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decision_making/daily/classification/Apple_Quality_B2/Apple_Quality_B2_001.json

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+{
+  "id": "001",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Apple_Quality",
+  "table_path": "kaggle/Apple_Quality",
+  "query": "My friend is counting on me to bring a good apple, not a \"bad\" one, for her baking demo. Looking at apple 1967.0, its size is 1.30070502, it weighs -3.782508357, has a sweetness of -2.517297769, a crunchiness score of -0.473818592, a juiciness of 2.07225142, and ripeness at 0.249530968. The alternative, apple 3037.0, measures -1.377766738 in size, -1.930203902 in weight, 1.073826092 in sweetness, 0.660535429 in crunchiness, 1.224800229 in juiciness, and 0.833628232 in ripeness. I'm going back and forth—could you tell me, from all this, which selection is the bad apple?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "A_id": "1967.0",
+          "Size": "1.30070502",
+          "Weight": "-3.782508357",
+          "Sweetness": "-2.517297769",
+          "Crunchiness": "-0.473818592",
+          "Juiciness": "2.07225142",
+          "Ripeness": "0.249530968",
+          "Acidity": null,
+          "Quality": "good"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "A_id": "3037.0",
+          "Size": "-1.377766738",
+          "Weight": "-1.930203902",
+          "Sweetness": "1.073826092",
+          "Crunchiness": "0.660535429",
+          "Juiciness": "1.224800229",
+          "Ripeness": "0.833628232",
+          "Acidity": null,
+          "Quality": "bad"
+        }
+      }
+    ],
+    "target_column": "Quality",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Apple_Quality_B2/Apple_Quality_B2_002.json

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+{
+  "id": "002",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Apple_Quality",
+  "table_path": "kaggle/Apple_Quality",
+  "query": "Let's lay it all out. Fruit number 207.0 has a Size of 4.490970426 and a Weight of -0.329475368. Its Sweetness level is -1.274028186, but it makes up for it with a solid Crunchiness of 3.159289343 and an outstanding Juiciness score of 4.450735119, even though its Ripeness is lagging at -2.135571344. The next one, 1840.0, measures at Size -1.943168085 and Weight -2.81966909. Sweetness is -1.754703383, its Crunchiness is just -0.408436682, it has a Juiciness of 2.404233277, and its big selling point is a Ripeness of 3.615822784. Lastly, we have 858.0 with a Size of -0.928644679 and Weight of -1.096734171. This one is really not sweet at -2.570144912, has a Crunchiness of -0.95699586, a Juiciness of 2.819405655, and a Ripeness of 0.338450512. They're all so different! I need the one that's going to be a winner in terms of overall excellence. Tell me, which of these three fits the bill for a truly good fruit?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "A_id": "207.0",
+          "Size": "4.490970426",
+          "Weight": "-0.329475368",
+          "Sweetness": "-1.274028186",
+          "Crunchiness": "3.159289343",
+          "Juiciness": "4.450735119",
+          "Ripeness": "-2.135571344",
+          "Acidity": null,
+          "Quality": "good"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "A_id": "1840.0",
+          "Size": "-1.943168085",
+          "Weight": "-2.81966909",
+          "Sweetness": "-1.754703383",
+          "Crunchiness": "-0.408436682",
+          "Juiciness": "2.404233277",
+          "Ripeness": "3.615822784",
+          "Acidity": null,
+          "Quality": "bad"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "A_id": "858.0",
+          "Size": "-0.928644679",
+          "Weight": "-1.096734171",
+          "Sweetness": "-2.570144912",
+          "Crunchiness": "-0.95699586",
+          "Juiciness": "2.819405655",
+          "Ripeness": "0.338450512",
+          "Acidity": null,
+          "Quality": "bad"
+        }
+      }
+    ],
+    "target_column": "Quality",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Apple_Quality_B2/Apple_Quality_B2_003.json

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+{
+  "id": "003",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Apple_Quality",
+  "table_path": "kaggle/Apple_Quality",
+  "query": "These three apples on the table each tell a different story. For ID 254.0, the size reads -0.845554468, it weighs 1.63692121, sweetness is -1.39745862, it has a mild crunch at 0.20748258, juiciness is quite low at -2.826069911, and ripeness is -0.210229378. The next, ID 445.0, is sized at -0.879061908, weighs 0.330725451, is a bit sweeter at -0.253714396, noticeably crunchier at 1.771869032, juicier at 0.765023954, but its ripeness is deeply negative at -2.885655074. Finally, ID 1489.0 comes in with a size of -1.79013844, a very light weight of -2.883487577, low sweetness of -2.286722473, high crunchiness of 2.225203847, juiciness at -0.703248983, and an unusually high ripeness of 3.738494175. It’s confusing how one can be so ripe yet light and not sweet—given all that, which one should I worry is bad quality?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "A_id": "254.0",
+          "Size": "-0.845554468",
+          "Weight": "1.63692121",
+          "Sweetness": "-1.39745862",
+          "Crunchiness": "0.20748258",
+          "Juiciness": "-2.826069911",
+          "Ripeness": "-0.210229378",
+          "Acidity": null,
+          "Quality": "good"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "A_id": "445.0",
+          "Size": "-0.879061908",
+          "Weight": "0.330725451",
+          "Sweetness": "-0.253714396",
+          "Crunchiness": "1.771869032",
+          "Juiciness": "0.765023954",
+          "Ripeness": "-2.885655074",
+          "Acidity": null,
+          "Quality": "good"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "A_id": "1489.0",
+          "Size": "-1.79013844",
+          "Weight": "-2.883487577",
+          "Sweetness": "-2.286722473",
+          "Crunchiness": "2.225203847",
+          "Juiciness": "-0.703248983",
+          "Ripeness": "3.738494175",
+          "Acidity": null,
+          "Quality": "bad"
+        }
+      }
+    ],
+    "target_column": "Quality",
+    "task_sub_type": "classification",
+    "final_decision": "003",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Apple_Quality_B2/Apple_Quality_B2_004.json

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+{
+  "id": "004",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Apple_Quality",
+  "table_path": "kaggle/Apple_Quality",
+  "query": "These two candidates from the new shipment have all their specs noted. Apple 2149 shows a size of 1.136051035, weight of -0.295270112, and a sweetness score of -1.619187353, while its crunchiness is 1.987419922, juiciness is 0.019736322, and ripeness is -0.840226087. The other, apple 2334, comes in at a size of 0.611046549, weighs -0.976546345, and has a sweetness as low as -2.349285088. Its crunchiness is 1.173038849, juiciness is -1.100054678, and it registers a ripeness of 0.119111973. Looking at the trends in the file I sent, which one appears to be the clearer example of a bad quality fruit?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "A_id": "2149.0",
+          "Size": "1.136051035",
+          "Weight": "-0.295270112",
+          "Sweetness": "-1.619187353",
+          "Crunchiness": "1.987419922",
+          "Juiciness": "0.019736322",
+          "Ripeness": "-0.840226087",
+          "Acidity": null,
+          "Quality": "bad"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "A_id": "2334.0",
+          "Size": "0.611046549",
+          "Weight": "-0.976546345",
+          "Sweetness": "-2.349285088",
+          "Crunchiness": "1.173038849",
+          "Juiciness": "-1.100054678",
+          "Ripeness": "0.119111973",
+          "Acidity": null,
+          "Quality": "good"
+        }
+      }
+    ],
+    "target_column": "Quality",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Apple_Quality_B2/Apple_Quality_B2_005.json

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+{
+  "id": "005",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Apple_Quality",
+  "table_path": "kaggle/Apple_Quality",
+  "query": "For this tasting, I need to avoid the bad ones. Looking at the first candidate, number 951, its size is 0.02565221 and it weighs -0.326161449. The sweetness reading is -1.239428425, it has a crunchiness of -0.486801683, a juiciness of 0.395589567, and a ripeness level of -0.127622496. The second, number 1231, measures -1.745322249 in size and -2.979970538 in weight. Its sweetness is 0.093208914, crunchiness is -0.84221055, juiciness is -0.801427776, and ripeness is a high 1.93038528. I've shared our historical quality logs to help spot patterns. Based on that, can you tell me if apple 951 or apple 1231 is the dud?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "A_id": "951.0",
+          "Size": "0.02565221",
+          "Weight": "-0.326161449",
+          "Sweetness": "-1.239428425",
+          "Crunchiness": "-0.486801683",
+          "Juiciness": "0.395589567",
+          "Ripeness": "-0.127622496",
+          "Acidity": null,
+          "Quality": "good"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "A_id": "1231.0",
+          "Size": "-1.745322249",
+          "Weight": "-2.979970538",
+          "Sweetness": "0.093208914",
+          "Crunchiness": "-0.84221055",
+          "Juiciness": "-0.801427776",
+          "Ripeness": "1.93038528",
+          "Acidity": null,
+          "Quality": "bad"
+        }
+      }
+    ],
+    "target_column": "Quality",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Apple_Quality_B2/Apple_Quality_B2_006.json

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+{
+  "id": "006",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Apple_Quality",
+  "table_path": "kaggle/Apple_Quality",
+  "query": "My job is to pick one apple guaranteed to be good for the display, but their specs are all over the place. Apple 1835 shows a size of -3.357510653, a weight of 0.067642937, sweetness at 0.813438932, crunchiness up at 2.979557228, but juiciness drops to -4.217111053, with ripeness 1.819288791. Then there’s apple 574, with size 0.389662204, weight way down at -4.386919432, sweetness high at 3.8699463, crunchiness 1.636602068, juiciness high too at 3.262054988, and ripeness 1.095093916. The last, apple 3055, has size -0.660428075, weight -2.391390997, sweetness low at -1.426340004, crunchiness 1.477891955, juiciness -1.197527435, yet ripeness peaks at 4.115822264. Looking at the trends in the file I sent, our historical logs tie these numbers to the final quality call. Honestly, I’m second-guessing myself—can you tell me, based on that past data, which apple should I bet on for the good quality rating?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "A_id": "1835.0",
+          "Size": "-3.357510653",
+          "Weight": "0.067642937",
+          "Sweetness": "0.813438932",
+          "Crunchiness": "2.979557228",
+          "Juiciness": "-4.217111053",
+          "Ripeness": "1.819288791",
+          "Acidity": null,
+          "Quality": "bad"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "A_id": "574.0",
+          "Size": "0.389662204",
+          "Weight": "-4.386919432",
+          "Sweetness": "3.8699463",
+          "Crunchiness": "1.636602068",
+          "Juiciness": "3.262054988",
+          "Ripeness": "1.095093916",
+          "Acidity": null,
+          "Quality": "good"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "A_id": "3055.0",
+          "Size": "-0.660428075",
+          "Weight": "-2.391390997",
+          "Sweetness": "-1.426340004",
+          "Crunchiness": "1.477891955",
+          "Juiciness": "-1.197527435",
+          "Ripeness": "4.115822264",
+          "Acidity": null,
+          "Quality": "bad"
+        }
+      }
+    ],
+    "target_column": "Quality",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Apple_Quality_B2/current.csv

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+A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality
+1967.0,1.30070502,-3.782508357,-2.517297769,-0.473818592,2.07225142,0.249530968,-1.94031386,good
+3037.0,-1.377766738,-1.930203902,1.073826092,0.660535429,1.224800229,0.833628232,-1.998524116,bad
+207.0,4.490970426,-0.329475368,-1.274028186,3.159289343,4.450735119,-2.135571344,0.268904126,good
+1840.0,-1.943168085,-2.81966909,-1.754703383,-0.408436682,2.404233277,3.615822784,-0.675343239,bad
+858.0,-0.928644679,-1.096734171,-2.570144912,-0.95699586,2.819405655,0.338450512,4.306908269,bad
+254.0,-0.845554468,1.63692121,-1.39745862,0.20748258,-2.826069911,-0.210229378,0.196019372,good
+445.0,-0.879061908,0.330725451,-0.253714396,1.771869032,0.765023954,-2.885655074,0.382914269,good
+1489.0,-1.79013844,-2.883487577,-2.286722473,2.225203847,-0.703248983,3.738494175,-1.614855762,bad
+2149.0,1.136051035,-0.295270112,-1.619187353,1.987419922,0.019736322,-0.840226087,2.568845781,bad
+2334.0,0.611046549,-0.976546345,-2.349285088,1.173038849,-1.100054678,0.119111973,-0.799352658,good
+951.0,0.02565221,-0.326161449,-1.239428425,-0.486801683,0.395589567,-0.127622496,2.477579568,good
+1231.0,-1.745322249,-2.979970538,0.093208914,-0.84221055,-0.801427776,1.93038528,-4.369480739,bad
+1835.0,-3.357510653,0.067642937,0.813438932,2.979557228,-4.217111053,1.819288791,-1.056070673,bad
+574.0,0.389662204,-4.386919432,3.8699463,1.636602068,3.262054988,1.095093916,-1.022076896,good
+3055.0,-0.660428075,-2.391390997,-1.426340004,1.477891955,-1.197527435,4.115822264,-2.14777319,bad

decision_making/daily/classification/Apple_Quality_B2/info.json

@@ -0,0 +1,66 @@
+{
+    "name": "Apple Quality",
+    "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data",
+    "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.",
+    "is_splited": false,
+    "overall_size": 4000,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 2,
+    "n_classes": 7,
+    "task_type": "classification",
+    "target": {
+        "Quality": "Build a model to predict the quality rating of fruits using various attributes."
+    },
+    "cat_feature_intro": {
+        "Acidity": "- Acidity: Acidity level of the fruit",
+        "Quality": "- Quality: Overall quality of the fruit."
+    },
+    "num_feature_intro": {
+        "A_id": "- A_id: Unique identifier for each fruit",
+        "Size": "- Size: Size of the fruit",
+        "Weight": "- Weight: Weight of the fruit",
+        "Sweetness": "- Sweetness: Degree of sweetness of the fruit",
+        "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit",
+        "Juiciness": "- Juiciness: Level of juiciness of the fruit",
+        "Ripeness": "- Ripeness: Stage of ripeness of the fruit"
+    },
+    "evaluation_metric": null,
+    "num_feature_value": {
+        "A_id": [
+            0.0,
+            3999.0
+        ],
+        "Crunchiness": [
+            -6.055057805,
+            7.619851801
+        ],
+        "Juiciness": [
+            -5.961897048,
+            7.364402864
+        ],
+        "Ripeness": [
+            -5.864598918,
+            7.237836684
+        ],
+        "Size": [
+            -7.151703059,
+            6.406366899
+        ],
+        "Sweetness": [
+            -6.894485494,
+            6.374915513
+        ],
+        "Weight": [
+            -7.149847675,
+            5.79071359
+        ]
+    },
+    "cat_feature_value": {
+        "Acidity": "string",
+        "Quality": [
+            "bad",
+            "good"
+        ]
+    }
+}

