": [ + 0.0, + 1.0 + ], + "wifi": [ + 0.0, + 1.0 + ] + }, + "cat_feature_value": {} +} \ No newline at end of file diff --git a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_002/test.csv b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_002/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c5e86fac3141d24b5ac1369bf66f4c1ac7ba2228 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_002/test.csv @@ -0,0 +1,201 @@ 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b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_003/Mobile_Price_Classification_B4_003_current.json @@ -0,0 +1,5243 @@ +{ + "id": "003", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Mobile Price Classification", + "table_path": "output/Mobile Price Classification_B4/Mobile Price Classification_B4_003/test.csv", + "query": "We’re planning a premium showcase event and need to curate a list of high-end candidate phones from the new shipment. I’ve provided our complete logs linking technical specifications to past pricing brackets. Review the new data against this history and retrieve all phones where the price_range is exactly 2, indicating the high-end category.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "battery_power": 1170, + "blue": 1, + "clock_speed": 0.9, + "dual_sim": 0, + "fc": 6, + "four_g": 1, + "int_memory": 14, + "m_dep": 0.4, + "mobile_wt": 199, + "n_cores": 2, + "pc": 13, + "px_height": 474, + "px_width": 709, + "ram": 715, + "sc_h": 18, + "sc_w": 5, + "talk_time": 20, + "three_g": 1, + "touch_screen": 0, + "wifi": 1, + "price_range": 0 + } + }, + { + "scenario_id": "002", + "features": { + "battery_power": 1414, + "blue": 0, + "clock_speed": 0.5, + "dual_sim": 0, + "fc": 0, + "four_g": 1, + "int_memory": 15, + "m_dep": 0.8, + "mobile_wt": 168, + "n_cores": 2, + "pc": 0, + "px_height": 294, + "px_width": 1233, + "ram": 1069, + "sc_h": 18, + "sc_w": 6, + "talk_time": 14, + "three_g": 1, + "touch_screen": 0, + "wifi": 0, + "price_range": 0 + } + }, + { + "scenario_id": "003", + "features": { + "battery_power": 1397, + "blue": 0, + "clock_speed": 2.9, + "dual_sim": 1, + "fc": 13, + "four_g": 0, + "int_memory": 9, + "m_dep": 0.5, + "mobile_wt": 87, + "n_cores": 6, + "pc": 18, + "px_height": 1308, + "px_width": 1991, + "ram": 3137, + "sc_h": 12, + "sc_w": 1, + "talk_time": 11, + "three_g": 1, + "touch_screen": 1, + "wifi": 1, + "price_range": 3 + } + }, + { + "scenario_id": "004", + "features": { + "battery_power": 1180, + "blue": 1, + "clock_speed": 2.5, + "dual_sim": 0, + "fc": 0, + "four_g": 1, + "int_memory": 27, + "m_dep": 0.1, + "mobile_wt": 90, + "n_cores": 6, + "pc": 1, + "px_height": 168, + "px_width": 540, + "ram": 348, + "sc_h": 7, + "sc_w": 5, + "talk_time": 19, + "three_g": 1, + "touch_screen": 1, + "wifi": 1, + "price_range": 0 + } + }, + { + "scenario_id": "005", + "features": { + "battery_power": 1164, + "blue": 1, + "clock_speed": 2.6, + "dual_sim": 1, + "fc": 0, + "four_g": 0, + "int_memory": 61, + "m_dep": 0.3, + "mobile_wt": 88, + "n_cores": 1, + "pc": 6, + "px_height": 1107, + "px_width": 1656, + "ram": 3859, + "sc_h": 11, + "sc_w": 10, + "talk_time": 17, + "three_g": 0, + "touch_screen": 1, + "wifi": 0, + "price_range": 3 + } + }, + { + "scenario_id": "006", + "features": { + "battery_power": 1924, + "blue": 0, + "clock_speed": 0.5, + "dual_sim": 0, + "fc": 3, + "four_g": 1, + "int_memory": 63, + "m_dep": 0.3, + "mobile_wt": 111, + "n_cores": 2, + "pc": 10, + "px_height": 122, + "px_width": 686, + "ram": 3423, + "sc_h": 13, + "sc_w": 3, + "talk_time": 19, + "three_g": 1, + "touch_screen": 0, + "wifi": 0, + "price_range": 3 + } + }, + { + "scenario_id": "007", + "features": { + "battery_power": 1449, + "blue": 0, + "clock_speed": 0.8, + "dual_sim": 1, + "fc": 3, + "four_g": 1, + "int_memory": 10, + "m_dep": 0.2, + "mobile_wt": 144, + "n_cores": 1, + "pc": 12, + "px_height": 1518, + "px_width": 1883, + "ram": 1032, + "sc_h": 10, + "sc_w": 0, + "talk_time": 8, + "three_g": 1, + "touch_screen": 1, + "wifi": 1, + "price_range": 1 + } + }, + { + "scenario_id": "008", + "features": { + "battery_power": 912, + "blue": 0, + "clock_speed": 0.5, + "dual_sim": 1, + "fc": 0, + "four_g": 0, + "int_memory": 58, + "m_dep": 0.3, + "mobile_wt": 107, + "n_cores": 5, + "pc": 1, + "px_height": 165, + "px_width": 1723, + "ram": 3573, + "sc_h": 17, + "sc_w": 3, + "talk_time": 20, + "three_g": 1, + "touch_screen": 1, + "wifi": 0, + "price_range": 3 + } + }, + { + "scenario_id": "009", + "features": { + "battery_power": 1062, + "blue": 0, + "clock_speed": 1.5, + "dual_sim": 0, + "fc": 3, + "four_g": 0, + "int_memory": 45, + "m_dep": 0.2, + "mobile_wt": 151, + "n_cores": 3, + "pc": 4, + "px_height": 798, + "px_width": 1435, + "ram": 911, + "sc_h": 14, + "sc_w": 2, + "talk_time": 9, + "three_g": 0, + "touch_screen": 0, + "wifi": 0, + "price_range": 0 + } + }, + { + "scenario_id": "010", + "features": { + "battery_power": 1772, + "blue": 1, + "clock_speed": 1.6, + "dual_sim": 0, + "fc": 17, + "four_g": 1, + "int_memory": 45, + "m_dep": 0.5, + "mobile_wt": 159, + "n_cores": 2, + "pc": 18, + "px_height": 837, + "px_width": 1405, + "ram": 1146, + "sc_h": 6, + "sc_w": 1, + "talk_time": 17, + "three_g": 1, + "touch_screen": 1, + "wifi": 0, + "price_range": 1 + } + }, + { + "scenario_id": "011", + "features": { + "battery_power": 1576, + "blue": 1, + "clock_speed": 2.0, + "dual_sim": 0, + "fc": 0, + "four_g": 1, + "int_memory": 20, + "m_dep": 0.6, + "mobile_wt": 148, + "n_cores": 3, + "pc": 0, + "px_height": 410, + "px_width": 1551, + "ram": 1379, + "sc_h": 17, + "sc_w": 1, + "talk_time": 16, + "three_g": 1, + "touch_screen": 0, + "wifi": 1, + "price_range": 1 + } + }, + { + "scenario_id": "012", + "features": { + "battery_power": 1821, + "blue": 0, + "clock_speed": 1.7, + "dual_sim": 0, + "fc": 4, + "four_g": 1, + "int_memory": 10, + "m_dep": 0.8, + "mobile_wt": 139, + "n_cores": 8, + "pc": 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+ { + "scenario_id": "015", + "features": { + "battery_power": 1974, + "blue": 0, + "clock_speed": 1.0, + "dual_sim": 0, + "fc": 4, + "four_g": 0, + "int_memory": 24, + "m_dep": 0.3, + "mobile_wt": 80, + "n_cores": 7, + "pc": 13, + "px_height": 250, + "px_width": 545, + "ram": 861, + "sc_h": 7, + "sc_w": 4, + "talk_time": 6, + "three_g": 1, + "touch_screen": 0, + "wifi": 1, + "price_range": 0 + } + }, + { + "scenario_id": "016", + "features": { + "battery_power": 1119, + "blue": 0, + "clock_speed": 0.5, + "dual_sim": 1, + "fc": 0, + "four_g": 1, + "int_memory": 23, + "m_dep": 0.4, + "mobile_wt": 164, + "n_cores": 4, + "pc": 1, + "px_height": 821, + "px_width": 915, + "ram": 3520, + "sc_h": 7, + "sc_w": 4, + "talk_time": 7, + "three_g": 1, + "touch_screen": 0, + "wifi": 0, + "price_range": 3 + } + }, + { + "scenario_id": "017", + "features": { + "battery_power": 1236, + "blue": 0, + "clock_speed": 0.9, + "dual_sim": 1, + "fc": 2, + "four_g": 1, + "int_memory": 57, + "m_dep": 0.1, + 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+ "wifi": 0, + "price_range": 1 + } + }, + { + "scenario_id": "020", + "features": { + "battery_power": 1979, + "blue": 1, + "clock_speed": 2.9, + "dual_sim": 1, + "fc": 0, + "four_g": 1, + "int_memory": 19, + "m_dep": 1.0, + "mobile_wt": 176, + "n_cores": 3, + "pc": 9, + "px_height": 1331, + "px_width": 1532, + "ram": 411, + "sc_h": 8, + "sc_w": 2, + "talk_time": 2, + "three_g": 1, + "touch_screen": 1, + "wifi": 1, + "price_range": 0 + } + }, + { + "scenario_id": "021", + "features": { + "battery_power": 706, + "blue": 0, + "clock_speed": 0.5, + "dual_sim": 0, + "fc": 13, + "four_g": 1, + "int_memory": 31, + "m_dep": 0.1, + "mobile_wt": 108, + "n_cores": 3, + "pc": 19, + "px_height": 543, + "px_width": 938, + "ram": 2419, + "sc_h": 8, + "sc_w": 7, + "talk_time": 4, + "three_g": 1, + "touch_screen": 0, + "wifi": 1, + "price_range": 1 + } + }, + { + "scenario_id": "022", + "features": { + "battery_power": 1719, + "blue": 1, + "clock_speed": 2.1, + "dual_sim": 1, + "fc": 