| from __future__ import annotations
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|
|
| from typing import Iterable, Tuple
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|
|
| import numpy as np
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| import pandas as pd
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|
|
| from core.hypothesis_tests import (
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| one_sample_ttest,
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| two_sample_ttest,
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| variance_test,
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| one_way_anova,
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| )
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|
|
| ROUND = 4
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|
|
|
|
| def _round_table(table: pd.DataFrame, decimals: int = ROUND) -> pd.DataFrame:
|
| """Round only numeric columns of the result table."""
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| if table is None:
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| return table
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| tbl = table.copy()
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| num_cols = tbl.select_dtypes(include="number").columns
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| if len(num_cols) > 0:
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| tbl[num_cols] = tbl[num_cols].round(decimals)
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| return tbl
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|
|
| def _ensure_numeric_series(df: pd.DataFrame, column: str) -> np.ndarray:
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| if df is None:
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| raise ValueError("No dataset loaded.")
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| if column not in df.columns:
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| raise ValueError(f"Column '{column}' not found in the dataset.")
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|
|
| series = df[column].dropna()
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| if series.empty:
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| raise ValueError("No valid data in the selected column.")
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| return series.to_numpy()
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|
|
|
|
| def _materialize_group(
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| df: pd.DataFrame,
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| numeric_col: str,
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| cat_col: str | None,
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| cat_vals: Iterable[str],
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| ) -> np.ndarray:
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| if cat_col is None:
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| raise ValueError("No categorical column selected.")
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|
|
| if cat_col not in df.columns:
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| raise ValueError(f"Categorical column '{cat_col}' not found in the dataset.")
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|
|
|
|
| if cat_vals is None:
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| values = []
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| else:
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| values = list(cat_vals)
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|
|
| if not values:
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| raise ValueError(f"No categories selected for column '{cat_col}'.")
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|
|
| cat_series = pd.Series(values).astype(df[cat_col].dtype)
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| mask = df[cat_col].isin(cat_series)
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| series = df.loc[mask, numeric_col].dropna()
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|
|
| if series.empty:
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| raise ValueError("One or more groups are empty after filtering.")
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| return series.to_numpy()
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|
|
|
|
| def run_hypothesis_testing(
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| *,
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| df: pd.DataFrame | None,
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| numeric_col: str,
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| hypo_test: str,
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| mu0_text: str,
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| alternative: str,
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| include_graph: bool,
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| bootstrap_samples: int,
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| cat_col1: str | None,
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| cat_vals1: list[str],
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| name_group1: str,
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| cat_col2: str | None,
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| cat_vals2: list[str],
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| name_group2: str,
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| cat_col3: str | None,
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| cat_vals3: list[str],
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| plot_type: str,
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| correction: bool,
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| test_type: str,
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| ) -> Tuple[pd.DataFrame, object | None]:
|
| """
|
| High-level dispatcher used by the Hypothesis Testing tab.
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|
|
| Returns:
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| (result_table, figure_or_none)
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| """
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| if df is None:
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| raise ValueError("No dataset loaded.")
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|
|
|
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| _ = _ensure_numeric_series(df, numeric_col)
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|
|
|
|
|
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|
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| if hypo_test == "One sample Student's t-test":
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| if not mu0_text.strip():
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| raise ValueError("μ₀ must be specified for the one-sample t-test.")
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| try:
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| mu0 = float(mu0_text)
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| except Exception:
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| raise ValueError("μ₀ must be a numeric value.")
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|
|
| sample = df[numeric_col].dropna().to_numpy()
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|
|
| table, fig = one_sample_ttest(
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| sample=sample,
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| mu0=mu0,
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| alternative=alternative,
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| numeric_col=numeric_col,
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| bootstrap_samples=bootstrap_samples,
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| include_graph=include_graph,
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| )
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| table = _round_table(table)
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| return table, fig
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|
|
|
|
|
|
|
|
| if hypo_test == "Two samples Student's t-test":
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| group1 = _materialize_group(df, numeric_col, cat_col1, cat_vals1)
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| group2 = _materialize_group(df, numeric_col, cat_col2, cat_vals2)
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|
|
|
|
| name1 = name_group1 or "Group 1"
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| name2 = name_group2 or "Group 2"
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|
|
| table, fig = two_sample_ttest(
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| group1=group1,
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| group2=group2,
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| numeric_col=numeric_col,
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| name_group1=name1,
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| name_group2=name2,
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| alternative=alternative,
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| correction=correction,
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| plot_type=plot_type,
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| bootstrap_samples=bootstrap_samples,
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| include_graph=include_graph,
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| )
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| table = _round_table(table)
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| return table, fig
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|
|
|
|
|
|
|
|
| if hypo_test == "Equal variance between two groups":
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| group1 = _materialize_group(df, numeric_col, cat_col1, cat_vals1)
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| group2 = _materialize_group(df, numeric_col, cat_col2, cat_vals2)
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|
|
| name1 = name_group1 or "Group 1"
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| name2 = name_group2 or "Group 2"
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|
|
| table, fig = variance_test(
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| group1=group1,
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| group2=group2,
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| name_group1=name1,
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| name_group2=name2,
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| test_type=test_type,
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| include_graph=include_graph,
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| bootstrap_samples=bootstrap_samples,
|
| )
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| table = _round_table(table)
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| return table, fig
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|
|
|
|
|
|
|
|
| if hypo_test == "One-way ANOVA":
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| if cat_col3 is None:
|
| raise ValueError("A categorical column must be selected for ANOVA.")
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|
|
| if cat_col3 not in df.columns:
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| raise ValueError(
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| f"Categorical column '{cat_col3}' not found in the dataset."
|
| )
|
|
|
| if not cat_vals3:
|
| raise ValueError("At least one category must be selected for ANOVA.")
|
|
|
| cat_series = pd.Series(cat_vals3).astype(df[cat_col3].dtype)
|
| data_group = df[df[cat_col3].isin(cat_series)][[numeric_col, cat_col3]].dropna()
|
|
|
| table, fig = one_way_anova(
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| data_group=data_group,
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| numeric_col=numeric_col,
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| cat_col=cat_col3,
|
| )
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| table = _round_table(table)
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| return table, fig
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|
|
|
|
|
|
|
|
| raise ValueError(f"Unknown hypothesis test: {hypo_test}")
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|
|
