From 1812a75f31f599721a285526121f08039a656273 Mon Sep 17 00:00:00 2001
From: maskedsyntax <aftaab@aftaab.xyz>
Date: Thu, 19 Feb 2026 16:21:20 +0530
Subject: [PATCH] refactor: centralize config and remove dead code

- Create hashprep/config.py with all thresholds as typed dataclasses
- Replace 80+ magic numbers across check files with config references
- Remove ~160 lines of commented-out code in summaries/variables.py
- Remove dead _check_feature_correlation() function in correlations.py
- Remove unused imports (f_oneway, infer_types, numpy) in correlations.py and type_inference.py
- Fix redundant infer_types() call: pass column_types from analyzer to summarize_variables()
- Remove unused dependencies: fastapi, starlette, brotli from pyproject.toml
- Clean up unused variables (text_cols, corr_methods, default_methods) in correlations.py
---
 hashprep/checks/columns.py        |   7 +-
 hashprep/checks/correlations.py   |  72 ++---------
 hashprep/checks/distribution.py   |  10 +-
 hashprep/checks/drift.py          |  12 +-
 hashprep/checks/imbalance.py      |   3 +-
 hashprep/checks/leakage.py        |  11 +-
 hashprep/checks/missing_values.py |  25 ++--
 hashprep/checks/outliers.py       |  25 ++--
 hashprep/config.py                | 168 +++++++++++++++++++++++++
 hashprep/core/analyzer.py         |   2 +-
 hashprep/summaries/variables.py   | 179 ++-------------------------
 hashprep/utils/sampling.py        |   7 +-
 hashprep/utils/type_inference.py  |  16 +--
 pyproject.toml                    |   3 -
 uv.lock                           | 199 ------------------------------
 15 files changed, 258 insertions(+), 481 deletions(-)
 create mode 100644 hashprep/config.py

diff --git a/hashprep/checks/columns.py b/hashprep/checks/columns.py
index 799f452..b37a5eb 100644
--- a/hashprep/checks/columns.py
+++ b/hashprep/checks/columns.py
@@ -1,4 +1,7 @@
 from .core import Issue
+from ..config import DEFAULT_CONFIG
+
+_COL_THRESHOLDS = DEFAULT_CONFIG.columns
 
 def _check_single_value_columns(analyzer):
     issues = []
@@ -23,7 +26,7 @@ def _check_single_value_columns(analyzer):
             )
     return issues
 
-def _check_high_cardinality(analyzer, threshold: int = 100, critical_threshold: float = 0.9):
+def _check_high_cardinality(analyzer, threshold: int = _COL_THRESHOLDS.high_cardinality_count, critical_threshold: float = _COL_THRESHOLDS.high_cardinality_ratio_critical):
     issues = []
     categorical_cols = analyzer.df.select_dtypes(include="object").columns.tolist()
     for col in categorical_cols:
@@ -54,7 +57,7 @@ def _check_duplicates(analyzer):
     duplicate_rows = int(analyzer.df.duplicated().sum())
     if duplicate_rows > 0:
         duplicate_ratio = float(duplicate_rows / len(analyzer.df))
-        severity = "critical" if duplicate_ratio > 0.1 else "warning"
+        severity = "critical" if duplicate_ratio > _COL_THRESHOLDS.duplicate_ratio_critical else "warning"
         impact = "high" if severity == "critical" else "medium"
         quick_fix = (
             "Options: \n- Drop duplicates: Ensures data integrity (Pros: Cleaner data; Cons: May lose valid repeats).\n- Verify duplicates: Check if intentional (e.g., time-series) (Pros: Validates data; Cons: Time-consuming)."
diff --git a/hashprep/checks/correlations.py b/hashprep/checks/correlations.py
index 04e7483..4854403 100644
--- a/hashprep/checks/correlations.py
+++ b/hashprep/checks/correlations.py
@@ -1,24 +1,16 @@
 from .core import Issue
 import pandas as pd
 import numpy as np
-from scipy.stats import f_oneway, spearmanr, pearsonr, kendalltau, chi2_contingency
+from scipy.stats import spearmanr, pearsonr, kendalltau, chi2_contingency
 from itertools import combinations
 from .discretizer import Discretizer, DiscretizationType
-from ..utils.type_inference import infer_types, is_usable_for_corr
-
-
-# Thresholds
-CORR_THRESHOLDS = {
-    'numeric': {
-        'spearman': {'warning': 0.7, 'critical': 0.95},
-        'pearson': {'warning': 0.7, 'critical': 0.95},
-        'kendall': {'warning': 0.6, 'critical': 0.85},  # Lower for Kendall (typically smaller values)
-    },
-    'categorical': {'warning': 0.5, 'critical': 0.8},
-    'mixed': {'warning': 0.5, 'critical': 0.8},  # Updated to coefficient thresholds (matching categorical) for Cramer's V
-}
-CAT_MAX_DISTINCT = 50
-LOW_CARD_NUM_THRESHOLD = 10  # From type_inference.py
+from ..utils.type_inference import is_usable_for_corr
+from ..config import DEFAULT_CONFIG
+
+_CORR = DEFAULT_CONFIG.correlations
+CORR_THRESHOLDS = _CORR.as_nested_dict()
+CAT_MAX_DISTINCT = _CORR.max_distinct_categories
+LOW_CARD_NUM_THRESHOLD = _CORR.low_cardinality_numeric
 
 def _cramers_v_corrected(table: pd.DataFrame) -> float:
     if table.empty or (table.shape[0] == 1 or table.shape[1] == 1):
@@ -52,28 +44,20 @@ def calculate_correlations(analyzer, thresholds=None):
                     typ == 'Numeric' and is_usable_for_corr(analyzer.df[col])]
     cat_cols = [col for col, typ in inferred_types.items() if typ == 'Categorical' and
                 1 < analyzer.df[col].nunique() <= CAT_MAX_DISTINCT and is_usable_for_corr(analyzer.df[col])]
-    text_cols = [col for col, typ in inferred_types.items() if typ == 'Text']
 