0, + "four_g": 0, + "int_memory": 52, + "m_dep": 0.1, + "mobile_wt": 187, + "n_cores": 3, + "pc": 1, + "px_height": 417, + "px_width": 520, + "ram": 2610, + "sc_h": 10, + "sc_w": 1, + "talk_time": 11, + "three_g": 1, + "touch_screen": 1, + "wifi": 1, + "price_range": 2 + } + }, + { + "scenario_id": "023", + "features": { + "battery_power": 558, + "blue": 1, + "clock_speed": 2.8, + "dual_sim": 0, + "fc": 1, + "four_g": 1, + "int_memory": 49, + "m_dep": 0.8, + "mobile_wt": 142, + "n_cores": 5, + "pc": 4, + "px_height": 997, + "px_width": 1605, + "ram": 392, + "sc_h": 10, + "sc_w": 5, + "talk_time": 8, + "three_g": 1, + "touch_screen": 1, + "wifi": 1, + "price_range": 0 + } + }, + { + "scenario_id": "024", + "features": { + "battery_power": 1538, + "blue": 1, + "clock_speed": 2.3, + "dual_sim": 0, + "fc": 6, + "four_g": 1, + "int_memory": 13, + "m_dep": 0.2, + "mobile_wt": 121, + "n_cores": 7, + "pc": 7, + "px_height": 567, + "px_width": 986, + "ram": 1494, + "sc_h": 18, + "sc_w": 10, + "talk_time": 12, 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"is_splited": true, + "overall_size": 3000, + "train_size": 2000, + "test_size": 1000, + "c_classes": 0, + "n_classes": 22, + "cat_feature_intro": {}, + "num_feature_intro": { + "id": "Unique identifier for each mobile phone", + "battery_power": "Total energy capacity of the battery in mAh", + "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)", + "clock_speed": "Speed at which microprocessor executes instructions (in GHz)", + "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)", + "fc": "Front Camera megapixels", + "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)", + "int_memory": "Internal memory in Gigabytes", + "m_dep": "Mobile depth in cm", + "mobile_wt": "Weight of the mobile phone in grams", + "n_cores": "Number of cores in the processor", + "pc": "Primary Camera megapixels", + "px_height": "Pixel resolution height", + "px_width": "Pixel resolution width", + "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.", + "sc_h": "Screen height of the mobile in cm", + "sc_w": "Screen width of the mobile in cm", + "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours", + "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)", + "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)", + "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)", + "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "price_range", + "num_feature_value": { + "battery_power": [ + 500.0, + 1999.0 + ], + "blue": [ + 0.0, + 1.0 + ], + "clock_speed": [ + 0.5, + 3.0 + ], + "dual_sim": [ + 0.0, + 1.0 + ], + "fc": [ + 0.0, + 19.0 + ], + "four_g": [ + 0.0, + 1.0 + ], + "id": [ + 1.0, + 1000.0 + ], + "int_memory": [ + 2.0, + 64.0 + ], + "m_dep": [ + 0.1, + 1.0 + ], + "mobile_wt": [ + 80.0, + 200.0 + ], + "n_cores": [ + 1.0, + 8.0 + ], + "pc": [ + 0.0, + 20.0 + ], + "price_range": [ + 0.0, + 3.0 + ], + "px_height": [ + 0.0, + 1960.0 + ], + "px_width": [ + 500.0, + 1998.0 + ], + "ram": [ + 256.0, + 3998.0 + ], + "sc_h": [ + 5.0, + 19.0 + ], + "sc_w": [ + 0.0, + 18.0 + ], + "talk_time": [ + 2.0, + 20.0 + ], + "three_g": [ + 0.0, + 1.0 + ], + "touch_screen": [ + 0.0, + 1.0 + ], + "wifi": [ + 0.0, + 1.0 + ] + }, + "cat_feature_value": {} +} \ No newline at end of file diff --git a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_003/test.csv b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_003/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..e3e2e88039d30664de7abd3857f70214bd552e87 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_003/test.csv @@ -0,0 +1,201 @@ 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b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_004/Mobile_Price_Classification_B4_004_current.json @@ -0,0 +1,5243 @@ +{ + "id": "004", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Mobile Price Classification", + "table_path": "output/Mobile Price Classification_B4/Mobile Price Classification_B4_004/test.csv", + "query": "For our upcoming luxury product launch, I need to isolate the flagship-tier models from the incoming stock. Based on the trends in the historical file I sent you, which correlates build quality and features with final price, please identify every new mobile that likely belongs to the 3 price range.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "battery_power": 1697, + "blue": 0, + "clock_speed": 0.5, + "dual_sim": 0, + "fc": 0, + "four_g": 1, + "int_memory": 60, + "m_dep": 0.1, + "mobile_wt": 90, + "n_cores": 4, + "pc": 0, + "px_height": 88, + "px_width": 1046, + "ram": 441, + "sc_h": 15, + "sc_w": 1, + "talk_time": 11, + "three_g": 1, + "touch_screen": 1, + "wifi": 0, + "price_range": 0 + } + }, + { + "scenario_id": "002", + "features": { + "battery_power": 1493, + "blue": 0, + "clock_speed": 1.5, + "dual_sim": 1, + "fc": 13, + "four_g": 0, + "int_memory": 53, + "m_dep": 0.5, + "mobile_wt": 102, + "n_cores": 5, + "pc": 16, + "px_height": 240, + "px_width": 1414, + "ram": 2083, + "sc_h": 19, + "sc_w": 15, + "talk_time": 20, + "three_g": 0, + "touch_screen": 1, + "wifi": 1, + "price_range": 2 + } + }, + { + "scenario_id": "003", + "features": { + "battery_power": 1871, + "blue": 0, + "clock_speed": 2.1, + "dual_sim": 0, + "fc": 4, + "four_g": 1, + "int_memory": 41, + "m_dep": 0.4, + "mobile_wt": 144, + "n_cores": 8, + "pc": 5, + "px_height": 275, + "px_width": 1966, + "ram": 2727, + "sc_h": 15, + "sc_w": 8, + "talk_time": 11, + "three_g": 1, + "touch_screen": 0, + "wifi": 0, + "price_range": 3 + } + }, + { + "scenario_id": "004", + "features": { + "battery_power": 1748, + "blue": 0, + "clock_speed": 1.7, + "dual_sim": 1, + "fc": 6, + "four_g": 1, + "int_memory": 14, + "m_dep": 0.5, + "mobile_wt": 146, + "n_cores": 3, + "pc": 7, + "px_height": 859, + "px_width": 1234, + "ram": 3968, + "sc_h": 16, + "sc_w": 12, + "talk_time": 3, + "three_g": 1, + "touch_screen": 0, + "wifi": 1, + "price_range": 3 + } + }, + { + "scenario_id": "005", + "features": 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b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_004/info.json new file mode 100644 index 0000000000000000000000000000000000000000..be3bbddec36292e76378cfc1e0a5057e7d16d99e --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_004/info.json @@ -0,0 +1,130 @@ +{ + "name": "Mobile Price Classification", + "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data", + "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.", + "is_splited": true, + "overall_size": 3000, + "train_size": 2000, + "test_size": 1000, + "c_classes": 0, + "n_classes": 22, + "cat_feature_intro": {}, + "num_feature_intro": { + "id": "Unique identifier for each mobile phone", + "battery_power": "Total energy capacity of the battery in mAh", + "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)", + "clock_speed": "Speed at which microprocessor executes instructions (in GHz)", + "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)", + "fc": "Front Camera megapixels", + "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)", + "int_memory": "Internal memory in Gigabytes", + "m_dep": "Mobile depth in cm", + "mobile_wt": "Weight of the mobile phone in grams", + "n_cores": "Number of cores in the processor", + "pc": "Primary Camera megapixels", + "px_height": "Pixel resolution height", + "px_width": "Pixel resolution width", + "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.", + "sc_h": "Screen height of the mobile in cm", + "sc_w": "Screen width of the mobile in cm", + "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours", + "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)", + "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)", + "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)", + "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "price_range", + "num_feature_value": { + "battery_power": [ + 500.0, + 1999.0 + ], + "blue": [ + 0.0, + 1.0 + ], + "clock_speed": [ + 0.5, + 3.0 + ], + "dual_sim": [ + 0.0, + 1.0 + ], + "fc": [ + 0.0, + 19.0 + ], + "four_g": [ + 0.0, + 1.0 + ], + "id": [ + 1.0, + 1000.0 + ], + "int_memory": [ + 2.0, + 64.0 + ], + "m_dep": [ + 0.1, + 1.0 + ], + "mobile_wt": [ + 80.0, + 200.0 + ], + "n_cores": [ + 1.0, + 8.0 + ], + "pc": [ + 0.0, + 20.0 + ], + "price_range": [ + 0.0, + 3.0 + ], + "px_height": [ + 0.0, + 1960.0 + ], + "px_width": [ + 500.0, + 1998.0 + ], + "ram": [ + 256.0, + 3998.0 + ], + "sc_h": [ + 5.0, + 19.0 + ], + "sc_w": [ + 0.0, + 18.0 + ], + "talk_time": [ + 2.0, + 20.0 + ], + "three_g": [ + 0.0, + 1.0 + ], + "touch_screen": [ + 0.0, + 1.0 + ], + "wifi": [ + 0.0, + 1.0 + ] + }, + "cat_feature_value": {} +} \ No newline at end of file diff --git a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_004/test.csv b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_004/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..28eb61de564fa32620666e4fe4bab3ef085e0686 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_004/test.csv @@ -0,0 +1,201 @@ 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a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_005/Mobile_Price_Classification_B4_005_current.json b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_005/Mobile_Price_Classification_B4_005_current.json new file mode 100644 index 0000000000000000000000000000000000000000..6a46e84f581f95837360490db104e60f7af3ce60 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_005/Mobile_Price_Classification_B4_005_current.json @@ -0,0 +1,5249 @@ +{ + "id": "005", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Mobile Price Classification", + "table_path": "output/Mobile Price Classification_B4/Mobile Price Classification_B4_005/test.csv", + "query": "We're planning a limited-time promotional bundle with our new fast-charging power bank. I've shared our historical sales archive. Looking at the trends in that file, can you shortlist all the mobile models in the new candidates that have a battery power of 1800 mAh or more? From that group, I want to identify which ones are likely to be a price_range of 3 so we can offer them as a premium bundle.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "battery_power": 860, + "blue": 1, + "clock_speed": 1.3, + "dual_sim": 1, + "fc": 8, + "four_g": 1, + "int_memory": 40, + "m_dep": 0.7, + "mobile_wt": 170, + "n_cores": 3, + "pc": 13, + "px_height": 1573, + "px_width": 1581, + "ram": 2832, + "sc_h": 13, + "sc_w": 7, + "talk_time": 20, + "three_g": 1, + "touch_screen": 1, + "wifi": 1, + "price_range": 2 + } + }, + { + "scenario_id": "002", + "features": { + "battery_power": 1701, + "blue": 1, + "clock_speed": 1.6, + "dual_sim": 1, + "fc": 3, + "four_g": 0, + "int_memory": 10, + "m_dep": 0.3, + "mobile_wt": 194, + "n_cores": 8, + "pc": 7, + "px_height": 550, + "px_width": 1338, + "ram": 2598, + "sc_h": 12, + "sc_w": 9, + "talk_time": 6, + "three_g": 0, + "touch_screen": 1, + "wifi": 0, + "price_range": 2 + } + }, + { + "scenario_id": "003", + "features": { + "battery_power": 580, + "blue": 1, + "clock_speed": 1.2, + "dual_sim": 1, + "fc": 0, + "four_g": 0, + "int_memory": 47, + "m_dep": 0.9, + "mobile_wt": 91, + "n_cores": 3, + "pc": 1, + "px_height": 1086, + "px_width": 1345, + "ram": 3451, + "sc_h": 15, + "sc_w": 1, + "talk_time": 11, + "three_g": 1, + "touch_screen": 1, + "wifi": 0, + "price_range": 3 + } + }, + { + "scenario_id": "004", + "features": { + "battery_power": 1885, + "blue": 0, + "clock_speed": 2.4, + "dual_sim": 1, + "fc": 12, + "four_g": 1, + "int_memory": 14, + "m_dep": 0.6, + "mobile_wt": 92, + "n_cores": 4, + "pc": 15, + "px_height": 1406, + "px_width": 1517, + "ram": 1277, + "sc_h": 9, + "sc_w": 6, + "talk_time": 16, + "three_g": 1, + "touch_screen": 0, + "wifi": 1, + "price_range": 1 + } + }, + { + "scenario_id": "005", + "features": { + "battery_power": 1417, + "blue": 1, + "clock_speed": 2.7, + 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"is_splited": true, + "overall_size": 3000, + "train_size": 2000, + "test_size": 1000, + "c_classes": 0, + "n_classes": 22, + "cat_feature_intro": {}, + "num_feature_intro": { + "id": "Unique identifier for each mobile phone", + "battery_power": "Total energy capacity of the battery in mAh", + "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)", + "clock_speed": "Speed at which microprocessor executes instructions (in GHz)", + "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)", + "fc": "Front Camera megapixels", + "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)", + "int_memory": "Internal memory in Gigabytes", + "m_dep": "Mobile depth in cm", + "mobile_wt": "Weight of the mobile phone in grams", + "n_cores": "Number of cores in the processor", + "pc": "Primary Camera megapixels", + "px_height": "Pixel resolution height", + "px_width": "Pixel resolution width", + "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.", + "sc_h": "Screen height of the mobile in cm", + "sc_w": "Screen width of the mobile in cm", + "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours", + "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)", + "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)", + "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)", + "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "price_range", + "num_feature_value": { + "battery_power": [ + 500.0, + 1999.0 + ], + "blue": [ + 0.0, + 1.0 + ], + "clock_speed": [ + 0.5, + 3.0 + ], + "dual_sim": [ + 0.0, + 1.0 + ], + "fc": [ + 0.0, + 19.0 + ], + "four_g": [ + 0.0, + 1.0 + ], + "id": [ + 1.0, + 1000.0 + ], + "int_memory": [ + 2.0, + 64.0 + ], + "m_dep": [ + 0.1, + 1.0 + ], + "mobile_wt": [ + 80.0, + 200.0 + ], + "n_cores": [ + 1.0, + 8.0 + ], + "pc": [ + 0.0, + 20.0 + ], + "price_range": [ + 0.0, + 3.0 + ], + "px_height": [ + 0.0, + 1960.0 + ], + "px_width": [ + 500.0, + 1998.0 + ], + "ram": [ + 256.0, + 3998.0 + ], + "sc_h": [ + 5.0, + 19.0 + ], + "sc_w": [ + 0.0, + 18.0 + ], + "talk_time": [ + 2.0, + 20.0 + ], + "three_g": [ + 0.0, + 1.0 + ], + "touch_screen": [ + 0.0, + 1.0 + ], + "wifi": [ + 0.0, + 1.0 + ] + }, + "cat_feature_value": {} +} \ No newline at end of file diff --git a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_005/test.csv b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_005/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..011cffa5cd7dfd97c24c161f20a4571aa81aa767 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_005/test.csv @@ -0,0 +1,201 @@ 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new file mode 100644 index 0000000000000000000000000000000000000000..c8f94fdb02d0035cdfe0f1b13eb8de4e7327fb16 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_006/Mobile_Price_Classification_B4_006_current.json @@ -0,0 +1,5249 @@ +{ + "id": "006", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Mobile Price Classification", + "table_path": "output/Mobile Price Classification_B4/Mobile Price Classification_B4_006/test.csv", + "query": "Our compliance team needs to ensure our low-cost educational device program meets affordability targets. I've uploaded our past product records for you to review. Please isolate all phones that do NOT have a touch screen. From that subset, flag the candidates where the price_range is 0, as these are our priority for the program.