Load a CSV or ZIP-compressed CSV file into memory under a given name. Call this ONCE before find_drivers or explain_segment — you do not need to reload the same file again within the same session.

Returns column names, row count, and for categorical columns their distinct values. Use this metadata to:

1. Identify the target variable and target class for find_drivers.

2. Spot columns that are direct encodings or duplicates of the target (e.g. a numeric "survived" column when the target is "alive") — pass those in the attributes exclusion list so find_drivers does not pick them as trivial drivers.

Args: name: Short label to refer to this dataset in later calls (e.g. "accidents") path: Absolute or relative path to a CSV or ZIP-compressed CSV file separator: Column delimiter — use "\t" for tab-separated files, default is "," encoding: File encoding, default "utf-8" (use "cp1250" for Windows Eastern European files)

List all currently loaded datasets with their names, row counts, and column names. Use this to remind yourself what data is available without reloading.

Find the key drivers and influencers of a target outcome in a loaded dataset. Call this ONCE — it returns a complete, multi-level nested JSON in a single response. Do NOT call it multiple times to "refine" results; use the parameters below to get it right on the first call.

The response contains a "drivers" list. Every top-level single-variable driver is guaranteed to have a "sub_drivers" list — if araxai did not produce one naturally, the server automatically runs a sub-analysis by filtering to that segment. Sub_drivers may themselves contain further "sub_drivers" up to max_depth levels. Always read the full nested structure before deciding whether more analysis is needed. Only call again with filters when the user asks about a specific sub-segment (e.g. "within women in 3rd class") that is not already covered by sub_drivers.

IMPORTANT — avoid trivial drivers: Before calling, check load_dataset output for columns that are direct encodings or recodings of the target (e.g. a numeric "survived=1" column when target is "alive=yes", or redundant label columns like "who"/"adult_male" that restate "sex"). Exclude these via the attributes parameter, otherwise they will dominate the results trivially.

Lift > 1 means the feature increases the probability of the target class. Lift < 1 means it decreases it. Strength shows +/- signs: more signs = stronger.

Args: dataset_name: Name of a dataset loaded with load_dataset target: Column name of the outcome variable to explain (e.g. "Severity") target_class: The specific outcome value to find drivers for (e.g. "Fatal") attributes: Explicit list of candidate driver columns. Use this to EXCLUDE columns that are redundant with or direct encodings of the target. If omitted, all non-target columns are used. filters: Optional dict of column→value pairs to restrict analysis to a specific segment before running (e.g. {"sex": "female", "pclass": "3"}). Use this when the user asks about a specific sub-group. Filtered columns are automatically excluded from driver candidates (they are constant). Values must match the raw dataset values before encoding. min_base: Minimum number of records a rule must cover (default 20) max_depth: Levels of nested sub-driver drill-down, 1–3 (default 2). Use 2 for standard analysis. Only increase to 3 when the user explicitly asks to drill deeper into a specific segment (e.g. "tell me more about women in 1st class"). The response already contains all levels nested under "sub_drivers" keys — do NOT call find_drivers again just to get deeper results. Only call again if a specific segment is entirely absent. auto_boundaries: If True, automatically tunes the lift threshold to return 2–10 drivers regardless of their absolute lift value

Find drivers of a target outcome within a specific segment (CLARA method). Call this ONCE — like find_drivers, it returns a complete multi-level nested JSON in a single response. Do NOT call it multiple times to refine results.

Use this for local/conditional analysis: "within 1st class passengers, what drives survival?" The condition_variables define which variables describe the segment — araxai will find what values of those variables define the strongest sub-segments, then find drivers within them.

Use find_drivers first for the global picture; use explain_segment only when you need to drill into a specific slice of the data.

Args: dataset_name: Name of a dataset loaded with load_dataset target: Column name of the outcome variable target_class: The specific outcome value to explain condition_variables: Columns that define the segment to focus on (e.g. ["Journey_Type"]) attributes: Optional subset of columns to use as candidate drivers. Exclude columns that are direct encodings of the target, same as for find_drivers. min_base: Minimum records a rule must cover