-    # Internal default methods
-    default_methods = ['spearman', 'pearson']
-    issues.extend(_check_numeric_correlation(analyzer, numeric_cols, thresholds['numeric'], default_methods))
+    issues.extend(_check_numeric_correlation(analyzer, numeric_cols, thresholds['numeric']))
     issues.extend(_check_categorical_correlation(analyzer, cat_cols, thresholds['categorical']))
     issues.extend(_check_mixed_correlation(analyzer, numeric_cols, cat_cols, thresholds['mixed']))
 
     return issues
 
 
-def _check_numeric_correlation(analyzer, numeric_cols: list, thresholds: dict, methods: list):
+def _check_numeric_correlation(analyzer, numeric_cols: list, thresholds: dict):
     issues = []
     if len(numeric_cols) < 2:
         return issues
 
     num_df = analyzer.df[numeric_cols].dropna(how='all')
-    corr_methods = {
-        'spearman': lambda x, y: spearmanr(x, y),
-        'pearson': lambda x, y: pearsonr(x, y),
-        'kendall': lambda x, y: kendalltau(x, y)
-    }
 
     for col1, col2 in combinations(numeric_cols, 2):
         series1, series2 = num_df[col1].dropna(), num_df[col2].dropna()
@@ -125,42 +109,6 @@ def _check_numeric_correlation(analyzer, numeric_cols: list, thresholds: dict, m
     return issues
 
 
-def _check_feature_correlation(
-    analyzer, threshold: float = 0.95, critical_threshold: float = 0.98
-):
-    issues = []
-    numeric_df = analyzer.df.select_dtypes(include="number")
-    if numeric_df.empty:
-        return issues
-    corr_matrix = numeric_df.corr().abs()
-    upper = corr_matrix.where(np.tril(np.ones(corr_matrix.shape)).astype(bool))
-    correlated_pairs = [
-        (col, row, float(val))
-        for row in upper.index
-        for col, val in upper[row].dropna().items()
-        if val > threshold and col != row
-    ]
-    for col1, col2, corr in correlated_pairs:
-        severity = "critical" if corr > critical_threshold else "warning"
-        impact = "high" if severity == "critical" else "medium"
-        quick_fix = (
-            "Options: \n- Drop one feature: Reduces multicollinearity (Pros: Simplifies model; Cons: Loses info).\n- Combine features: Create composite feature (e.g., PCA) (Pros: Retains info; Cons: Less interpretable).\n- Retain and test: Use robust models (e.g., trees) (Pros: Keeps info; Cons: May affect sensitive models)."
-            if severity == "critical"
-            else "Options: \n- Drop one feature: If less predictive (Pros: Simplifies model; Cons: Loses info).\n- Retain and test: Evaluate with robust models (Pros: Keeps info; Cons: Risk of multicollinearity).\n- Engineer feature: Combine or transform features (Pros: Reduces redundancy; Cons: Adds complexity)."
-        )
-        issues.append(
-            Issue(
-                category="feature_correlation",
-                severity=severity,
-                column=f"{col1},{col2}",
-                description=f"Columns '{col1}' and '{col2}' are highly correlated ({corr:.2f})",
-                impact_score=impact,
-                quick_fix=quick_fix,
-            )
-        )
-    return issues
-
-
 def _check_categorical_correlation(analyzer, cat_cols: list, thresholds: dict):
     issues = []
     if len(cat_cols) < 2:
diff --git a/hashprep/checks/distribution.py b/hashprep/checks/distribution.py
index 475b304..82fa866 100644
--- a/hashprep/checks/distribution.py
+++ b/hashprep/checks/distribution.py
@@ -3,9 +3,11 @@
 from scipy.stats import kstest
 
 from .core import Issue
+from ..config import DEFAULT_CONFIG
 
+_DIST = DEFAULT_CONFIG.distribution
 
-def _check_uniform_distribution(analyzer, p_threshold: float = 0.1) -> List[Issue]:
+def _check_uniform_distribution(analyzer, p_threshold: float = _DIST.uniform_p_value) -> List[Issue]:
     """
     Detect uniformly distributed numeric columns using Kolmogorov-Smirnov test.
     Uniform distributions often indicate synthetic IDs or sequential data.
@@ -14,7 +16,7 @@ def _check_uniform_distribution(analyzer, p_threshold: float = 0.1) -> List[Issu
 
     for col in analyzer.df.select_dtypes(include="number").columns:
         series = analyzer.df[col].dropna()
-        if len(series) < 20:
+        if len(series) < _DIST.uniform_min_samples:
             continue
 
         min_val, max_val = series.min(), series.max()
@@ -46,7 +48,7 @@ def _check_uniform_distribution(analyzer, p_threshold: float = 0.1) -> List[Issu
     return issues
 
 
-def _check_unique_values(analyzer, threshold: float = 0.95) -> List[Issue]:
+def _check_unique_values(analyzer, threshold: float = _DIST.unique_value_ratio) -> List[Issue]:
     """
     Detect columns where nearly all values are unique.
     High uniqueness often indicates identifiers, names, or free-text fields.
@@ -55,7 +57,7 @@ def _check_unique_values(analyzer, threshold: float = 0.95) -> List[Issue]:
 
     for col in analyzer.df.columns:
         series = analyzer.df[col].dropna()
-        if len(series) < 10:
+        if len(series) < _DIST.unique_min_samples:
             continue
 
         unique_count = series.nunique()
diff --git a/hashprep/checks/drift.py b/hashprep/checks/drift.py
index cea28f7..e50b8fe 100644
--- a/hashprep/checks/drift.py
+++ b/hashprep/checks/drift.py
@@ -3,15 +3,17 @@
 from scipy.stats import chisquare, ks_2samp
 