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "battery_power": 1686, + "blue": 1, + "clock_speed": 0.5, + "dual_sim": 1, + "fc": 4, + "four_g": 0, + "int_memory": 28, + "m_dep": 0.8, + "mobile_wt": 182, + "n_cores": 5, + "pc": 8, + "px_height": 599, + "px_width": 1073, + "ram": 780, + "sc_h": 11, + "sc_w": 10, + "talk_time": 5, + "three_g": 0, + "touch_screen": 0, + "wifi": 0, + "price_range": 0 + } + }, + { + "scenario_id": "002", + "features": { + "battery_power": 1276, + "blue": 1, + "clock_speed": 2.9, + "dual_sim": 1, + "fc": 2, + "four_g": 0, + "int_memory": 53, + "m_dep": 0.3, + "mobile_wt": 130, + "n_cores": 2, + "pc": 3, + "px_height": 217, + "px_width": 735, + "ram": 3798, + "sc_h": 19, + "sc_w": 10, + "talk_time": 8, + "three_g": 1, + "touch_screen": 1, + "wifi": 1, + "price_range": 3 + } + }, + { + "scenario_id": "003", + "features": { + "battery_power": 1493, + "blue": 0, + "clock_speed": 1.5, + "dual_sim": 1, + "fc": 13, + "four_g": 0, + "int_memory": 53, + "m_dep": 0.5, + "mobile_wt": 102, + "n_cores": 5, + "pc": 16, + "px_height": 240, + "px_width": 1414, + "ram": 2083, + "sc_h": 19, + "sc_w": 15, + "talk_time": 20, + "three_g": 0, + "touch_screen": 1, + "wifi": 1, + "price_range": 2 + } + }, + { + "scenario_id": "004", + "features": { + "battery_power": 860, + "blue": 1, + "clock_speed": 1.3, + "dual_sim": 1, + "fc": 8, + "four_g": 1, + "int_memory": 40, + "m_dep": 0.7, + "mobile_wt": 170, + "n_cores": 3, + "pc": 13, + "px_height": 1573, + "px_width": 1581, + "ram": 2832, + "sc_h": 13, + "sc_w": 7, + "talk_time": 20, + "three_g": 1, + "touch_screen": 1, + "wifi": 1, + "price_range": 2 + } + }, + { + "scenario_id": "005", + "features": { + "battery_power": 1092, + "blue": 1, + "clock_speed": 0.5, + "dual_sim": 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b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_006/info.json new file mode 100644 index 0000000000000000000000000000000000000000..be3bbddec36292e76378cfc1e0a5057e7d16d99e --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_006/info.json @@ -0,0 +1,130 @@ +{ + "name": "Mobile Price Classification", + "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data", + "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.", + "is_splited": true, + "overall_size": 3000, + "train_size": 2000, + "test_size": 1000, + "c_classes": 0, + "n_classes": 22, + "cat_feature_intro": {}, + "num_feature_intro": { + "id": "Unique identifier for each mobile phone", + "battery_power": "Total energy capacity of the battery in mAh", + "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)", + "clock_speed": "Speed at which microprocessor executes instructions (in GHz)", + "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)", + "fc": "Front Camera megapixels", + "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)", + "int_memory": "Internal memory in Gigabytes", + "m_dep": "Mobile depth in cm", + "mobile_wt": "Weight of the mobile phone in grams", + "n_cores": "Number of cores in the processor", + "pc": "Primary Camera megapixels", + "px_height": "Pixel resolution height", + "px_width": "Pixel resolution width", + "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.", + "sc_h": "Screen height of the mobile in cm", + "sc_w": "Screen width of the mobile in cm", + "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours", + "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)", + "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)", + "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)", + "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "price_range", + "num_feature_value": { + "battery_power": [ + 500.0, + 1999.0 + ], + "blue": [ + 0.0, + 1.0 + ], + "clock_speed": [ + 0.5, + 3.0 + ], + "dual_sim": [ + 0.0, + 1.0 + ], + "fc": [ + 0.0, + 19.0 + ], + "four_g": [ + 0.0, + 1.0 + ], + "id": [ + 1.0, + 1000.0 + ], + "int_memory": [ + 2.0, + 64.0 + ], + "m_dep": [ + 0.1, + 1.0 + ], + "mobile_wt": [ + 80.0, + 200.0 + ], + "n_cores": [ + 1.0, + 8.0 + ], + "pc": [ + 0.0, + 20.0 + ], + "price_range": [ + 0.0, + 3.0 + ], + "px_height": [ + 0.0, + 1960.0 + ], + "px_width": [ + 500.0, + 1998.0 + ], + "ram": [ + 256.0, + 3998.0 + ], + "sc_h": [ + 5.0, + 19.0 + ], + "sc_w": [ + 0.0, + 18.0 + ], + "talk_time": [ + 2.0, + 20.0 + ], + "three_g": [ + 0.0, + 1.0 + ], + "touch_screen": [ + 0.0, + 1.0 + ], + "wifi": [ + 0.0, + 1.0 + ] + }, + "cat_feature_value": {} +} \ No newline at end of file diff --git a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_006/test.csv b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_006/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..6de82b4a2ae4fddcf607940388ecb4334d3ee4d8 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_006/test.csv @@ -0,0 +1,201 @@ 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0000000000000000000000000000000000000000..18ada0d6a78132870b6e0e9012d337d96493f45b --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_007/Mobile_Price_Classification_B4_007_current.json @@ -0,0 +1,5249 @@ +{ + "id": "007", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Mobile Price Classification", + "table_path": "output/Mobile Price Classification_B4/Mobile Price Classification_B4_007/test.csv", + "query": "For our upcoming marketing campaign targeting photography enthusiasts, I'm sharing our device database. Based on the history I'm providing, can you pull all models from the NEW batch where the primary camera is 12 megapixels or higher? Within that list, I need you to group the ones that are typically positioned in the 'High' price category for a focused ad spend.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "battery_power": 1652, + "blue": 0, + "clock_speed": 0.6, + "dual_sim": 1, + "fc": 4, + "four_g": 1, + "int_memory": 40, + "m_dep": 1.0, + "mobile_wt": 198, + "n_cores": 7, + "pc": 6, + "px_height": 352, + "px_width": 1084, + "ram": 3592, + "sc_h": 7, + "sc_w": 6, + "talk_time": 6, + "three_g": 1, + "touch_screen": 0, + "wifi": 0, + "price_range": 3 + } + }, + { + "scenario_id": "002", + "features": { + "battery_power": 1344, + "blue": 0, + "clock_speed": 1.5, + "dual_sim": 0, + "fc": 1, + "four_g": 0, + "int_memory": 14, + "m_dep": 0.5, + "mobile_wt": 190, + "n_cores": 1, + "pc": 3, + "px_height": 419, + "px_width": 556, + "ram": 2074, + "sc_h": 8, + "sc_w": 1, + "talk_time": 5, + 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b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_007/info.json new file mode 100644 index 0000000000000000000000000000000000000000..be3bbddec36292e76378cfc1e0a5057e7d16d99e --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_007/info.json @@ -0,0 +1,130 @@ +{ + "name": "Mobile Price Classification", + "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data", + "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.", + "is_splited": true, + "overall_size": 3000, + "train_size": 2000, + "test_size": 1000, + "c_classes": 0, + "n_classes": 22, + "cat_feature_intro": {}, + "num_feature_intro": { + "id": "Unique identifier for each mobile phone", + "battery_power": "Total energy capacity of the battery in mAh", + "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)", + "clock_speed": "Speed at which microprocessor executes instructions (in GHz)", + "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)", + "fc": "Front Camera megapixels", + "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)", + "int_memory": "Internal memory in Gigabytes", + "m_dep": "Mobile depth in cm", + "mobile_wt": "Weight of the mobile phone in grams", + "n_cores": "Number of cores in the processor", + "pc": "Primary Camera megapixels", + "px_height": "Pixel resolution height", + "px_width": "Pixel resolution width", + "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.", + "sc_h": "Screen height of the mobile in cm", + "sc_w": "Screen width of the mobile in cm", + "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours", + "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)", + "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)", + "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)", + "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "price_range", + "num_feature_value": { + "battery_power": [ + 500.0, + 1999.0 + ], + "blue": [ + 0.0, + 1.0 + ], + "clock_speed": [ + 0.5, + 3.0 + ], + "dual_sim": [ + 0.0, + 1.0 + ], + "fc": [ + 0.0, + 19.0 + ], + "four_g": [ + 0.0, + 1.0 + ], + "id": [ + 1.0, + 1000.0 + ], + "int_memory": [ + 2.0, + 64.0 + ], + "m_dep": [ + 0.1, + 1.0 + ], + "mobile_wt": [ + 80.0, + 200.0 + ], + "n_cores": [ + 1.0, + 8.0 + ], + "pc": [ + 0.0, + 20.0 + ], + "price_range": [ + 0.0, + 3.0 + ], + "px_height": [ + 0.0, + 1960.0 + ], + "px_width": [ + 500.0, + 1998.0 + ], + "ram": [ + 256.0, + 3998.0 + ], + "sc_h": [ + 5.0, + 19.0 + ], + "sc_w": [ + 0.0, + 18.0 + ], + "talk_time": [ + 2.0, + 20.0 + ], + "three_g": [ + 0.0, + 1.0 + ], + "touch_screen": [ + 0.0, + 1.0 + ], + "wifi": [ + 0.0, + 1.0 + ] + }, + "cat_feature_value": {} +} \ No newline at end of file diff --git a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_007/test.csv b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_007/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..50bb13d0e17ccc5d3e1d66d1221e985952d48ce9 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_007/test.csv @@ -0,0 +1,201 @@ 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b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_008/Mobile_Price_Classification_B4_008_current.json @@ -0,0 +1,5249 @@ +{ + "id": "008", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Mobile Price Classification", + "table_path": "output/Mobile Price Classification_B4/Mobile Price Classification_B4_008/test.csv", + "query": "We're sourcing components for a new rugged phone line and need to analyze weight constraints. I've sent you our archive of past mobile specs. Please find all devices with a weight under 120 grams in the new batch. From that lightweight group, identify which models have historically been classified in the 'Medium' price bracket to understand the typical cost structure.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "battery_power": 1076, + "blue": 0, + "clock_speed": 2.5, + "dual_sim": 0, + "fc": 3, + "four_g": 0, + "int_memory": 14, + "m_dep": 0.2, + "mobile_wt": 105, + "n_cores": 5, + "pc": 4, + "px_height": 545, + "px_width": 1300, + "ram": 2043, + "sc_h": 7, + "sc_w": 5, + "talk_time": 14, + "three_g": 0, + "touch_screen": 0, + "wifi": 0, + "price_range": 1 + } + }, + { + "scenario_id": "002", + "features": { + "battery_power": 1958, + "blue": 0, + "clock_speed": 0.5, + "dual_sim": 0, + "fc": 6, + "four_g": 1, + "int_memory": 52, + "m_dep": 0.4, + "mobile_wt": 153, + "n_cores": 1, + "pc": 14, + "px_height": 1524, + "px_width": 1848, + "ram": 3064, + "sc_h": 7, + 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mode 100644 index 0000000000000000000000000000000000000000..be3bbddec36292e76378cfc1e0a5057e7d16d99e --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_008/info.json @@ -0,0 +1,130 @@ +{ + "name": "Mobile Price Classification", + "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data", + "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.", + "is_splited": true, + "overall_size": 3000, + "train_size": 2000, + "test_size": 1000, + "c_classes": 0, + "n_classes": 22, + "cat_feature_intro": {}, + "num_feature_intro": { + "id": "Unique identifier for each mobile phone", + "battery_power": "Total energy capacity of the battery in mAh", + "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)", + "clock_speed": "Speed at which microprocessor executes instructions (in GHz)", + "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)", + "fc": "Front Camera megapixels", + "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)", + "int_memory": "Internal memory in Gigabytes", + "m_dep": "Mobile depth in cm", + "mobile_wt": "Weight of the mobile phone in grams", + "n_cores": "Number of cores in the processor", + "pc": "Primary Camera megapixels", + "px_height": "Pixel resolution height", + "px_width": "Pixel resolution width", + "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.", + "sc_h": "Screen height of the mobile in cm", + "sc_w": "Screen width of the mobile in cm", + "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours", + "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)", + "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)", + "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)", + "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "price_range", + "num_feature_value": { + "battery_power": [ + 500.0, + 1999.0 + ], + "blue": [ + 0.0, + 1.0 + ], + "clock_speed": [ + 0.5, + 3.0 + ], + "dual_sim": [ + 0.0, + 1.0 + ], + "fc": [ + 0.0, + 19.0 + ], + "four_g": [ + 0.0, + 1.0 + ], + "id": [ + 1.0, + 1000.0 + ], + "int_memory": [ + 2.0, + 64.0 + ], + "m_dep": [ + 0.1, + 1.0 + ], + "mobile_wt": [ + 80.0, + 200.0 + ], + "n_cores": [ + 1.0, + 8.0 + ], + "pc": [ + 0.0, + 20.0 + ], + "price_range": [ + 0.0, + 3.0 + ], + "px_height": [ + 0.0, + 1960.0 + ], + "px_width": [ + 500.0, + 1998.0 + ], + "ram": [ + 256.0, + 3998.0 + ], + "sc_h": [ + 5.0, + 19.0 + ], + "sc_w": [ + 0.0, + 18.0 + ], + "talk_time": [ + 2.0, + 20.0 + ], + "three_g": [ + 0.0, + 1.0 + ], + "touch_screen": [ + 0.0, + 1.0 + ], + "wifi": [ + 0.0, + 1.0 + ] + }, + "cat_feature_value": {} +} \ No newline at end of file diff --git a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_008/test.csv b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_008/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..2eefb6fbf11afded04a1bfbfa147bfb258e75916 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_008/test.csv @@ -0,0 +1,201 @@ 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a/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_008/test_current.csv b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_008/test_current.csv new file mode 100644 index 0000000000000000000000000000000000000000..d5b4d797cb7a6daab92b82396e4c572683be833d --- /dev/null +++ b/ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_008/test_current.csv @@ -0,0 +1,16 @@ +battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range +1076,0,2.5,0,3,0,14,0.2,105,5,4,545,1300,2043,7,5,14,0,0,0,1 +1524,0,2.5,1,11,1,41,1.0,96,4,20,184,740,1936,7,0,9,1,1,1,1 +1656,1,0.5,1,8,1,38,0.1,105,7,17,506,519,1499,12,8,19,1,0,1,1 +1973,1,0.5,1,3,0,21,0.8,95,8,7,127,507,1724,12,10,8,0,0,1,1 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b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_001/Wine_Quality_Dataset_B4_001_current.json @@ -0,0 +1,3643 @@ +{ + "id": "001", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Wine Quality Dataset", + "table_path": "output/daily/classification/Wine Quality Dataset_B4/Wine Quality Dataset_B4_001/test.csv", + "query": "I'm reviewing this quarter's batch of new wine samples. We need to identify the ones that are likely to be of superior quality for our premium reserve line. Using the historical quality assessments I've provided, can you flag all the new candidates that have a quality rating of 8, matching the profile of our past top-tier vintages?", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "fixed acidity": 6.1, + "volatile acidity": 0.21, + "citric acid": 0.4, + "residual sugar": 1.4, + "chlorides": 0.066, + "free sulfur dioxide": 40.5, + "total sulfur dioxide": 165.0, + "density": 0.9912, + "pH": 3.25, + "sulphates": 0.59, + "alcohol": 11.9, + "quality": 6, + "Id": 354 + } + }, + { + "scenario_id": "002", + "features": { + "fixed acidity": 8.9, + "volatile acidity": 0.48, + "citric acid": 0.53, + "residual sugar": 4.0, + "chlorides": 0.1009999999999999, + "free sulfur dioxide": 3.0, + "total sulfur dioxide": 10.0, + "density": 0.99586, + "pH": 3.21, + "sulphates": 0.59, + "alcohol": 12.1, + "quality": 7, + "Id": 1059 + } + 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b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_001/info.json @@ -0,0 +1,86 @@ +{ + "name": "Wine Quality Dataset", + "source": "https://www.kaggle.com/datasets/yasserh/wine-quality-dataset/data", + "data_intro": "This datasets is related to red variants of the Portuguese \"Vinho Verde\" wine.The dataset describes the amount of various chemicals present in wine and their effect on it's quality. The datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.", + "is_splited": false, + "overall_size": 1143, + "train_size": 0, + "test_size": 0, + "c_classes": 0, + "n_classes": 13, + "cat_feature_intro": {}, + "num_feature_intro": { + "fixed acidity": "fixed acidity", + "volatile acidity": "volatile