diff --git a/docs/outrank/algorithms/feature_ranking/ranking_mi_numba.html b/docs/outrank/algorithms/feature_ranking/ranking_mi_numba.html index 4045afa..af09096 100644 --- a/docs/outrank/algorithms/feature_ranking/ranking_mi_numba.html +++ b/docs/outrank/algorithms/feature_ranking/ranking_mi_numba.html @@ -146,114 +146,115 @@

81 82 initial_prob = _f_value_counts / all_events 83 x_value_subspace = np.where(X == f_values[f_index]) - 84 Y_classes = Y[x_value_subspace] - 85 index = 0 - 86 nonzero_class_counts = np.zeros(len(class_values), dtype=np.int32) + 84 + 85 Y_classes = Y[x_value_subspace] + 86 Y_classes_spoofed = np.roll(Y, _f_value_counts)[x_value_subspace] 87 - 88 # Cache nonzero counts - 89 for c in class_values: - 90 nonzero_class_counts[index] = np.count_nonzero(Y_classes == c) - 91 index += 1 - 92 conditional_entropy += compute_conditional_entropy( - 93 Y_classes, class_values, _f_value_counts, initial_prob, nonzero_class_counts, - 94 ) + 88 nonzero_class_counts = np.zeros(len(class_values), dtype=np.int32) + 89 nonzero_class_counts_spoofed = np.zeros(len(class_values), dtype=np.int32) + 90 + 91 # Cache nonzero counts + 92 for index, c in enumerate(class_values): + 93 nonzero_class_counts[index] = np.count_nonzero(Y_classes == c) + 94 nonzero_class_counts_spoofed[index] = np.count_nonzero(Y_classes_spoofed == c) 95 - 96 if cardinality_correction: - 97 # A neat hack that seems to work fine (permutations are expensive) - 98 Y_classes = np.roll(Y, _f_value_counts)[x_value_subspace] + 96 conditional_entropy += compute_conditional_entropy( + 97 Y_classes, class_values, _f_value_counts, initial_prob, nonzero_class_counts, + 98 ) 99 -100 background_cond_entropy += compute_conditional_entropy( -101 Y_classes, class_values, _f_value_counts, initial_prob, nonzero_class_counts, -102 ) -103 -104 if not cardinality_correction: -105 return full_entropy - conditional_entropy -106 -107 else: -108 # note: full entropy falls out during derivation of final term -109 core_joint_entropy = -conditional_entropy + background_cond_entropy -110 return core_joint_entropy -111 +100 if cardinality_correction: +101 background_cond_entropy += compute_conditional_entropy( +102 Y_classes_spoofed, class_values, _f_value_counts, initial_prob, nonzero_class_counts_spoofed, +103 ) +104 +105 if not cardinality_correction: +106 return full_entropy - conditional_entropy +107 +108 else: +109 # note: full entropy falls out during derivation of final term +110 core_joint_entropy = -conditional_entropy + background_cond_entropy +111 return core_joint_entropy 112 -113@njit( -114 'float32(int32[:], int32[:], float32, b1)', -115 cache=True, -116 fastmath=True, -117 error_model='numpy', -118 boundscheck=True, -119) -120def mutual_info_estimator_numba( -121 Y, X, approximation_factor=1, cardinality_correction=False, -122): -123 """Core estimator logic. Compute unique elements, subset if required""" -124 -125 all_events = len(X) -126 f_values, f_value_counts = numba_unique(X) -127 -128 # Diagonal entries -129 if np.sum(X - Y) == 0: -130 cardinality_correction = False -131 -132 if approximation_factor < 1: -133 subspace_size = int(approximation_factor * all_events) -134 if subspace_size != 0: -135 subspace = np.random.randint(0, all_events, size=subspace_size) -136 X = X[subspace] -137 Y = Y[subspace] -138 -139 joint_entropy_core = compute_entropies( -140 X, Y, all_events, f_values, f_value_counts, cardinality_correction, -141 ) -142 -143 return approximation_factor * joint_entropy_core -144 +113 +114@njit( +115 'float32(int32[:], int32[:], float32, b1)', +116 cache=True, +117 fastmath=True, +118 error_model='numpy', +119 boundscheck=True, +120) +121def mutual_info_estimator_numba( +122 Y, X, approximation_factor=1, cardinality_correction=False, +123): +124 """Core estimator logic. Compute unique elements, subset if required""" +125 +126 all_events = len(X) +127 f_values, f_value_counts = numba_unique(X) +128 +129 # Diagonal entries +130 if np.sum(X - Y) == 0: +131 cardinality_correction = False +132 +133 if approximation_factor < 1: +134 subspace_size = int(approximation_factor * all_events) +135 if subspace_size != 0: +136 subspace = np.random.randint(0, all_events, size=subspace_size) +137 X = X[subspace] +138 Y = Y[subspace] +139 +140 joint_entropy_core = compute_entropies( +141 X, Y, all_events, f_values, f_value_counts, cardinality_correction, +142 ) +143 +144 return approximation_factor * joint_entropy_core 145 -146if __name__ == '__main__': -147 import pandas as pd -148 from sklearn.feature_selection import mutual_info_classif -149 -150 np.random.seed(123) -151 import time -152 -153 final_times = [] -154 for algo in ['MI-numba-randomized']: -155 for order in range(20, 21): -156 for j in range(1): -157 start = time.time() -158 a = np.random.randint(1000, size=2**order).astype(np.int32) -159 b = np.random.randint(1000, size=2**order).astype(np.int32) -160 if algo == 'MI': -161 final_score = mutual_info_classif( -162 a.reshape(-1, 1), b.reshape(-1), discrete_features=True, -163 ) -164 elif algo == 'MI-numba-randomized': -165 final_score = mutual_info_estimator_numba( -166 a, b, np.float32(1.0), True, -167 ) -168 elif algo == 'MI-numba': -169 final_score = mutual_info_estimator_numba( -170 a, b, np.float32(1.0), False, -171 ) -172 elif algo == 'MI-numba-randomized-ap': -173 final_score = mutual_info_estimator_numba( -174 a, b, np.float32(0.3), True, -175 ) -176 elif algo == 'MI-numba-ap': -177 final_score = mutual_info_estimator_numba( -178 a, b, np.float32(0.3), False, -179 ) -180 -181 end = time.time() -182 tdiff = end - start -183 instance = { -184 'time': tdiff, -185 'samples 2e': order, 'algorithm': algo, -186 } -187 final_times.append(instance) -188 print(instance) -189 dfx = pd.DataFrame(final_times) -190 dfx = dfx.sort_values(by=['samples 2e']) -191 print(dfx) +146 +147if __name__ == '__main__': +148 import pandas as pd +149 from sklearn.feature_selection import mutual_info_classif +150 +151 np.random.seed(123) +152 import time +153 +154 final_times = [] +155 for algo in ['MI-numba-randomized']: +156 for order in range(20, 21): +157 for j in range(1): +158 start = time.time() +159 a = np.random.randint(1000, size=2**order).astype(np.int32) +160 b = np.random.randint(1000, size=2**order).astype(np.int32) +161 if algo == 'MI': +162 final_score = mutual_info_classif( +163 a.reshape(-1, 1), b.reshape(-1), discrete_features=True, +164 ) +165 elif algo == 'MI-numba-randomized': +166 final_score = mutual_info_estimator_numba( +167 a, b, np.float32(1.0), True, +168 ) +169 elif algo == 'MI-numba': +170 final_score = mutual_info_estimator_numba( +171 a, b, np.float32(1.0), False, +172 ) +173 elif algo == 'MI-numba-randomized-ap': +174 final_score = mutual_info_estimator_numba( +175 a, b, np.float32(0.3), True, +176 ) +177 elif algo == 'MI-numba-ap': +178 final_score = mutual_info_estimator_numba( +179 a, b, np.float32(0.3), False, +180 ) +181 +182 end = time.time() +183 tdiff = end - start +184 instance = { +185 'time': tdiff, +186 'samples 2e': order, 'algorithm': algo, +187 } +188 final_times.append(instance) +189 print(instance) +190 dfx = pd.DataFrame(final_times) +191 dfx = dfx.sort_values(by=['samples 2e']) +192 print(dfx) @@ -377,33 +378,34 @@

82 83 initial_prob = _f_value_counts / all_events 84 x_value_subspace = np.where(X == f_values[f_index]) - 85 Y_classes = Y[x_value_subspace] - 86 index = 0 - 87 nonzero_class_counts = np.zeros(len(class_values), dtype=np.int32) + 85 + 86 Y_classes = Y[x_value_subspace] + 87 Y_classes_spoofed = np.roll(Y, _f_value_counts)[x_value_subspace] 88 - 89 # Cache nonzero counts - 90 for c in class_values: - 91 nonzero_class_counts[index] = np.count_nonzero(Y_classes == c) - 92 index += 1 - 93 conditional_entropy += compute_conditional_entropy( - 94 Y_classes, class_values, _f_value_counts, initial_prob, nonzero_class_counts, - 95 ) + 89 nonzero_class_counts = np.zeros(len(class_values), dtype=np.int32) + 90 nonzero_class_counts_spoofed = np.zeros(len(class_values), dtype=np.int32) + 91 + 92 # Cache nonzero counts + 93 for index, c in enumerate(class_values): + 94 nonzero_class_counts[index] = np.count_nonzero(Y_classes == c) + 95 nonzero_class_counts_spoofed[index] = np.count_nonzero(Y_classes_spoofed == c) 96 - 97 if cardinality_correction: - 98 # A neat hack that seems to work fine (permutations are expensive) - 99 Y_classes = np.roll(Y, _f_value_counts)[x_value_subspace] + 97 conditional_entropy += compute_conditional_entropy( + 98 Y_classes, class_values, _f_value_counts, initial_prob, nonzero_class_counts, + 99 ) 100 -101 background_cond_entropy += compute_conditional_entropy( -102 Y_classes, class_values, _f_value_counts, initial_prob, nonzero_class_counts, -103 ) -104 -105 if not cardinality_correction: -106 return full_entropy - conditional_entropy -107 -108 else: -109 # note: full entropy falls out during derivation of final term -110 core_joint_entropy = -conditional_entropy + background_cond_entropy -111 return core_joint_entropy +101 if cardinality_correction: +102 background_cond_entropy += compute_conditional_entropy( +103 Y_classes_spoofed, class_values, _f_value_counts, initial_prob, nonzero_class_counts_spoofed, +104 ) +105 +106 if not cardinality_correction: +107 return full_entropy - conditional_entropy +108 +109 else: +110 # note: full entropy falls out during derivation of final term +111 core_joint_entropy = -conditional_entropy + background_cond_entropy +112 return core_joint_entropy @@ -424,37 +426,37 @@