 from .core import Issue
+from ..config import DEFAULT_CONFIG
 
-CRITICAL_P_VALUE = 0.001
-MAX_CATEGORIES_FOR_CHI2 = 50
+_DRIFT = DEFAULT_CONFIG.drift
+CRITICAL_P_VALUE = _DRIFT.critical_p_value
+MAX_CATEGORIES_FOR_CHI2 = _DRIFT.max_categories_for_chi2
 
 
 def check_drift(
     df_train: pd.DataFrame,
     df_test: pd.DataFrame,
-    threshold: float = 0.05,
+    threshold: float = _DRIFT.p_value,
 ) -> list[Issue]:
     """
     Check for distribution shift between two datasets.
@@ -80,13 +82,13 @@ def _check_categorical_drift(
 
         new_categories = set(test_counts.index) - set(train_counts.index)
         if new_categories:
-            sample_new = list(new_categories)[:5]
+            sample_new = list(new_categories)[:_DRIFT.max_new_category_samples]
             issues.append(
                 Issue(
                     category="dataset_drift",
                     severity="warning",
                     column=col,
-                    description=f"New categories in test set for '{col}': {sample_new}{'...' if len(new_categories) > 5 else ''}",
+                    description=f"New categories in test set for '{col}': {sample_new}{'...' if len(new_categories) > _DRIFT.max_new_category_samples else ''}",
                     impact_score="medium",
                     quick_fix="Handle unseen categories in preprocessing pipeline (e.g., OrdinalEncoder with unknown_value).",
                 )
diff --git a/hashprep/checks/imbalance.py b/hashprep/checks/imbalance.py
index 15c2507..081ee62 100644
--- a/hashprep/checks/imbalance.py
+++ b/hashprep/checks/imbalance.py
@@ -1,6 +1,7 @@
 from .core import Issue
+from ..config import DEFAULT_CONFIG
 
-def _check_class_imbalance(analyzer, threshold: float = 0.9):
+def _check_class_imbalance(analyzer, threshold: float = DEFAULT_CONFIG.imbalance.majority_class_ratio):
     issues = []
     if analyzer.target_col and analyzer.target_col in analyzer.df.columns:
         counts = analyzer.df[analyzer.target_col].value_counts(normalize=True)
diff --git a/hashprep/checks/leakage.py b/hashprep/checks/leakage.py
index 304e518..26d5901 100644
--- a/hashprep/checks/leakage.py
+++ b/hashprep/checks/leakage.py
@@ -2,6 +2,9 @@
 import pandas as pd
 from scipy.stats import chi2_contingency, f_oneway
 import numpy as np
+from ..config import DEFAULT_CONFIG
+
+_LEAK = DEFAULT_CONFIG.leakage
 
 def _check_data_leakage(analyzer):
     issues = []
@@ -36,7 +39,7 @@ def _check_target_leakage_patterns(analyzer):
                 corrs = numeric_cols.corrwith(target).abs()
                 for col, corr in corrs.items():
                     severity = (
-                        "critical" if corr > 0.98 else "warning" if corr > 0.95 else None
+                        "critical" if corr > _LEAK.numeric_critical else "warning" if corr > _LEAK.numeric_warning else None
                     )
                     if severity:
                         impact = "high" if severity == "critical" else "medium"
@@ -69,7 +72,7 @@ def _check_target_leakage_patterns(analyzer):
                     r, k = table.shape
                     cramers_v = np.sqrt(phi2 / min(k - 1, r - 1))
                     severity = (
-                        "critical" if cramers_v > 0.95 else "warning" if cramers_v > 0.8 else None
+                        "critical" if cramers_v > _LEAK.categorical_critical else "warning" if cramers_v > _LEAK.categorical_warning else None
                     )
                     if severity:
                         impact = "high" if severity == "critical" else "medium"
@@ -104,8 +107,8 @@ def _check_target_leakage_patterns(analyzer):
                 try:
                     f_stat, p_val = f_oneway(*groups)
                     severity = (
-                        "critical" if f_stat > 20.0 and p_val < 0.001
-                        else "warning" if f_stat > 10.0 and p_val < 0.001 else None
+                        "critical" if f_stat > _LEAK.f_stat_critical and p_val < _LEAK.f_stat_p_value
+                        else "warning" if f_stat > _LEAK.f_stat_warning and p_val < _LEAK.f_stat_p_value else None
                     )
                     if severity:
                         impact = "high" if severity == "critical" else "medium"
diff --git a/hashprep/checks/missing_values.py b/hashprep/checks/missing_values.py
index 2e4e1d5..36f7ab5 100644
--- a/hashprep/checks/missing_values.py
+++ b/hashprep/checks/missing_values.py
@@ -3,8 +3,11 @@
 import pandas as pd
 from collections import defaultdict
 import numpy as np
+from ..config import DEFAULT_CONFIG
 
-def _check_high_missing_values(analyzer, threshold: float = 0.4, critical_threshold: float = 0.7):
+_THRESHOLDS = DEFAULT_CONFIG.missing_values
+
+def _check_high_missing_values(analyzer, threshold: float = _THRESHOLDS.warning, critical_threshold: float = _THRESHOLDS.critical):
     issues = []
     for col in analyzer.df.columns:
         missing_pct = float(analyzer.df[col].isna().mean())
@@ -44,7 +47,7 @@ def _check_empty_columns(analyzer):
             )
     return issues
 
-def _check_dataset_missingness(analyzer, threshold: float = 20.0, critical_threshold: float = 50.0):
+def _check_dataset_missingness(analyzer, threshold: float = _THRESHOLDS.dataset_warning_pct, critical_threshold: float = _THRESHOLDS.dataset_critical_pct):
     issues = []
     missing_pct = float(
         (analyzer.df.isnull().sum().sum() / (analyzer.df.shape[0] * analyzer.df.shape[1])) * 100
@@ -70,11 +73,11 @@ def _check_dataset_missingness(analyzer, threshold: float = 20.0, critical_thres
     return issues
 