acidity", + "citric acid": "citric acid", + "residual sugar": "residual sugar", + "chlorides": "chlorides", + "free sulfur dioxide": "free sulfur dioxide", + "total sulfur dioxide": "total sulfur dioxide", + "density": "density", + "pH": "pH", + "sulphates": "sulphates", + "alcohol": "alcohol", + "quality": "the quality of wine(classes are ordered and imbalanced)", + "Id": "identifier for each sample" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "quality", + "example": "https://www.kaggle.com/code/qusaybtoush1990/wine-quality", + "num_feature_value": { + "Id": [ + 0.0, + 1597.0 + ], + "alcohol": [ + 8.4, + 14.9 + ], + "chlorides": [ + 0.012, + 0.611 + ], + "citric acid": [ + 0.0, + 1.0 + ], + "density": [ + 0.99007, + 1.00369 + ], + "fixed acidity": [ + 4.6, + 15.9 + ], + "free sulfur dioxide": [ + 1.0, + 68.0 + ], + "pH": [ + 2.74, + 4.01 + ], + "quality": [ + 3.0, + 8.0 + ], + "residual sugar": [ + 0.9, + 15.5 + ], + "sulphates": [ + 0.33, + 2.0 + ], + "total sulfur dioxide": [ + 6.0, + 289.0 + ], + "volatile acidity": [ + 0.12, + 1.58 + ] + }, + "cat_feature_value": {} +} \ No newline at end of file diff --git a/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_001/test.csv b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_001/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..f437eb1a47aa094f1fe61013595301f64519dad1 --- /dev/null +++ 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b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/Wine_Quality_Dataset_B4_002_current.json new file mode 100644 index 0000000000000000000000000000000000000000..8b066242ce4965b94558d74b502ce2f39aea747b --- /dev/null +++ b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/Wine_Quality_Dataset_B4_002_current.json @@ -0,0 +1,3643 @@ +{ + "id": "002", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Wine Quality Dataset", + "table_path": "output/daily/classification/Wine Quality Dataset_B4/Wine Quality Dataset_B4_002/test.csv", + "query": "Our logistics team needs to know which new wines from the incoming inventory are probable high-scoring candidates for our exclusive client club. Based on the chemical profiles and outcomes in the file I sent you, please identify all entries that are strong contenders for our highest quality tier.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "fixed acidity": 8.6, + "volatile acidity": 0.315, + "citric acid": 0.4, + "residual sugar": 2.2, + "chlorides": 0.079, + "free sulfur dioxide": 3.0, + "total sulfur dioxide": 6.0, + "density": 0.99512, + "pH": 3.27, + "sulphates": 0.67, + "alcohol": 11.9, + "quality": 6, + "Id": 915 + } + }, + { + "scenario_id": "002", + "features": { + "fixed acidity": 7.9, + "volatile acidity": 0.65, + "citric acid": 0.01, + "residual sugar": 2.5, + "chlorides": 0.078, + "free sulfur dioxide": 17.0, + "total sulfur dioxide": 38.0, + "density": 0.9963, + "pH": 3.34, + "sulphates": 0.74, + "alcohol": 11.7, + "quality": 7, + "Id": 334 + } + }, + { + "scenario_id": "003", + 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+10.2,0.42,0.57,3.4,0.07,4.0,10.0,0.9971,3.04,0.63,9.6,5,56 +7.3,0.39,0.31,2.4,0.074,9.0,46.0,0.9962,3.41,0.54,9.4,6,59 +8.8,0.4,0.4,2.2,0.079,19.0,52.0,0.998,3.44,0.64,9.2,5,60 +7.7,0.69,0.49,1.8,0.115,20.0,112.0,0.9968,3.21,0.71,9.3,5,61 +7.0,0.735,0.05,2.0,0.081,13.0,54.0,0.9966,3.39,0.57,9.8,5,63 +7.2,0.725,0.05,4.65,0.086,4.0,11.0,0.9962,3.41,0.39,10.9,5,64 +7.2,0.725,0.05,4.65,0.086,4.0,11.0,0.9962,3.41,0.39,10.9,5,65 +6.6,0.705,0.07,1.6,0.076,6.0,15.0,0.9962,3.44,0.58,10.7,5,67 +8.0,0.705,0.05,1.9,0.074,8.0,19.0,0.9962,3.34,0.95,10.5,6,69 +7.7,0.69,0.22,1.9,0.084,18.0,94.0,0.9961,3.31,0.48,9.5,5,72 +8.3,0.675,0.26,2.1,0.084,11.0,43.0,0.9976,3.31,0.53,9.2,4,73 +8.8,0.41,0.64,2.2,0.093,9.0,42.0,0.9986,3.54,0.66,10.5,5,76 +6.8,0.785,0.0,2.4,0.104,14.0,30.0,0.9966,3.52,0.55,10.7,6,77 +6.7,0.75,0.12,2.0,0.086,12.0,80.0,0.9958,3.38,0.52,10.1,5,78 +8.3,0.625,0.2,1.5,0.08,27.0,119.0,0.9972,3.16,1.12,9.1,4,79 +6.2,0.45,0.2,1.6,0.069,3.0,15.0,0.9958,3.41,0.56,9.2,5,80 +7.4,0.5,0.47,2.0,0.086,21.0,73.0,0.997,3.36,0.57,9.1,5,82 +6.3,0.3,0.48,1.8,0.069,18.0,61.0,0.9959,3.44,0.78,10.3,6,84 +6.9,0.55,0.15,2.2,0.076,19.0,40.0,0.9961,3.41,0.59,10.1,5,85 +8.6,0.49,0.28,1.9,0.11,20.0,136.0,0.9972,2.93,1.95,9.9,6,86 +7.7,0.49,0.26,1.9,0.062,9.0,31.0,0.9966,3.39,0.64,9.6,5,87 +9.3,0.39,0.44,2.1,0.107,34.0,125.0,0.9978,3.14,1.22,9.5,5,88 +7.0,0.62,0.08,1.8,0.076,8.0,24.0,0.9978,3.48,0.53,9.0,5,89 +7.9,0.52,0.26,1.9,0.079,42.0,140.0,0.9964,3.23,0.54,9.5,5,90 +7.7,0.49,0.26,1.9,0.062,9.0,31.0,0.9966,3.39,0.64,9.6,5,93 +6.8,0.775,0.0,3.0,0.102,8.0,23.0,0.9965,3.45,0.56,10.7,5,96 +8.1,0.545,0.18,1.9,0.08,13.0,35.0,0.9972,3.3,0.59,9.0,6,99 +8.3,0.61,0.3,2.1,0.084,11.0,50.0,0.9972,3.4,0.61,10.2,6,100 +8.1,0.545,0.18,1.9,0.08,13.0,35.0,0.9972,3.3,0.59,9.0,6,102 +7.2,0.49,0.24,2.2,0.07,5.0,36.0,0.996,3.33,0.48,9.4,5,104 +8.1,0.575,0.22,2.1,0.077,12.0,65.0,0.9967,3.29,0.51,9.2,5,105 +6.2,0.63,0.31,1.7,0.088,15.0,64.0,0.9969,3.46,0.79,9.3,5,107 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a/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/info.json b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3971b7775a2d98f4de4ba2319684b43be1e49fcb --- /dev/null +++ b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/info.json @@ -0,0 +1,86 @@ +{ + "name": "Wine Quality Dataset", + "source": "https://www.kaggle.com/datasets/yasserh/wine-quality-dataset/data", + "data_intro": "This datasets is related to red variants of the Portuguese \"Vinho Verde\" wine.The dataset describes the amount of various chemicals present in wine and their effect on it's quality. The datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.", + "is_splited": false, + "overall_size": 1143, + "train_size": 0, + "test_size": 0, + "c_classes": 0, + "n_classes": 13, + "cat_feature_intro": {}, + "num_feature_intro": { + "fixed acidity": "fixed acidity", + "volatile acidity": "volatile acidity", + "citric acid": "citric acid", + "residual sugar": "residual sugar", + "chlorides": "chlorides", + "free sulfur dioxide": "free sulfur dioxide", + "total sulfur dioxide": "total sulfur dioxide", + "density": "density", + "pH": "pH", + "sulphates": "sulphates", + "alcohol": "alcohol", + "quality": "the quality of wine(classes are ordered and imbalanced)", + "Id": "identifier for each sample" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "quality", + "example": "https://www.kaggle.com/code/qusaybtoush1990/wine-quality", + 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a/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/train.csv b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..c29529f32e16e9cece35c1f34ff57354b3bafbc8 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/train.csv @@ -0,0 +1,916 @@ +fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality,Id +7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4,5,0 +7.8,0.88,0.0,2.6,0.098,25.0,67.0,0.9968,3.2,0.68,9.8,5,1 +7.8,0.76,0.04,2.3,0.092,15.0,54.0,0.997,3.26,0.65,9.8,5,2 +11.2,0.28,0.56,1.9,0.075,17.0,60.0,0.998,3.16,0.58,9.8,6,3 +7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4,5,4 +7.9,0.6,0.06,1.6,0.069,15.0,59.0,0.9964,3.3,0.46,9.4,5,6 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The datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.", + "is_splited": false, + "overall_size": 1143, + "train_size": 0, + "test_size": 0, + "c_classes": 0, + "n_classes": 13, + "cat_feature_intro": {}, + "num_feature_intro": { + "fixed acidity": "fixed acidity", + "volatile acidity": "volatile acidity", + "citric acid": "citric acid", + "residual sugar": "residual sugar", + "chlorides": "chlorides", + "free sulfur dioxide": "free sulfur dioxide", + "total sulfur dioxide": "total sulfur dioxide", + "density": "density", + "pH": "pH", + "sulphates": "sulphates", + "alcohol": "alcohol", + "quality": "the quality of wine(classes are ordered and imbalanced)", + "Id": "identifier for each sample" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "quality", + "example": "https://www.kaggle.com/code/qusaybtoush1990/wine-quality", + 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Dataset_B4_004/test.csv", + "query": "For a quality control deep-dive, we need to investigate all new vintages that are at risk of being classified as flawed. 