decision_making/daily/classification/Apple_Quality_B2/info_mod.json

@@ -0,0 +1,94 @@
+{
+  "name": "Apple Quality",
+  "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data",
+  "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.",
+  "is_splited": false,
+  "overall_size": 4000,
+  "train_size": 0,
+  "test_size": 0,
+  "c_classes": 2,
+  "n_classes": 7,
+  "task_type": "classification",
+  "target": "Quality",
+  "cat_feature_intro": {},
+  "num_feature_intro": {
+    "A_id": "- A_id: Unique identifier for each fruit",
+    "Size": "- Size: Size of the fruit",
+    "Weight": "- Weight: Weight of the fruit",
+    "Sweetness": "- Sweetness: Degree of sweetness of the fruit",
+    "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit",
+    "Juiciness": "- Juiciness: Level of juiciness of the fruit",
+    "Ripeness": "- Ripeness: Stage of ripeness of the fruit",
+    "Acidity": "- Acidity: Acidity level of the fruit"
+  },
+  "evaluation_metric": null,
+  "num_feature_value": {
+    "A_id": [
+      0.0,
+      3999.0
+    ],
+    "Size": [
+      -7.151703059,
+      6.406366899
+    ],
+    "Weight": [
+      -7.149847675,
+      5.79071359
+    ],
+    "Sweetness": [
+      -6.894485494,
+      6.374915513
+    ],
+    "Crunchiness": [
+      -6.055057805,
+      7.619851801
+    ],
+    "Juiciness": [
+      -5.961897048,
+      7.364402864
+    ],
+    "Ripeness": [
+      -5.864598918,
+      7.237836684
+    ],
+    "Acidity": [
+      -6.46098926,
+      6.70139469
+    ]
+  },
+  "cat_feature_value": {},
+  "columns": [
+    "A_id",
+    "Size",
+    "Weight",
+    "Sweetness",
+    "Crunchiness",
+    "Juiciness",
+    "Ripeness",
+    "Acidity",
+    "Quality"
+  ],
+  "feature_columns": [
+    "A_id",
+    "Size",
+    "Weight",
+    "Sweetness",
+    "Crunchiness",
+    "Juiciness",
+    "Ripeness",
+    "Acidity"
+  ],
+  "feature_types": {
+    "A_id": "numeric",
+    "Size": "numeric",
+    "Weight": "numeric",
+    "Sweetness": "numeric",
+    "Crunchiness": "numeric",
+    "Juiciness": "numeric",
+    "Ripeness": "numeric",
+    "Acidity": "numeric"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": []
+}

decision_making/daily/classification/Apple_Quality_B2/test.csv

@@ -0,0 +1,16 @@
+A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality
+1967.0,1.30070502,-3.782508357,-2.517297769,-0.473818592,2.07225142,0.249530968,-1.94031386,good
+3037.0,-1.377766738,-1.930203902,1.073826092,0.660535429,1.224800229,0.833628232,-1.998524116,bad
+207.0,4.490970426,-0.329475368,-1.274028186,3.159289343,4.450735119,-2.135571344,0.268904126,good
+1840.0,-1.943168085,-2.81966909,-1.754703383,-0.408436682,2.404233277,3.615822784,-0.675343239,bad
+858.0,-0.928644679,-1.096734171,-2.570144912,-0.95699586,2.819405655,0.338450512,4.306908269,bad
+254.0,-0.845554468,1.63692121,-1.39745862,0.20748258,-2.826069911,-0.210229378,0.196019372,good
+445.0,-0.879061908,0.330725451,-0.253714396,1.771869032,0.765023954,-2.885655074,0.382914269,good
+1489.0,-1.79013844,-2.883487577,-2.286722473,2.225203847,-0.703248983,3.738494175,-1.614855762,bad
+2149.0,1.136051035,-0.295270112,-1.619187353,1.987419922,0.019736322,-0.840226087,2.568845781,bad
+2334.0,0.611046549,-0.976546345,-2.349285088,1.173038849,-1.100054678,0.119111973,-0.799352658,good
+951.0,0.02565221,-0.326161449,-1.239428425,-0.486801683,0.395589567,-0.127622496,2.477579568,good
+1231.0,-1.745322249,-2.979970538,0.093208914,-0.84221055,-0.801427776,1.93038528,-4.369480739,bad
+1835.0,-3.357510653,0.067642937,0.813438932,2.979557228,-4.217111053,1.819288791,-1.056070673,bad
+574.0,0.389662204,-4.386919432,3.8699463,1.636602068,3.262054988,1.095093916,-1.022076896,good
+3055.0,-0.660428075,-2.391390997,-1.426340004,1.477891955,-1.197527435,4.115822264,-2.14777319,bad

decision_making/daily/classification/Apple_Quality_B2/test_001.csv

@@ -0,0 +1,3 @@
+A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality
+1967.0,1.30070502,-3.782508357,-2.517297769,-0.473818592,2.07225142,0.249530968,-1.94031386,good
+3037.0,-1.377766738,-1.930203902,1.073826092,0.660535429,1.224800229,0.833628232,-1.998524116,bad

decision_making/daily/classification/Apple_Quality_B2/test_002.csv

@@ -0,0 +1,4 @@
+A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality
+207.0,4.490970426,-0.329475368,-1.274028186,3.159289343,4.450735119,-2.135571344,0.268904126,good
+1840.0,-1.943168085,-2.81966909,-1.754703383,-0.408436682,2.404233277,3.615822784,-0.675343239,bad
+858.0,-0.928644679,-1.096734171,-2.570144912,-0.95699586,2.819405655,0.338450512,4.306908269,bad

decision_making/daily/classification/Apple_Quality_B2/test_003.csv

@@ -0,0 +1,4 @@
+A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality
+254.0,-0.845554468,1.63692121,-1.39745862,0.20748258,-2.826069911,-0.210229378,0.196019372,good
+445.0,-0.879061908,0.330725451,-0.253714396,1.771869032,0.765023954,-2.885655074,0.382914269,good
+1489.0,-1.79013844,-2.883487577,-2.286722473,2.225203847,-0.703248983,3.738494175,-1.614855762,bad

decision_making/daily/classification/Apple_Quality_B2/test_004.csv

@@ -0,0 +1,3 @@
+A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality
+2149.0,1.136051035,-0.295270112,-1.619187353,1.987419922,0.019736322,-0.840226087,2.568845781,bad
+2334.0,0.611046549,-0.976546345,-2.349285088,1.173038849,-1.100054678,0.119111973,-0.799352658,good

decision_making/daily/classification/Apple_Quality_B2/test_005.csv

@@ -0,0 +1,3 @@
+A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality
+951.0,0.02565221,-0.326161449,-1.239428425,-0.486801683,0.395589567,-0.127622496,2.477579568,good
+1231.0,-1.745322249,-2.979970538,0.093208914,-0.84221055,-0.801427776,1.93038528,-4.369480739,bad

decision_making/daily/classification/Apple_Quality_B2/test_006.csv

@@ -0,0 +1,4 @@
+A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality
+1835.0,-3.357510653,0.067642937,0.813438932,2.979557228,-4.217111053,1.819288791,-1.056070673,bad
+574.0,0.389662204,-4.386919432,3.8699463,1.636602068,3.262054988,1.095093916,-1.022076896,good
+3055.0,-0.660428075,-2.391390997,-1.426340004,1.477891955,-1.197527435,4.115822264,-2.14777319,bad

decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_001.json

@@ -0,0 +1,99 @@
+{
+  "id": "001",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Hotel_booking_demand",
+  "table_path": "kaggle/Hotel_booking_demand",
+  "query": "It’s tricky comparing these two cases side by side. The February booking, which was canceled, had those specific details: arrival year 2016, month February, week number 7, day of month 7, one weekend night stay, two adults, zero children and zero babies, a BB meal, country Thailand, market segment Online TA, distribution channel TA/TO, not a repeated guest, zero previous bookings not canceled, reserved room A and assigned room A, zero booking changes, zero days on a waiting list, customer type Transient, an average daily rate of 93.0, and zero required parking spaces. Then there’s the March booking with a 43-day lead time, arrival year 2016, month March, zero weekend nights but one weekday night, two adults, zero babies, BB meal, country Italy, market segment Online TA, not a repeated guest, zero previous cancellations, zero previous bookings not canceled, reserved and assigned room F, zero booking changes, deposit type No Deposit, agent 9.0, zero waiting list days, customer type Transient, an ADR of 153.9, zero parking spaces, and zero special requests. Flipping between them, the differences in rate, lead time, and the fact one is already canceled make it hard to judge. Given what we know about no-shows, does one of these stand out as more likely to have been a complete no-show?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "hotel": null,
+          "is_canceled": "1",
+          "lead_time": null,
+          "arrival_date_year": "2016",
+          "arrival_date_month": "February",
+          "arrival_date_week_number": "7",
+          "arrival_date_day_of_month": "7",
+          "stays_in_weekend_nights": "1",
+          "stays_in_week_nights": null,
+          "adults": "2",
+          "children": "0.0",
+          "babies": "0",
+          "meal": "BB",
+          "country": "THA",
+          "market_segment": "Online TA",
+          "distribution_channel": "TA/TO",
+          "is_repeated_guest": "0",
+          "previous_cancellations": null,
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": "A",
+          "booking_changes": "0",
+          "deposit_type": null,
+          "agent": null,
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": "Transient",
+          "adr": "93.0",
+          "required_car_parking_spaces": "0",
+          "total_of_special_requests": null,
+          "reservation_status": "No-Show",
+          "reservation_status_date": null
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "hotel": null,
+          "is_canceled": null,
+          "lead_time": "43",
+          "arrival_date_year": "2016",
+          "arrival_date_month": "March",
+          "arrival_date_week_number": null,
+          "arrival_date_day_of_month": null,
+          "stays_in_weekend_nights": "0",
+          "stays_in_week_nights": "1",
+          "adults": "2",
+          "children": null,
+          "babies": "0",
+          "meal": "BB",
+          "country": "ITA",
+          "market_segment": "Online TA",
+          "distribution_channel": null,
+          "is_repeated_guest": "0",
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "F",
+          "assigned_room_type": "F",
+          "booking_changes": "0",
+          "deposit_type": "No Deposit",
+          "agent": "9.0",
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": "Transient",
+          "adr": "153.9",
+          "required_car_parking_spaces": "0",
+          "total_of_special_requests": "0",
+          "reservation_status": "Canceled",
+          "reservation_status_date": null
+        }
+      }
+    ],
+    "target_column": "reservation_status",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_002.json

@@ -0,0 +1,136 @@
+{
+  "id": "002",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Hotel_booking_demand",
+  "table_path": "kaggle/Hotel_booking_demand",
+  "query": "Take a look: the first is a City Hotel reservation with a 9-day lead, for 2016, week 2, the 8th, planning 2 weekend and 3 weeknight stays. Just one adult, no children or babies, a BB meal, booked through Offline TA/TO. The guest isn't a repeat, has no history of cancellations or prior kept bookings, reserved and got room A, made no changes, paid no deposit, wasn't on a waiting list, is a Transient, and had no special requests. The second entry, for the City Hotel, is flagged canceled, booked 104 days ahead for June 9, 2016, week 24, with 0 weekend nights and 3 weeknights. Two adults, no babies, BB meal, guest from Brazil, booked via Online TA on the TA/TO channel, not a repeat guest, with zero previous bookings not canceled, reserved and assigned room A, no deposit, agent ID 9.0, average rate 126.9, and no parking needed. Third, another City Hotel cancellation, lead time 5 days for 2017 week 19, includes 2 weekend nights, two adults, no babies, from Brazil, Online TA/TA/TO booking. Not a repeat guest, zero previous cancellations, zero previous uncanceled bookings, room A reserved and assigned, zero changes, agent 9.0, zero waiting list days, Transient type, rate 150.0, and one special request. With all this info, can you point to the booking that successfully reached check-out?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "hotel": "City Hotel",
+          "is_canceled": null,
+          "lead_time": "9",
+          "arrival_date_year": "2016",
+          "arrival_date_month": null,
+          "arrival_date_week_number": "2",
+          "arrival_date_day_of_month": "8",
+          "stays_in_weekend_nights": "2",
+          "stays_in_week_nights": "3",
+          "adults": "1",
+          "children": "0.0",
+          "babies": "0",
+          "meal": "BB",
+          "country": null,
+          "market_segment": "Offline TA/TO",
+          "distribution_channel": null,
+          "is_repeated_guest": "0",
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": "A",
+          "booking_changes": "0",
+          "deposit_type": "No Deposit",
+          "agent": null,
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": "Transient",
+          "adr": null,
+          "required_car_parking_spaces": null,
+          "total_of_special_requests": "0",
+          "reservation_status": "Check-Out",
+          "reservation_status_date": null
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "hotel": "City Hotel",
+          "is_canceled": "1",
+          "lead_time": "104",
+          "arrival_date_year": "2016",
+          "arrival_date_month": "June",
+          "arrival_date_week_number": "24",
+          "arrival_date_day_of_month": "9",
+          "stays_in_weekend_nights": "0",
+          "stays_in_week_nights": "3",
+          "adults": "2",
+          "children": null,
+          "babies": "0",
+          "meal": "BB",
+          "country": "BRA",
+          "market_segment": "Online TA",
+          "distribution_channel": "TA/TO",
+          "is_repeated_guest": "0",
+          "previous_cancellations": null,
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": "A",
+          "booking_changes": null,
+          "deposit_type": "No Deposit",
+          "agent": "9.0",
+          "company": null,
+          "days_in_waiting_list": null,
+          "customer_type": null,
+          "adr": "126.9",
+          "required_car_parking_spaces": "0",
+          "total_of_special_requests": null,
+          "reservation_status": "Canceled",
+          "reservation_status_date": null
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "hotel": "City Hotel",
+          "is_canceled": "1",
+          "lead_time": "5",
+          "arrival_date_year": "2017",
+          "arrival_date_month": null,
+          "arrival_date_week_number": "19",
+          "arrival_date_day_of_month": null,
+          "stays_in_weekend_nights": "2",
+          "stays_in_week_nights": null,
+          "adults": "2",
+          "children": null,
+          "babies": "0",
+          "meal": null,
+          "country": "BRA",
+          "market_segment": "Online TA",
+          "distribution_channel": "TA/TO",
+          "is_repeated_guest": "0",
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": "A",
+          "booking_changes": "0",
+          "deposit_type": null,
+          "agent": "9.0",
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": "Transient",
+          "adr": "150.0",
+          "required_car_parking_spaces": null,
+          "total_of_special_requests": "1",
+          "reservation_status": "No-Show",
+          "reservation_status_date": null
+        }
+      }
+    ],
+    "target_column": "reservation_status",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_003.json

