|
common_hypothesis_features = [
|
|
'1-2 sentences',
|
|
'surprising finding',
|
|
'includes numeric concepts',
|
|
'includes categorical concepts',
|
|
'includes binary concepts',
|
|
]
|
|
hypothesis_features = [
|
|
['requires within-cluster analysis'],
|
|
['requires across-cluster analysis'],
|
|
['corresponds to a polynomial relationship of some columns'],
|
|
['corresponds to a ratio between some columns'],
|
|
['requires temporal analysis'],
|
|
['relationship is based on descriptive statistics of some columns'],
|
|
['requires concepts based on percentage or percentiles'],
|
|
['relationship is only applicable to one cluster in the data and not the others'],
|
|
]
|
|
|
|
column_features = [
|
|
[
|
|
'must have one target column',
|
|
'must have quantifiable columns',
|
|
'must have a few categorical columns',
|
|
'make sure the categorical column values do not contain special characters',
|
|
'include a few distractor columns',
|
|
]
|
|
]
|
|
|
|
common_pandas_features = [
|
|
'must be executable using python `eval` to create the target column in variable `df` (pandas dataframe)',
|
|
"for e.g., df['A']**2 + 3*df['B'] + 9, np.where(df['A'] > 3, 'Yes', 'No'), etc.",
|
|
'variables in pandas_expression must be from the existing columns listed above',
|
|
'variables in pandas_expression must NOT contain the target column itself',
|
|
]
|
|
pandas_features = [
|
|
['expression is a quadratic polynomial'],
|
|
['expression is a cubic polynomial'],
|
|
['expression is a ratio of existing columns'],
|
|
['expression is derived through logical combination of existing columns'],
|
|
|
|
]
|
|
pandas_features = [common_pandas_features + p for p in pandas_features]
|
|
|
|
common_derived_features = [
|
|
'1-2 sentences',
|
|
'includes numeric concepts',
|
|
'includes categorical concepts',
|
|
'includes binary concepts',
|
|
]
|
|
derived_features = [common_derived_features + h for h in hypothesis_features]
|
|
hypothesis_features = [common_hypothesis_features + h for h in hypothesis_features]
|
|
|
|
PROMPT_HYP = """\
|
|
Given a dataset topic and description, generate an interesting hypothesis based on \
|
|
the provided instructions. Be creative and come up with an unusual finding.
|
|
|
|
```json
|
|
{
|
|
"topic": "%s",
|
|
"description": "%s",
|
|
"hypothesis_features": %s,
|
|
"hypothesis": "..."
|
|
}```
|
|
|
|
Give your answer as a new JSON with the following format:
|
|
```json
|
|
{
|
|
"hypothesis": "..."
|
|
}
|
|
```"""
|
|
|
|
PROMPT_COL = """\
|
|
Given a dataset topic, its description, and a true hypothesis that can be determined from it, \
|
|
generate a list of valid columns based on the provided instructions.
|
|
|
|
```json
|
|
{
|
|
"topic": "%s",
|
|
"description": "%s",
|
|
"hypothesis": "%s",
|
|
"column_instructions": %s,
|
|
"columns": [
|
|
{
|
|
"col_name": "...", # should be an "_"-separated string
|
|
"description": "...",
|
|
"data_type": "...", # should be executable using python's `eval` function. E.g., str, float, int, bool
|
|
"data_range": {...}, # should be either {"min": ..., "max": ...} or {"values": [...]}
|
|
"is_distractor": true/false, # boolean indicating whether this is a distractor that could cause confusion during data analysis
|
|
"is_target": true/false # boolean indicating whether this is the target variable for the hypothesis; at least one column should be the target
|
|
},
|
|
...
|
|
],
|
|
"pandas_instructions": %s,
|
|
"pandas_equation_for_hypothesis": {
|
|
"target_col": "...",
|
|
"target_col_type": "...",
|
|
"target_col_range": {...},
|
|
"independent_cols_in_pandas_expression": [], # list of column names that will be used to derive the target column
|
|
"pandas_expression": "..." # expression to derive df[target_col] using df[ind_col1], df[ind_col2], etc.
|
|
}
|
|
}```
|
|
|
|
Give your answer as a new JSON with the "columns" and "pandas_equation_for_hypothesis" keys filled using the following format:
|
|
```json
|
|
{
|
|
"columns": [...],
|
|
"pandas_equation_for_hypothesis": {...}
|
|
}
|
|
```"""
|
|
|
|
PROMPT_DER = """\
|
|
Given a dataset topic, description, a true hypothesis that can be determined from the data, \
|
|
and a target column from the dataset, generate a hypothesis for the target column using new independent columns not present in the existing columns.
|
|
|
|
```json
|
|
{
|
|
"topic": "%s",
|
|
"description": "%s",
|
|
"hypothesis": "%s",
|
|
"existing_columns": %s,
|
|
"target_column": "%s",
|
|
"new_to_target_instructions": %s,
|
|
"new_to_target_hypothesis": "...", # describe a relationship between new columns that explains the target column
|
|
"new_columns_for_target": [ # do not repeat any of the existing columns in the dataset
|
|
{
|
|
"col_name": "...", # should be an "_"-separated string
|
|
"description": "...",
|
|
"data_type": "...", # should be executable using python's `eval` function. E.g., str, float, int, bool
|
|
"data_range": {...}, # should be either {"min": ..., "max": ...} or {"values": [...]}
|
|
},
|
|
...
|
|
],
|
|
"pandas_instructions": %s,
|
|
"pandas_equation_for_new_to_target_hypothesis": {
|
|
"target_col": "...",
|
|
"target_col_type": "...",
|
|
"target_col_range": {...},
|
|
"independent_cols_in_pandas_expression": [], # list of column names from new_columns_for_target that will be used to derive target_col
|
|
"pandas_expression": "..." # expression to derive df[target_col] using df[ind_col1], df[ind_col2], etc.
|
|
}
|
|
}```
|
|
|
|
Give your answer as a new JSON with the "new_to_target_hypothesis", "new_columns_for_target", and \
|
|
"pandas_equation_for_new_to_target_hypothesis" keys filled using the following format:
|
|
```json
|
|
{
|
|
"new_to_target_hypothesis": "...",
|
|
"new_columns_for_target": [...],
|
|
"pandas_equation_for_new_to_target_hypothesis": {...}
|
|
}
|
|
```"""
|
|
|