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The datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.", + "is_splited": false, + "overall_size": 1143, + "train_size": 0, + "test_size": 0, + "c_classes": 0, + "n_classes": 13, + "cat_feature_intro": {}, + "num_feature_intro": { + "fixed acidity": "fixed acidity", + "volatile acidity": "volatile acidity", + "citric acid": "citric acid", + "residual sugar": "residual sugar", + "chlorides": "chlorides", + "free sulfur dioxide": "free sulfur dioxide", + "total sulfur dioxide": "total sulfur dioxide", + "density": "density", + "pH": "pH", + "sulphates": "sulphates", + "alcohol": "alcohol", + "quality": "the quality of wine(classes are ordered and imbalanced)", + "Id": "identifier for each sample" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "quality", + "example": "https://www.kaggle.com/code/qusaybtoush1990/wine-quality", + 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+8.6,0.38,0.36,3.0,0.081,30.0,119.0,0.997,3.2,0.56,9.4,5,53 +7.6,0.51,0.15,2.8,0.11,33.0,73.0,0.9955,3.17,0.63,10.2,6,54 +7.3,0.39,0.31,2.4,0.074,9.0,46.0,0.9962,3.41,0.54,9.4,6,59 +8.8,0.4,0.4,2.2,0.079,19.0,52.0,0.998,3.44,0.64,9.2,5,60 +7.0,0.735,0.05,2.0,0.081,13.0,54.0,0.9966,3.39,0.57,9.8,5,63 +7.2,0.725,0.05,4.65,0.086,4.0,11.0,0.9962,3.41,0.39,10.9,5,65 +8.0,0.705,0.05,1.9,0.074,8.0,19.0,0.9962,3.34,0.95,10.5,6,69 +8.3,0.675,0.26,2.1,0.084,11.0,43.0,0.9976,3.31,0.53,9.2,4,73 +8.8,0.41,0.64,2.2,0.093,9.0,42.0,0.9986,3.54,0.66,10.5,5,76 +8.3,0.625,0.2,1.5,0.08,27.0,119.0,0.9972,3.16,1.12,9.1,4,79 +6.2,0.45,0.2,1.6,0.069,3.0,15.0,0.9958,3.41,0.56,9.2,5,80 +7.4,0.5,0.47,2.0,0.086,21.0,73.0,0.997,3.36,0.57,9.1,5,82 +6.3,0.3,0.48,1.8,0.069,18.0,61.0,0.9959,3.44,0.78,10.3,6,84 +7.7,0.49,0.26,1.9,0.062,9.0,31.0,0.9966,3.39,0.64,9.6,5,87 +9.3,0.39,0.44,2.1,0.107,34.0,125.0,0.9978,3.14,1.22,9.5,5,88 +7.0,0.62,0.08,1.8,0.076,8.0,24.0,0.9978,3.48,0.53,9.0,5,89 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+8.3,0.54,0.28,1.9,0.077,11.0,40.0,0.9978,3.39,0.61,10.0,6,116 +7.3,1.07,0.09,1.7,0.178,10.0,89.0,0.9962,3.3,0.57,9.0,5,120 +8.8,0.55,0.04,2.2,0.119,14.0,56.0,0.9962,3.21,0.6,10.9,6,121 +7.8,0.5,0.17,1.6,0.0819999999999999,21.0,102.0,0.996,3.39,0.48,9.5,5,124 +8.2,1.33,0.0,1.7,0.081,3.0,12.0,0.9964,3.53,0.49,10.9,5,126 +8.1,1.33,0.0,1.8,0.0819999999999999,3.0,12.0,0.9964,3.54,0.48,10.9,5,127 +8.0,0.59,0.16,1.8,0.065,3.0,16.0,0.9962,3.42,0.92,10.5,7,128 +8.0,0.745,0.56,2.0,0.118,30.0,134.0,0.9968,3.24,0.66,9.4,5,130 +5.6,0.5,0.09,2.3,0.049,17.0,99.0,0.9937,3.63,0.63,13.0,5,131 +7.9,1.04,0.05,2.2,0.084,13.0,29.0,0.9959,3.22,0.55,9.9,6,134 +8.4,0.745,0.11,1.9,0.09,16.0,63.0,0.9965,3.19,0.82,9.6,5,135 +7.2,0.415,0.36,2.0,0.081,13.0,45.0,0.9972,3.48,0.64,9.2,5,137 +8.4,0.745,0.11,1.9,0.09,16.0,63.0,0.9965,3.19,0.82,9.6,5,140 +6.3,0.39,0.08,1.7,0.066,3.0,20.0,0.9954,3.34,0.58,9.4,5,143 +5.2,0.34,0.0,1.8,0.05,27.0,63.0,0.9916,3.68,0.79,14.0,6,144 +8.1,0.67,0.55,1.8,0.1169999999999999,32.0,141.0,0.9968,3.17,0.62,9.4,5,145 +5.8,0.68,0.02,1.8,0.087,21.0,94.0,0.9944,3.54,0.52,10.0,5,146 +6.9,0.49,0.1,2.3,0.074,12.0,30.0,0.9959,3.42,0.58,10.2,6,148 +7.3,0.33,0.47,2.1,0.077,5.0,11.0,0.9958,3.33,0.53,10.3,6,150 +9.2,0.52,1.0,3.4,0.61,32.0,69.0,0.9996,2.74,2.0,9.4,4,151 +7.5,0.6,0.03,1.8,0.095,25.0,99.0,0.995,3.35,0.54,10.1,5,152 +7.1,0.43,0.42,5.5,0.071,28.0,128.0,0.9973,3.42,0.71,10.5,5,155 +7.1,0.43,0.42,5.5,0.07,29.0,129.0,0.9973,3.42,0.72,10.5,5,156 +7.1,0.43,0.42,5.5,0.071,28.0,128.0,0.9973,3.42,0.71,10.5,5,157 +7.1,0.68,0.0,2.2,0.073,12.0,22.0,0.9969,3.48,0.5,9.3,5,158 +6.8,0.6,0.18,1.9,0.079,18.0,86.0,0.9968,3.59,0.57,9.3,6,159 +7.6,0.95,0.03,2.0,0.09,7.0,20.0,0.9959,3.2,0.56,9.6,5,160 +7.6,0.68,0.02,1.3,0.072,9.0,20.0,0.9965,3.17,1.08,9.2,4,161 +7.8,0.53,0.04,1.7,0.076,17.0,31.0,0.9964,3.33,0.56,10.0,6,162 +7.3,0.59,0.26,7.2,0.07,35.0,121.0,0.9981,3.37,0.49,9.4,5,164 +7.8,0.63,0.48,1.7,0.1,14.0,96.0,0.9961,3.19,0.62,9.5,5,165 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chemicals present in wine and their effect on it's quality. The datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.", + "is_splited": false, + "overall_size": 1143, + "train_size": 0, + "test_size": 0, + "c_classes": 0, + "n_classes": 13, + "cat_feature_intro": {}, + "num_feature_intro": { + "fixed acidity": "fixed acidity", + "volatile acidity": "volatile acidity", + "citric acid": "citric acid", + "residual sugar": "residual sugar", + "chlorides": "chlorides", + "free sulfur dioxide": "free sulfur dioxide", + "total sulfur dioxide": "total sulfur dioxide", + "density": "density", + "pH": "pH", + "sulphates": "sulphates", + "alcohol": "alcohol", + "quality": "the quality of wine(classes are ordered and imbalanced)", + "Id": "identifier for each sample" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "quality", + "example": "https://www.kaggle.com/code/qusaybtoush1990/wine-quality", + 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b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_006/Wine_Quality_Dataset_B4_006_current.json new file mode 100644 index 0000000000000000000000000000000000000000..ceb5f52d5ed3b879a58f26781d5dc163e365a713 --- /dev/null +++ b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_006/Wine_Quality_Dataset_B4_006_current.json @@ -0,0 +1,3635 @@ +{ + "id": "006", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Wine Quality Dataset", + "table_path": "output/daily/classification/Wine Quality Dataset_B4/Wine Quality Dataset_B4_006/test.csv", + "query": "We've received some customer complaints about wines tasting overly sharp or acidic. Using the quality logs I've provided, can you flag the bottles in our current batch that have a volatile acidity level greater than 1.0 and are also predicted to be of poor quality (3)? We need to review these before they go to market.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "fixed acidity": 7.7, + "volatile acidity": 0.96, + "citric acid": 0.2, + "residual sugar": 2.0, + "chlorides": 0.047, + "free sulfur dioxide": 15.0, + "total sulfur dioxide": 60.0, + "density": 0.9955, + "pH": 3.36, + "sulphates": 0.44, + "alcohol": 10.9, + "quality": 5, + "Id": 422 + } + }, + { + "scenario_id": "002", + "features": { + "fixed acidity": 9.3, + "volatile acidity": 0.43, + "citric acid": 0.44, + "residual sugar": 1.9, + "chlorides": 0.085, + "free sulfur dioxide": 9.0, + "total sulfur dioxide": 22.0, + "density": 0.99708, + "pH": 3.28, + "sulphates": 0.55, + "alcohol": 9.5, + "quality": 5, + "Id": 850 + } + }, + { + "scenario_id": "003", + "features": { + "fixed acidity": 6.6, + "volatile acidity": 0.725, + "citric acid": 0.09, + "residual sugar": 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+5.9,0.645,0.12,2.0,0.075,32.0,44.0,0.99547,3.57,0.71,10.2,5,1597 diff --git a/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_006/info.json b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_006/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3971b7775a2d98f4de4ba2319684b43be1e49fcb --- /dev/null +++ b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_006/info.json @@ -0,0 +1,86 @@ +{ + "name": "Wine Quality Dataset", + "source": "https://www.kaggle.com/datasets/yasserh/wine-quality-dataset/data", + "data_intro": "This datasets is related to red variants of the Portuguese \"Vinho Verde\" wine.The dataset describes the amount of various chemicals present in wine and their effect on it's quality. The datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.", + "is_splited": false, + "overall_size": 1143, + "train_size": 0, + "test_size": 0, + "c_classes": 0, + "n_classes": 