@@ -0,0 +1,99 @@
+{
+  "id": "003",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Hotel_booking_demand",
+  "table_path": "kaggle/Hotel_booking_demand",
+  "query": "I’ve got two booking profiles here and I’m trying to spot the one that was canceled, but every detail seems to tell a different story. First, a Resort Hotel booking, canceled, with a lead time of 48 days for an arrival in July 2017, specifically the 11th, which is in week 28. The stay is three weeknights with zero weekend nights, for two adults, zero children, zero babies. The meal is Half Board, the country is Portugal, and it was booked in the Online TA market segment via the TA/TO distribution channel. The guest is not a repeated guest, has zero previous cancellations and zero previous bookings not canceled, reserved room type A and was assigned the same, made zero booking changes, selected No Deposit, and was handled by agent 240.0. They spent zero days on a waiting list, are a Transient customer, have an average daily rate of 180.67, required zero car parking spaces, and made zero special requests. The second is a City Hotel booking, not canceled, booked 263 days ahead for arrival on June 23rd, 2017, in week 25, with two weekend nights and two weeknights, also two adults, zero children, zero babies, but with a Bed & Breakfast meal. Also from Portugal, but this was a Groups market segment booking via the TA/TO channel. Again, not a repeated guest, with zero previous cancellations and zero previous successful bookings, reserved and assigned room A, zero changes, No Deposit, agent 37.0, zero waiting list days, but the customer type is Transient-Party, the rate is 105.0, zero parking needed, and they made one special request. Juggling all these numbers and categories—from the agent IDs 240.0 and 37.0 to the rates of 180.67 and 105.0—leaves me uncertain. Between these two, which reservation actually ended up being canceled?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "hotel": "Resort Hotel",
+          "is_canceled": "1",
+          "lead_time": "48",
+          "arrival_date_year": "2017",
+          "arrival_date_month": "July",
+          "arrival_date_week_number": "28",
+          "arrival_date_day_of_month": "11",
+          "stays_in_weekend_nights": "0",
+          "stays_in_week_nights": "3",
+          "adults": "2",
+          "children": "0.0",
+          "babies": "0",
+          "meal": "HB",
+          "country": "PRT",
+          "market_segment": "Online TA",
+          "distribution_channel": "TA/TO",
+          "is_repeated_guest": "0",
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": "A",
+          "booking_changes": "0",
+          "deposit_type": "No Deposit",
+          "agent": "240.0",
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": "Transient",
+          "adr": "180.67",
+          "required_car_parking_spaces": "0",
+          "total_of_special_requests": "0",
+          "reservation_status": "Canceled",
+          "reservation_status_date": null
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "hotel": "City Hotel",
+          "is_canceled": "0",
+          "lead_time": "263",
+          "arrival_date_year": "2017",
+          "arrival_date_month": "June",
+          "arrival_date_week_number": "25",
+          "arrival_date_day_of_month": "23",
+          "stays_in_weekend_nights": "2",
+          "stays_in_week_nights": "2",
+          "adults": "2",
+          "children": "0.0",
+          "babies": "0",
+          "meal": "BB",
+          "country": "PRT",
+          "market_segment": "Groups",
+          "distribution_channel": "TA/TO",
+          "is_repeated_guest": "0",
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": "A",
+          "booking_changes": "0",
+          "deposit_type": "No Deposit",
+          "agent": "37.0",
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": "Transient-Party",
+          "adr": "105.0",
+          "required_car_parking_spaces": "0",
+          "total_of_special_requests": "1",
+          "reservation_status": "Check-Out",
+          "reservation_status_date": null
+        }
+      }
+    ],
+    "target_column": "reservation_status",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_004.json

@@ -0,0 +1,99 @@
+{
+  "id": "004",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Hotel_booking_demand",
+  "table_path": "kaggle/Hotel_booking_demand",
+  "query": "Having to decide which upcoming booking is most likely to be a \"Canceled\" reservation is giving me a headache. I’ve got two different ones here. The first is for a City Hotel, it’s marked as canceled already with a lead time of 20 days, set to arrive in May on the 3rd in 2017. That guest is staying for zero weekend nights and two week nights, just one adult with no children or babies from Portugal, booked through the Online TA market segment via the TA/TO channel. They’re not a repeated guest, have no previous cancellations or non-canceled bookings, reserved room type A, made zero booking changes, had no deposit, zero days on a waiting list, are a Transient customer type, and required zero car parking spaces. The second booking is for a Resort Hotel, currently not canceled, with an 18-day lead time, arriving in the 12th week of 2016. That stay involves zero weekend nights and one week night, for two adults with no children or babies, on a BB meal plan, also from Portugal via the Online TA segment. They also have zero previous cancellations and non-canceled bookings, reserved room type A, made zero changes, had no deposit, were booked by agent 240.0, spent zero days on a waiting list, are a Transient customer, and needed zero parking spaces. I’ve shared our historical booking archives with you. Considering all these details from my records, is the Resort Hotel booking actually the one more prone to end up canceled?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "hotel": "City Hotel",
+          "is_canceled": "1",
+          "lead_time": "20",
+          "arrival_date_year": "2017",
+          "arrival_date_month": "May",
+          "arrival_date_week_number": null,
+          "arrival_date_day_of_month": "3",
+          "stays_in_weekend_nights": "0",
+          "stays_in_week_nights": "2",
+          "adults": "1",
+          "children": "0.0",
+          "babies": "0",
+          "meal": null,
+          "country": "PRT",
+          "market_segment": "Online TA",
+          "distribution_channel": "TA/TO",
+          "is_repeated_guest": "0",
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": null,
+          "booking_changes": "0",
+          "deposit_type": "No Deposit",
+          "agent": null,
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": "Transient",
+          "adr": null,
+          "required_car_parking_spaces": "0",
+          "total_of_special_requests": null,
+          "reservation_status": "Canceled",
+          "reservation_status_date": null
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "hotel": "Resort Hotel",
+          "is_canceled": "0",
+          "lead_time": "18",
+          "arrival_date_year": "2016",
+          "arrival_date_month": null,
+          "arrival_date_week_number": "12",
+          "arrival_date_day_of_month": null,
+          "stays_in_weekend_nights": "0",
+          "stays_in_week_nights": "1",
+          "adults": "2",
+          "children": "0.0",
+          "babies": "0",
+          "meal": "BB",
+          "country": "PRT",
+          "market_segment": "Online TA",
+          "distribution_channel": null,
+          "is_repeated_guest": null,
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": null,
+          "booking_changes": "0",
+          "deposit_type": "No Deposit",
+          "agent": "240.0",
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": "Transient",
+          "adr": null,
+          "required_car_parking_spaces": "0",
+          "total_of_special_requests": null,
+          "reservation_status": "Check-Out",
+          "reservation_status_date": null
+        }
+      }
+    ],
+    "target_column": "reservation_status",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_006.json

@@ -0,0 +1,136 @@
+{
+  "id": "006",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Hotel_booking_demand",
+  "table_path": "kaggle/Hotel_booking_demand",
+  "query": "This City Hotel booking for December 2016 was made way back, 195 days before arrival, for a two-night stay during the week with two adults on a BB plan, booked online by a guest from France. They’re not a repeat customer, have no history of cancellations, were assigned the B room they reserved, changed the booking once, didn’t pay a deposit, worked with agent 9.0, are considered a transient-party, and required no parking or special extras. Meanwhile, another City Hotel booking for August 14th was canceled—it was booked with no advance notice, for two weeknights, no children, on an HB meal plan, arranged through an offline travel agent. The guest isn’t a repeat visitor either, has no prior cancellations, got the A room they wanted, paid no deposit, used agent 6.0, waited zero days, was charged 109.0 per night, and made no special requests. Then there’s a third reservation for December 6th, 2016, in week 50, not canceled, booked 27 days out for a four-weeknight stay with two adults on an SC meal, from France via a travel agent channel. This guest also isn’t a repeat visitor, has no cancellation history, received their reserved A room, made no changes, gave no deposit, used agent 9.0, had zero waiting time, is a transient customer, paid 74.8 nightly, and made one special request. I’ve sent you our archive of past bookings—from your analysis of the trends, do you think the first, second, or third one will be checked out?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "hotel": "City Hotel",
+          "is_canceled": null,
+          "lead_time": "195",
+          "arrival_date_year": "2016",
+          "arrival_date_month": "December",
+          "arrival_date_week_number": null,
+          "arrival_date_day_of_month": null,
+          "stays_in_weekend_nights": "0",
+          "stays_in_week_nights": "2",
+          "adults": "2",
+          "children": null,
+          "babies": null,
+          "meal": "BB",
+          "country": "FRA",
+          "market_segment": "Online TA",
+          "distribution_channel": null,
+          "is_repeated_guest": "0",
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "B",
+          "assigned_room_type": "B",
+          "booking_changes": "1",
+          "deposit_type": "No Deposit",
+          "agent": "9.0",
+          "company": null,
+          "days_in_waiting_list": null,
+          "customer_type": "Transient-Party",
+          "adr": null,
+          "required_car_parking_spaces": "0",
+          "total_of_special_requests": "0",
+          "reservation_status": "Canceled",
+          "reservation_status_date": null
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "hotel": "City Hotel",
+          "is_canceled": "1",
+          "lead_time": "0",
+          "arrival_date_year": null,
+          "arrival_date_month": "August",
+          "arrival_date_week_number": null,
+          "arrival_date_day_of_month": "14",
+          "stays_in_weekend_nights": "0",
+          "stays_in_week_nights": "2",
+          "adults": null,
+          "children": "0.0",
+          "babies": null,
+          "meal": "HB",
+          "country": null,
+          "market_segment": "Offline TA/TO",
+          "distribution_channel": "TA/TO",
+          "is_repeated_guest": "0",
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": "A",
+          "booking_changes": null,
+          "deposit_type": "No Deposit",
+          "agent": "6.0",
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": null,
+          "adr": "109.0",
+          "required_car_parking_spaces": null,
+          "total_of_special_requests": "0",
+          "reservation_status": "Canceled",
+          "reservation_status_date": null
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "hotel": null,
+          "is_canceled": "0",
+          "lead_time": "27",
+          "arrival_date_year": "2016",
+          "arrival_date_month": "December",
+          "arrival_date_week_number": "50",
+          "arrival_date_day_of_month": "6",
+          "stays_in_weekend_nights": "0",
+          "stays_in_week_nights": "4",
+          "adults": "2",
+          "children": null,
+          "babies": null,
+          "meal": "SC",
+          "country": "FRA",
+          "market_segment": null,
+          "distribution_channel": "TA/TO",
+          "is_repeated_guest": "0",
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": "A",
+          "booking_changes": "0",
+          "deposit_type": "No Deposit",
+          "agent": "9.0",
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": "Transient",
+          "adr": "74.8",
+          "required_car_parking_spaces": null,
+          "total_of_special_requests": "1",
+          "reservation_status": "Check-Out",
+          "reservation_status_date": null
+        }
+      }
+    ],
+    "target_column": "reservation_status",
+    "task_sub_type": "classification",
+    "final_decision": "003",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Hotel_booking_demand_B2/current.csv

@@ -0,0 +1,15 @@
+hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date
+City Hotel,1,12,2016,February,7,7,1,0,2,0.0,0,BB,THA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,93.0,0,2,No-Show,2016-02-07
+City Hotel,1,43,2016,March,10,1,0,1,2,2.0,0,BB,ITA,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,153.9,0,0,Canceled,2016-02-26
+City Hotel,0,9,2016,January,2,8,2,3,1,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,44.8,0,0,Check-Out,2016-01-13
+City Hotel,1,104,2016,June,24,9,0,3,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.9,0,0,Canceled,2016-03-01
+City Hotel,1,5,2017,May,19,11,2,3,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,150.0,0,1,No-Show,2017-05-11
+Resort Hotel,1,48,2017,July,28,11,0,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,180.67,0,0,Canceled,2017-05-25
+City Hotel,0,263,2017,June,25,23,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,105.0,0,1,Check-Out,2017-06-27
+City Hotel,1,20,2017,May,18,3,0,2,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,130.0,0,1,Canceled,2017-04-28
+Resort Hotel,0,18,2016,March,12,19,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,60.0,0,0,Check-Out,2016-03-20
+City Hotel,1,293,2015,August,32,6,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,Canceled,2015-01-01
+City Hotel,1,19,2016,February,6,1,1,5,2,2.0,0,BB,CHN,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient-Party,79.88,0,0,No-Show,2016-02-01
+City Hotel,1,195,2016,December,49,2,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient-Party,71.22,0,0,Canceled,2016-06-23
+City Hotel,1,0,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0,Canceled,2015-08-14
+City Hotel,0,27,2016,December,50,6,0,4,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,1,Check-Out,2016-12-10