- 
114@njit(
-115    'float32(int32[:], int32[:], float32, b1)',
-116    cache=True,
-117    fastmath=True,
-118    error_model='numpy',
-119    boundscheck=True,
-120)
-121def mutual_info_estimator_numba(
-122    Y, X, approximation_factor=1, cardinality_correction=False,
-123):
-124    """Core estimator logic. Compute unique elements, subset if required"""
-125
-126    all_events = len(X)
-127    f_values, f_value_counts = numba_unique(X)
-128
-129    # Diagonal entries
-130    if np.sum(X - Y) == 0:
-131        cardinality_correction = False
-132
-133    if approximation_factor < 1:
-134        subspace_size = int(approximation_factor * all_events)
-135        if subspace_size != 0:
-136            subspace = np.random.randint(0, all_events, size=subspace_size)
-137            X = X[subspace]
-138            Y = Y[subspace]
-139
-140    joint_entropy_core = compute_entropies(
-141        X, Y, all_events, f_values, f_value_counts, cardinality_correction,
-142    )
-143
-144    return approximation_factor * joint_entropy_core
+            
115@njit(
+116    'float32(int32[:], int32[:], float32, b1)',
+117    cache=True,
+118    fastmath=True,
+119    error_model='numpy',
+120    boundscheck=True,
+121)
+122def mutual_info_estimator_numba(
+123    Y, X, approximation_factor=1, cardinality_correction=False,
+124):
+125    """Core estimator logic. Compute unique elements, subset if required"""
+126
+127    all_events = len(X)
+128    f_values, f_value_counts = numba_unique(X)
+129
+130    # Diagonal entries
+131    if np.sum(X - Y) == 0:
+132        cardinality_correction = False
+133
+134    if approximation_factor < 1:
+135        subspace_size = int(approximation_factor * all_events)
+136        if subspace_size != 0:
+137            subspace = np.random.randint(0, all_events, size=subspace_size)
+138            X = X[subspace]
+139            Y = Y[subspace]
+140
+141    joint_entropy_core = compute_entropies(
+142        X, Y, all_events, f_values, f_value_counts, cardinality_correction,
+143    )
+144
+145    return approximation_factor * joint_entropy_core
 

 
 
diff --git a/docs/outrank/algorithms/importance_estimator.html b/docs/outrank/algorithms/importance_estimator.html
index bb90b6d..1b57d14 100644
--- a/docs/outrank/algorithms/importance_estimator.html
+++ b/docs/outrank/algorithms/importance_estimator.html
@@ -30,6 +30,9 @@
 
             
API Documentation

                 
    
+            
  • 
+                    logger
+            
    • 
             
  • 
                     sklearn_MI
             
    • 
@@ -75,203 +78,234 @@ 
    
                         
      1# A module for pairwise computation of importances -- entrypoint for the core ranking algorighm(s)
   2from __future__ import annotations
   3
-  4import operator
-  5import traceback
-  6from typing import Any
-  7from typing import Dict
-  8
-  9import numpy as np
- 10import pandas as pd
- 11from scipy.stats import pearsonr
- 12from sklearn.feature_selection import mutual_info_classif
- 13from sklearn.linear_model import LogisticRegression
- 14from sklearn.metrics import adjusted_mutual_info_score
- 15from sklearn.model_selection import cross_val_score
- 16from sklearn.preprocessing import OneHotEncoder
- 17from sklearn.svm import SVC
- 18
- 19try:
- 20    from outrank.algorithms.feature_ranking import ranking_mi_numba
- 21
- 22    numba_available = True
- 23
- 24except Exception as es:
- 25    traceback.print_exc(0)
- 26    numba_available = False
+  4import logging
+  5import operator
+  6import traceback
+  7from typing import Any
+  8from typing import Dict
+  9
+ 10import numpy as np
+ 11import pandas as pd
+ 12from scipy.stats import pearsonr
+ 13from sklearn.feature_selection import mutual_info_classif
+ 14from sklearn.linear_model import LogisticRegression
+ 15from sklearn.metrics import adjusted_mutual_info_score
+ 16from sklearn.model_selection import cross_val_score
+ 17from sklearn.preprocessing import OneHotEncoder
+ 18from sklearn.svm import SVC
+ 19
+ 20logger = logging.getLogger('syn-logger')
+ 21logger.setLevel(logging.DEBUG)
+ 22
+ 23try:
+ 24    from outrank.algorithms.feature_ranking import ranking_mi_numba
+ 25
+ 26    numba_available = True
  27
- 28
- 29def sklearn_MI(vector_first: Any, vector_second: Any) -> float:
- 30    estimate_feature_importance = mutual_info_classif(
- 31        vector_first.reshape(-1, 1), vector_second.reshape(-1), discrete_features=True,
- 32    )[0]
- 33    return estimate_feature_importance
- 34
- 35
- 36def sklearn_surrogate(
- 37    vector_first: Any, vector_second: Any, surrogate_model: str,
- 38) -> float:
- 39    if surrogate_model == 'surrogate-LR':
- 40        clf = LogisticRegression(max_iter=100000)
- 41    elif surrogate_model == 'surrogate-SVM':
- 42        clf = SVC(gamma='auto', probability=True)
- 43
- 44    transf = OneHotEncoder()
- 45
- 46    # They do not commute, swap if needed
- 47    if len(np.unique(vector_second) > 2):
- 48        vector_third = vector_second
- 49        vector_second = vector_first
- 50        vector_first = vector_third
- 51        del vector_third
- 52
- 53    unique_values, counts = np.unique(vector_second, return_counts=True)
- 54
- 55    # Establish min support for this type of ranking.
- 56    if counts[0] < len(unique_values) * (2**5):
- 57        estimate_feature_importance = 0
+ 28except Exception as es:
+ 29    traceback.print_exc(0)
+ 30    numba_available = False
+ 31
+ 32
+ 33def sklearn_MI(vector_first: Any, vector_second: Any) -> float:
+ 34    estimate_feature_importance = mutual_info_classif(
+ 35        vector_first.reshape(-1, 1), vector_second.reshape(-1), discrete_features=True,
+ 36    )[0]
+ 37    return estimate_feature_importance
+ 38
+ 39
+ 40def sklearn_surrogate(
+ 41    vector_first: Any, vector_second: Any, surrogate_model: str,
+ 42) -> float:
+ 43    if surrogate_model == 'surrogate-LR':
+ 44        clf = LogisticRegression(max_iter=100000)
+ 45    elif surrogate_model == 'surrogate-SVM':
+ 46        clf = SVC(gamma='auto', probability=True)
+ 47
+ 48    transf = OneHotEncoder()
+ 49
+ 50    # They do not commute, swap if needed
+ 51    if len(np.unique(vector_second) > 2):
+ 52        vector_third = vector_second
+ 53        vector_second = vector_first
+ 54        vector_first = vector_third
+ 55        del vector_third
+ 56
+ 57    unique_values, counts = np.unique(vector_second, return_counts=True)
  58
- 59    else:
- 60        vector_first = transf.fit_transform(vector_first.reshape(-1, 1))
- 61        estimate_feature_importance_list = cross_val_score(
- 62            clf, vector_first, vector_second, scoring='neg_log_loss', cv=4,
- 63        )
- 64
- 65        estimate_feature_importance = 1 + \
- 66            np.median(estimate_feature_importance_list)
- 67
- 68    return estimate_feature_importance
- 69
- 70
- 71def numba_mi(vector_first, vector_second, heuristic):
- 72    if heuristic == 'MI-numba-randomized':
- 73        cardinality_correction = True
+ 59    # Establish min support for this type of ranking.
+ 60    if counts[0] < len(unique_values) * (2**5):
+ 61        estimate_feature_importance = 0
+ 62
+ 63    else:
+ 64        vector_first = transf.fit_transform(vector_first.reshape(-1, 1))
+ 65        estimate_feature_importance_list = cross_val_score(
+ 66            clf, vector_first, vector_second, scoring='neg_log_loss', cv=4,
+ 67        )
+ 68
+ 69        estimate_feature_importance = 1 + \
+ 70            np.median(estimate_feature_importance_list)
+ 71
+ 72    return estimate_feature_importance
+ 73
  74
- 75    else:
- 76        cardinality_correction = False
- 77
- 78    estimate_feature_importance = ranking_mi_numba.mutual_info_estimator_numba(
- 79        vector_first.reshape(-1).astype(np.int32),
- 80        vector_second.reshape(-1).astype(np.int32),
- 81        approximation_factor=np.float32(1.0),
- 82        cardinality_correction=cardinality_correction,
- 83    )
- 84
- 85    return estimate_feature_importance
- 86
- 87
- 88def sklearn_mi_adj(vector_first, vector_second):
- 89    # AMI(U, V) = [MI(U, V) - E(MI(U, V))] / [avg(H(U), H(V)) - E(MI(U, V))]
- 90    estimate_feature_importance = adjusted_mutual_info_score(
- 91        vector_first.reshape(-1), vector_second.reshape(-1),
- 92    )
- 93    return estimate_feature_importance
- 94
- 95
- 96def get_importances_estimate_pairwise(combination, args, tmp_df):
- 97    """A method for parallel importances estimation. As interaction scoring is independent, individual scores can be computed in parallel."""
+ 75def numba_mi(vector_first, vector_second, heuristic):
+ 76    if heuristic == 'MI-numba-randomized':
+ 77        cardinality_correction = True
+ 78
+ 79    else:
+ 80        cardinality_correction = False
+ 81
+ 82    estimate_feature_importance = ranking_mi_numba.mutual_info_estimator_numba(
+ 83        vector_first.reshape(-1).astype(np.int32),
+ 84        vector_second.reshape(-1).astype(np.int32),
+ 85        approximation_factor=np.float32(1.0),
+ 86        cardinality_correction=cardinality_correction,
+ 87    )
+ 88
+ 89    return estimate_feature_importance
+ 90
+ 91
+ 92def sklearn_mi_adj(vector_first, vector_second):
+ 93    # AMI(U, V) = [MI(U, V) - E(MI(U, V))] / [avg(H(U), H(V)) - E(MI(U, V))]
+ 94    estimate_feature_importance = adjusted_mutual_info_score(
+ 95        vector_first.reshape(-1), vector_second.reshape(-1),
+ 96    )
+ 97    return estimate_feature_importance
  98
- 99    feature_one = combination[0]
-100    feature_two = combination[1]
-101
-102    vector_first = tmp_df[[feature_one]].values.ravel()
-103    vector_second = tmp_df[[feature_two]].values.ravel()
-104
-105    if len(vector_first) == 0 or len(vector_second) == 0:
-106        return [feature_one, feature_two, 0]
-107
-108    # Compute score based on the selected heuristic.
-109    if args.heuristic == 'MI':
-110        # Compute the infoGain
-111        estimate_feature_importance = sklearn_MI(vector_first, vector_second)
+ 99
+100def get_importances_estimate_pairwise(combination, args, tmp_df):
+101    """A method for parallel importances estimation. As interaction scoring is independent, individual scores can be computed in parallel."""
+102
+103    feature_one = combination[0]
+104    feature_two = combination[1]
+105
+106    if feature_one not in tmp_df.columns:
+107        logging.info(f'{feature_one} not found in the constructed data frame - consider increasing --combination_number_upper_bound for better coverage.')
+108        return [feature_one, feature_two, 0]
+109    elif feature_two not in tmp_df.columns:
+110        logging.info(f'{feature_two} not found in the constructed data frame - consider increasing --combination_number_upper_bound for better coverage.')
+111        return [feature_one, feature_two, 0]
 112
-113    elif 'surrogate-' in args.heuristic:
-114        estimate_feature_importance = sklearn_surrogate(
-115            vector_first, vector_second, args.heuristic,
-116        )
-117
-118    elif 'MI-numba' in args.heuristic:
-119        estimate_feature_importance = numba_mi(
-120            vector_first, vector_second, args.heuristic,
-121        )
-122
-123    elif args.heuristic == 'AMI':
-124        estimate_feature_importance = sklearn_mi_adj(
-125            vector_first, vector_second,
-126        )
-127
-128    elif args.heuristic == 'correlation-Pearson':
-129        estimate_feature_importance = pearsonr(vector_first, vector_second)[0]
-130
-131    elif args.heuristic == 'Constant':
-132        estimate_feature_importance = 0.0
+113    vector_first = tmp_df[[feature_one]].values.ravel()
+114    vector_second = tmp_df[[feature_two]].values.ravel()
+115
+116    if len(vector_first) == 0 or len(vector_second) == 0:
+117        return [feature_one, feature_two, 0]
+118
+119    # Compute score based on the selected heuristic.
+120    if args.heuristic == 'MI':
+121        # Compute the infoGain
+122        estimate_feature_importance = sklearn_MI(vector_first, vector_second)
+123
+124    elif 'surrogate-' in args.heuristic:
+125        estimate_feature_importance = sklearn_surrogate(
+126            vector_first, vector_second, args.heuristic,
+127        )
+128
+129    elif 'MI-numba' in args.heuristic:
+130        estimate_feature_importance = numba_mi(
+131            vector_first, vector_second, args.heuristic,
+132        )
 133
-134    else:
-135        raise ValueError(
-136            'Please select one of the possible heuristics (MI, chi2)',
+134    elif args.heuristic == 'AMI':
+135        estimate_feature_importance = sklearn_mi_adj(
+136            vector_first, vector_second,
 137        )
 138
-139    return (feature_one, feature_two, estimate_feature_importance)
-140
+139    elif args.heuristic == 'correlation-Pearson':
+140        estimate_feature_importance = pearsonr(vector_first, vector_second)[0]
 141
-142def rank_features_3MR(
-143    relevance_dict: dict[str, float],
-144    redundancy_dict: dict[tuple[Any, Any], Any],
-145    relational_dict: dict[tuple[Any, Any], Any],
-146    strategy: str = 'median',
-147    alpha: float = 1,
-148    beta: float = 1,
-149) -> pd.DataFrame:
-150    all_features = relevance_dict.keys()
-151    most_important_feature = max(
-152        relevance_dict.items(), key=operator.itemgetter(1),
-153    )[0]
-154    ranked_features = [most_important_feature]
-155
-156    def calc_higher_order(feature, is_redundancy=True):
-157        values = []
-158        for feat in ranked_features:
-159            if is_redundancy:
-160                values.append(redundancy_dict[(feat, feature)])
-161            else:
-162                values.append(relational_dict[(feat, feature)])
-163        if strategy == 'sum':
-164            return sum(values)
-165        if strategy == 'mean':
-166            return np.mean(values)
-167        return np.median(values)
-168
-169    while len(ranked_features) != len(all_features):
-170        top_importance = 0
-171        most_important_feature = ''
-172
-173        for ind, feat in enumerate(set(all_features) - set(ranked_features)):
-174            feature_redundancy = calc_higher_order(feat)
-175            feature_relation = calc_higher_order(feat, False)
-176            feature_relevance = relevance_dict[feat]
-177            importance = (
-178                feature_relevance - alpha * feature_redundancy + beta * feature_relation
-179            )
-180
-181            if (importance > top_importance) or (ind == 0):
-182                top_importance = importance
-183                most_important_feature = feat
-184        ranked_features.append(most_important_feature)
-185    return pd.DataFrame(
-186        {
-187            'Feature': ranked_features,
-188            '3mr_ranking': list(range(1, len(ranked_features) + 1)),
-189        },
-190    )
+142    elif args.heuristic == 'Constant':
+143        estimate_feature_importance = 0.0
+144
+145    else:
+146        raise ValueError(
+147            'Please select one of the possible heuristics (MI, chi2)',
+148        )
+149
+150    return (feature_one, feature_two, estimate_feature_importance)
+151
+152
+153def rank_features_3MR(
+154    relevance_dict: dict[str, float],
+155    redundancy_dict: dict[tuple[Any, Any], Any],
+156    relational_dict: dict[tuple[Any, Any], Any],
+157    strategy: str = 'median',
+158    alpha: float = 1,
+159    beta: float = 1,
+160) -> pd.DataFrame:
+161    all_features = relevance_dict.keys()
+162    most_important_feature = max(
+163        relevance_dict.items(), key=operator.itemgetter(1),
+164    )[0]
+165    ranked_features = [most_important_feature]
+166
+167    def calc_higher_order(feature, is_redundancy=True):
+168        values = []
+169        for feat in ranked_features:
+170            interaction_tuple = (feat, feature)
+171            if is_redundancy:
+172                if interaction_tuple in redundancy_dict:
+173                    values.append(redundancy_dict[interaction_tuple])
+174                else:
+175                    logging.info('Not accounting for redundancy tuple {} - please increase the --combination_number_upper_bound for beter coverage of interactions/redundancies.')
+176            else:
+177                if interaction_tuple in relational_dict:
+178                    values.append(relational_dict[interaction_tuple])
+179                else:
+180                    logging.info('Not accounting for interaction tuple {} - please increase the --combination_number_upper_bound for beter coverage of interactions/redundancies.')
+181
+182        if strategy == 'sum':
+183            return sum(values)
+184        if strategy == 'mean':
+185            return np.mean(values)
+186        return np.median(values)
+187
+188    while len(ranked_features) != len(all_features):
+189        top_importance = 0
+190        most_important_feature = ''
 191
-192
-193def get_importances_estimate_nonmyopic(args: Any, tmp_df: pd.DataFrame):
-194    # TODO - nonmyopic algorithms - tmp_df \ args.label vs. label
-195    # TODO - this is to be executed directly on df - no need for parallel kernel(s)
-196    pass
+192        for ind, feat in enumerate(set(all_features) - set(ranked_features)):
+193            feature_redundancy = calc_higher_order(feat)
+194            feature_relation = calc_higher_order(feat, False)
+195            feature_relevance = relevance_dict[feat]
+196            importance = (
+197                feature_relevance - alpha * feature_redundancy + beta * feature_relation
+198            )
+199
+200            if (importance > top_importance) or (ind == 0):
+201                top_importance = importance
+202                most_important_feature = feat
+203        ranked_features.append(most_important_feature)
+204    return pd.DataFrame(
+205        {
+206            'Feature': ranked_features,
+207            '3mr_ranking': list(range(1, len(ranked_features) + 1)),
+208        },
+209    )
+210
+211
+212def get_importances_estimate_nonmyopic(args: Any, tmp_df: pd.DataFrame):
+213    # TODO - nonmyopic algorithms - tmp_df \ args.label vs. label
+214    # TODO - this is to be executed directly on df - no need for parallel kernel(s)
+215    pass
 