13, + "cat_feature_intro": {}, + "num_feature_intro": { + "fixed acidity": "fixed acidity", + "volatile acidity": "volatile acidity", + "citric acid": "citric acid", + "residual sugar": "residual sugar", + "chlorides": "chlorides", + "free sulfur dioxide": "free sulfur dioxide", + "total sulfur dioxide": "total sulfur dioxide", + "density": "density", + "pH": "pH", + "sulphates": "sulphates", + "alcohol": "alcohol", + "quality": "the quality of wine(classes are ordered and imbalanced)", + "Id": "identifier for each sample" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "quality", + "example": "https://www.kaggle.com/code/qusaybtoush1990/wine-quality", + "num_feature_value": { + "Id": [ + 0.0, + 1597.0 + ], + "alcohol": [ + 8.4, + 14.9 + ], + "chlorides": [ + 0.012, + 0.611 + ], + "citric acid": [ + 0.0, + 1.0 + ], + "density": [ + 0.99007, + 1.00369 + ], + "fixed acidity": [ + 4.6, + 15.9 + ], + "free sulfur dioxide": [ + 1.0, + 68.0 + ], + "pH": [ + 2.74, + 4.01 + ], + "quality": [ + 3.0, + 8.0 + ], + "residual sugar": [ + 0.9, + 15.5 + ], + "sulphates": [ + 0.33, + 2.0 + ], + "total sulfur dioxide": [ + 6.0, + 289.0 + ], + "volatile acidity": [ + 0.12, + 1.58 + ] + }, + "cat_feature_value": {} +} \ No newline at end of file diff --git a/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_006/test.csv b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_006/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..020cd489dfab63194fb782df0a9ad36a8ed1a426 --- /dev/null +++ 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From the product file I sent over, can you isolate the candidates where the total sulfur dioxide is under 50 and determine which of them are still projected to achieve a good quality score of 7? We want to prove that quality isn't compromised.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "fixed acidity": 9.4, + "volatile acidity": 0.685, + "citric acid": 0.11, + "residual sugar": 2.7, + "chlorides": 0.077, + "free sulfur dioxide": 6.0, + "total sulfur dioxide": 31.0, + "density": 0.9984, + "pH": 3.19, + "sulphates": 0.7, + "alcohol": 10.1, + "quality": 6, + "Id": 479 + } + }, + { + "scenario_id": "002", + "features": { + "fixed acidity": 7.1, + "volatile acidity": 0.46, + "citric acid": 0.2, + "residual sugar": 1.9, + "chlorides": 0.077, + "free sulfur dioxide": 28.0, + "total sulfur dioxide": 54.0, + "density": 0.9956, + "pH": 3.37, + "sulphates": 0.64, + "alcohol": 10.4, + "quality": 6, + "Id": 1281 + } + }, + { + "scenario_id": "003", + "features": { + "fixed acidity": 8.2, + "volatile acidity": 0.74, + "citric acid": 0.09, + "residual sugar": 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a/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_008/Wine_Quality_Dataset_B4_008_current.json b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_008/Wine_Quality_Dataset_B4_008_current.json new file mode 100644 index 0000000000000000000000000000000000000000..d64a711d62ab211b78ca52dc7fceb6c932a7257b --- /dev/null +++ b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_008/Wine_Quality_Dataset_B4_008_current.json @@ -0,0 +1,3642 @@ +{ + "id": "008", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Wine Quality Dataset", + "table_path": "output/daily/classification/Wine Quality Dataset_B4/Wine Quality Dataset_B4_008/test.csv", + "query": "We are targeting a market segment that prefers sweeter wines. Looking at the trends in our historical quality data, could you identify the wines from our new vintage that have a residual sugar content above 2 g/L and are likely to fall into the high-quality bracket (8)? We need to adjust their marketing placement accordingly.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "fixed acidity": 10.2, + "volatile acidity": 0.34, + "citric acid": 0.48, + "residual sugar": 2.1, + "chlorides": 0.052, + "free sulfur dioxide": 5.0, + "total sulfur dioxide": 9.0, + "density": 0.99458, + "pH": 3.2, + "sulphates": 0.69, + "alcohol": 12.1, + "quality": 7, + "Id": 953 + } + }, + { + "scenario_id": "002", + "features": { + "fixed acidity": 11.6, + "volatile acidity": 0.475, + "citric acid": 0.4, + "residual sugar": 1.4, + "chlorides": 0.091, + "free sulfur dioxide": 6.0, + "total sulfur dioxide": 28.0, + "density": 0.99704, + "pH": 3.07, + "sulphates": 0.65, + "alcohol": 10.0333333333333, + "quality": 6, + "Id": 1359 + } + }, + { + "scenario_id": "003", + "features": { + "fixed acidity": 8.1, + "volatile acidity": 0.87, + "citric acid": 0.0, 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b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_008/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3971b7775a2d98f4de4ba2319684b43be1e49fcb --- /dev/null +++ b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_008/info.json @@ -0,0 +1,86 @@ +{ + "name": "Wine Quality Dataset", + "source": "https://www.kaggle.com/datasets/yasserh/wine-quality-dataset/data", + "data_intro": "This datasets is related to red variants of the Portuguese \"Vinho Verde\" wine.The dataset describes the amount of various chemicals present in wine and their effect on it's quality. The datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.", + "is_splited": false, + "overall_size": 1143, + "train_size": 0, + "test_size": 0, + "c_classes": 0, + "n_classes": 13, + "cat_feature_intro": {}, + "num_feature_intro": { + "fixed acidity": "fixed acidity", + "volatile acidity": "volatile acidity", + "citric acid": "citric acid", + "residual sugar": "residual sugar", + "chlorides": "chlorides", + "free sulfur dioxide": "free sulfur dioxide", + "total sulfur dioxide": "total sulfur dioxide", + "density": "density", + "pH": "pH", + "sulphates": "sulphates", + "alcohol": "alcohol", + "quality": "the quality of wine(classes are ordered and imbalanced)", + "Id": "identifier for each sample" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "quality", + "example": "https://www.kaggle.com/code/qusaybtoush1990/wine-quality", + 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+9.4,0.4,0.31,2.2,0.09,13.0,62.0,0.9966,3.07,0.63,10.5,6,115 +8.3,0.54,0.28,1.9,0.077,11.0,40.0,0.9978,3.39,0.61,10.0,6,116 +7.3,1.07,0.09,1.7,0.178,10.0,89.0,0.9962,3.3,0.57,9.0,5,120 +8.8,0.55,0.04,2.2,0.119,14.0,56.0,0.9962,3.21,0.6,10.9,6,121 +7.3,0.695,0.0,2.5,0.075,3.0,13.0,0.998,3.49,0.52,9.2,5,122 +8.2,1.33,0.0,1.7,0.081,3.0,12.0,0.9964,3.53,0.49,10.9,5,126 +8.1,1.33,0.0,1.8,0.0819999999999999,3.0,12.0,0.9964,3.54,0.48,10.9,5,127 +8.0,0.59,0.16,1.8,0.065,3.0,16.0,0.9962,3.42,0.92,10.5,7,128 +8.0,0.745,0.56,2.0,0.118,30.0,134.0,0.9968,3.24,0.66,9.4,5,130 +7.9,1.04,0.05,2.2,0.084,13.0,29.0,0.9959,3.22,0.55,9.9,6,134 +8.4,0.745,0.11,1.9,0.09,16.0,63.0,0.9965,3.19,0.82,9.6,5,140 +6.3,0.39,0.08,1.7,0.066,3.0,20.0,0.9954,3.34,0.58,9.4,5,143 +5.2,0.34,0.0,1.8,0.05,27.0,63.0,0.9916,3.68,0.79,14.0,6,144 +8.1,0.67,0.55,1.8,0.1169999999999999,32.0,141.0,0.9968,3.17,0.62,9.4,5,145 +5.8,0.68,0.02,1.8,0.087,21.0,94.0,0.9944,3.54,0.52,10.0,5,146 +6.9,0.49,0.1,2.3,0.074,12.0,30.0,0.9959,3.42,0.58,10.2,6,148 +7.3,0.33,0.47,2.1,0.077,5.0,11.0,0.9958,3.33,0.53,10.3,6,150 +9.2,0.52,1.0,3.4,0.61,32.0,69.0,0.9996,2.74,2.0,9.4,4,151 +7.5,0.6,0.03,1.8,0.095,25.0,99.0,0.995,3.35,0.54,10.1,5,152 +7.1,0.43,0.42,5.5,0.071,28.0,128.0,0.9973,3.42,0.71,10.5,5,155 +7.1,0.43,0.42,5.5,0.07,29.0,129.0,0.9973,3.42,0.72,10.5,5,156 +6.8,0.6,0.18,1.9,0.079,18.0,86.0,0.9968,3.59,0.57,9.3,6,159 +7.6,0.95,0.03,2.0,0.09,7.0,20.0,0.9959,3.2,0.56,9.6,5,160 +7.6,0.68,0.02,1.3,0.072,9.0,20.0,0.9965,3.17,1.08,9.2,4,161 +7.8,0.53,0.04,1.7,0.076,17.0,31.0,0.9964,3.33,0.56,10.0,6,162 +7.4,0.6,0.26,7.3,0.07,36.0,121.0,0.9982,3.37,0.49,9.4,5,163 +7.3,0.59,0.26,7.2,0.07,35.0,121.0,0.9981,3.37,0.49,9.4,5,164 +7.8,0.63,0.48,1.7,0.1,14.0,96.0,0.9961,3.19,0.62,9.5,5,165 +6.8,0.64,0.1,2.1,0.085,18.0,101.0,0.9956,3.34,0.52,10.2,5,166 +7.3,0.55,0.03,1.6,0.072,17.0,42.0,0.9956,3.37,0.48,9.0,4,167 +8.0,0.42,0.17,2.0,0.073,6.0,18.0,0.9972,3.29,0.61,9.2,6,172 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