 
-def _check_missing_patterns(analyzer, threshold: float = 0.01,
-                            critical_p_threshold: float = 0.001):
+def _check_missing_patterns(analyzer, threshold: float = _THRESHOLDS.pattern_p_value,
+                            critical_p_threshold: float = _THRESHOLDS.pattern_critical_p_value):
     issues = []
     missing_cols = [
-        col for col in analyzer.df.columns if int(analyzer.df[col].isna().sum()) >= 10
+        col for col in analyzer.df.columns if int(analyzer.df[col].isna().sum()) >= _THRESHOLDS.pattern_min_missing_count
     ]
 
     # grouping logic
@@ -89,7 +92,7 @@ def _check_missing_patterns(analyzer, threshold: float = 0.01,
                 continue
             try:
                 value_counts = analyzer.df[other_col].value_counts()
-                rare_cats = value_counts[value_counts < 5].index
+                rare_cats = value_counts[value_counts < _THRESHOLDS.pattern_rare_category_count].index
                 temp_col = analyzer.df[other_col].copy()
                 if not rare_cats.empty:
                     temp_col = temp_col.where(~temp_col.isin(rare_cats), "Other")
@@ -112,7 +115,7 @@ def cramers_v(table):
                     return np.sqrt(phi2corr / rkcorr)
 
                 cramers = cramers_v(table)
-                if p_val < threshold and cramers > 0.1:
+                if p_val < threshold and cramers > _THRESHOLDS.pattern_cramers_v_min:
                     cat_patterns[col].append((other_col, p_val, cramers))
             except Exception:
                 continue
@@ -125,7 +128,7 @@ def cramers_v(table):
             try:
                 missing = analyzer.df[analyzer.df[col].isna()][other_col].dropna()
                 non_missing = analyzer.df[analyzer.df[col].notna()][other_col].dropna()
-                if len(missing) < 10 or len(non_missing) < 10:
+                if len(missing) < _THRESHOLDS.pattern_min_group_size or len(non_missing) < _THRESHOLDS.pattern_min_group_size:
                     continue
 
                 # Replaced f_oneway with mannwhitneyu
@@ -135,7 +138,7 @@ def cramers_v(table):
                 pooled_std = np.sqrt((np.std(missing) ** 2 + np.std(non_missing) ** 2) / 2)
                 cohens_d = abs(np.mean(missing) - np.mean(non_missing)) / pooled_std if pooled_std > 0 else 0
 
-                if p_val < threshold and cohens_d > 0.2:
+                if p_val < threshold and cohens_d > _THRESHOLDS.pattern_cohens_d_min:
                     num_patterns[col].append((other_col, p_val, cohens_d))
             except Exception:
                 continue
@@ -151,7 +154,7 @@ def cramers_v(table):
         if all_patterns:
             # Sort by effect size (descending) and take top 3
             all_patterns.sort(key=lambda x: x[2], reverse=True)  # x[2] is effect size
-            top_corrs = [pat[0] for pat in all_patterns[:3]]
+            top_corrs = [pat[0] for pat in all_patterns[:_THRESHOLDS.pattern_top_correlations]]
             total_count = len(all_patterns)
 
             desc = f"Missingness in '{col}' correlates with {total_count} columns ({', '.join(top_corrs)})"
@@ -161,7 +164,7 @@ def cramers_v(table):
             is_target_correlated = any(pat[0] == analyzer.target_col for pat in all_patterns)
             severity = (
                 "critical"
-                if p_val < critical_p_threshold and is_target_correlated and max_effect > 0.3  # medium effect threshold
+                if p_val < critical_p_threshold and is_target_correlated and max_effect > _THRESHOLDS.pattern_effect_critical
                 else "warning"
             )
             impact = "high" if severity == "critical" else "medium"
diff --git a/hashprep/checks/outliers.py b/hashprep/checks/outliers.py
index debca39..9f79638 100644
--- a/hashprep/checks/outliers.py
+++ b/hashprep/checks/outliers.py
@@ -1,8 +1,11 @@
 from .core import Issue
 import pandas as pd
 import numpy as np
+from ..config import DEFAULT_CONFIG
 
-def _check_outliers(analyzer, z_threshold: float = 4.0):
+_THRESHOLDS = DEFAULT_CONFIG.outliers
+
+def _check_outliers(analyzer, z_threshold: float = _THRESHOLDS.z_score):
     issues = []
     for col in analyzer.df.select_dtypes(include="number").columns:
         series = analyzer.df[col].dropna()
@@ -12,7 +15,7 @@ def _check_outliers(analyzer, z_threshold: float = 4.0):
         outlier_count = int((abs(z_scores) > z_threshold).sum())
         if outlier_count > 0:
             outlier_ratio = float(outlier_count / len(series))
-            severity = "critical" if outlier_ratio > 0.1 else "warning"
+            severity = "critical" if outlier_ratio > _THRESHOLDS.outlier_ratio_critical else "warning"
             impact = "high" if severity == "critical" else "medium"
             quick_fix = (
                 "Options: \n- Remove outliers: Improves model stability (Pros: Reduces noise; Cons: Loses data).\n- Winsorize: Cap extreme values (Pros: Retains data; Cons: Alters distribution).\n- Transform: Apply log/sqrt to reduce impact (Pros: Preserves info; Cons: Changes interpretation)."
@@ -31,7 +34,7 @@ def _check_outliers(analyzer, z_threshold: float = 4.0):
             )
     return issues
 
-def _check_high_zero_counts(analyzer, threshold: float = 0.5, critical_threshold: float = 0.8):
+def _check_high_zero_counts(analyzer, threshold: float = _THRESHOLDS.zero_count_warning, critical_threshold: float = _THRESHOLDS.zero_count_critical):
     issues = []
     for col in analyzer.df.select_dtypes(include="number").columns:
         series = analyzer.df[col].dropna()
@@ -58,7 +61,7 @@ def _check_high_zero_counts(analyzer, threshold: float = 0.5, critical_threshold
             )
     return issues
 