    
 
 
             
+                
    
+                    
    
+            logger        =
+<Logger syn-logger (DEBUG)>
+
+
+    
    
+    
+
+
+
+                
    
                 
    
                             
 
    
@@ -283,11 +317,11 @@ 
    
 
     
    
     
-            
    30def sklearn_MI(vector_first: Any, vector_second: Any) -> float:
-31    estimate_feature_importance = mutual_info_classif(
-32        vector_first.reshape(-1, 1), vector_second.reshape(-1), discrete_features=True,
-33    )[0]
-34    return estimate_feature_importance
+            
    34def sklearn_MI(vector_first: Any, vector_second: Any) -> float:
+35    estimate_feature_importance = mutual_info_classif(
+36        vector_first.reshape(-1, 1), vector_second.reshape(-1), discrete_features=True,
+37    )[0]
+38    return estimate_feature_importance
 
    
 
 
@@ -305,39 +339,39 @@ 
    
 
     
    
     
-            
    37def sklearn_surrogate(
-38    vector_first: Any, vector_second: Any, surrogate_model: str,
-39) -> float:
-40    if surrogate_model == 'surrogate-LR':
-41        clf = LogisticRegression(max_iter=100000)
-42    elif surrogate_model == 'surrogate-SVM':
-43        clf = SVC(gamma='auto', probability=True)
-44
-45    transf = OneHotEncoder()
-46
-47    # They do not commute, swap if needed
-48    if len(np.unique(vector_second) > 2):
-49        vector_third = vector_second
-50        vector_second = vector_first
-51        vector_first = vector_third
-52        del vector_third
-53
-54    unique_values, counts = np.unique(vector_second, return_counts=True)
-55
-56    # Establish min support for this type of ranking.
-57    if counts[0] < len(unique_values) * (2**5):
-58        estimate_feature_importance = 0
+            
    41def sklearn_surrogate(
+42    vector_first: Any, vector_second: Any, surrogate_model: str,
+43) -> float:
+44    if surrogate_model == 'surrogate-LR':
+45        clf = LogisticRegression(max_iter=100000)
+46    elif surrogate_model == 'surrogate-SVM':
+47        clf = SVC(gamma='auto', probability=True)
+48
+49    transf = OneHotEncoder()
+50
+51    # They do not commute, swap if needed
+52    if len(np.unique(vector_second) > 2):
+53        vector_third = vector_second
+54        vector_second = vector_first
+55        vector_first = vector_third
+56        del vector_third
+57
+58    unique_values, counts = np.unique(vector_second, return_counts=True)
 59
-60    else:
-61        vector_first = transf.fit_transform(vector_first.reshape(-1, 1))
-62        estimate_feature_importance_list = cross_val_score(
-63            clf, vector_first, vector_second, scoring='neg_log_loss', cv=4,
-64        )
-65
-66        estimate_feature_importance = 1 + \
-67            np.median(estimate_feature_importance_list)
-68
-69    return estimate_feature_importance
+60    # Establish min support for this type of ranking.
+61    if counts[0] < len(unique_values) * (2**5):
+62        estimate_feature_importance = 0
+63
+64    else:
+65        vector_first = transf.fit_transform(vector_first.reshape(-1, 1))
+66        estimate_feature_importance_list = cross_val_score(
+67            clf, vector_first, vector_second, scoring='neg_log_loss', cv=4,
+68        )
+69
+70        estimate_feature_importance = 1 + \
+71            np.median(estimate_feature_importance_list)
+72
+73    return estimate_feature_importance
 
    
 
 
@@ -355,21 +389,21 @@ 
    
 
     
    
     
-            
    72def numba_mi(vector_first, vector_second, heuristic):
-73    if heuristic == 'MI-numba-randomized':
-74        cardinality_correction = True
-75
-76    else:
-77        cardinality_correction = False
-78
-79    estimate_feature_importance = ranking_mi_numba.mutual_info_estimator_numba(
-80        vector_first.reshape(-1).astype(np.int32),
-81        vector_second.reshape(-1).astype(np.int32),
-82        approximation_factor=np.float32(1.0),
-83        cardinality_correction=cardinality_correction,
-84    )
-85
-86    return estimate_feature_importance
+            
    76def numba_mi(vector_first, vector_second, heuristic):
+77    if heuristic == 'MI-numba-randomized':
+78        cardinality_correction = True
+79
+80    else:
+81        cardinality_correction = False
+82
+83    estimate_feature_importance = ranking_mi_numba.mutual_info_estimator_numba(
+84        vector_first.reshape(-1).astype(np.int32),
+85        vector_second.reshape(-1).astype(np.int32),
+86        approximation_factor=np.float32(1.0),
+87        cardinality_correction=cardinality_correction,
+88    )
+89
+90    return estimate_feature_importance
 
    
 
 
@@ -387,12 +421,12 @@ 
    
 
     
    
     
-            
    89def sklearn_mi_adj(vector_first, vector_second):
-90    # AMI(U, V) = [MI(U, V) - E(MI(U, V))] / [avg(H(U), H(V)) - E(MI(U, V))]
-91    estimate_feature_importance = adjusted_mutual_info_score(
-92        vector_first.reshape(-1), vector_second.reshape(-1),
-93    )
-94    return estimate_feature_importance
+            
    93def sklearn_mi_adj(vector_first, vector_second):
+94    # AMI(U, V) = [MI(U, V) - E(MI(U, V))] / [avg(H(U), H(V)) - E(MI(U, V))]
+95    estimate_feature_importance = adjusted_mutual_info_score(
+96        vector_first.reshape(-1), vector_second.reshape(-1),
+97    )
+98    return estimate_feature_importance
 