decision_making/daily/classification/Hotel_booking_demand_B2/info.json

@@ -0,0 +1,398 @@
+{
+    "name": "Hotel Booking Demand",
+    "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data",
+    "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.",
+    "is_splited": false,
+    "overall_size": 119390,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 12,
+    "n_classes": 20,
+    "cat_feature_intro": {
+        "hotel": "Type of hotel (City Hotel or Resort Hotel).",
+        "arrival_date_month": "Month of arrival for the booking.",
+        "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).",
+        "country": "Country of origin of the guest.",
+        "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).",
+        "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).",
+        "reserved_room_type": "Type of room reserved by the guest before any changes.",
+        "assigned_room_type": "Final room type assigned to the guest.",
+        "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).",
+        "customer_type": "Type of customer (e.g., transient, group, contract).",
+        "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).",
+        "reservation_status_date": "Date when the reservation status was last updated."
+    },
+    "num_feature_intro": {
+        "is_canceled": "Indicates whether the booking was canceled (1) or not (0).",
+        "lead_time": "Number of days between booking and arrival.",
+        "arrival_date_year": "Year of arrival for the booking.",
+        "arrival_date_week_number": "Week number of the arrival date.",
+        "arrival_date_day_of_month": "Day of the month of arrival.",
+        "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.",
+        "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.",
+        "adults": "Number of adults included in the booking.",
+        "children": "Number of children included in the booking.",
+        "babies": "Number of babies included in the booking.",
+        "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).",
+        "previous_cancellations": "Number of times the guest has canceled previous bookings.",
+        "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.",
+        "booking_changes": "Number of changes made to the booking.",
+        "agent": "ID of the booking agent (if applicable).",
+        "company": "ID of the company making the booking (if applicable).",
+        "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.",
+        "adr": "Average daily rate (price per night per room).",
+        "required_car_parking_spaces": "Number of car parking spaces required by the guest.",
+        "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)."
+    },
+    "evaluation_metric": null,
+    "task_type": "classification",
+    "target": "reservation_status",
+    "num_feature_value": {
+        "adr": [
+            -6.38,
+            5400.0
+        ],
+        "adults": [
+            0.0,
+            55.0
+        ],
+        "agent": [
+            1.0,
+            535.0
+        ],
+        "arrival_date_day_of_month": [
+            1.0,
+            31.0
+        ],
+        "arrival_date_week_number": [
+            1.0,
+            53.0
+        ],
+        "arrival_date_year": [
+            2015.0,
+            2017.0
+        ],
+        "babies": [
+            0.0,
+            10.0
+        ],
+        "booking_changes": [
+            0.0,
+            21.0
+        ],
+        "children": [
+            0.0,
+            10.0
+        ],
+        "company": [
+            6.0,
+            543.0
+        ],
+        "days_in_waiting_list": [
+            0.0,
+            391.0
+        ],
+        "is_canceled": [
+            0.0,
+            1.0
+        ],
+        "is_repeated_guest": [
+            0.0,
+            1.0
+        ],
+        "lead_time": [
+            0.0,
+            737.0
+        ],
+        "previous_bookings_not_canceled": [
+            0.0,
+            72.0
+        ],
+        "previous_cancellations": [
+            0.0,
+            26.0
+        ],
+        "required_car_parking_spaces": [
+            0.0,
+            8.0
+        ],
+        "stays_in_week_nights": [
+            0.0,
+            50.0
+        ],
+        "stays_in_weekend_nights": [
+            0.0,
+            19.0
+        ],
+        "total_of_special_requests": [
+            0.0,
+            5.0
+        ]
+    },
+    "cat_feature_value": {
+        "arrival_date_month": [
+            "April",
+            "August",
+            "December",
+            "February",
+            "January",
+            "July",
+            "June",
+            "March",
+            "May",
+            "November",
+            "October",
+            "September"
+        ],
+        "assigned_room_type": [
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+            "H",
+            "I",
+            "K",
+            "L",
+            "P"
+        ],
+        "country": [
+            "ABW",
+            "AGO",
+            "AIA",
+            "ALB",
+            "AND",
+            "ARE",
+            "ARG",
+            "ARM",
+            "ASM",
+            "ATA",
+            "ATF",
+            "AUS",
+            "AUT",
+            "AZE",
+            "BDI",
+            "BEL",
+            "BEN",
+            "BFA",
+            "BGD",
+            "BGR",
+            "BHR",
+            "BHS",
+            "BIH",
+            "BLR",
+            "BOL",
+            "BRA",
+            "BRB",
+            "BWA",
+            "CAF",
+            "CHE",
+            "CHL",
+            "CHN",
+            "CIV",
+            "CMR",
+            "CN",
+            "COL",
+            "COM",
+            "CPV",
+            "CRI",
+            "CUB",
+            "CYM",
+            "CYP",
+            "CZE",
+            "DEU",
+            "DJI",
+            "DMA",
+            "DNK",
+            "DOM",
+            "DZA",
+            "ECU",
+            "EGY",
+            "ESP",
+            "EST",
+            "ETH",
+            "FIN",
+            "FJI",
+            "FRA",
+            "FRO",
+            "GAB",
+            "GBR",
+            "GEO",
+            "GGY",
+            "GHA",
+            "GIB",
+            "GLP",
+            "GNB",
+            "GRC",
+            "GTM",
+            "GUY",
+            "HKG",
+            "HND",
+            "HRV",
+            "HUN",
+            "IDN",
+            "IMN",
+            "IND",
+            "IRL",
+            "IRN",
+            "IRQ",
+            "ISL",
+            "ISR",
+            "ITA",
+            "JAM",
+            "JEY",
+            "JOR",
+            "JPN",
+            "KAZ",
+            "KEN",
+            "KHM",
+            "KIR",
+            "KNA",
+            "KOR",
+            "KWT",
+            "LAO",
+            "LBN",
+            "LBY",
+            "LCA",
+            "LIE",
+            "LKA",
+            "LTU",
+            "LUX",
+            "LVA",
+            "MAC",
+            "MAR",
+            "MCO",
+            "MDG",
+            "MDV",
+            "MEX",
+            "MKD",
+            "MLI",
+            "MLT",
+            "MMR",
+            "MNE",
+            "MOZ",
+            "MRT",
+            "MUS",
+            "MWI",
+            "MYS",
+            "MYT",
+            "NAM",
+            "NCL",
+            "NGA",
+            "NIC",
+            "NLD",
+            "NOR",
+            "NPL",
+            "NZL",
+            "OMN",
+            "PAK",
+            "PAN",
+            "PER",
+            "PHL",
+            "PLW",
+            "POL",
+            "PRI",
+            "PRT",
+            "PRY",
+            "PYF",
+            "QAT",
+            "ROU",
+            "RUS",
+            "RWA",
+            "SAU",
+            "SDN",
+            "SEN",
+            "SGP",
+            "SLE",
+            "SLV",
+            "SMR",
+            "SRB",
+            "STP",
+            "SUR",
+            "SVK",
+            "SVN",
+            "SWE",
+            "SYC",
+            "SYR",
+            "TGO",
+            "THA",
+            "TJK",
+            "TMP",
+            "TUN",
+            "TUR",
+            "TWN",
+            "TZA",
+            "UGA",
+            "UKR",
+            "UMI",
+            "URY",
+            "USA",
+            "UZB",
+            "VEN",
+            "VGB",
+            "VNM",
+            "ZAF",
+            "ZMB",
+            "ZWE"
+        ],
+        "customer_type": [
+            "Contract",
+            "Group",
+            "Transient",
+            "Transient-Party"
+        ],
+        "deposit_type": [
+            "No Deposit",
+            "Non Refund",
+            "Refundable"
+        ],
+        "distribution_channel": [
+            "Corporate",
+            "Direct",
+            "GDS",
+            "TA/TO",
+            "Undefined"
+        ],
+        "hotel": [
+            "City Hotel",
+            "Resort Hotel"
+        ],
+        "market_segment": [
+            "Aviation",
+            "Complementary",
+            "Corporate",
+            "Direct",
+            "Groups",
+            "Offline TA/TO",
+            "Online TA",
+            "Undefined"
+        ],
+        "meal": [
+            "BB",
+            "FB",
+            "HB",
+            "SC",
+            "Undefined"
+        ],
+        "reservation_status": [
+            "Canceled",
+            "Check-Out",
+            "No-Show"
+        ],
+        "reservation_status_date": "string",
+        "reserved_room_type": [
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+            "H",
+            "L",
+            "P"
+        ]
+    }
+}

decision_making/daily/classification/Hotel_booking_demand_B2/info_mod.json

@@ -0,0 +1,318 @@
+{
+  "name": "Hotel Booking Demand",
+  "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data",
+  "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.",
+  "is_splited": false,
+  "overall_size": 119390,
+  "train_size": 0,
+  "test_size": 0,
+  "c_classes": 12,
+  "n_classes": 20,
+  "cat_feature_intro": {
+    "hotel": "Type of hotel (City Hotel or Resort Hotel).",
+    "arrival_date_month": "Month of arrival for the booking.",
+    "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).",
+    "country": "Country of origin of the guest.",
+    "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).",
+    "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).",
+    "reserved_room_type": "Type of room reserved by the guest before any changes.",
+    "assigned_room_type": "Final room type assigned to the guest.",
+    "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).",
+    "customer_type": "Type of customer (e.g., transient, group, contract)."
+  },
+  "num_feature_intro": {
+    "is_canceled": "Indicates whether the booking was canceled (1) or not (0).",
+    "lead_time": "Number of days between booking and arrival.",
+    "arrival_date_year": "Year of arrival for the booking.",
+    "arrival_date_week_number": "Week number of the arrival date.",
+    "arrival_date_day_of_month": "Day of the month of arrival.",
+    "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.",
+    "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.",
+    "adults": "Number of adults included in the booking.",
+    "children": "Number of children included in the booking.",
+    "babies": "Number of babies included in the booking.",
+    "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).",
+    "previous_cancellations": "Number of times the guest has canceled previous bookings.",
+    "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.",
+    "booking_changes": "Number of changes made to the booking.",
+    "agent": "ID of the booking agent (if applicable).",
+    "company": "ID of the company making the booking (if applicable).",
+    "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.",
+    "adr": "Average daily rate (price per night per room).",
+    "required_car_parking_spaces": "Number of car parking spaces required by the guest.",
+    "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)."
+  },
+  "evaluation_metric": null,
+  "task_type": "classification",
+  "target": "reservation_status",
+  "num_feature_value": {
+    "is_canceled": [
+      0.0,
+      1.0
+    ],
+    "lead_time": [
+      0.0,
+      737.0
+    ],
+    "arrival_date_year": [
+      2015.0,
+      2017.0
+    ],
+    "arrival_date_week_number": [
+      1.0,
+      53.0
+    ],
+    "arrival_date_day_of_month": [
+      1.0,
+      31.0
+    ],
+    "stays_in_weekend_nights": [
+      0.0,
+      19.0
+    ],
+    "stays_in_week_nights": [
+      0.0,
+      50.0
+    ],
+    "adults": [
+      0.0,
+      55.0
+    ],
+    "children": [
+      0.0,
+      10.0
+    ],
+    "babies": [
+      0.0,
+      10.0
+    ],
+    "is_repeated_guest": [
+      0.0,
+      1.0
+    ],
+    "previous_cancellations": [
+      0.0,
+      26.0
+    ],
+    "previous_bookings_not_canceled": [
+      0.0,
+      72.0
+    ],
+    "booking_changes": [
+      0.0,
+      21.0
+    ],
+    "agent": [
+      1.0,
+      535.0
+    ],
+    "company": [
+      6.0,
+      543.0
+    ],
+    "days_in_waiting_list": [
+      0.0,
+      391.0
+    ],
+    "adr": [
+      -6.38,
+      5400.0
+    ],
+    "required_car_parking_spaces": [
+      0.0,
+      8.0
+    ],
+    "total_of_special_requests": [
+      0.0,
+      5.0
+    ]
+  },
+  "cat_feature_value": {
+    "hotel": [
+      "City Hotel",
+      "Resort Hotel"
+    ],
+    "arrival_date_month": [
+      "April",
+      "August",
+      "December",
+      "February",
+      "January",
+      "July",
+      "June",
+      "March",
+      "May",
+      "November",
+      "October",
+      "September"
+    ],
+    "meal": [
+      "BB",
+      "FB",
+      "HB",
+      "SC",
+      "Undefined"
+    ],
+    "market_segment": [
+      "Aviation",
+      "Complementary",
+      "Corporate",
+      "Direct",
+      "Groups",
+      "Offline TA/TO",
+      "Online TA",
+      "Undefined"
+    ],
+    "distribution_channel": [
+      "Corporate",
+      "Direct",
+      "GDS",
+      "TA/TO",
+      "Undefined"
+    ],
+    "reserved_room_type": [
+      "A",
+      "B",
+      "C",
+      "D",
+      "E",
+      "F",
+      "G",
+      "H",
+      "L",
+      "P"
+    ],
+    "assigned_room_type": [
+      "A",
+      "B",
+      "C",
+      "D",
+      "E",
+      "F",
+      "G",
+      "H",
+      "I",
+      "K",
+      "L",
+      "P"
+    ],
+    "deposit_type": [
+      "No Deposit",
+      "Non Refund",
+      "Refundable"
+    ],
+    "customer_type": [
+      "Contract",
+      "Group",
+      "Transient",
+      "Transient-Party"
+    ]
+  },
+  "columns": [
+    "hotel",
+    "is_canceled",
+    "lead_time",
+    "arrival_date_year",
+    "arrival_date_month",
+    "arrival_date_week_number",
+    "arrival_date_day_of_month",
+    "stays_in_weekend_nights",
+    "stays_in_week_nights",
+    "adults",
+    "children",
+    "babies",
+    "meal",
+    "country",
+    "market_segment",
+    "distribution_channel",
+    "is_repeated_guest",
+    "previous_cancellations",
+    "previous_bookings_not_canceled",
+    "reserved_room_type",
+    "assigned_room_type",
+    "booking_changes",
+    "deposit_type",
+    "agent",
+    "company",
+    "days_in_waiting_list",
+    "customer_type",
+    "adr",
+    "required_car_parking_spaces",
+    "total_of_special_requests",
+    "reservation_status",
+    "reservation_status_date"
+  ],
+  "feature_columns": [
+    "hotel",
+    "is_canceled",
+    "lead_time",
+    "arrival_date_year",
+    "arrival_date_month",
+    "arrival_date_week_number",
+    "arrival_date_day_of_month",
+    "stays_in_weekend_nights",
+    "stays_in_week_nights",
+    "adults",
+    "children",
+    "babies",
+    "meal",
+    "country",
+    "market_segment",
+    "distribution_channel",
+    "is_repeated_guest",
+    "previous_cancellations",
+    "previous_bookings_not_canceled",
+    "reserved_room_type",
+    "assigned_room_type",
+    "booking_changes",
+    "deposit_type",
+    "agent",
+    "company",
+    "days_in_waiting_list",
+    "customer_type",
+    "adr",
+    "required_car_parking_spaces",
+    "total_of_special_requests",
+    "reservation_status_date"
+  ],
+  "feature_types": {
+    "hotel": "categorical",
+    "is_canceled": "numeric",
+    "lead_time": "numeric",
+    "arrival_date_year": "numeric",
+    "arrival_date_month": "categorical",
+    "arrival_date_week_number": "numeric",
+    "arrival_date_day_of_month": "numeric",
+    "stays_in_weekend_nights": "numeric",
+    "stays_in_week_nights": "numeric",
+    "adults": "numeric",
+    "children": "numeric",
+    "babies": "numeric",
+    "meal": "categorical",
+    "country": "categorical",
+    "market_segment": "categorical",
+    "distribution_channel": "categorical",
+    "is_repeated_guest": "numeric",
+    "previous_cancellations": "numeric",
+    "previous_bookings_not_canceled": "numeric",
+    "reserved_room_type": "categorical",
+    "assigned_room_type": "categorical",
+    "booking_changes": "numeric",
+    "deposit_type": "categorical",
+    "agent": "numeric",
+    "company": "numeric",
+    "days_in_waiting_list": "numeric",
+    "customer_type": "categorical",
+    "adr": "numeric",
+    "required_car_parking_spaces": "numeric",
+    "total_of_special_requests": "numeric",
+    "reservation_status_date": "open_text"
+  },
+  "open_text_feature_intro": {
+    "reservation_status_date": "Date when the reservation status was last updated."
+  },
+  "open_text_features": [
+    "reservation_status_date"
+  ],
+  "missing_from_original_info": []
+}

decision_making/daily/classification/Hotel_booking_demand_B2/test.csv

@@ -0,0 +1,15 @@
+hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date
+City Hotel,1,12,2016,February,7,7,1,0,2,0.0,0,BB,THA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,93.0,0,2,No-Show,2016-02-07
+City Hotel,1,43,2016,March,10,1,0,1,2,2.0,0,BB,ITA,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,153.9,0,0,Canceled,2016-02-26
+City Hotel,0,9,2016,January,2,8,2,3,1,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,44.8,0,0,Check-Out,2016-01-13
+City Hotel,1,104,2016,June,24,9,0,3,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.9,0,0,Canceled,2016-03-01
+City Hotel,1,5,2017,May,19,11,2,3,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,150.0,0,1,No-Show,2017-05-11
+Resort Hotel,1,48,2017,July,28,11,0,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,180.67,0,0,Canceled,2017-05-25
+City Hotel,0,263,2017,June,25,23,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,105.0,0,1,Check-Out,2017-06-27
+City Hotel,1,20,2017,May,18,3,0,2,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,130.0,0,1,Canceled,2017-04-28
+Resort Hotel,0,18,2016,March,12,19,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,60.0,0,0,Check-Out,2016-03-20
+City Hotel,1,293,2015,August,32,6,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,Canceled,2015-01-01
+City Hotel,1,19,2016,February,6,1,1,5,2,2.0,0,BB,CHN,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient-Party,79.88,0,0,No-Show,2016-02-01
+City Hotel,1,195,2016,December,49,2,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient-Party,71.22,0,0,Canceled,2016-06-23
+City Hotel,1,0,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0,Canceled,2015-08-14
+City Hotel,0,27,2016,December,50,6,0,4,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,1,Check-Out,2016-12-10

decision_making/daily/classification/Hotel_booking_demand_B2/test_003.csv

@@ -0,0 +1,3 @@
+hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date
+Resort Hotel,1,48,2017,July,28,11,0,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,180.67,0,0,Canceled,2017-05-25
+City Hotel,0,263,2017,June,25,23,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,105.0,0,1,Check-Out,2017-06-27

decision_making/daily/classification/Hotel_booking_demand_B2/test_004.csv

@@ -0,0 +1,3 @@
+hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date
+City Hotel,1,20,2017,May,18,3,0,2,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,130.0,0,1,Canceled,2017-04-28
+Resort Hotel,0,18,2016,March,12,19,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,60.0,0,0,Check-Out,2016-03-20

decision_making/daily/classification/Hotel_booking_demand_B2/test_005.csv

@@ -0,0 +1,3 @@
+hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date
+City Hotel,1,293,2015,August,32,6,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,Canceled,2015-01-01
+City Hotel,1,19,2016,February,6,1,1,5,2,2.0,0,BB,CHN,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient-Party,79.88,0,0,No-Show,2016-02-01

decision_making/daily/classification/Hotel_booking_demand_B2/test_006.csv

@@ -0,0 +1,4 @@
+hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date
+City Hotel,1,195,2016,December,49,2,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient-Party,71.22,0,0,Canceled,2016-06-23
+City Hotel,1,0,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0,Canceled,2015-08-14
+City Hotel,0,27,2016,December,50,6,0,4,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,1,Check-Out,2016-12-10