-def _check_extreme_text_lengths(analyzer, max_threshold: int = 1000, min_threshold: int = 1):
+def _check_extreme_text_lengths(analyzer, max_threshold: int = _THRESHOLDS.text_length_max, min_threshold: int = _THRESHOLDS.text_length_min):
     issues = []
     for col in analyzer.df.select_dtypes(include="object").columns:
         series = analyzer.df[col].dropna().astype(str)
@@ -69,7 +72,7 @@ def _check_extreme_text_lengths(analyzer, max_threshold: int = 1000, min_thresho
             extreme_ratio = float(
                 ((lengths > max_threshold) | (lengths < min_threshold)).mean()
             )
-            severity = "critical" if extreme_ratio > 0.1 else "warning"
+            severity = "critical" if extreme_ratio > _THRESHOLDS.extreme_ratio_critical else "warning"
             impact = "high" if severity == "critical" else "medium"
             quick_fix = (
                 "Options: \n- Truncate values: Cap extreme lengths (Pros: Stabilizes model; Cons: Loses info).\n- Filter outliers: Remove extreme entries (Pros: Reduces noise; Cons: Loses data).\n- Transform: Normalize lengths (e.g., log) (Pros: Retains info; Cons: Changes interpretation)."
@@ -88,11 +91,11 @@ def _check_extreme_text_lengths(analyzer, max_threshold: int = 1000, min_thresho
             )
     return issues
 
-def _check_skewness(analyzer, skew_threshold: float = 3.0, critical_skew_threshold: float = 10.0):
+def _check_skewness(analyzer, skew_threshold: float = _THRESHOLDS.skewness_warning, critical_skew_threshold: float = _THRESHOLDS.skewness_critical):
     issues = []
     for col in analyzer.df.select_dtypes(include="number").columns:
         series = analyzer.df[col].dropna()
-        if len(series) < 10:
+        if len(series) < _THRESHOLDS.min_sample_size:
             continue
         skewness = float(series.skew())
         abs_skew = abs(skewness)
@@ -117,7 +120,7 @@ def _check_skewness(analyzer, skew_threshold: float = 3.0, critical_skew_thresho
             )
     return issues
 
-def _check_datetime_skew(analyzer, threshold: float = 0.8):
+def _check_datetime_skew(analyzer, threshold: float = _THRESHOLDS.datetime_skew):
     issues = []
     for col in analyzer.df.select_dtypes(include="datetime64").columns:
         series = pd.to_datetime(analyzer.df[col], errors="coerce").dropna()
@@ -138,7 +141,7 @@ def _check_datetime_skew(analyzer, threshold: float = 0.8):
     return issues
 
 
-def _check_infinite_values(analyzer, threshold: float = 0.01):
+def _check_infinite_values(analyzer, threshold: float = _THRESHOLDS.infinite_ratio_critical):
     """Detect columns with infinite values."""
     issues = []
     for col in analyzer.df.select_dtypes(include="number").columns:
@@ -166,12 +169,12 @@ def _check_infinite_values(analyzer, threshold: float = 0.01):
     return issues
 