    
 
 
@@ -410,50 +444,57 @@ 
    
 
     
    
     
-            
     97def get_importances_estimate_pairwise(combination, args, tmp_df):
- 98    """A method for parallel importances estimation. As interaction scoring is independent, individual scores can be computed in parallel."""
- 99
-100    feature_one = combination[0]
-101    feature_two = combination[1]
-102
-103    vector_first = tmp_df[[feature_one]].values.ravel()
-104    vector_second = tmp_df[[feature_two]].values.ravel()
-105
-106    if len(vector_first) == 0 or len(vector_second) == 0:
-107        return [feature_one, feature_two, 0]
-108
-109    # Compute score based on the selected heuristic.
-110    if args.heuristic == 'MI':
-111        # Compute the infoGain
-112        estimate_feature_importance = sklearn_MI(vector_first, vector_second)
+            
    101def get_importances_estimate_pairwise(combination, args, tmp_df):
+102    """A method for parallel importances estimation. As interaction scoring is independent, individual scores can be computed in parallel."""
+103
+104    feature_one = combination[0]
+105    feature_two = combination[1]
+106
+107    if feature_one not in tmp_df.columns:
+108        logging.info(f'{feature_one} not found in the constructed data frame - consider increasing --combination_number_upper_bound for better coverage.')
+109        return [feature_one, feature_two, 0]
+110    elif feature_two not in tmp_df.columns:
+111        logging.info(f'{feature_two} not found in the constructed data frame - consider increasing --combination_number_upper_bound for better coverage.')
+112        return [feature_one, feature_two, 0]
 113
-114    elif 'surrogate-' in args.heuristic:
-115        estimate_feature_importance = sklearn_surrogate(
-116            vector_first, vector_second, args.heuristic,
-117        )
-118
-119    elif 'MI-numba' in args.heuristic:
-120        estimate_feature_importance = numba_mi(
-121            vector_first, vector_second, args.heuristic,
-122        )
-123
-124    elif args.heuristic == 'AMI':
-125        estimate_feature_importance = sklearn_mi_adj(
-126            vector_first, vector_second,
-127        )
-128
-129    elif args.heuristic == 'correlation-Pearson':
-130        estimate_feature_importance = pearsonr(vector_first, vector_second)[0]
-131
-132    elif args.heuristic == 'Constant':
-133        estimate_feature_importance = 0.0
+114    vector_first = tmp_df[[feature_one]].values.ravel()
+115    vector_second = tmp_df[[feature_two]].values.ravel()
+116
+117    if len(vector_first) == 0 or len(vector_second) == 0:
+118        return [feature_one, feature_two, 0]
+119
+120    # Compute score based on the selected heuristic.
+121    if args.heuristic == 'MI':
+122        # Compute the infoGain
+123        estimate_feature_importance = sklearn_MI(vector_first, vector_second)
+124
+125    elif 'surrogate-' in args.heuristic:
+126        estimate_feature_importance = sklearn_surrogate(
+127            vector_first, vector_second, args.heuristic,
+128        )
+129
+130    elif 'MI-numba' in args.heuristic:
+131        estimate_feature_importance = numba_mi(
+132            vector_first, vector_second, args.heuristic,
+133        )
 134
-135    else:
-136        raise ValueError(
-137            'Please select one of the possible heuristics (MI, chi2)',
+135    elif args.heuristic == 'AMI':
+136        estimate_feature_importance = sklearn_mi_adj(
+137            vector_first, vector_second,
 138        )
 139
-140    return (feature_one, feature_two, estimate_feature_importance)
+140    elif args.heuristic == 'correlation-Pearson':
+141        estimate_feature_importance = pearsonr(vector_first, vector_second)[0]
+142
+143    elif args.heuristic == 'Constant':
+144        estimate_feature_importance = 0.0
+145
+146    else:
+147        raise ValueError(
+148            'Please select one of the possible heuristics (MI, chi2)',
+149        )
+150
+151    return (feature_one, feature_two, estimate_feature_importance)
 
    
 
 
@@ -473,55 +514,63 @@ 
    
 
     
    
     
-            
    143def rank_features_3MR(
-144    relevance_dict: dict[str, float],
-145    redundancy_dict: dict[tuple[Any, Any], Any],
-146    relational_dict: dict[tuple[Any, Any], Any],
-147    strategy: str = 'median',
-148    alpha: float = 1,
-149    beta: float = 1,
-150) -> pd.DataFrame:
-151    all_features = relevance_dict.keys()
-152    most_important_feature = max(
-153        relevance_dict.items(), key=operator.itemgetter(1),
-154    )[0]
-155    ranked_features = [most_important_feature]
-156
-157    def calc_higher_order(feature, is_redundancy=True):
-158        values = []
-159        for feat in ranked_features:
-160            if is_redundancy:
-161                values.append(redundancy_dict[(feat, feature)])
-162            else:
-163                values.append(relational_dict[(feat, feature)])
-164        if strategy == 'sum':
-165            return sum(values)
-166        if strategy == 'mean':
-167            return np.mean(values)
-168        return np.median(values)
-169
-170    while len(ranked_features) != len(all_features):
-171        top_importance = 0
-172        most_important_feature = ''
-173
-174        for ind, feat in enumerate(set(all_features) - set(ranked_features)):
-175            feature_redundancy = calc_higher_order(feat)
-176            feature_relation = calc_higher_order(feat, False)
-177            feature_relevance = relevance_dict[feat]
-178            importance = (
-179                feature_relevance - alpha * feature_redundancy + beta * feature_relation
-180            )
-181
-182            if (importance > top_importance) or (ind == 0):
-183                top_importance = importance
-184                most_important_feature = feat
-185        ranked_features.append(most_important_feature)
-186    return pd.DataFrame(
-187        {
-188            'Feature': ranked_features,
-189            '3mr_ranking': list(range(1, len(ranked_features) + 1)),
-190        },
-191    )
+            
    154def rank_features_3MR(
+155    relevance_dict: dict[str, float],
+156    redundancy_dict: dict[tuple[Any, Any], Any],
+157    relational_dict: dict[tuple[Any, Any], Any],
+158    strategy: str = 'median',
+159    alpha: float = 1,
+160    beta: float = 1,
+161) -> pd.DataFrame:
+162    all_features = relevance_dict.keys()
+163    most_important_feature = max(
+164        relevance_dict.items(), key=operator.itemgetter(1),
+165    )[0]
+166    ranked_features = [most_important_feature]
+167
+168    def calc_higher_order(feature, is_redundancy=True):
+169        values = []
+170        for feat in ranked_features:
+171            interaction_tuple = (feat, feature)
+172            if is_redundancy:
+173                if interaction_tuple in redundancy_dict:
+174                    values.append(redundancy_dict[interaction_tuple])
+175                else:
+176                    logging.info('Not accounting for redundancy tuple {} - please increase the --combination_number_upper_bound for beter coverage of interactions/redundancies.')
+177            else:
+178                if interaction_tuple in relational_dict:
+179                    values.append(relational_dict[interaction_tuple])
+180                else:
+181                    logging.info('Not accounting for interaction tuple {} - please increase the --combination_number_upper_bound for beter coverage of interactions/redundancies.')
+182
+183        if strategy == 'sum':
+184            return sum(values)
+185        if strategy == 'mean':
+186            return np.mean(values)
+187        return np.median(values)
+188
+189    while len(ranked_features) != len(all_features):
+190        top_importance = 0
+191        most_important_feature = ''
+192
+193        for ind, feat in enumerate(set(all_features) - set(ranked_features)):
+194            feature_redundancy = calc_higher_order(feat)
+195            feature_relation = calc_higher_order(feat, False)
+196            feature_relevance = relevance_dict[feat]
+197            importance = (
+198                feature_relevance - alpha * feature_redundancy + beta * feature_relation
+199            )
+200
+201            if (importance > top_importance) or (ind == 0):
+202                top_importance = importance
+203                most_important_feature = feat
+204        ranked_features.append(most_important_feature)
+205    return pd.DataFrame(
+206        {
+207            'Feature': ranked_features,
+208            '3mr_ranking': list(range(1, len(ranked_features) + 1)),
+209        },
+210    )
 
    
 
 
@@ -539,10 +588,10 @@ 
    
 
     
    
     
-            
    194def get_importances_estimate_nonmyopic(args: Any, tmp_df: pd.DataFrame):
-195    # TODO - nonmyopic algorithms - tmp_df \ args.label vs. label
-196    # TODO - this is to be executed directly on df - no need for parallel kernel(s)
-197    pass
+            
    213def get_importances_estimate_nonmyopic(args: Any, tmp_df: pd.DataFrame):
+214    # TODO - nonmyopic algorithms - tmp_df \ args.label vs. label
+215    # TODO - this is to be executed directly on df - no need for parallel kernel(s)
+216    pass
 
    
 
 
diff --git a/docs/outrank/core_ranking.html b/docs/outrank/core_ranking.html
index ebded07..35caebf 100644
--- a/docs/outrank/core_ranking.html
+++ b/docs/outrank/core_ranking.html
@@ -194,9 +194,9 @@ 
    
  72
  73    # Handle cont. types prior to interaction evaluation
  74    pbar.set_description('Encoding columns')
- 75    col_dots = '.'
- 76    start_enc_timer = timer()
- 77    tmp_df = pd.DataFrame({k : tmp_df[k].cat.codes for k in all_columns})
+ 75    start_enc_timer = timer()
+ 76    tmp_df = pd.DataFrame({k : tmp_df[k].cat.codes for k in all_columns})
+ 77
  78    end_enc_timer = timer()
  79    out_time_struct['encoding_columns'] = end_enc_timer - start_enc_timer
  80
@@ -610,7 +610,7 @@ 
    
 488
 489        focus_set.add(args.label_column)
 490        focus_set = {x for x in focus_set if x in input_dataframe.columns}
-491        input_dataframe = input_dataframe[focus_set]
+491        input_dataframe = input_dataframe[list(focus_set)]
 492
 493    if args.transformers != 'none':
 494        pbar.set_description('Adding transformations')
@@ -986,9 +986,9 @@ 
    
  73
  74    # Handle cont. types prior to interaction evaluation
  75    pbar.set_description('Encoding columns')
- 76    col_dots = '.'
- 77    start_enc_timer = timer()
- 78    tmp_df = pd.DataFrame({k : tmp_df[k].cat.codes for k in all_columns})
+ 76    start_enc_timer = timer()
+ 77    tmp_df = pd.DataFrame({k : tmp_df[k].cat.codes for k in all_columns})
+ 78
  79    end_enc_timer = timer()
  80    out_time_struct['encoding_columns'] = end_enc_timer - start_enc_timer
  81
@@ -1589,7 +1589,7 @@ 
    
 489
 490        focus_set.add(args.label_column)
 491        focus_set = {x for x in focus_set if x in input_dataframe.columns}
-492        input_dataframe = input_dataframe[focus_set]
+492        input_dataframe = input_dataframe[list(focus_set)]
 493
 494    if args.transformers != 'none':
 495        pbar.set_description('Adding transformations')
diff --git a/docs/outrank/core_utils.html b/docs/outrank/core_utils.html
index 7d47cd0..dc6d242 100644
--- a/docs/outrank/core_utils.html
+++ b/docs/outrank/core_utils.html
@@ -379,458 +379,462 @@ 
    
