{ "candidate_parameters": [ "gravity acceleration", "plane inclination angle", "Coulomb friction coefficient", "breakaway friction coefficient" ], "download_link": "https://huggingface.co/datasets/eth-siplab/tsenvbenchmark/tree/main/questions/MassSlide", "environment_id": "MassSlide", "name": "MassSlide", "observed_channels": [ { "id": "mass_velocity", "label": "velocity of the mass along the plane", "unit": "" }, { "id": "friction_force", "label": "friction force", "unit": "" }, { "id": "normal_force", "label": "normal force", "unit": "" } ], "prompt_combinations": [ { "agent_instruction": "The time series in the test_samples/ folder were generated by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.\n\nObserved Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time\n\nFor each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.\n\nAllowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]\n\nUse \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.\n\nTask:\nCreate a file named results.json in the current working directory.\nFor each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}\n\nRequirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n\nEvaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.\n\nAdditional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", "desc_level": "high", "task_type": "direct", "training_samples": "none" }, { "agent_instruction": "The time series in the test_samples/ and train_samples/ folders were generated by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.\n\nObserved Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time\n\nFor each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.\n\nAllowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]\n\nUse \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.\n\nTask:\nCreate a file named results.json in the current working directory.\nFor each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}\n\nTo help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.\n\nRequirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n\nEvaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.\n\nAdditional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", "desc_level": "high", "task_type": "direct", "training_samples": ">0" }, { "agent_instruction": "Context:\nThe time series in the test_samples/ folder were generated by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.\n\nObserved Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time\n\nFor each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.\n\nAllowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]\n\nUse \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.\n\nTask:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:\nThe input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.\n\nRequirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.\n\nEvaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.\n\nAdditional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", "desc_level": "high", "task_type": "code", "training_samples": "none" }, { "agent_instruction": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.\n\nObserved Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time\n\nFor each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.\n\nAllowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]\n\nUse \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.\n\nTask:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:\nThe input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.\n\nTo help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.\n\nRequirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.\n\nEvaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.\n\nAdditional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", "desc_level": "high", "task_type": "code", "training_samples": ">0" }, { "agent_instruction": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.\nThe column meanings are unknown, except for the last column, which represents time.\n\nFor each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.\n\n[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.\n\nTask:\nCreate a file named results.json in the current working directory.\nFor each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}\n\nRequirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n\nEvaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.\n\nAdditional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", "desc_level": "none", "task_type": "direct", "training_samples": "none" }, { "agent_instruction": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.\nThe column meanings are unknown, except for the last column, which represents time.\n\nFor each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.\n\n[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.\n\nTask:\nCreate a file named results.json in the current working directory.\nFor each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}\n\nTo help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.\n\nRequirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n\nEvaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.\n\nAdditional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", "desc_level": "none", "task_type": "direct", "training_samples": ">0" }, { "agent_instruction": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.\nThe column meanings are unknown, except for the last column, which represents time.\n\nFor each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.\n\n[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.\n\nTask:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:\nThe input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.\n\nRequirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.\n\nEvaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.\n\nAdditional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", "desc_level": "none", "task_type": "code", "training_samples": "none" }, { "agent_instruction": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.\nThe column meanings are unknown, except for the last column, which represents time.\n\nFor each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.\n\n[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.\n\nTask:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:\nThe input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.\n\nTo help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.\n\nRequirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.\n\nEvaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.\n\nAdditional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", "desc_level": "none", "task_type": "code", "training_samples": ">0" } ], "sample_count": 5, "short_one_line_description": "A mass slides under force while friction and velocity are observed over time." }