 
-def _check_constant_length(analyzer, threshold: float = 0.95):
+def _check_constant_length(analyzer, threshold: float = _THRESHOLDS.constant_length_ratio):
     """Detect string columns where all values have the same length (e.g., IDs, codes)."""
     issues = []
     for col in analyzer.df.select_dtypes(include="object").columns:
         series = analyzer.df[col].dropna().astype(str)
-        if len(series) < 10:
+        if len(series) < _THRESHOLDS.min_sample_size:
             continue
         lengths = series.str.len()
         most_common_length_ratio = lengths.value_counts(normalize=True).iloc[0] if len(lengths) > 0 else 0
diff --git a/hashprep/config.py b/hashprep/config.py
new file mode 100644
index 0000000..9c73c00
--- /dev/null
+++ b/hashprep/config.py
@@ -0,0 +1,168 @@
+"""Centralized configuration for all HashPrep thresholds and defaults.
+
+All magic numbers and thresholds are defined here to make them:
+- Discoverable: one place to find all tuning knobs
+- Configurable: easy to override for different use cases
+- Documented: each threshold explains its purpose
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict
+
+
+@dataclass(frozen=True)
+class MissingValueThresholds:
+    """Thresholds for missing value detection."""
+    warning: float = 0.4
+    critical: float = 0.7
+    dataset_warning_pct: float = 20.0
+    dataset_critical_pct: float = 50.0
+    pattern_p_value: float = 0.01
+    pattern_critical_p_value: float = 0.001
+    pattern_cramers_v_min: float = 0.1
+    pattern_cohens_d_min: float = 0.2
+    pattern_effect_critical: float = 0.3
+    pattern_min_missing_count: int = 10
+    pattern_min_group_size: int = 10
+    pattern_rare_category_count: int = 5
+    pattern_top_correlations: int = 3
+
+
+@dataclass(frozen=True)
+class OutlierThresholds:
+    """Thresholds for outlier detection."""
+    z_score: float = 4.0
+    outlier_ratio_critical: float = 0.1
+    zero_count_warning: float = 0.5
+    zero_count_critical: float = 0.8
+    text_length_max: int = 1000
+    text_length_min: int = 1
+    extreme_ratio_critical: float = 0.1
+    skewness_warning: float = 3.0
+    skewness_critical: float = 10.0
+    datetime_skew: float = 0.8
+    infinite_ratio_critical: float = 0.01
+    constant_length_ratio: float = 0.95
+    min_sample_size: int = 10
+
+
+@dataclass(frozen=True)
+class ColumnThresholds:
+    """Thresholds for column-level checks."""
+    high_cardinality_count: int = 100
+    high_cardinality_ratio_critical: float = 0.9
+    duplicate_ratio_critical: float = 0.1
+
+
+@dataclass(frozen=True)
+class CorrelationThresholds:
+    """Thresholds for correlation analysis."""
+    spearman_warning: float = 0.7
+    spearman_critical: float = 0.95
+    pearson_warning: float = 0.7
+    pearson_critical: float = 0.95
+    kendall_warning: float = 0.6
+    kendall_critical: float = 0.85
+    categorical_warning: float = 0.5
+    categorical_critical: float = 0.8
+    mixed_warning: float = 0.5
+    mixed_critical: float = 0.8
+    max_distinct_categories: int = 50
+    low_cardinality_numeric: int = 10
+
+    def as_nested_dict(self) -> dict:
+        """Return thresholds in the nested dict format used by correlation checks."""
+        return {
+            'numeric': {
+                'spearman': {'warning': self.spearman_warning, 'critical': self.spearman_critical},
+                'pearson': {'warning': self.pearson_warning, 'critical': self.pearson_critical},
+                'kendall': {'warning': self.kendall_warning, 'critical': self.kendall_critical},
+            },
+            'categorical': {'warning': self.categorical_warning, 'critical': self.categorical_critical},
+            'mixed': {'warning': self.mixed_warning, 'critical': self.mixed_critical},
+        }
+
+
+@dataclass(frozen=True)
+class LeakageThresholds:
+    """Thresholds for data leakage detection."""
+    numeric_critical: float = 0.98
+    numeric_warning: float = 0.95
+    categorical_critical: float = 0.95
+    categorical_warning: float = 0.8
+    f_stat_critical: float = 20.0
+    f_stat_warning: float = 10.0
+    f_stat_p_value: float = 0.001
+
+
+@dataclass(frozen=True)
+class DriftThresholds:
+    """Thresholds for dataset drift detection."""
+    p_value: float = 0.05
+    critical_p_value: float = 0.001
+    max_categories_for_chi2: int = 50
+    max_new_category_samples: int = 5
+
+
+@dataclass(frozen=True)
+class DistributionThresholds:
+    """Thresholds for distribution checks."""
+    uniform_p_value: float = 0.1
+    uniform_min_samples: int = 20
+    unique_value_ratio: float = 0.95
+    unique_min_samples: int = 10
+
+
+@dataclass(frozen=True)
+class ImbalanceThresholds:
+    """Thresholds for class imbalance detection."""
+    majority_class_ratio: float = 0.9
+
+
+@dataclass(frozen=True)
+class TypeInferenceConfig:
+    """Configuration for type inference."""
+    cat_cardinality_threshold: int = 50
+    cat_percentage_threshold: float = 0.05
+    num_low_cat_threshold: int = 10
+    bool_mappings: Dict[str, bool] = field(default_factory=lambda: {
+        'true': True, 'false': False,
+        'yes': True, 'no': False,
+        't': True, 'f': False,
+    })
+
+
+@dataclass(frozen=True)
+class SamplingDefaults:
+    """Default values for dataset sampling."""
+    max_rows: int = 100_000
+    memory_threshold_mb: float = 500.0
+
+
+@dataclass(frozen=True)
+class SummaryDefaults:
+    """Defaults for summary generation."""
+    histogram_bins: int = 10
+    top_n_values: int = 10
+    extreme_values_count: int = 10
+    top_n_words: int = 10
+
+
+@dataclass(frozen=True)
+class HashPrepConfig:
+    """Root configuration aggregating all threshold groups."""
+    missing_values: MissingValueThresholds = field(default_factory=MissingValueThresholds)
+    outliers: OutlierThresholds = field(default_factory=OutlierThresholds)
+    columns: ColumnThresholds = field(default_factory=ColumnThresholds)
+    correlations: CorrelationThresholds = field(default_factory=CorrelationThresholds)
+    leakage: LeakageThresholds = field(default_factory=LeakageThresholds)
+    drift: DriftThresholds = field(default_factory=DriftThresholds)
+    distribution: DistributionThresholds = field(default_factory=DistributionThresholds)
+    imbalance: ImbalanceThresholds = field(default_factory=ImbalanceThresholds)
+    type_inference: TypeInferenceConfig = field(default_factory=TypeInferenceConfig)
+    sampling: SamplingDefaults = field(default_factory=SamplingDefaults)
+    summaries: SummaryDefaults = field(default_factory=SummaryDefaults)
+
+
+# Global default config instance
+DEFAULT_CONFIG = HashPrepConfig()
diff --git a/hashprep/core/analyzer.py b/hashprep/core/analyzer.py
index 32c7f24..5062c6a 100644
--- a/hashprep/core/analyzer.py
+++ b/hashprep/core/analyzer.py
@@ -112,7 +112,7 @@ def analyze(self) -> Dict:
             self.df, column_types=self.column_types
         )
         self.summaries["reproduction_info"] = add_reproduction_info(self.df)
-        self.summaries["variables"] = summarize_variables(self.df)
+        self.summaries["variables"] = summarize_variables(self.df, column_types=self.column_types)
         self.summaries.update(summarize_interactions(self.df))
         self.summaries.update(summarize_missing_values(self.df))
 
diff --git a/hashprep/summaries/variables.py b/hashprep/summaries/variables.py
index fbbbfe3..6b5243f 100644
--- a/hashprep/summaries/variables.py
+++ b/hashprep/summaries/variables.py
@@ -4,9 +4,9 @@
 import re
 from collections import defaultdict
 from scipy.stats import median_abs_deviation
+from ..config import DEFAULT_CONFIG
 
-from ..utils.type_inference import infer_types
-
+_SUMMARY = DEFAULT_CONFIG.summaries
 
 def get_monotonicity(series: pd.Series) -> str:
     if series.is_monotonic_increasing:
@@ -17,8 +17,11 @@ def get_monotonicity(series: pd.Series) -> str:
         return "none"
 