 182
 183    # Hash multi-value tuples and store name-val mappings
 184    for remaining_part in remainder:
-185        core_parts = remaining_part.split(' ')
+185        core_parts = remaining_part.strip().split(' ')
 186        namespace_part = core_parts[0]
 187        other_parts = '-'.join(x for x in core_parts[1:] if x != '')
-188        if namespace_part in fw_col_mapping:
-189            remainder_hash[fw_col_mapping[namespace_part]] = other_parts
-190
-191    # Construct the consistently-mapped instance based on the remainder mapping
-192    the_real_instance = [
-193        remainder_hash.get(
-194            el, None,
-195        ) for el in table_header[1:]
-196    ]
-197    if not include_namespace_info:
-198        the_real_instance = [
-199            x[2:] if not x is None else None for x in the_real_instance
-200        ]
-201
-202    parts = [label] + the_real_instance
-203    return parts
+188
+189        if namespace_part in fw_col_mapping:
+190            remainder_hash[fw_col_mapping[namespace_part]] = other_parts
+191        else:
+192            logging.error(f"Didn't find namespace {namespace_part}")
+193
+194    # Construct the consistently-mapped instance based on the remainder mapping
+195    the_real_instance = [
+196        remainder_hash.get(
+197            el, None,
+198        ) for el in table_header[1:]
+199    ]
+200    if not include_namespace_info:
+201        the_real_instance = [
+202            x[2:] if not x is None else None for x in the_real_instance
+203        ]
 204
-205
-206def parse_ob_csv_line(
-207    line_string: str, delimiter: str = ',', args: Any = None,
-208) -> list[str]:
-209    """Data can have commas within JSON field dumps"""
-210
-211    clx = list(csv.reader([line_string])).pop()
-212    return clx
-213
+205    parts = [label] + the_real_instance
+206
+207    return parts
+208
+209
+210def parse_ob_csv_line(
+211    line_string: str, delimiter: str = ',', args: Any = None,
+212) -> list[str]:
+213    """Data can have commas within JSON field dumps"""
 214
-215def generic_line_parser(
-216    line_string: str,
-217    delimiter: str,
-218    args: Any = None,
-219    fw_col_mapping: Any = None,
-220    table_header: Any = None,
-221) -> list[Any]:
-222    """A generic method aimed to parse data from different sources."""
-223
-224    if args.data_source == 'ob-raw-dump':
-225        return parse_ob_line(line_string, delimiter, args)
-226
-227    elif args.data_source == 'ob-vw':
-228        return parse_ob_line_vw(
-229            line_string, delimiter, args, fw_col_mapping, table_header,
-230        )
-231
-232    elif args.data_source == 'ob-csv' or args.data_source == 'csv-raw':
-233        return parse_ob_csv_line(line_string, delimiter, args)
-234
-235    else:
-236        raise NotImplementedError(
-237            'Please, specify a valid --data_source argument!',
-238        )
-239
-240
-241def read_reference_json(json_path) -> dict[str, dict]:
-242    """A helper method for reading a JSON"""
-243    with open(json_path) as jp:
-244        return json.load(jp)
-245
-246
-247def parse_namespace(namespace_path: str) -> tuple[set[str], dict[str, str]]:
-248    """Parse the feature namespace for type awareness"""
+215    clx = list(csv.reader([line_string])).pop()
+216    return clx
+217
+218
+219def generic_line_parser(
+220    line_string: str,
+221    delimiter: str,
+222    args: Any = None,
+223    fw_col_mapping: Any = None,
+224    table_header: Any = None,
+225) -> list[Any]:
+226    """A generic method aimed to parse data from different sources."""
+227
+228    if args.data_source == 'ob-raw-dump':
+229        return parse_ob_line(line_string, delimiter, args)
+230
+231    elif args.data_source == 'ob-vw':
+232        return parse_ob_line_vw(
+233            line_string, delimiter, args, fw_col_mapping, table_header,
+234        )
+235
+236    elif args.data_source == 'ob-csv' or args.data_source == 'csv-raw':
+237        return parse_ob_csv_line(line_string, delimiter, args)
+238
+239    else:
+240        raise NotImplementedError(
+241            'Please, specify a valid --data_source argument!',
+242        )
+243
+244
+245def read_reference_json(json_path) -> dict[str, dict]:
+246    """A helper method for reading a JSON"""
+247    with open(json_path) as jp:
+248        return json.load(jp)
 249
-250    float_set = set()
-251    id_feature_map = {}
-252
-253    with open(namespace_path) as nm:
-254        for line in nm:
-255            try:
-256                namespace_parts = line.strip().split(',')
-257                if len(namespace_parts) == 2 and '_' not in namespace_parts[0]:
-258                    fw_id, feature = namespace_parts
-259                    type_name = 'generic'
-260
-261                else:
-262                    fw_id, feature, type_name = namespace_parts
-263
-264                id_feature_map[fw_id] = feature
-265                if type_name == 'f32':
-266                    float_set.add(feature)
-267            except Exception as es:
-268                logging.error(f'\U0001F631 {es} -- {namespace_parts}')
-269
-270    return float_set, id_feature_map
-271
-272
-273def read_column_names(mapping_file: str) -> list[str]:
-274    """Read the col. header"""
+250
+251def parse_namespace(namespace_path: str) -> tuple[set[str], dict[str, str]]:
+252    """Parse the feature namespace for type awareness"""
+253
+254    float_set = set()
+255    id_feature_map = {}
+256
+257    with open(namespace_path) as nm:
+258        for line in nm:
+259            try:
+260                namespace_parts = line.strip().split(',')
+261                if len(namespace_parts) == 2 and '_' not in namespace_parts[0]:
+262                    fw_id, feature = namespace_parts
+263                    type_name = 'generic'
+264
+265                else:
+266                    fw_id, feature, type_name = namespace_parts
+267
+268                id_feature_map[fw_id] = feature
+269                if type_name == 'f32':
+270                    float_set.add(feature)
+271            except Exception as es:
+272                logging.error(f'\U0001F631 {es} -- {namespace_parts}')
+273
+274    return float_set, id_feature_map
 275
-276    with open(mapping_file, encoding='utf-8') as mf:
-277        columns = mf.read().strip().split('\t')
-278    return columns
+276
+277def read_column_names(mapping_file: str) -> list[str]:
+278    """Read the col. header"""
 279
-280
-281def parse_ob_vw_feature_information(data_path) -> DatasetInformationStorage:
-282    """A generic parser of ob-based data"""
+280    with open(mapping_file, encoding='utf-8') as mf:
+281        columns = mf.read().strip().split('\t')
+282    return columns
 283
-284    # Get column names
-285    column_descriptions = os.path.join(data_path, 'vw_namespace_map.csv')
-286    column_types, fw_map = parse_namespace(column_descriptions)
+284
+285def parse_ob_vw_feature_information(data_path) -> DatasetInformationStorage:
+286    """A generic parser of ob-based data"""
 287
-288    # We establish column order here
-289    column_names = ['label'] + list(fw_map.values())
-290
-291    data_path = os.path.join(data_path, 'data.vw.gz')
-292    col_delimiter = None
-293    encoding = 'utf-8'
+288    # Get column names
+289    column_descriptions = os.path.join(data_path, 'vw_namespace_map.csv')
+290    column_types, fw_map = parse_namespace(column_descriptions)
+291
+292    # We establish column order here
+293    column_names = ['label'] + list(fw_map.values())
 294
-295    return DatasetInformationStorage(
-296        data_path, column_names, column_types, col_delimiter, encoding, fw_map,
-297    )
+295    data_path = os.path.join(data_path, 'data.vw.gz')
+296    col_delimiter = None
+297    encoding = 'utf-8'
 298
-299
-300def parse_ob_raw_feature_information(data_path) -> DatasetInformationStorage:
-301    """A generic parser of ob-based data"""
+299    return DatasetInformationStorage(
+300        data_path, column_names, column_types, col_delimiter, encoding, fw_map,
+301    )
 302
-303    # Get column names
-304    column_types: list[str] = []
-305
-306    # Get set of numeric columns
-307    table_header_path = os.path.join(data_path, 'raw_data/0_header/header.csv')
-308    table_header = read_column_names(table_header_path)
+303
+304def parse_ob_raw_feature_information(data_path) -> DatasetInformationStorage:
+305    """A generic parser of ob-based data"""
+306
+307    # Get column names
+308    column_types: list[str] = []
 309
-310    data_path_train = os.path.join(data_path, 'raw_data/1_train/*')
-311    col_delimiter = '\t'
-312    encoding = 'utf-8'
+310    # Get set of numeric columns
+311    table_header_path = os.path.join(data_path, 'raw_data/0_header/header.csv')
+312    table_header = read_column_names(table_header_path)
 313
-314    final_df = []
-315    core_data_folders = glob.glob(data_path_train)
-316    for actual_data in core_data_folders:
-317        for dump in glob.glob(actual_data + '/*'):
-318            tmp_df = pd.read_csv(
-319                dump, sep='\t', low_memory=True, dtype='object',
-320            )
-321            assert tmp_df.shape[1] == len(table_header)
-322            tmp_df.columns = table_header
-323            final_df.append(tmp_df)
-324
-325    final_df_concat = pd.concat(final_df, axis=0)
-326    final_path = os.path.join(data_path, 'raw_dump.tsv')
-327    logging.info(
-328        f'Stored data dump of dimension {final_df_concat.shape} to {final_path}',
-329    )
-330    final_df_concat.to_csv(final_path, sep='\t', index=False)
-331    data_path = os.path.join(data_path, 'raw_dump.tsv')
-332
-333    return DatasetInformationStorage(
-334        data_path, table_header, set(column_types), col_delimiter, encoding, None,
-335    )
+314    data_path_train = os.path.join(data_path, 'raw_data/1_train/*')
+315    col_delimiter = '\t'
+316    encoding = 'utf-8'
+317
+318    final_df = []
+319    core_data_folders = glob.glob(data_path_train)
+320    for actual_data in core_data_folders:
+321        for dump in glob.glob(actual_data + '/*'):
+322            tmp_df = pd.read_csv(
+323                dump, sep='\t', low_memory=True, dtype='object',
+324            )
+325            assert tmp_df.shape[1] == len(table_header)
+326            tmp_df.columns = table_header
+327            final_df.append(tmp_df)
+328
+329    final_df_concat = pd.concat(final_df, axis=0)
+330    final_path = os.path.join(data_path, 'raw_dump.tsv')
+331    logging.info(
+332        f'Stored data dump of dimension {final_df_concat.shape} to {final_path}',
+333    )
+334    final_df_concat.to_csv(final_path, sep='\t', index=False)
+335    data_path = os.path.join(data_path, 'raw_dump.tsv')
 336
-337
-338def parse_ob_feature_information(data_path) -> DatasetInformationStorage:
-339    """A generic parser of ob-based data"""
+337    return DatasetInformationStorage(
+338        data_path, table_header, set(column_types), col_delimiter, encoding, None,
+339    )
 340
-341    # Get column names
-342    column_names = os.path.join(data_path, 'vw_namespace_map.csv')
-343    column_types, _ = parse_namespace(column_names)
+341
+342def parse_ob_feature_information(data_path) -> DatasetInformationStorage:
+343    """A generic parser of ob-based data"""
 344
-345    # Get set of numeric columns
-346    table_header_path = os.path.join(data_path, 'raw_data/0_header/header.csv')
-347    table_header = read_column_names(table_header_path)
+345    # Get column names
+346    column_names = os.path.join(data_path, 'vw_namespace_map.csv')
+347    column_types, _ = parse_namespace(column_names)
 348
-349    data_path = os.path.join(data_path, 'raw_data/1_train/*')
-350    col_delimiter = '\t'
-351    encoding = 'utf-8'
+349    # Get set of numeric columns
+350    table_header_path = os.path.join(data_path, 'raw_data/0_header/header.csv')
+351    table_header = read_column_names(table_header_path)
 352
-353    return DatasetInformationStorage(
-354        data_path, table_header, column_types, col_delimiter, encoding, None,
-355    )
+353    data_path = os.path.join(data_path, 'raw_data/1_train/*')
+354    col_delimiter = '\t'
+355    encoding = 'utf-8'
 356
-357
-358def parse_csv_with_description_information(data_path) -> DatasetInformationStorage:
-359    dataset_description = read_reference_json(
-360        os.path.join(data_path, 'dataset_desc.json'),
-361    )
-362    column_names = []
-363    column_types = set()
-364    for feature in dataset_description.get('data_features', []):
-365        feature_name = feature.get('name')
-366        column_names.append(feature_name)
-367        feature_type = feature.get('type', '')
-368        if 'float' in feature_type or 'Float' in feature_type:
-369            column_types.add(feature_name)
-370    col_delimiter = ','
-371    data_path = os.path.join(data_path, 'data.csv')
-372    encoding = 'latin1'
-373    return DatasetInformationStorage(
-374        data_path, column_names, column_types, col_delimiter, encoding, None,
-375    )
-376
-377
-378def parse_csv_raw(data_path) -> DatasetInformationStorage:
-379    column_types: set[str] = set()
+357    return DatasetInformationStorage(
+358        data_path, table_header, column_types, col_delimiter, encoding, None,
+359    )
+360
+361
+362def parse_csv_with_description_information(data_path) -> DatasetInformationStorage:
+363    dataset_description = read_reference_json(
+364        os.path.join(data_path, 'dataset_desc.json'),
+365    )
+366    column_names = []
+367    column_types = set()
+368    for feature in dataset_description.get('data_features', []):
+369        feature_name = feature.get('name')
+370        column_names.append(feature_name)
+371        feature_type = feature.get('type', '')
+372        if 'float' in feature_type or 'Float' in feature_type:
+373            column_types.add(feature_name)
+374    col_delimiter = ','
+375    data_path = os.path.join(data_path, 'data.csv')
+376    encoding = 'latin1'
+377    return DatasetInformationStorage(
+378        data_path, column_names, column_types, col_delimiter, encoding, None,
+379    )
 380
-381    data_path = os.path.join(data_path, 'data.csv')
-382    with open(data_path) as inp_data:
-383        header = inp_data.readline()
-384    col_delimiter = ','
-385    column_names = header.strip().split(col_delimiter)
-386    encoding = 'latin1'
-387    return DatasetInformationStorage(
-388        data_path, column_names, column_types, col_delimiter, encoding, None,
-389    )
-390
-391
-392def extract_features_from_reference_JSON(json_path: str) -> set[Any]:
-393    """Given a model's JSON, extract unique features"""
+381
+382def parse_csv_raw(data_path) -> DatasetInformationStorage:
+383    column_types: set[str] = set()
+384
+385    data_path = os.path.join(data_path, 'data.csv')
+386    with open(data_path) as inp_data:
+387        header = inp_data.readline()
+388    col_delimiter = ','
+389    column_names = header.strip().split(col_delimiter)
+390    encoding = 'latin1'
+391    return DatasetInformationStorage(
+392        data_path, column_names, column_types, col_delimiter, encoding, None,
+393    )
 394
-395    with open(json_path) as jp:
-396        content = json.load(jp)
-397
-398    unique_features = set()
-399    feature_space = content['desc'].get('features', [])
-400    fields_space = content['desc'].get('fields', [])
-401    joint_space = feature_space + fields_space
-402
-403    for feature_tuple in joint_space:
-404        for individual_feature in feature_tuple.split(','):
-405            unique_features.add(individual_feature)
+395
+396def extract_features_from_reference_JSON(json_path: str) -> set[Any]:
+397    """Given a model's JSON, extract unique features"""
+398
+399    with open(json_path) as jp:
+400        content = json.load(jp)
+401
+402    unique_features = set()
+403    feature_space = content['desc'].get('features', [])
+404    fields_space = content['desc'].get('fields', [])
+405    joint_space = feature_space + fields_space
 406
-407    return unique_features
-408
-409
-410def summarize_feature_bounds_for_transformers(
-411    bounds_object_storage: Any,
-412    feature_types: list[str],
-413    task_name: str,
-414    label_name: str,
-415    granularity: int = 15,
-416    output_summary_table_only: bool = False,
-417):
-418    """summarization auxilliary method for generating JSON-based specs"""
-419
-420    if bounds_object_storage is None:
-421        logging.info('Bounds storage object is empty.')
-422        exit()
+407    for feature_tuple in joint_space:
+408        for individual_feature in feature_tuple.split(','):
+409            unique_features.add(individual_feature)
+410
+411    return unique_features
+412
+413
+414def summarize_feature_bounds_for_transformers(
+415    bounds_object_storage: Any,
+416    feature_types: list[str],
+417    task_name: str,
+418    label_name: str,
+419    granularity: int = 15,
+420    output_summary_table_only: bool = False,
+421):
+422    """summarization auxilliary method for generating JSON-based specs"""
 423
-424    final_storage = defaultdict(list)
-425    for el in bounds_object_storage:
-426        if isinstance(el, dict):
-427            for k, v in el.items():
-428                final_storage[k].append(v)
-429
-430    summary_table_rows = []
-431    for k, v in final_storage.items():
-432        # Conduct local aggregation + bound changes
-433        if k in feature_types and k != label_name:
-434            minima, maxima, medians, uniques = [], [], [], []
-435            for feature_summary in v:
-436                minima.append(feature_summary.minimum)
-437                maxima.append(feature_summary.maximum)
-438                medians.append(feature_summary.median)
-439                uniques.append(feature_summary.num_unique)
-440            summary_table_rows.append(
-441                [
-442                    k,
-443                    round(np.min(minima), 2),
-444                    round(np.max(maxima), 2),
-445                    round(np.median(medians), 2),
-446                    int(np.mean(uniques)),
-447                ],
-448            )
-449
-450    if len(summary_table_rows) == 0:
-451        logging.info('No numeric features to summarize.')
-452        return None
+424    if bounds_object_storage is None:
+425        logging.info('Bounds storage object is empty.')
+426        exit()
+427
+428    final_storage = defaultdict(list)
+429    for el in bounds_object_storage:
+430        if isinstance(el, dict):
+431            for k, v in el.items():
+432                final_storage[k].append(v)
+433
+434    summary_table_rows = []
+435    for k, v in final_storage.items():
+436        # Conduct local aggregation + bound changes
+437        if k in feature_types and k != label_name:
+438            minima, maxima, medians, uniques = [], [], [], []
+439            for feature_summary in v:
+440                minima.append(feature_summary.minimum)
+441                maxima.append(feature_summary.maximum)
+442                medians.append(feature_summary.median)
+443                uniques.append(feature_summary.num_unique)
+444            summary_table_rows.append(
+445                [
+446                    k,
+447                    round(np.min(minima), 2),
+448                    round(np.max(maxima), 2),
+449                    round(np.median(medians), 2),
+450                    int(np.mean(uniques)),
+451                ],
+452            )
 453
-454    summary_table: pd.Dataframe = pd.DataFrame(summary_table_rows)
-455    summary_table.columns = [
-456        'Feature',
-457        'Minimum',
-458        'Maximum',
-459        'Median',
-460        'Num avg. unique (batch)',
-461    ]
-462
-463    if output_summary_table_only:
-464        return summary_table
-465
-466    if len(summary_table) == 0:
-467        logging.info('Summary table empty, skipping transformer generation ..')
-468        return
+454    if len(summary_table_rows) == 0:
+455        logging.info('No numeric features to summarize.')
+456        return None
+457
+458    summary_table: pd.Dataframe = pd.DataFrame(summary_table_rows)
+459    summary_table.columns = [
+460        'Feature',
+461        'Minimum',
+462        'Maximum',
+463        'Median',
+464        'Num avg. unique (batch)',
+465    ]
+466
+467    if output_summary_table_only:
+468        return summary_table
 469
-470    if task_name == 'feature_summary_transformers':
-471        transformers_per_feature = defaultdict(list)
-472
-473        # Take care of weights first -> range is pre-defined
-474        for k, v in final_storage.items():
-475            if label_name in k or 'dummy' in k:
-476                continue
-477
-478            weight_template = {
-479                'feature': k,
-480                'src_features': [k],
-481                'transformations': ['Weight'],
-482                'weights': [0, 0.5, 1.5, 2, 3, 10],
-483            }
-484            transformers_per_feature[k].append(weight_template)
-485
-486        # Consider numeric transformations - pairs and single ones
-487        for enx, row in summary_table.iterrows():
-488            if row.Feature == 'dummy':
-489                continue
-490            try:
-491                actual_range = (
-492                    np.arange(
-493                        row['Minimum'],
-494                        row['Maximum'],
-495                        (row['Maximum'] - row['Minimum']) / granularity,
-496                    )
-497                    .round(2)
-498                    .tolist()
-499                )
-500                binner_template = {
-501                    'feature': f'{row.Feature}',
-502                    'src_features': [row.Feature],
-503                    'transformations': [
-504                        'BinnerSqrt',
-505                        'BinnerLog',
-506                        'BinnerSqrtPlain',
-507                        'BinnerLogPlain',
-508                    ],
-509                    'n': actual_range,
-510                    'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
-511                }
-512
-513            except Exception as es:
-514                logging.info(
-515                    f'\U0001F631 Encountered {es}. The problematic feature is: {row}, skipping transformer for this feature ..',
-516                )
-517
-518            transformers_per_feature[row.Feature].append(binner_template)
-519
-520            # We want the full loop here, due to asymmetry of transformation(s)
-521            for enx_second, row_second in summary_table.iterrows():
-522                if enx_second < enx:
-523                    continue
-524
-525                # The n values are defined based on maxima of the second feature
-526                if row_second.Feature != row.Feature:
-527                    n_bound = round(row_second['Median'] + row['Median'], 2)
-528                    max_bound = round(
-529                        min(row_second['Maximum'], row['Maximum']), 2,
-530                    )
-531                    min_bound = round(
-532                        row_second['Minimum'] + row['Minimum'], 2,
-533                    )
-534                    range_spectrum = sorted(
-535                        list(
-536                            {
-537                                0.0,
-538                                min_bound,
-539                                n_bound / 10,
-540                                n_bound / 5,
-541                                n_bound,
-542                                max_bound,
-543                            },
-544                        ),
-545                    )
-546
-547                    range_spectrum = [x for x in range_spectrum if x >= 0]
-548                    binner_pair_template = {
-549                        'feature': f'{row.Feature}Ratio{row_second.Feature}',
-550                        'src_features': [row.Feature, row_second.Feature],
-551                        'transformations': ['BinnerLogRatioPlain', 'BinnerLogRatio'],
-552                        'n': range_spectrum,
-553                        'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
-554                    }
-555
-556                    binner_pair_template_second = {
-557                        'feature': f'{row_second.Feature}Ratio{row.Feature}',
-558                        'src_features': [row_second.Feature, row.Feature],
-559                        'transformations': ['BinnerLogRatioPlain', 'BinnerLogRatio'],
-560                        'n': range_spectrum,
-561                        'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
-562                    }
-563
-564                    transformers_per_feature[row.Feature].append(
-565                        binner_pair_template,
-566                    )
-567                    transformers_per_feature[row.Feature].append(
-568                        binner_pair_template_second,
-569                    )
-570
-571        binner_templates = []
-572        for k, v in transformers_per_feature.items():
-573            for transformer_struct in v:
-574                binner_templates.append(transformer_struct)
-575
-576        logging.info(
-577            f'Generated {len(binner_templates)} transformation search specifications.\n',
-578        )
-579        namespace_full = f'"random_grid_feature_transform": {json.dumps(binner_templates)}, "random_grid_epochs": 512'
-580        logging.info('Generated transformations below:\n')
-581        print(namespace_full)
-582
-583
-584def summarize_rare_counts(
-585    term_counter: Any,
-586    args: Any,
-587    cardinality_object: Any,
-588    object_info: DatasetInformationStorage,
-589) -> None:
-590    """Write rare values"""
-591
-592    out_df_rows = []
-593    logging.info(
-594        f'Rare value summary (freq <= {args.rare_value_count_upper_bound}) follows ..',
-595    )
-596
-597    for namespace_tuple, count in term_counter.items():
-598        namespace, value = namespace_tuple
-599        out_df_rows.append([namespace, value, count])
-600    out_df: pd.DataFrame = pd.DataFrame(out_df_rows)
-601    out_df.columns = ['Namespace', 'value', 'Count']
-602    out_df.to_csv(
-603        os.path.join(args.output_folder, 'rare_values.tsv'), sep='\t', index=False,
-604    )
-605    logging.info(f'Wrote rare values to {args.output_folder}/rare_values.tsv')
-606
-607    overall_rare_counts = Counter(out_df.Namespace.values)
-608    sorted_counts = sorted(
-609        overall_rare_counts.items(), key=lambda pair: pair[1], reverse=True,
-610    )
-611    for k, v in sorted_counts:
-612        logging.info(f'Namespace: {k} ---- Rare values observed: {v}')
-613
-614    final_df_rows = []
+470    if len(summary_table) == 0:
+471        logging.info('Summary table empty, skipping transformer generation ..')
+472        return
+473
+474    if task_name == 'feature_summary_transformers':
+475        transformers_per_feature = defaultdict(list)
+476
+477        # Take care of weights first -> range is pre-defined
+478        for k, v in final_storage.items():
+479            if label_name in k or 'dummy' in k:
+480                continue
+481
+482            weight_template = {
+483                'feature': k,
+484                'src_features': [k],
+485                'transformations': ['Weight'],
+486                'weights': [0, 0.5, 1.5, 2, 3, 10],
+487            }
+488            transformers_per_feature[k].append(weight_template)
+489
+490        # Consider numeric transformations - pairs and single ones
+491        for enx, row in summary_table.iterrows():
+492            if row.Feature == 'dummy':
+493                continue
+494            try:
+495                actual_range = (
+496                    np.arange(
+497                        row['Minimum'],
+498                        row['Maximum'],
+499                        (row['Maximum'] - row['Minimum']) / granularity,
+500                    )
+501                    .round(2)
+502                    .tolist()
+503                )
+504                binner_template = {
+505                    'feature': f'{row.Feature}',
+506                    'src_features': [row.Feature],
+507                    'transformations': [
+508                        'BinnerSqrt',
+509                        'BinnerLog',
+510                        'BinnerSqrtPlain',
+511                        'BinnerLogPlain',
+512                    ],
+513                    'n': actual_range,
+514                    'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
+515                }
+516
+517            except Exception as es:
+518                logging.info(
+519                    f'\U0001F631 Encountered {es}. The problematic feature is: {row}, skipping transformer for this feature ..',
+520                )
+521
+522            transformers_per_feature[row.Feature].append(binner_template)
+523
+524            # We want the full loop here, due to asymmetry of transformation(s)
+525            for enx_second, row_second in summary_table.iterrows():
+526                if enx_second < enx:
+527                    continue
+528
+529                # The n values are defined based on maxima of the second feature
+530                if row_second.Feature != row.Feature:
+531                    n_bound = round(row_second['Median'] + row['Median'], 2)
+532                    max_bound = round(
+533                        min(row_second['Maximum'], row['Maximum']), 2,
+534                    )
+535                    min_bound = round(
+536                        row_second['Minimum'] + row['Minimum'], 2,
+537                    )
+538                    range_spectrum = sorted(
+539                        list(
+540                            {
+541                                0.0,
+542                                min_bound,
+543                                n_bound / 10,
+544                                n_bound / 5,
+545                                n_bound,
+546                                max_bound,
+547                            },
+548                        ),
+549                    )
+550
+551                    range_spectrum = [x for x in range_spectrum if x >= 0]
+552                    binner_pair_template = {
+553                        'feature': f'{row.Feature}Ratio{row_second.Feature}',
+554                        'src_features': [row.Feature, row_second.Feature],
+555                        'transformations': ['BinnerLogRatioPlain', 'BinnerLogRatio'],
+556                        'n': range_spectrum,
+557                        'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
+558                    }
+559
+560                    binner_pair_template_second = {
+561                        'feature': f'{row_second.Feature}Ratio{row.Feature}',
+562                        'src_features': [row_second.Feature, row.Feature],
+563                        'transformations': ['BinnerLogRatioPlain', 'BinnerLogRatio'],
+564                        'n': range_spectrum,
+565                        'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
+566                    }
+567
+568                    transformers_per_feature[row.Feature].append(
+569                        binner_pair_template,
+570                    )
+571                    transformers_per_feature[row.Feature].append(
+572                        binner_pair_template_second,
+573                    )
+574
+575        binner_templates = []
+576        for k, v in transformers_per_feature.items():
+577            for transformer_struct in v:
+578                binner_templates.append(transformer_struct)
+579
+580        logging.info(
+581            f'Generated {len(binner_templates)} transformation search specifications.\n',
+582        )
+583        namespace_full = f'"random_grid_feature_transform": {json.dumps(binner_templates)}, "random_grid_epochs": 512'
+584        logging.info('Generated transformations below:\n')
+585        print(namespace_full)
+586
+587
+588def summarize_rare_counts(
+589    term_counter: Any,
+590    args: Any,
+591    cardinality_object: Any,
+592    object_info: DatasetInformationStorage,
+593) -> None:
+594    """Write rare values"""
+595
+596    out_df_rows = []
+597    logging.info(
+598        f'Rare value summary (freq <= {args.rare_value_count_upper_bound}) follows ..',
+599    )
+600
+601    for namespace_tuple, count in term_counter.items():
+602        namespace, value = namespace_tuple
+603        out_df_rows.append([namespace, value, count])
+604    out_df: pd.DataFrame = pd.DataFrame(out_df_rows)
+605    out_df.columns = ['Namespace', 'value', 'Count']
+606    out_df.to_csv(
+607        os.path.join(args.output_folder, 'rare_values.tsv'), sep='\t', index=False,
+608    )
+609    logging.info(f'Wrote rare values to {args.output_folder}/rare_values.tsv')
+610
+611    overall_rare_counts = Counter(out_df.Namespace.values)
+612    sorted_counts = sorted(
+613        overall_rare_counts.items(), key=lambda pair: pair[1], reverse=True,
+614    )
 615    for k, v in sorted_counts:
-616        cardinality = len(cardinality_object[k])
-617        rare_proportion = np.round(100 * (v / cardinality), 2)
-618        col_type = 'nominal'
-619        if k in object_info.column_types:
-620            col_type = 'numeric'
-621        final_df_rows.append(
-622            {
-623                'rare_proportion': rare_proportion,
-624                'feature_type': col_type,
-625                'feature_name': k,
-626            },
-627        )
-628
-629    final_df: pd.DataFrame = pd.DataFrame(final_df_rows)
-630    final_df = final_df.sort_values(by=['rare_proportion'])
-631    logging.info(
-632        f'Wrote feature sparsity summary to {args.output_folder}/feature_sparsity_summary.tsv',
-633    )
-634    final_df.to_csv(
-635        f'{args.output_folder}/feature_sparsity_summary.tsv', index=False, sep='\t',
-636    )
+616        logging.info(f'Namespace: {k} ---- Rare values observed: {v}')
+617
+618    final_df_rows = []
+619    for k, v in sorted_counts:
+620        cardinality = len(cardinality_object[k])
+621        rare_proportion = np.round(100 * (v / cardinality), 2)
+622        col_type = 'nominal'
+623        if k in object_info.column_types:
+624            col_type = 'numeric'
+625        final_df_rows.append(
+626            {
+627                'rare_proportion': rare_proportion,
+628                'feature_type': col_type,
+629                'feature_name': k,
+630            },
+631        )
+632
+633    final_df: pd.DataFrame = pd.DataFrame(final_df_rows)
+634    final_df = final_df.sort_values(by=['rare_proportion'])
+635    logging.info(
+636        f'Wrote feature sparsity summary to {args.output_folder}/feature_sparsity_summary.tsv',
+637    )
+638    final_df.to_csv(
+639        f'{args.output_folder}/feature_sparsity_summary.tsv', index=False, sep='\t',
+640    )
 