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2_002.json

@@ -0,0 +1,145 @@
+{
+  "id": "002",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "IBM_HR_Analytics_Employee_Attrition_&_Performance",
+  "table_path": "kaggle/IBM_HR_Analytics_Employee_Attrition_&_Performance",
+  "query": "Deciding on retention risks for these three profiles is giving me a real headache. Employee 1818’s details: business travel is rare, 920 daily rate, in the Human Resources department. Education level 2, employee count 1, and they find their environment very satisfying with a 4. Their job involvement scores a 3, but they’re only at level 1 in their job, with job satisfaction at a middling 2. Married, monthly income 2148 against a monthly rate of 6889, and they’ve never switched companies. Over 18, puts in overtime, salary hike was 11 percent, performance rating 3. They rate relationship satisfaction as a high 3, work standard 80 hours, have zero stock options. Six years total work experience, attended 3 training sessions last year, feels a better work-life balance at 3, been here five years, promoted just one year back, and reporting to the same manager for four years. Then there's 284: travels rarely, 1136 daily, in R&D. Education 3, count 1, environment satisfaction a top score of 4, and shows very high job involvement at 4, though still at job level 1 with job satisfaction at 2. Divorced, earns 2328 monthly with a high monthly rate of 12392, has worked at one other place, over 18, does overtime, got a 16 percent raise, performance 3, but relationship satisfaction is low at 1. Hours 80, stock option level 1. Four years total working, 2 trainings last year, work-life balance is a 2, four years at the firm, two years since last promotion, two years with current manager. Finally, 65: rare travel, 1434 daily, also in R&D. Education is 4, count 1, environment satisfaction 3, job involvement 3, job level 1, but job satisfaction is a higher 3. Single, monthly income 3441, monthly rate 11179, one other company before this, over 18, works overtime, 13 percent hike, performance 3, relationship satisfaction 3. Hours 80, stock option level 0. Only two years total career experience, 3 trainings last year, work-life balance 2, two years at the company, two years since promotion, two years with manager. After laying all that out, I’m stumped—can you tell me which one of these three seems most likely to remain with the company?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Age": null,
+          "Attrition": "Yes",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "920",
+          "Department": "Human Resources",
+          "DistanceFromHome": null,
+          "Education": "2",
+          "EducationField": null,
+          "EmployeeCount": "1",
+          "EmployeeNumber": "1818",
+          "EnvironmentSatisfaction": "4",
+          "Gender": null,
+          "HourlyRate": null,
+          "JobInvolvement": "3",
+          "JobLevel": "1",
+          "JobRole": null,
+          "JobSatisfaction": "2",
+          "MaritalStatus": "Married",
+          "MonthlyIncome": "2148",
+          "MonthlyRate": "6889",
+          "NumCompaniesWorked": "0",
+          "Over18": "Y",
+          "OverTime": "Yes",
+          "PercentSalaryHike": "11",
+          "PerformanceRating": "3",
+          "RelationshipSatisfaction": "3",
+          "StandardHours": "80",
+          "StockOptionLevel": "0",
+          "TotalWorkingYears": "6",
+          "TrainingTimesLastYear": "3",
+          "WorkLifeBalance": "3",
+          "YearsAtCompany": "5",
+          "YearsInCurrentRole": null,
+          "YearsSinceLastPromotion": "1",
+          "YearsWithCurrManager": "4"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "Age": null,
+          "Attrition": "No",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "1136",
+          "Department": "Research & Development",
+          "DistanceFromHome": null,
+          "Education": "3",
+          "EducationField": null,
+          "EmployeeCount": "1",
+          "EmployeeNumber": "284",
+          "EnvironmentSatisfaction": "4",
+          "Gender": null,
+          "HourlyRate": null,
+          "JobInvolvement": "4",
+          "JobLevel": "1",
+          "JobRole": null,
+          "JobSatisfaction": "2",
+          "MaritalStatus": "Divorced",
+          "MonthlyIncome": "2328",
+          "MonthlyRate": "12392",
+          "NumCompaniesWorked": "1",
+          "Over18": "Y",
+          "OverTime": "Yes",
+          "PercentSalaryHike": "16",
+          "PerformanceRating": "3",
+          "RelationshipSatisfaction": "1",
+          "StandardHours": "80",
+          "StockOptionLevel": "1",
+          "TotalWorkingYears": "4",
+          "TrainingTimesLastYear": "2",
+          "WorkLifeBalance": "2",
+          "YearsAtCompany": "4",
+          "YearsInCurrentRole": null,
+          "YearsSinceLastPromotion": "2",
+          "YearsWithCurrManager": "2"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "Age": null,
+          "Attrition": "Yes",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "1434",
+          "Department": "Research & Development",
+          "DistanceFromHome": null,
+          "Education": "4",
+          "EducationField": null,
+          "EmployeeCount": "1",
+          "EmployeeNumber": "65",
+          "EnvironmentSatisfaction": "3",
+          "Gender": null,
+          "HourlyRate": null,
+          "JobInvolvement": "3",
+          "JobLevel": "1",
+          "JobRole": null,
+          "JobSatisfaction": "3",
+          "MaritalStatus": "Single",
+          "MonthlyIncome": "3441",
+          "MonthlyRate": "11179",
+          "NumCompaniesWorked": "1",
+          "Over18": "Y",
+          "OverTime": "Yes",
+          "PercentSalaryHike": "13",
+          "PerformanceRating": "3",
+          "RelationshipSatisfaction": "3",
+          "StandardHours": "80",
+          "StockOptionLevel": "0",
+          "TotalWorkingYears": "2",
+          "TrainingTimesLastYear": "3",
+          "WorkLifeBalance": "2",
+          "YearsAtCompany": "2",
+          "YearsInCurrentRole": null,
+          "YearsSinceLastPromotion": "2",
+          "YearsWithCurrManager": "2"
+        }
+      }
+    ],
+    "target_column": "Attrition",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2_003.json

@@ -0,0 +1,145 @@
+{
+  "id": "003",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "IBM_HR_Analytics_Employee_Attrition_&_Performance",
+  "table_path": "kaggle/IBM_HR_Analytics_Employee_Attrition_&_Performance",
+  "query": "Faced with these three employees, it’s tricky to judge their likelihood of staying. Look at the 53-year-old male Sales Executive, ID 1968: travels rarely, 24 km from home, daily rate 1168, master’s education (4) in Life Sciences, hourly 66, monthly income 10448, monthly rate 5843, overtime yes, 13% salary hike, performance 3, stock options 0, 15 total working years, 2 years here, 2 trainings, work-life balance 2, job involvement 3, level 3. Compare that to the 30-year-old female Research Scientist, number 297: travels rarely, 3 km commute, daily 1005, bachelor’s (3) in Technical Degree, hourly 88, income 2657, rate 8556, overtime yes, 11% hike, performance 3, stock 0, 8 years total, 5 here, 5 trainings, balance 3, involvement 3, level 1. Then the 39-year-old female HR specialist, ID 909: travels rarely, 2 km away, daily 1383, bachelor’s (3) in Life Sciences, hourly 42, income 5204, rate 7790, overtime no, 11% hike, performance 3, stock level 2, 13 years total, 5 here, 2 trainings, balance 3, involvement 2, level 2. Each has such different combinations—age, department, income, overtime, stock—and I’m really hesitating. Based on all that, pick the one you feel is most probable to not leave the company.",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Age": "53",
+          "Attrition": "Yes",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "1168",
+          "Department": "Sales",
+          "DistanceFromHome": "24",
+          "Education": "4",
+          "EducationField": "Life Sciences",
+          "EmployeeCount": "1",
+          "EmployeeNumber": "1968",
+          "EnvironmentSatisfaction": null,
+          "Gender": "Male",
+          "HourlyRate": "66",
+          "JobInvolvement": "3",
+          "JobLevel": "3",
+          "JobRole": "Sales Executive",
+          "JobSatisfaction": null,
+          "MaritalStatus": null,
+          "MonthlyIncome": "10448",
+          "MonthlyRate": "5843",
+          "NumCompaniesWorked": null,
+          "Over18": null,
+          "OverTime": "Yes",
+          "PercentSalaryHike": "13",
+          "PerformanceRating": "3",
+          "RelationshipSatisfaction": null,
+          "StandardHours": null,
+          "StockOptionLevel": "0",
+          "TotalWorkingYears": "15",
+          "TrainingTimesLastYear": "2",
+          "WorkLifeBalance": "2",
+          "YearsAtCompany": "2",
+          "YearsInCurrentRole": null,
+          "YearsSinceLastPromotion": null,
+          "YearsWithCurrManager": null
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "Age": "30",
+          "Attrition": "Yes",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "1005",
+          "Department": "Research & Development",
+          "DistanceFromHome": "3",
+          "Education": "3",
+          "EducationField": "Technical Degree",
+          "EmployeeCount": "1",
+          "EmployeeNumber": "297",
+          "EnvironmentSatisfaction": null,
+          "Gender": "Female",
+          "HourlyRate": "88",
+          "JobInvolvement": "3",
+          "JobLevel": "1",
+          "JobRole": "Research Scientist",
+          "JobSatisfaction": null,
+          "MaritalStatus": null,
+          "MonthlyIncome": "2657",
+          "MonthlyRate": "8556",
+          "NumCompaniesWorked": null,
+          "Over18": null,
+          "OverTime": "Yes",
+          "PercentSalaryHike": "11",
+          "PerformanceRating": "3",
+          "RelationshipSatisfaction": null,
+          "StandardHours": null,
+          "StockOptionLevel": "0",
+          "TotalWorkingYears": "8",
+          "TrainingTimesLastYear": "5",
+          "WorkLifeBalance": "3",
+          "YearsAtCompany": "5",
+          "YearsInCurrentRole": null,
+          "YearsSinceLastPromotion": null,
+          "YearsWithCurrManager": null
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "Age": "39",
+          "Attrition": "No",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "1383",
+          "Department": "Human Resources",
+          "DistanceFromHome": "2",
+          "Education": "3",
+          "EducationField": "Life Sciences",
+          "EmployeeCount": "1",
+          "EmployeeNumber": "909",
+          "EnvironmentSatisfaction": null,
+          "Gender": "Female",
+          "HourlyRate": "42",
+          "JobInvolvement": "2",
+          "JobLevel": "2",
+          "JobRole": "Human Resources",
+          "JobSatisfaction": null,
+          "MaritalStatus": null,
+          "MonthlyIncome": "5204",
+          "MonthlyRate": "7790",
+          "NumCompaniesWorked": null,
+          "Over18": null,
+          "OverTime": "No",
+          "PercentSalaryHike": "11",
+          "PerformanceRating": "3",
+          "RelationshipSatisfaction": null,
+          "StandardHours": null,
+          "StockOptionLevel": "2",
+          "TotalWorkingYears": "13",
+          "TrainingTimesLastYear": "2",
+          "WorkLifeBalance": "3",
+          "YearsAtCompany": "5",
+          "YearsInCurrentRole": null,
+          "YearsSinceLastPromotion": null,
+          "YearsWithCurrManager": null
+        }
+      }
+    ],
+    "target_column": "Attrition",
+    "task_sub_type": "classification",
+    "final_decision": "003",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2_004.json