 
-def summarize_variables(df):
-    inferred_types = infer_types(df)
+def summarize_variables(df, column_types=None):
+    if column_types is None:
+        from ..utils.type_inference import infer_types
+        column_types = infer_types(df)
+    inferred_types = column_types
     variables = {}
     for column in df.columns:
         typ = inferred_types.get(column, "Unsupported")
@@ -107,19 +110,19 @@ def _summarize_numeric(df, col):
         "variance": float(series.var()),
         "monotonicity": get_monotonicity(series),
     }
-    hist, bin_edges = np.histogram(series, bins=10, range=(min_val, max_val))
+    hist, bin_edges = np.histogram(series, bins=_SUMMARY.histogram_bins, range=(min_val, max_val))
     histogram = {
         "bin_edges": [float(x) for x in bin_edges],
         "counts": [int(x) for x in hist],
     }
-    vc = series.value_counts().head(10)
+    vc = series.value_counts().head(_SUMMARY.top_n_values)
     common_values = {
         str(v): {"count": int(c), "percentage": float(c / n * 100)}
         for v, c in vc.items()
     }
     extremes = {
-        "minimum_10": [float(x) for x in sorted(series)[:10]],
-        "maximum_10": [float(x) for x in sorted(series)[-10:]],
+        "minimum_10": [float(x) for x in sorted(series)[:_SUMMARY.extreme_values_count]],
+        "maximum_10": [float(x) for x in sorted(series)[-_SUMMARY.extreme_values_count:]],
     }
     stats = {
         "infinite_count": infinite_count,
@@ -375,163 +378,3 @@ def _summarize_boolean(df, col):
     }
     stats = {"common_values": common_values}
     return stats
-
-
-# def summarize_variables(df, include_plots=False):
-#     variables = {}
-#     for column in df.columns:
-#         if pd.api.types.is_numeric_dtype(df[column]):
-#             variables[column] = _summarize_numeric_column(df, column, include_plots)
-#         elif pd.api.types.is_datetime64_any_dtype(df[column]):
-#             variables[column] = _summarize_datetime_column(df, column, include_plots)
-#         elif pd.api.types.is_string_dtype(df[column]):
-#             variables[column] = _summarize_text_column(df, column, include_plots)
-#         else:
-#             variables[column] = _summarize_categorical_column(df, column, include_plots)
-#     return variables
-
-
-# def _summarize_numeric_column(df, col, include_plots):
-#     series = df[col].dropna()
-#     stats = {
-#         "count": int(series.count()),
-#         "mean": float(series.mean().item()) if not series.empty else None,
-#         "std": float(series.std().item()) if not series.empty else None,
-#         "min": float(series.min().item()) if not series.empty else None,
-#         "max": float(series.max().item()) if not series.empty else None,
-#         "quantiles": (
-#             {
-#                 "25%": float(series.quantile(0.25).item()),
-#                 "50%": float(series.quantile(0.50).item()),
-#                 "75%": float(series.quantile(0.75).item()),
-#             }
-#             if not series.empty
-#             else None
-#         ),
-#         "missing": int(df[col].isna().sum()),
-#         "zeros": int((series == 0).sum()),
-#     }
-#     if not series.empty:
-#         hist, bin_edges = np.histogram(
-#             series, bins=10, range=(series.min(), series.max())
-#         )
-#         stats["histogram"] = {
-#             "bin_edges": [float(x) for x in bin_edges],
-#             "counts": [int(x) for x in hist],
-#         }
-#         if include_plots:
-#             fig, ax = plt.subplots(figsize=(4, 3))
-#             sns.histplot(series, bins=10, ax=ax)
-#             ax.set_title(f"Histogram of {col}")
-#             ax.set_xlabel(col)
-#             ax.set_ylabel("Count")
-#             buf = io.BytesIO()
-#             fig.savefig(buf, format="png", bbox_inches="tight")
-#             buf.seek(0)
-#             img_str = base64.b64encode(buf.getvalue()).decode("utf-8")
-#             plt.close(fig)
-#             stats["plot"] = img_str  # Store plot directly in stats["plot"]
-#     else:
-#         stats["histogram"] = {"bin_edges": None, "counts": None}
-#     return stats
-
-
-# def _summarize_categorical_column(df, col, include_plots):
-#     series = df[col].dropna().astype(str)
-#     stats = {
-#         "count": int(series.count()),
-#         "unique": int(series.nunique()),
-#         "top_values": series.value_counts().head(10).to_dict(),
-#         "most_frequent": str(series.mode().iloc[0]) if not series.empty else None,
-#         "missing": int(df[col].isna().sum()),
-#     }
-#     if include_plots and not series.empty:
-#         fig, ax = plt.subplots(figsize=(4, 3))
-#         series.value_counts().head(10).plot(kind="bar", ax=ax)
-#         ax.set_title(f"Top Values of {col}")
-#         ax.set_xlabel(col)
-#         ax.set_ylabel("Count")
-#         plt.xticks(rotation=45, ha="right")
-#         buf = io.BytesIO()
-#         fig.savefig(buf, format="png", bbox_inches="tight")
-#         buf.seek(0)
-#         img_str = base64.b64encode(buf.getvalue()).decode("utf-8")
-#         plt.close(fig)
-#         stats["plot"] = img_str
-#     return stats
-
-
-# def _summarize_text_column(df, col, include_plots):
-#     series = df[col].dropna().astype(str)
-#     lengths = series.str.len()
-#     stats = {
-#         "count": int(series.count()),
-#         "missing": int(df[col].isna().sum()),
-#         "avg_length": float(lengths.mean().item()) if not lengths.empty else None,
-#         "min_length": float(lengths.min().item()) if not lengths.empty else None,
-#         "max_length": float(lengths.max().item()) if not lengths.empty else None,
-#         "common_lengths": lengths.value_counts().head(5).to_dict(),
-#         "char_freq": (
-#             dict(
-#                 zip(
-#                     list(
-#                         pd.Series(list("".join(series))).value_counts().head(10).index
-#                     ),
-#                     [
-#                         int(x)
-#                         for x in pd.Series(list("".join(series)))
-#                         .value_counts()
-#                         .head(10)
-#                         .values
-#                     ],
-#                 )
-#             )
-#             if not series.empty
-#             else None
-#         ),
-#     }
-#     if include_plots and not lengths.empty:
-#         fig, ax = plt.subplots(figsize=(4, 3))
-#         sns.histplot(lengths, bins=10, ax=ax)
-#         ax.set_title(f"Length Distribution of {col}")
-#         ax.set_xlabel("Length")
-#         ax.set_ylabel("Count")
-#         buf = io.BytesIO()
-#         fig.savefig(buf, format="png", bbox_inches="tight")
-#         buf.seek(0)
-#         img_str = base64.b64encode(buf.getvalue()).decode("utf-8")
-#         plt.close(fig)
-#         stats["plot"] = img_str
-#     return stats
-
-
-# def _summarize_datetime_column(df, col, include_plots):
-#     series = pd.to_datetime(df[col], errors="coerce").dropna()
-#     stats = {
-#         "count": int(series.count()),
-#         "missing": int(df[col].isna().sum()),
-#         "min": str(series.min()) if not series.empty else None,
-#         "max": str(series.max()) if not series.empty else None,
-#         "year_counts": (
-#             series.dt.year.value_counts().to_dict() if not series.empty else None
-#         ),
-#         "month_counts": (
-#             series.dt.month.value_counts().to_dict() if not series.empty else None
-#         ),
-#         "day_counts": (
-#             series.dt.day.value_counts().to_dict() if not series.empty else None
-#         ),
-#     }
-#     if include_plots and not series.empty:
-#         fig, ax = plt.subplots(figsize=(4, 3))
-#         series.dt.year.value_counts().sort_index().plot(kind="bar", ax=ax)
-#         ax.set_title(f"Year Distribution of {col}")
-#         ax.set_xlabel("Year")
-#         ax.set_ylabel("Count")
-#         buf = io.BytesIO()
-#         fig.savefig(buf, format="png", bbox_inches="tight")
-#         buf.seek(0)
-#         img_str = base64.b64encode(buf.getvalue()).decode("utf-8")
-#         plt.close(fig)
-#         stats["plot"] = img_str
-#     return stats
diff --git a/hashprep/utils/sampling.py b/hashprep/utils/sampling.py
index dfe6e86..8c7aa5f 100644
--- a/hashprep/utils/sampling.py
+++ b/hashprep/utils/sampling.py
@@ -3,8 +3,11 @@
 