    
 
 
@@ -1378,25 +1382,29 @@ 
    
 183
 184    # Hash multi-value tuples and store name-val mappings
 185    for remaining_part in remainder:
-186        core_parts = remaining_part.split(' ')
+186        core_parts = remaining_part.strip().split(' ')
 187        namespace_part = core_parts[0]
 188        other_parts = '-'.join(x for x in core_parts[1:] if x != '')
-189        if namespace_part in fw_col_mapping:
-190            remainder_hash[fw_col_mapping[namespace_part]] = other_parts
-191
-192    # Construct the consistently-mapped instance based on the remainder mapping
-193    the_real_instance = [
-194        remainder_hash.get(
-195            el, None,
-196        ) for el in table_header[1:]
-197    ]
-198    if not include_namespace_info:
-199        the_real_instance = [
-200            x[2:] if not x is None else None for x in the_real_instance
-201        ]
-202
-203    parts = [label] + the_real_instance
-204    return parts
+189
+190        if namespace_part in fw_col_mapping:
+191            remainder_hash[fw_col_mapping[namespace_part]] = other_parts
+192        else:
+193            logging.error(f"Didn't find namespace {namespace_part}")
+194
+195    # Construct the consistently-mapped instance based on the remainder mapping
+196    the_real_instance = [
+197        remainder_hash.get(
+198            el, None,
+199        ) for el in table_header[1:]
+200    ]
+201    if not include_namespace_info:
+202        the_real_instance = [
+203            x[2:] if not x is None else None for x in the_real_instance
+204        ]
+205
+206    parts = [label] + the_real_instance
+207
+208    return parts
@@ -1416,13 +1424,13 @@

- 
207def parse_ob_csv_line(
-208    line_string: str, delimiter: str = ',', args: Any = None,
-209) -> list[str]:
-210    """Data can have commas within JSON field dumps"""
-211
-212    clx = list(csv.reader([line_string])).pop()
-213    return clx
+            
211def parse_ob_csv_line(
+212    line_string: str, delimiter: str = ',', args: Any = None,
+213) -> list[str]:
+214    """Data can have commas within JSON field dumps"""
+215
+216    clx = list(csv.reader([line_string])).pop()
+217    return clx
 

 
 