@@ -0,0 +1,105 @@
+{
+  "id": "004",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "IBM_HR_Analytics_Employee_Attrition_&_Performance",
+  "table_path": "kaggle/IBM_HR_Analytics_Employee_Attrition_&_Performance",
+  "query": "I’ve got two employee profiles that are really making me pause. The first person is a 36-year-old married woman in Sales, a Sales Executive. She travels rarely for business, lives 16 km from the office, and has a Master’s education in Marketing. Her daily pay is 329, hourly rate 98, and she earns a monthly income of 5647 with a monthly rate of 13,494. She’s at job level 2, with environment satisfaction at 3, but job satisfaction is low at 1, and relationship satisfaction is also low at 1. Job involvement is medium (2), performance rating is good (3), and she got a 13% salary hike. She’s worked at 4 companies before, with 11 total working years, but only 3 years here at the company. She’s been in her current role for 2 years, had a promotion just now (0 years since), and has had her current manager for 2 years. Stock option level is 2, work-life balance is good (2), and she had 3 trainings last year. Employee number is 1436, standard hours are 80, and she doesn’t work overtime. The other is a 24-year-old married man in Human Resources, an HR specialist. He also travels rarely, lives 22 km away, has a Below College education in Human Resources. His daily rate is 240, hourly rate 58, monthly income is 1555 with a monthly rate of 11,585. He’s at job level 1, with high environment satisfaction (4) and good job satisfaction (3). Relationship satisfaction is high (3), but job involvement is low (1). Performance rating is good (3) with an 11% hike. He’s only worked at 1 other company, with just 1 total working year and 1 year at our company. He’s been in his current role for 0 years, 0 years since last promotion, and 0 years with his manager. Stock option level is 1, work-life balance is better (3), and he had 2 trainings. Employee number is 1714, standard hours 80, no overtime. Given the historical data on who tends to leave, does the first one or the second one seem more likely to be considering leaving the company?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Age": "36",
+          "Attrition": "No",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "329",
+          "Department": "Sales",
+          "DistanceFromHome": "16",
+          "Education": "4",
+          "EducationField": "Marketing",
+          "EmployeeCount": "1",
+          "EmployeeNumber": "1436",
+          "EnvironmentSatisfaction": "3",
+          "Gender": "Female",
+          "HourlyRate": "98",
+          "JobInvolvement": "2",
+          "JobLevel": "2",
+          "JobRole": "Sales Executive",
+          "JobSatisfaction": "1",
+          "MaritalStatus": "Married",
+          "MonthlyIncome": "5647",
+          "MonthlyRate": "13494",
+          "NumCompaniesWorked": "4",
+          "Over18": "Y",
+          "OverTime": "No",
+          "PercentSalaryHike": "13",
+          "PerformanceRating": "3",
+          "RelationshipSatisfaction": "1",
+          "StandardHours": "80",
+          "StockOptionLevel": "2",
+          "TotalWorkingYears": "11",
+          "TrainingTimesLastYear": "3",
+          "WorkLifeBalance": "2",
+          "YearsAtCompany": "3",
+          "YearsInCurrentRole": "2",
+          "YearsSinceLastPromotion": "0",
+          "YearsWithCurrManager": "2"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "Age": "24",
+          "Attrition": "Yes",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "240",
+          "Department": "Human Resources",
+          "DistanceFromHome": "22",
+          "Education": "1",
+          "EducationField": "Human Resources",
+          "EmployeeCount": "1",
+          "EmployeeNumber": "1714",
+          "EnvironmentSatisfaction": "4",
+          "Gender": "Male",
+          "HourlyRate": "58",
+          "JobInvolvement": "1",
+          "JobLevel": "1",
+          "JobRole": "Human Resources",
+          "JobSatisfaction": "3",
+          "MaritalStatus": "Married",
+          "MonthlyIncome": "1555",
+          "MonthlyRate": "11585",
+          "NumCompaniesWorked": "1",
+          "Over18": "Y",
+          "OverTime": "No",
+          "PercentSalaryHike": "11",
+          "PerformanceRating": "3",
+          "RelationshipSatisfaction": "3",
+          "StandardHours": "80",
+          "StockOptionLevel": "1",
+          "TotalWorkingYears": "1",
+          "TrainingTimesLastYear": "2",
+          "WorkLifeBalance": "3",
+          "YearsAtCompany": "1",
+          "YearsInCurrentRole": "0",
+          "YearsSinceLastPromotion": "0",
+          "YearsWithCurrManager": "0"
+        }
+      }
+    ],
+    "target_column": "Attrition",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2_005.json

@@ -0,0 +1,145 @@
+{
+  "id": "005",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "IBM_HR_Analytics_Employee_Attrition_&_Performance",
+  "table_path": "kaggle/IBM_HR_Analytics_Employee_Attrition_&_Performance",
+  "query": "Honestly, I’m torn between these three cases for our upcoming talent review. I’ve attached our archive of past employee situations to help. Take the first one: a married male, 30 years old, in Sales, traveling rarely. He earns 1288 daily and 9250 monthly, lives 29 km away, has a technical degree with education level 4, and his ID is 1568. He feels good about his environment (rating 3), is highly involved in his job (3), but only moderately satisfied with it (2), and he’s at job level 3. No overtime, hourly rate 33, a 12% raise, performance rating 3, stock level 1, 3 trainings last year, work-life balance rated 3. Tenure shows 4 years with the company, 2 in his role, 3 with his manager, and 9 total working years. The second is a single male, 35, also in Sales traveling rarely. Daily pay 1204, monthly 9582, commutes just 4 km, technical degree with education level 3, ID 1100. He’s very satisfied with his environment (4), highly involved (3), but dissatisfied with his job (1), at job level 3. He works overtime, hourly rate 86, got a 22% hike with a top performance rating of 4, but no stock options, 2 trainings, work-life balance 3. He’s been here 8 years, 7 in his role, 7 with his manager, 9 total years working. The third is a single male, 37, in R&D traveling rarely. Daily rate 1373, monthly 2090, lives 2 km away, education field ‘other’ with level 2, ID 4. Very high environment satisfaction (4), medium job involvement (2), high job satisfaction (3), but only job level 1. Does overtime, hourly rate 92, 15% hike, performance 3, no stock, 3 trainings, work-life balance 3. He’s new with 0 years at the company, 0 in role, 0 with manager, and 7 total working years. Scrutinizing the trends from the data, do you think the first, second, or third employee is the one who will probably stay?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Age": "30",
+          "Attrition": "No",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "1288",
+          "Department": "Sales",
+          "DistanceFromHome": "29",
+          "Education": "4",
+          "EducationField": "Technical Degree",
+          "EmployeeCount": "1",
+          "EmployeeNumber": "1568",
+          "EnvironmentSatisfaction": "3",
+          "Gender": "Male",
+          "HourlyRate": "33",
+          "JobInvolvement": "3",
+          "JobLevel": "3",
+          "JobRole": null,
+          "JobSatisfaction": "2",
+          "MaritalStatus": "Married",
+          "MonthlyIncome": "9250",
+          "MonthlyRate": null,
+          "NumCompaniesWorked": null,
+          "Over18": "Y",
+          "OverTime": "No",
+          "PercentSalaryHike": "12",
+          "PerformanceRating": "3",
+          "RelationshipSatisfaction": null,
+          "StandardHours": null,
+          "StockOptionLevel": "1",
+          "TotalWorkingYears": "9",
+          "TrainingTimesLastYear": "3",
+          "WorkLifeBalance": "3",
+          "YearsAtCompany": "4",
+          "YearsInCurrentRole": "2",
+          "YearsSinceLastPromotion": null,
+          "YearsWithCurrManager": "3"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "Age": "35",
+          "Attrition": "Yes",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "1204",
+          "Department": "Sales",
+          "DistanceFromHome": "4",
+          "Education": "3",
+          "EducationField": "Technical Degree",
+          "EmployeeCount": "1",
+          "EmployeeNumber": "1100",
+          "EnvironmentSatisfaction": "4",
+          "Gender": "Male",
+          "HourlyRate": "86",
+          "JobInvolvement": "3",
+          "JobLevel": "3",
+          "JobRole": null,
+          "JobSatisfaction": "1",
+          "MaritalStatus": "Single",
+          "MonthlyIncome": "9582",
+          "MonthlyRate": null,
+          "NumCompaniesWorked": null,
+          "Over18": "Y",
+          "OverTime": "Yes",
+          "PercentSalaryHike": "22",
+          "PerformanceRating": "4",
+          "RelationshipSatisfaction": null,
+          "StandardHours": null,
+          "StockOptionLevel": "0",
+          "TotalWorkingYears": "9",
+          "TrainingTimesLastYear": "2",
+          "WorkLifeBalance": "3",
+          "YearsAtCompany": "8",
+          "YearsInCurrentRole": "7",
+          "YearsSinceLastPromotion": null,
+          "YearsWithCurrManager": "7"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "Age": "37",
+          "Attrition": "Yes",
+          "BusinessTravel": "Travel_Rarely",
+          "DailyRate": "1373",
+          "Department": "Research & Development",
+          "DistanceFromHome": "2",
+          "Education": "2",
+          "EducationField": "Other",
+          "EmployeeCount": "1",
+          "EmployeeNumber": "4",
+          "EnvironmentSatisfaction": "4",
+          "Gender": "Male",
+          "HourlyRate": "92",
+          "JobInvolvement": "2",
+          "JobLevel": "1",
+          "JobRole": null,
+          "JobSatisfaction": "3",
+          "MaritalStatus": "Single",
+          "MonthlyIncome": "2090",
+          "MonthlyRate": null,
+          "NumCompaniesWorked": null,
+          "Over18": "Y",
+          "OverTime": "Yes",
+          "PercentSalaryHike": "15",
+          "PerformanceRating": "3",
+          "RelationshipSatisfaction": null,
+          "StandardHours": null,
+          "StockOptionLevel": "0",
+          "TotalWorkingYears": "7",
+          "TrainingTimesLastYear": "3",
+          "WorkLifeBalance": "3",
+          "YearsAtCompany": "0",
+          "YearsInCurrentRole": "0",
+          "YearsSinceLastPromotion": null,
+          "YearsWithCurrManager": "0"
+        }
+      }
+    ],
+    "target_column": "Attrition",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/info.json

@@ -0,0 +1,210 @@
+{
+    "name": "IBM HR Analytics Employee Attrition & Performance",
+    "source": "https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset/data",
+    "data_intro": "This dataset contains various attributes of employees at IBM, which can be used to predict employee attrition.",
+    "is_splited": false,
+    "overall_size": 1470,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 9,
+    "n_classes": 26,
+    "cat_feature_intro": {
+        "Attrition": "Whether the employee left the company or not.",
+        "BusinessTravel": "Frequency of business travel (Non-Travel, Travel_Rarely, Travel_Frequently).",
+        "Department": "Department in which the employee works (Sales, Research & Development, Human Resources).",
+        "EducationField": "Field of education (Life Sciences, Medical, Marketing, Technical Degree, Human Resources, Other).",
+        "Gender": "Gender of the employee (Male, Female).",
+        "JobRole": "Role of the employee (Sales Executive, Research Scientist, Laboratory Technician, Manufacturing Director, Healthcare Representative, Manager, Sales Representative, Research Director, Human Resources).",
+        "MaritalStatus": "Marital status of the employee (Single, Married, Divorced).",
+        "Over18": "Whether the employee is over 18 years old (Yes).",
+        "OverTime": "Whether the employee works overtime (Yes, No)."
+    },
+    "num_feature_intro": {
+        "Age": "Age of the employee.",
+        "DailyRate": "Daily rate of the employee.",
+        "DistanceFromHome": "Distance from home to work (in kilometers).",
+        "Education": "Education level (1 'Below College', 2 'College', 3 'Bachelor', 4 'Master', 5 'Doctor').",
+        "EmployeeCount": "Number of employees (always 1).",
+        "EmployeeNumber": "Unique identifier for the employee.",
+        "EnvironmentSatisfaction": "Environment satisfaction level (1 'Low', 2 'Medium', 3 'High', 4 'Very High').",
+        "HourlyRate": "Hourly rate of the employee.",
+        "JobInvolvement": "Job involvement level (1 'Low', 2 'Medium', 3 'High', 4 'Very High').",
+        "JobLevel": "Job level of the employee.",
+        "JobSatisfaction": "Job satisfaction level (1 'Low', 2 'Medium', 3 'High', 4 'Very High').",
+        "MonthlyIncome": "Monthly income of the employee.",
+        "MonthlyRate": "Monthly rate of the employee.",
+        "NumCompaniesWorked": "Number of companies the employee has worked for.",
+        "PercentSalaryHike": "Percentage increase in salary.",
+        "PerformanceRating": "Performance rating (1 'Low', 2 'Good', 3 'Excellent', 4 'Outstanding').",
+        "RelationshipSatisfaction": "Relationship satisfaction level (1 'Low', 2 'Medium', 3 'High', 4 'Very High').",
+        "StandardHours": "Standard working hours (always 80).",
+        "StockOptionLevel": "Stock option level (0 'None', 1 'Low', 2 'Medium', 3 'High').",
+        "TotalWorkingYears": "Total number of years the employee has worked.",
+        "TrainingTimesLastYear": "Number of training sessions attended last year.",
+        "WorkLifeBalance": "Work-life balance level (1 'Bad', 2 'Good', 3 'Better', 4 'Best').",
+        "YearsAtCompany": "Number of years the employee has been at the company.",
+        "YearsInCurrentRole": "Number of years the employee has been in the current role.",
+        "YearsSinceLastPromotion": "Number of years since the employee's last promotion.",
+        "YearsWithCurrManager": "Number of years the employee has been with the current manager."
+    },
+    "evaluation_metric": "Accuracy",
+    "task_type": "classification",
+    "target": "Attrition",
+    "num_feature_value": {
+        "Age": [
+            18.0,
+            60.0
+        ],
+        "DailyRate": [
+            102.0,
+            1499.0
+        ],
+        "DistanceFromHome": [
+            1.0,
+            29.0
+        ],
+        "Education": [
+            1.0,
+            5.0
+        ],
+        "EmployeeCount": [
+            1.0,
+            1.0
+        ],
+        "EmployeeNumber": [
+            1.0,
+            2068.0
+        ],
+        "EnvironmentSatisfaction": [
+            1.0,
+            4.0
+        ],
+        "HourlyRate": [
+            30.0,
+            100.0
+        ],
+        "JobInvolvement": [
+            1.0,
+            4.0
+        ],
+        "JobLevel": [
+            1.0,
+            5.0
+        ],
+        "JobSatisfaction": [
+            1.0,
+            4.0
+        ],
+        "MonthlyIncome": [
+            1009.0,
+            19999.0
+        ],
+        "MonthlyRate": [
+            2094.0,
+            26999.0
+        ],
+        "NumCompaniesWorked": [
+            0.0,
+            9.0
+        ],
+        "PercentSalaryHike": [
+            11.0,
+            25.0
+        ],
+        "PerformanceRating": [
+            3.0,
+            4.0
+        ],
+        "RelationshipSatisfaction": [
+            1.0,
+            4.0
+        ],
+        "StandardHours": [
+            80.0,
+            80.0
+        ],
+        "StockOptionLevel": [
+            0.0,
+            3.0
+        ],
+        "TotalWorkingYears": [
+            0.0,
+            40.0
+        ],
+        "TrainingTimesLastYear": [
+            0.0,
+            6.0
+        ],
+        "WorkLifeBalance": [
+            1.0,
+            4.0
+        ],
+        "YearsAtCompany": [
+            0.0,
+            40.0
+        ],
+        "YearsInCurrentRole": [
+            0.0,
+            18.0
+        ],
+        "YearsSinceLastPromotion": [
+            0.0,
+            15.0
+        ],
+        "YearsWithCurrManager": [
+            0.0,
+            17.0
+        ]
+    },
+    "cat_feature_value": {
+        "Attrition": [
+            "No",
+            "Yes"
+        ],
+        "BusinessTravel": [
+            "Non-Travel",
+            "Travel_Frequently",
+            "Travel_Rarely"
+        ],
+        "Department": [
+            "Human Resources",
+            "Research & Development",
+            "Sales"
+        ],
+        "EducationField": [
+            "Human Resources",
+            "Life Sciences",
+            "Marketing",
+            "Medical",
+            "Other",
+            "Technical Degree"
+        ],
+        "Gender": [
+            "Female",
+            "Male"
+        ],
+        "JobRole": [
+            "Healthcare Representative",
+            "Human Resources",
+            "Laboratory Technician",
+            "Manager",
+            "Manufacturing Director",
+            "Research Director",
+            "Research Scientist",
+            "Sales Executive",
+            "Sales Representative"
+        ],
+        "MaritalStatus": [
+            "Divorced",
+            "Married",
+            "Single"
+        ],
+        "Over18": [
+            "Y"
+        ],
+        "OverTime": [
+            "No",
+            "Yes"
+        ]
+    }
+}