 import pandas as pd
 
-DEFAULT_MAX_ROWS = 100_000
-DEFAULT_MEMORY_THRESHOLD_MB = 500.0
+from ..config import DEFAULT_CONFIG
+
+_SAMPLING = DEFAULT_CONFIG.sampling
+DEFAULT_MAX_ROWS = _SAMPLING.max_rows
+DEFAULT_MEMORY_THRESHOLD_MB = _SAMPLING.memory_threshold_mb
 
 
 @dataclass
diff --git a/hashprep/utils/type_inference.py b/hashprep/utils/type_inference.py
index 7fa4233..ded5d3f 100644
--- a/hashprep/utils/type_inference.py
+++ b/hashprep/utils/type_inference.py
@@ -1,14 +1,14 @@
 import pandas as pd
-import numpy as np
-from typing import Dict, List
-from scipy.stats import chi2_contingency  # For potential future use
+from typing import Dict
 
-# Config-like thresholds (mimic ydata's Settings; tune as needed)
+from ..config import DEFAULT_CONFIG
+
+_TYPE_CFG = DEFAULT_CONFIG.type_inference
 CONFIG = {
-    'cat_cardinality_threshold': 50,  # Max unique for Categorical (ydata default ~50)
-    'cat_percentage_threshold': 0.05,  # % unique for Categorical
-    'num_low_cat_threshold': 10,       # Low unique numerics → Categorical
-    'bool_mappings': {'true': True, 'false': False, 'yes': True, 'no': False, 't': True, 'f': False},  # For bool inference
+    'cat_cardinality_threshold': _TYPE_CFG.cat_cardinality_threshold,
+    'cat_percentage_threshold': _TYPE_CFG.cat_percentage_threshold,
+    'num_low_cat_threshold': _TYPE_CFG.num_low_cat_threshold,
+    'bool_mappings': _TYPE_CFG.bool_mappings,
 }
 
 def infer_types(df: pd.DataFrame) -> Dict[str, str]:
diff --git a/pyproject.toml b/pyproject.toml
index 9c61596..9438449 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,6 @@ authors = [
 
 dependencies = [
     "click>=8.3.0",
-    "fastapi>=0.116.1",
     "fuzzybunny",
     "jinja2>=3.1.6",
     "matplotlib>=3.10.6",
@@ -25,8 +24,6 @@ dependencies = [
     "scikit-learn>=1.7.2",
     "scipy>=1.15.3",
     "seaborn>=0.13.2",
-    "starlette>=0.49.1",
-    "brotli>=1.2.0",
     "fonttools>=4.60.2",
     "tabulate>=0.9.0",
     "weasyprint>=68.0",
diff --git a/uv.lock b/uv.lock
index 2cd7cd1..674d497 100644
--- a/uv.lock
+++ b/uv.lock
@@ -7,39 +7,6 @@ resolution-markers = [
     "python_full_version < '3.11'",
 ]
 
-[[package]]
-name = "annotated-doc"
-version = "0.0.4"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
-]
-
-[[package]]
-name = "annotated-types"
-version = "0.7.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
-]
-
-[[package]]
-name = "anyio"
-version = "4.10.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
-    { name = "idna" },
-    { name = "sniffio" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/6f/12/e5e0282d673bb9746bacfb6e2dba8719989d3660cdb2ea79aee9a9651afb/anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1", size = 107213, upload-time = "2025-08-04T08:54:24.882Z" },
-]
-
 [[package]]
 name = "brotli"
 version = "1.2.0"
@@ -413,21 +380,6 @@ wheels = [
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