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/info_mod.json

@@ -0,0 +1,317 @@
+{
+  "name": "IBM HR Analytics Employee Attrition & Performance",
+  "source": "https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset/data",
+  "data_intro": "This dataset contains various attributes of employees at IBM, which can be used to predict employee attrition.",
+  "is_splited": false,
+  "overall_size": 1470,
+  "train_size": 0,
+  "test_size": 0,
+  "c_classes": 9,
+  "n_classes": 26,
+  "cat_feature_intro": {
+    "BusinessTravel": "Frequency of business travel (Non-Travel, Travel_Rarely, Travel_Frequently).",
+    "Department": "Department in which the employee works (Sales, Research & Development, Human Resources).",
+    "EducationField": "Field of education (Life Sciences, Medical, Marketing, Technical Degree, Human Resources, Other).",
+    "Gender": "Gender of the employee (Male, Female).",
+    "JobRole": "Role of the employee (Sales Executive, Research Scientist, Laboratory Technician, Manufacturing Director, Healthcare Representative, Manager, Sales Representative, Research Director, Human Resources).",
+    "MaritalStatus": "Marital status of the employee (Single, Married, Divorced).",
+    "Over18": "Whether the employee is over 18 years old (Yes).",
+    "OverTime": "Whether the employee works overtime (Yes, No)."
+  },
+  "num_feature_intro": {
+    "Age": "Age of the employee.",
+    "DailyRate": "Daily rate of the employee.",
+    "DistanceFromHome": "Distance from home to work (in kilometers).",
+    "Education": "Education level (1 'Below College', 2 'College', 3 'Bachelor', 4 'Master', 5 'Doctor').",
+    "EmployeeCount": "Number of employees (always 1).",
+    "EmployeeNumber": "Unique identifier for the employee.",
+    "EnvironmentSatisfaction": "Environment satisfaction level (1 'Low', 2 'Medium', 3 'High', 4 'Very High').",
+    "HourlyRate": "Hourly rate of the employee.",
+    "JobInvolvement": "Job involvement level (1 'Low', 2 'Medium', 3 'High', 4 'Very High').",
+    "JobLevel": "Job level of the employee.",
+    "JobSatisfaction": "Job satisfaction level (1 'Low', 2 'Medium', 3 'High', 4 'Very High').",
+    "MonthlyIncome": "Monthly income of the employee.",
+    "MonthlyRate": "Monthly rate of the employee.",
+    "NumCompaniesWorked": "Number of companies the employee has worked for.",
+    "PercentSalaryHike": "Percentage increase in salary.",
+    "PerformanceRating": "Performance rating (1 'Low', 2 'Good', 3 'Excellent', 4 'Outstanding').",
+    "RelationshipSatisfaction": "Relationship satisfaction level (1 'Low', 2 'Medium', 3 'High', 4 'Very High').",
+    "StandardHours": "Standard working hours (always 80).",
+    "StockOptionLevel": "Stock option level (0 'None', 1 'Low', 2 'Medium', 3 'High').",
+    "TotalWorkingYears": "Total number of years the employee has worked.",
+    "TrainingTimesLastYear": "Number of training sessions attended last year.",
+    "WorkLifeBalance": "Work-life balance level (1 'Bad', 2 'Good', 3 'Better', 4 'Best').",
+    "YearsAtCompany": "Number of years the employee has been at the company.",
+    "YearsInCurrentRole": "Number of years the employee has been in the current role.",
+    "YearsSinceLastPromotion": "Number of years since the employee's last promotion.",
+    "YearsWithCurrManager": "Number of years the employee has been with the current manager."
+  },
+  "evaluation_metric": "Accuracy",
+  "task_type": "classification",
+  "target": "Attrition",
+  "num_feature_value": {
+    "Age": [
+      18.0,
+      60.0
+    ],
+    "DailyRate": [
+      102.0,
+      1499.0
+    ],
+    "DistanceFromHome": [
+      1.0,
+      29.0
+    ],
+    "Education": [
+      1.0,
+      5.0
+    ],
+    "EmployeeCount": [
+      1.0,
+      1.0
+    ],
+    "EmployeeNumber": [
+      1.0,
+      2068.0
+    ],
+    "EnvironmentSatisfaction": [
+      1.0,
+      4.0
+    ],
+    "HourlyRate": [
+      30.0,
+      100.0
+    ],
+    "JobInvolvement": [
+      1.0,
+      4.0
+    ],
+    "JobLevel": [
+      1.0,
+      5.0
+    ],
+    "JobSatisfaction": [
+      1.0,
+      4.0
+    ],
+    "MonthlyIncome": [
+      1009.0,
+      19999.0
+    ],
+    "MonthlyRate": [
+      2094.0,
+      26999.0
+    ],
+    "NumCompaniesWorked": [
+      0.0,
+      9.0
+    ],
+    "PercentSalaryHike": [
+      11.0,
+      25.0
+    ],
+    "PerformanceRating": [
+      3.0,
+      4.0
+    ],
+    "RelationshipSatisfaction": [
+      1.0,
+      4.0
+    ],
+    "StandardHours": [
+      80.0,
+      80.0
+    ],
+    "StockOptionLevel": [
+      0.0,
+      3.0
+    ],
+    "TotalWorkingYears": [
+      0.0,
+      40.0
+    ],
+    "TrainingTimesLastYear": [
+      0.0,
+      6.0
+    ],
+    "WorkLifeBalance": [
+      1.0,
+      4.0
+    ],
+    "YearsAtCompany": [
+      0.0,
+      40.0
+    ],
+    "YearsInCurrentRole": [
+      0.0,
+      18.0
+    ],
+    "YearsSinceLastPromotion": [
+      0.0,
+      15.0
+    ],
+    "YearsWithCurrManager": [
+      0.0,
+      17.0
+    ]
+  },
+  "cat_feature_value": {
+    "BusinessTravel": [
+      "Non-Travel",
+      "Travel_Frequently",
+      "Travel_Rarely"
+    ],
+    "Department": [
+      "Human Resources",
+      "Research & Development",
+      "Sales"
+    ],
+    "EducationField": [
+      "Human Resources",
+      "Life Sciences",
+      "Marketing",
+      "Medical",
+      "Other",
+      "Technical Degree"
+    ],
+    "Gender": [
+      "Female",
+      "Male"
+    ],
+    "JobRole": [
+      "Healthcare Representative",
+      "Human Resources",
+      "Laboratory Technician",
+      "Manager",
+      "Manufacturing Director",
+      "Research Director",
+      "Research Scientist",
+      "Sales Executive",
+      "Sales Representative"
+    ],
+    "MaritalStatus": [
+      "Divorced",
+      "Married",
+      "Single"
+    ],
+    "Over18": [
+      "Y"
+    ],
+    "OverTime": [
+      "No",
+      "Yes"
+    ]
+  },
+  "columns": [
+    "Age",
+    "Attrition",
+    "BusinessTravel",
+    "DailyRate",
+    "Department",
+    "DistanceFromHome",
+    "Education",
+    "EducationField",
+    "EmployeeCount",
+    "EmployeeNumber",
+    "EnvironmentSatisfaction",
+    "Gender",
+    "HourlyRate",
+    "JobInvolvement",
+    "JobLevel",
+    "JobRole",
+    "JobSatisfaction",
+    "MaritalStatus",
+    "MonthlyIncome",
+    "MonthlyRate",
+    "NumCompaniesWorked",
+    "Over18",
+    "OverTime",
+    "PercentSalaryHike",
+    "PerformanceRating",
+    "RelationshipSatisfaction",
+    "StandardHours",
+    "StockOptionLevel",
+    "TotalWorkingYears",
+    "TrainingTimesLastYear",
+    "WorkLifeBalance",
+    "YearsAtCompany",
+    "YearsInCurrentRole",
+    "YearsSinceLastPromotion",
+    "YearsWithCurrManager"
+  ],
+  "feature_columns": [
+    "Age",
+    "BusinessTravel",
+    "DailyRate",
+    "Department",
+    "DistanceFromHome",
+    "Education",
+    "EducationField",
+    "EmployeeCount",
+    "EmployeeNumber",
+    "EnvironmentSatisfaction",
+    "Gender",
+    "HourlyRate",
+    "JobInvolvement",
+    "JobLevel",
+    "JobRole",
+    "JobSatisfaction",
+    "MaritalStatus",
+    "MonthlyIncome",
+    "MonthlyRate",
+    "NumCompaniesWorked",
+    "Over18",
+    "OverTime",
+    "PercentSalaryHike",
+    "PerformanceRating",
+    "RelationshipSatisfaction",
+    "StandardHours",
+    "StockOptionLevel",
+    "TotalWorkingYears",
+    "TrainingTimesLastYear",
+    "WorkLifeBalance",
+    "YearsAtCompany",
+    "YearsInCurrentRole",
+    "YearsSinceLastPromotion",
+    "YearsWithCurrManager"
+  ],
+  "feature_types": {
+    "Age": "numeric",
+    "BusinessTravel": "categorical",
+    "DailyRate": "numeric",
+    "Department": "categorical",
+    "DistanceFromHome": "numeric",
+    "Education": "numeric",
+    "EducationField": "categorical",
+    "EmployeeCount": "numeric",
+    "EmployeeNumber": "numeric",
+    "EnvironmentSatisfaction": "numeric",
+    "Gender": "categorical",
+    "HourlyRate": "numeric",
+    "JobInvolvement": "numeric",
+    "JobLevel": "numeric",
+    "JobRole": "categorical",
+    "JobSatisfaction": "numeric",
+    "MaritalStatus": "categorical",
+    "MonthlyIncome": "numeric",
+    "MonthlyRate": "numeric",
+    "NumCompaniesWorked": "numeric",
+    "Over18": "categorical",
+    "OverTime": "categorical",
+    "PercentSalaryHike": "numeric",
+    "PerformanceRating": "numeric",
+    "RelationshipSatisfaction": "numeric",
+    "StandardHours": "numeric",
+    "StockOptionLevel": "numeric",
+    "TotalWorkingYears": "numeric",
+    "TrainingTimesLastYear": "numeric",
+    "WorkLifeBalance": "numeric",
+    "YearsAtCompany": "numeric",
+    "YearsInCurrentRole": "numeric",
+    "YearsSinceLastPromotion": "numeric",
+    "YearsWithCurrManager": "numeric"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": []
+}

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/test.csv

@@ -0,0 +1,17 @@
+Age,Attrition,BusinessTravel,DailyRate,Department,DistanceFromHome,Education,EducationField,EmployeeCount,EmployeeNumber,EnvironmentSatisfaction,Gender,HourlyRate,JobInvolvement,JobLevel,JobRole,JobSatisfaction,MaritalStatus,MonthlyIncome,MonthlyRate,NumCompaniesWorked,Over18,OverTime,PercentSalaryHike,PerformanceRating,RelationshipSatisfaction,StandardHours,StockOptionLevel,TotalWorkingYears,TrainingTimesLastYear,WorkLifeBalance,YearsAtCompany,YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager
+29,Yes,Travel_Rarely,408,Research & Development,25,5,Technical Degree,1,565,3,Female,71,2,1,Research Scientist,2,Married,2546,18300,5,Y,No,16,3,2,80,0,6,2,4,2,2,1,1
+47,No,Non-Travel,543,Sales,2,4,Marketing,1,1731,3,Male,87,3,2,Sales Executive,2,Married,4978,3536,7,Y,No,11,3,4,80,1,4,3,1,1,0,0,0
+26,Yes,Travel_Rarely,920,Human Resources,20,2,Medical,1,1818,4,Female,69,3,1,Human Resources,2,Married,2148,6889,0,Y,Yes,11,3,3,80,0,6,3,3,5,1,1,4
+22,No,Travel_Rarely,1136,Research & Development,5,3,Life Sciences,1,284,4,Male,60,4,1,Research Scientist,2,Divorced,2328,12392,1,Y,Yes,16,3,1,80,1,4,2,2,4,2,2,2
+28,Yes,Travel_Rarely,1434,Research & Development,5,4,Technical Degree,1,65,3,Male,50,3,1,Laboratory Technician,3,Single,3441,11179,1,Y,Yes,13,3,3,80,0,2,3,2,2,2,2,2
+53,Yes,Travel_Rarely,1168,Sales,24,4,Life Sciences,1,1968,1,Male,66,3,3,Sales Executive,1,Single,10448,5843,6,Y,Yes,13,3,2,80,0,15,2,2,2,2,2,2
+30,Yes,Travel_Rarely,1005,Research & Development,3,3,Technical Degree,1,297,4,Female,88,3,1,Research Scientist,1,Single,2657,8556,5,Y,Yes,11,3,3,80,0,8,5,3,5,2,0,4
+39,No,Travel_Rarely,1383,Human Resources,2,3,Life Sciences,1,909,4,Female,42,2,2,Human Resources,4,Married,5204,7790,8,Y,No,11,3,3,80,2,13,2,3,5,4,0,4
+36,No,Travel_Rarely,329,Sales,16,4,Marketing,1,1436,3,Female,98,2,2,Sales Executive,1,Married,5647,13494,4,Y,No,13,3,1,80,2,11,3,2,3,2,0,2
+24,Yes,Travel_Rarely,240,Human Resources,22,1,Human Resources,1,1714,4,Male,58,1,1,Human Resources,3,Married,1555,11585,1,Y,No,11,3,3,80,1,1,2,3,1,0,0,0
+30,No,Travel_Rarely,1288,Sales,29,4,Technical Degree,1,1568,3,Male,33,3,3,Sales Executive,2,Married,9250,17799,3,Y,No,12,3,2,80,1,9,3,3,4,2,1,3
+35,Yes,Travel_Rarely,1204,Sales,4,3,Technical Degree,1,1100,4,Male,86,3,3,Sales Executive,1,Single,9582,10333,0,Y,Yes,22,4,1,80,0,9,2,3,8,7,4,7
+37,Yes,Travel_Rarely,1373,Research & Development,2,2,Other,1,4,4,Male,92,2,1,Laboratory Technician,3,Single,2090,2396,6,Y,Yes,15,3,2,80,0,7,3,3,0,0,0,0
+42,No,Travel_Rarely,188,Research & Development,29,3,Medical,1,1148,2,Male,56,1,2,Laboratory Technician,4,Single,4272,9558,4,Y,No,19,3,1,80,0,16,3,3,1,0,0,0
+44,Yes,Travel_Frequently,920,Research & Development,24,3,Life Sciences,1,392,4,Male,43,3,1,Laboratory Technician,3,Divorced,3161,19920,3,Y,Yes,22,4,4,80,1,19,0,1,1,0,0,0
+40,Yes,Travel_Rarely,676,Research & Development,9,4,Life Sciences,1,1534,4,Male,86,3,1,Laboratory Technician,1,Single,2018,21831,3,Y,No,14,3,2,80,0,15,3,1,5,4,1,0

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/test_001.csv

@@ -0,0 +1,3 @@
+Age,Attrition,BusinessTravel,DailyRate,Department,DistanceFromHome,Education,EducationField,EmployeeCount,EmployeeNumber,EnvironmentSatisfaction,Gender,HourlyRate,JobInvolvement,JobLevel,JobRole,JobSatisfaction,MaritalStatus,MonthlyIncome,MonthlyRate,NumCompaniesWorked,Over18,OverTime,PercentSalaryHike,PerformanceRating,RelationshipSatisfaction,StandardHours,StockOptionLevel,TotalWorkingYears,TrainingTimesLastYear,WorkLifeBalance,YearsAtCompany,YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager
+29,Yes,Travel_Rarely,408,Research & Development,25,5,Technical Degree,1,565,3,Female,71,2,1,Research Scientist,2,Married,2546,18300,5,Y,No,16,3,2,80,0,6,2,4,2,2,1,1
+47,No,Non-Travel,543,Sales,2,4,Marketing,1,1731,3,Male,87,3,2,Sales Executive,2,Married,4978,3536,7,Y,No,11,3,4,80,1,4,3,1,1,0,0,0

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/test_002.csv

@@ -0,0 +1,4 @@
+Age,Attrition,BusinessTravel,DailyRate,Department,DistanceFromHome,Education,EducationField,EmployeeCount,EmployeeNumber,EnvironmentSatisfaction,Gender,HourlyRate,JobInvolvement,JobLevel,JobRole,JobSatisfaction,MaritalStatus,MonthlyIncome,MonthlyRate,NumCompaniesWorked,Over18,OverTime,PercentSalaryHike,PerformanceRating,RelationshipSatisfaction,StandardHours,StockOptionLevel,TotalWorkingYears,TrainingTimesLastYear,WorkLifeBalance,YearsAtCompany,YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager
+26,Yes,Travel_Rarely,920,Human Resources,20,2,Medical,1,1818,4,Female,69,3,1,Human Resources,2,Married,2148,6889,0,Y,Yes,11,3,3,80,0,6,3,3,5,1,1,4
+22,No,Travel_Rarely,1136,Research & Development,5,3,Life Sciences,1,284,4,Male,60,4,1,Research Scientist,2,Divorced,2328,12392,1,Y,Yes,16,3,1,80,1,4,2,2,4,2,2,2
+28,Yes,Travel_Rarely,1434,Research & Development,5,4,Technical Degree,1,65,3,Male,50,3,1,Laboratory Technician,3,Single,3441,11179,1,Y,Yes,13,3,3,80,0,2,3,2,2,2,2,2

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/test_003.csv

@@ -0,0 +1,4 @@
+Age,Attrition,BusinessTravel,DailyRate,Department,DistanceFromHome,Education,EducationField,EmployeeCount,EmployeeNumber,EnvironmentSatisfaction,Gender,HourlyRate,JobInvolvement,JobLevel,JobRole,JobSatisfaction,MaritalStatus,MonthlyIncome,MonthlyRate,NumCompaniesWorked,Over18,OverTime,PercentSalaryHike,PerformanceRating,RelationshipSatisfaction,StandardHours,StockOptionLevel,TotalWorkingYears,TrainingTimesLastYear,WorkLifeBalance,YearsAtCompany,YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager
+53,Yes,Travel_Rarely,1168,Sales,24,4,Life Sciences,1,1968,1,Male,66,3,3,Sales Executive,1,Single,10448,5843,6,Y,Yes,13,3,2,80,0,15,2,2,2,2,2,2
+30,Yes,Travel_Rarely,1005,Research & Development,3,3,Technical Degree,1,297,4,Female,88,3,1,Research Scientist,1,Single,2657,8556,5,Y,Yes,11,3,3,80,0,8,5,3,5,2,0,4
+39,No,Travel_Rarely,1383,Human Resources,2,3,Life Sciences,1,909,4,Female,42,2,2,Human Resources,4,Married,5204,7790,8,Y,No,11,3,3,80,2,13,2,3,5,4,0,4

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/test_004.csv

@@ -0,0 +1,3 @@
+Age,Attrition,BusinessTravel,DailyRate,Department,DistanceFromHome,Education,EducationField,EmployeeCount,EmployeeNumber,EnvironmentSatisfaction,Gender,HourlyRate,JobInvolvement,JobLevel,JobRole,JobSatisfaction,MaritalStatus,MonthlyIncome,MonthlyRate,NumCompaniesWorked,Over18,OverTime,PercentSalaryHike,PerformanceRating,RelationshipSatisfaction,StandardHours,StockOptionLevel,TotalWorkingYears,TrainingTimesLastYear,WorkLifeBalance,YearsAtCompany,YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager
+36,No,Travel_Rarely,329,Sales,16,4,Marketing,1,1436,3,Female,98,2,2,Sales Executive,1,Married,5647,13494,4,Y,No,13,3,1,80,2,11,3,2,3,2,0,2
+24,Yes,Travel_Rarely,240,Human Resources,22,1,Human Resources,1,1714,4,Male,58,1,1,Human Resources,3,Married,1555,11585,1,Y,No,11,3,3,80,1,1,2,3,1,0,0,0

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/test_005.csv

@@ -0,0 +1,4 @@
+Age,Attrition,BusinessTravel,DailyRate,Department,DistanceFromHome,Education,EducationField,EmployeeCount,EmployeeNumber,EnvironmentSatisfaction,Gender,HourlyRate,JobInvolvement,JobLevel,JobRole,JobSatisfaction,MaritalStatus,MonthlyIncome,MonthlyRate,NumCompaniesWorked,Over18,OverTime,PercentSalaryHike,PerformanceRating,RelationshipSatisfaction,StandardHours,StockOptionLevel,TotalWorkingYears,TrainingTimesLastYear,WorkLifeBalance,YearsAtCompany,YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager
+30,No,Travel_Rarely,1288,Sales,29,4,Technical Degree,1,1568,3,Male,33,3,3,Sales Executive,2,Married,9250,17799,3,Y,No,12,3,2,80,1,9,3,3,4,2,1,3
+35,Yes,Travel_Rarely,1204,Sales,4,3,Technical Degree,1,1100,4,Male,86,3,3,Sales Executive,1,Single,9582,10333,0,Y,Yes,22,4,1,80,0,9,2,3,8,7,4,7
+37,Yes,Travel_Rarely,1373,Research & Development,2,2,Other,1,4,4,Male,92,2,1,Laboratory Technician,3,Single,2090,2396,6,Y,Yes,15,3,2,80,0,7,3,3,0,0,0,0

decision_making/daily/classification/IBM_HR_Analytics_Employee_Attrition_&_Performance_B2/test_006.csv

@@ -0,0 +1,4 @@
+Age,Attrition,BusinessTravel,DailyRate,Department,DistanceFromHome,Education,EducationField,EmployeeCount,EmployeeNumber,EnvironmentSatisfaction,Gender,HourlyRate,JobInvolvement,JobLevel,JobRole,JobSatisfaction,MaritalStatus,MonthlyIncome,MonthlyRate,NumCompaniesWorked,Over18,OverTime,PercentSalaryHike,PerformanceRating,RelationshipSatisfaction,StandardHours,StockOptionLevel,TotalWorkingYears,TrainingTimesLastYear,WorkLifeBalance,YearsAtCompany,YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager
+42,No,Travel_Rarely,188,Research & Development,29,3,Medical,1,1148,2,Male,56,1,2,Laboratory Technician,4,Single,4272,9558,4,Y,No,19,3,1,80,0,16,3,3,1,0,0,0
+44,Yes,Travel_Frequently,920,Research & Development,24,3,Life Sciences,1,392,4,Male,43,3,1,Laboratory Technician,3,Divorced,3161,19920,3,Y,Yes,22,4,4,80,1,19,0,1,1,0,0,0
+40,Yes,Travel_Rarely,676,Research & Development,9,4,Life Sciences,1,1534,4,Male,86,3,1,Laboratory Technician,1,Single,2018,21831,3,Y,No,14,3,2,80,0,15,3,1,5,4,1,0

single_point_prediction/finance/Credit_Card_customers_B1/Credit_Card_customers_B1_001.json

@@ -0,0 +1,51 @@
+{
+  "id": "001",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Credit Card customers",
+  "table_path": "kaggle/Credit Card customers",
+  "query": "My statement just arrived for client number 801366783, and it's got me a bit worried. This 51-year-old customer, who has one dependent and a college education, is only making less than $40K a year. They have the basic Blue card and three different accounts with us, but they've already been inactive for three months in the last year, and we've had to contact them twice. With a revolving balance of $1,772, they still have about $3,088 in available credit. Their spending amount dropped by about 44% from the first to the fourth quarter, they only made 80 transactions all year, and the number of those transactions also fell by 14%. Their card usage sits at 36.5%. With all these signs, do you think this person is about to close their account and leave?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "CLIENTNUM": 801366783,
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": 51,
+          "Gender": "NULL",
+          "Dependent_count": 1,
+          "Education_Level": "College",
+          "Marital_Status": "NULL",
+          "Income_Category": "Less than $40K",
+          "Card_Category": "Blue",
+          "Months_on_book": "NULL",
+          "Total_Relationship_Count": 3,
+          "Months_Inactive_12_mon": 3,
+          "Contacts_Count_12_mon": 2,
+          "Credit_Limit": "NULL",
+          "Total_Revolving_Bal": 1772,
+          "Avg_Open_To_Buy": 3088.0,
+          "Total_Amt_Chng_Q4_Q1": 0.561,
+          "Total_Trans_Amt": "NULL",
+          "Total_Trans_Ct": 80,
+          "Total_Ct_Chng_Q4_Q1": 0.86,
+          "Avg_Utilization_Ratio": 0.365
+        }
+      }
+    ],
+    "target_column": "Attrition_Flag",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Credit_Card_customers_B1/Credit_Card_customers_B1_004.json

@@ -0,0 +1,51 @@
+{
+  "id": "004",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Credit Card customers",
+  "table_path": "kaggle/Credit Card customers",
+  "query": "Sitting here with my colleague reviewing account #778353183, we noticed this 31-year-old married woman only has a high school education and makes under $40K. She carries a Blue card with a $3,201 limit, has no dependents, and has only been with the bank for 13 months. Despite holding 5 different products with us, she's been inactive for 2 of the last 12 months, we've contacted her 3 times, and her revolving balance is $984 with 30.7% utilization. Her transaction amount changed by 0.639 last quarter with $5,155 total spent across 77 transactions, and her open-to-buy credit sits at $2,217. Given everything we're seeing, is she at risk of closing her account?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "CLIENTNUM": 778353183,
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": 31,
+          "Gender": "F",
+          "Dependent_count": 0,
+          "Education_Level": "High School",
+          "Marital_Status": "Married",
+          "Income_Category": "Less than $40K",
+          "Card_Category": "Blue",
+          "Months_on_book": 13,
+          "Total_Relationship_Count": 5,
+          "Months_Inactive_12_mon": 2,
+          "Contacts_Count_12_mon": 3,
+          "Credit_Limit": 3201.0,
+          "Total_Revolving_Bal": 984,
+          "Avg_Open_To_Buy": 2217.0,
+          "Total_Amt_Chng_Q4_Q1": 0.639,
+          "Total_Trans_Amt": 5155,
+          "Total_Trans_Ct": 77,
+          "Total_Ct_Chng_Q4_Q1": 0.925,
+          "Avg_Utilization_Ratio": 0.307
+        }
+      }
+    ],
+    "target_column": "Attrition_Flag",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Credit_Card_customers_B1/Credit_Card_customers_B1_005.json

@@ -0,0 +1,51 @@
+{
+  "id": "005",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Credit Card customers",
+  "table_path": "kaggle/Credit Card customers",
+  "query": "Hey there, I was just reviewing a client's file that's got me a little concerned. The customer is a 39-year-old married man with two kids, and his annual income is under $40,000. He's had his Blue card with us for 34 months and holds four of our products. Over the last year, his account was inactive for two months, but we still contacted him four times. His credit limit is $3,702, with a revolving balance of $1,118. His transaction amount is $2,585 across 64 transactions, and his spending only went up by about 4% from Q1 to Q4, while his transaction count actually dropped by around 22%. I've shared our entire customer history with you for context. Given all this, do you think he's at risk of closing his account?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "CLIENTNUM": "NULL",
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": 39,
+          "Gender": "M",
+          "Dependent_count": 2,
+          "Education_Level": "NULL",
+          "Marital_Status": "Married",
+          "Income_Category": "Less than $40K",
+          "Card_Category": "Blue",
+          "Months_on_book": 34,
+          "Total_Relationship_Count": 4,
+          "Months_Inactive_12_mon": 2,
+          "Contacts_Count_12_mon": 4,
+          "Credit_Limit": 3702.0,
+          "Total_Revolving_Bal": 1118,
+          "Avg_Open_To_Buy": "NULL",
+          "Total_Amt_Chng_Q4_Q1": 1.04,
+          "Total_Trans_Amt": 2585,
+          "Total_Trans_Ct": 64,
+          "Total_Ct_Chng_Q4_Q1": 0.778,
+          "Avg_Utilization_Ratio": "NULL"
+        }
+      }
+    ],
+    "target_column": "Attrition_Flag",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Credit_Card_customers_B1/Credit_Card_customers_B1_006.json

@@ -0,0 +1,51 @@
+{
+  "id": "006",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Credit Card customers",
+  "table_path": "kaggle/Credit Card customers",
+  "query": "Okay, so I was just reviewing a client file, number 712955433, and wanted to get your thoughts. This woman is 36, has four dependents, and her education level is listed as uneducated with an unknown marital status. Her income is between forty and sixty thousand a year, and she's got a Blue card. She's only been with the bank for 16 months and only has one product with us. In the last year, she's been inactive for 2 months and we've contacted her twice. Her credit limit is $4,370, with a revolving balance of $1,222, leaving about $3,148 open to buy. Her transaction amount change from Q4 to Q1 was 0.648, with a total transaction amount of $7,223 over 90 transactions last year. The change in her transaction count was 0.552, and her average card utilization is 0.28. I’ve shared our complete customer history with you. Looking at all this together, do you think she’s going to close her account?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "CLIENTNUM": 712955433,
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": 36,
+          "Gender": "F",
+          "Dependent_count": 4,
+          "Education_Level": "Uneducated",
+          "Marital_Status": "Unknown",
+          "Income_Category": "$40K - $60K",
+          "Card_Category": "Blue",
+          "Months_on_book": 16,
+          "Total_Relationship_Count": 1,
+          "Months_Inactive_12_mon": 2,
+          "Contacts_Count_12_mon": 2,
+          "Credit_Limit": 4370.0,
+          "Total_Revolving_Bal": 1222,
+          "Avg_Open_To_Buy": 3148.0,
+          "Total_Amt_Chng_Q4_Q1": 0.648,
+          "Total_Trans_Amt": 7223,
+          "Total_Trans_Ct": 90,
+          "Total_Ct_Chng_Q4_Q1": 0.552,
+          "Avg_Utilization_Ratio": 0.28
+        }
+      }
+    ],
+    "target_column": "Attrition_Flag",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}