@@ -1442,30 +1450,30 @@
- 
216def generic_line_parser(
-217    line_string: str,
-218    delimiter: str,
-219    args: Any = None,
-220    fw_col_mapping: Any = None,
-221    table_header: Any = None,
-222) -> list[Any]:
-223    """A generic method aimed to parse data from different sources."""
-224
-225    if args.data_source == 'ob-raw-dump':
-226        return parse_ob_line(line_string, delimiter, args)
-227
-228    elif args.data_source == 'ob-vw':
-229        return parse_ob_line_vw(
-230            line_string, delimiter, args, fw_col_mapping, table_header,
-231        )
-232
-233    elif args.data_source == 'ob-csv' or args.data_source == 'csv-raw':
-234        return parse_ob_csv_line(line_string, delimiter, args)
-235
-236    else:
-237        raise NotImplementedError(
-238            'Please, specify a valid --data_source argument!',
-239        )
+            
220def generic_line_parser(
+221    line_string: str,
+222    delimiter: str,
+223    args: Any = None,
+224    fw_col_mapping: Any = None,
+225    table_header: Any = None,
+226) -> list[Any]:
+227    """A generic method aimed to parse data from different sources."""
+228
+229    if args.data_source == 'ob-raw-dump':
+230        return parse_ob_line(line_string, delimiter, args)
+231
+232    elif args.data_source == 'ob-vw':
+233        return parse_ob_line_vw(
+234            line_string, delimiter, args, fw_col_mapping, table_header,
+235        )
+236
+237    elif args.data_source == 'ob-csv' or args.data_source == 'csv-raw':
+238        return parse_ob_csv_line(line_string, delimiter, args)
+239
+240    else:
+241        raise NotImplementedError(
+242            'Please, specify a valid --data_source argument!',
+243        )
 

 
 
@@ -1485,10 +1493,10 @@
- 
242def read_reference_json(json_path) -> dict[str, dict]:
-243    """A helper method for reading a JSON"""
-244    with open(json_path) as jp:
-245        return json.load(jp)
+            
246def read_reference_json(json_path) -> dict[str, dict]:
+247    """A helper method for reading a JSON"""
+248    with open(json_path) as jp:
+249        return json.load(jp)
 

 
 
@@ -1508,30 +1516,30 @@
- 
248def parse_namespace(namespace_path: str) -> tuple[set[str], dict[str, str]]:
-249    """Parse the feature namespace for type awareness"""
-250
-251    float_set = set()
-252    id_feature_map = {}
-253
-254    with open(namespace_path) as nm:
-255        for line in nm:
-256            try:
-257                namespace_parts = line.strip().split(',')
-258                if len(namespace_parts) == 2 and '_' not in namespace_parts[0]:
-259                    fw_id, feature = namespace_parts
-260                    type_name = 'generic'
-261
-262                else:
-263                    fw_id, feature, type_name = namespace_parts
-264
-265                id_feature_map[fw_id] = feature
-266                if type_name == 'f32':
-267                    float_set.add(feature)
-268            except Exception as es:
-269                logging.error(f'\U0001F631 {es} -- {namespace_parts}')
-270
-271    return float_set, id_feature_map
+            
252def parse_namespace(namespace_path: str) -> tuple[set[str], dict[str, str]]:
+253    """Parse the feature namespace for type awareness"""
+254
+255    float_set = set()
+256    id_feature_map = {}
+257
+258    with open(namespace_path) as nm:
+259        for line in nm:
+260            try:
+261                namespace_parts = line.strip().split(',')
+262                if len(namespace_parts) == 2 and '_' not in namespace_parts[0]:
+263                    fw_id, feature = namespace_parts
+264                    type_name = 'generic'
+265
+266                else:
+267                    fw_id, feature, type_name = namespace_parts
+268
+269                id_feature_map[fw_id] = feature
+270                if type_name == 'f32':
+271                    float_set.add(feature)
+272            except Exception as es:
+273                logging.error(f'\U0001F631 {es} -- {namespace_parts}')
+274
+275    return float_set, id_feature_map
 

 
 
@@ -1551,12 +1559,12 @@
- 
274def read_column_names(mapping_file: str) -> list[str]:
-275    """Read the col. header"""
-276
-277    with open(mapping_file, encoding='utf-8') as mf:
-278        columns = mf.read().strip().split('\t')
-279    return columns
+            
278def read_column_names(mapping_file: str) -> list[str]:
+279    """Read the col. header"""
+280
+281    with open(mapping_file, encoding='utf-8') as mf:
+282        columns = mf.read().strip().split('\t')
+283    return columns
 

 
 
@@ -1576,23 +1584,23 @@
- 
282def parse_ob_vw_feature_information(data_path) -> DatasetInformationStorage:
-283    """A generic parser of ob-based data"""
-284
-285    # Get column names
-286    column_descriptions = os.path.join(data_path, 'vw_namespace_map.csv')
-287    column_types, fw_map = parse_namespace(column_descriptions)
+            
286def parse_ob_vw_feature_information(data_path) -> DatasetInformationStorage:
+287    """A generic parser of ob-based data"""
 288
-289    # We establish column order here
-290    column_names = ['label'] + list(fw_map.values())
-291
-292    data_path = os.path.join(data_path, 'data.vw.gz')
-293    col_delimiter = None
-294    encoding = 'utf-8'
+289    # Get column names
+290    column_descriptions = os.path.join(data_path, 'vw_namespace_map.csv')
+291    column_types, fw_map = parse_namespace(column_descriptions)
+292
+293    # We establish column order here
+294    column_names = ['label'] + list(fw_map.values())
 295
-296    return DatasetInformationStorage(
-297        data_path, column_names, column_types, col_delimiter, encoding, fw_map,
-298    )
+296    data_path = os.path.join(data_path, 'data.vw.gz')
+297    col_delimiter = None
+298    encoding = 'utf-8'
+299
+300    return DatasetInformationStorage(
+301        data_path, column_names, column_types, col_delimiter, encoding, fw_map,
+302    )
 

 
 
@@ -1612,42 +1620,42 @@
- 
301def parse_ob_raw_feature_information(data_path) -> DatasetInformationStorage:
-302    """A generic parser of ob-based data"""
-303
-304    # Get column names
-305    column_types: list[str] = []
-306
-307    # Get set of numeric columns
-308    table_header_path = os.path.join(data_path, 'raw_data/0_header/header.csv')
-309    table_header = read_column_names(table_header_path)
+            
305def parse_ob_raw_feature_information(data_path) -> DatasetInformationStorage:
+306    """A generic parser of ob-based data"""
+307
+308    # Get column names
+309    column_types: list[str] = []
 310
-311    data_path_train = os.path.join(data_path, 'raw_data/1_train/*')
-312    col_delimiter = '\t'
-313    encoding = 'utf-8'
+311    # Get set of numeric columns
+312    table_header_path = os.path.join(data_path, 'raw_data/0_header/header.csv')
+313    table_header = read_column_names(table_header_path)
 314
-315    final_df = []
-316    core_data_folders = glob.glob(data_path_train)
-317    for actual_data in core_data_folders:
-318        for dump in glob.glob(actual_data + '/*'):
-319            tmp_df = pd.read_csv(
-320                dump, sep='\t', low_memory=True, dtype='object',
-321            )
-322            assert tmp_df.shape[1] == len(table_header)
-323            tmp_df.columns = table_header
-324            final_df.append(tmp_df)
-325
-326    final_df_concat = pd.concat(final_df, axis=0)
-327    final_path = os.path.join(data_path, 'raw_dump.tsv')
-328    logging.info(
-329        f'Stored data dump of dimension {final_df_concat.shape} to {final_path}',
-330    )
-331    final_df_concat.to_csv(final_path, sep='\t', index=False)
-332    data_path = os.path.join(data_path, 'raw_dump.tsv')
-333
-334    return DatasetInformationStorage(
-335        data_path, table_header, set(column_types), col_delimiter, encoding, None,
-336    )
+315    data_path_train = os.path.join(data_path, 'raw_data/1_train/*')
+316    col_delimiter = '\t'
+317    encoding = 'utf-8'
+318
+319    final_df = []
+320    core_data_folders = glob.glob(data_path_train)
+321    for actual_data in core_data_folders:
+322        for dump in glob.glob(actual_data + '/*'):
+323            tmp_df = pd.read_csv(
+324                dump, sep='\t', low_memory=True, dtype='object',
+325            )
+326            assert tmp_df.shape[1] == len(table_header)
+327            tmp_df.columns = table_header
+328            final_df.append(tmp_df)
+329
+330    final_df_concat = pd.concat(final_df, axis=0)
+331    final_path = os.path.join(data_path, 'raw_dump.tsv')
+332    logging.info(
+333        f'Stored data dump of dimension {final_df_concat.shape} to {final_path}',
+334    )
+335    final_df_concat.to_csv(final_path, sep='\t', index=False)
+336    data_path = os.path.join(data_path, 'raw_dump.tsv')
+337
+338    return DatasetInformationStorage(
+339        data_path, table_header, set(column_types), col_delimiter, encoding, None,
+340    )
 

 
 
@@ -1667,24 +1675,24 @@
- 
339def parse_ob_feature_information(data_path) -> DatasetInformationStorage:
-340    """A generic parser of ob-based data"""
-341
-342    # Get column names
-343    column_names = os.path.join(data_path, 'vw_namespace_map.csv')
-344    column_types, _ = parse_namespace(column_names)
+            
343def parse_ob_feature_information(data_path) -> DatasetInformationStorage:
+344    """A generic parser of ob-based data"""
 345
-346    # Get set of numeric columns
-347    table_header_path = os.path.join(data_path, 'raw_data/0_header/header.csv')
-348    table_header = read_column_names(table_header_path)
+346    # Get column names
+347    column_names = os.path.join(data_path, 'vw_namespace_map.csv')
+348    column_types, _ = parse_namespace(column_names)
 349
-350    data_path = os.path.join(data_path, 'raw_data/1_train/*')
-351    col_delimiter = '\t'
-352    encoding = 'utf-8'
+350    # Get set of numeric columns
+351    table_header_path = os.path.join(data_path, 'raw_data/0_header/header.csv')
+352    table_header = read_column_names(table_header_path)
 353
-354    return DatasetInformationStorage(
-355        data_path, table_header, column_types, col_delimiter, encoding, None,
-356    )
+354    data_path = os.path.join(data_path, 'raw_data/1_train/*')
+355    col_delimiter = '\t'
+356    encoding = 'utf-8'
+357
+358    return DatasetInformationStorage(
+359        data_path, table_header, column_types, col_delimiter, encoding, None,
+360    )
 

 
 
@@ -1704,24 +1712,24 @@
- 
359def parse_csv_with_description_information(data_path) -> DatasetInformationStorage:
-360    dataset_description = read_reference_json(
-361        os.path.join(data_path, 'dataset_desc.json'),
-362    )
-363    column_names = []
-364    column_types = set()
-365    for feature in dataset_description.get('data_features', []):
-366        feature_name = feature.get('name')
-367        column_names.append(feature_name)
-368        feature_type = feature.get('type', '')
-369        if 'float' in feature_type or 'Float' in feature_type:
-370            column_types.add(feature_name)
-371    col_delimiter = ','
-372    data_path = os.path.join(data_path, 'data.csv')
-373    encoding = 'latin1'
-374    return DatasetInformationStorage(
-375        data_path, column_names, column_types, col_delimiter, encoding, None,
-376    )
+            
363def parse_csv_with_description_information(data_path) -> DatasetInformationStorage:
+364    dataset_description = read_reference_json(
+365        os.path.join(data_path, 'dataset_desc.json'),
+366    )
+367    column_names = []
+368    column_types = set()
+369    for feature in dataset_description.get('data_features', []):
+370        feature_name = feature.get('name')
+371        column_names.append(feature_name)
+372        feature_type = feature.get('type', '')
+373        if 'float' in feature_type or 'Float' in feature_type:
+374            column_types.add(feature_name)
+375    col_delimiter = ','
+376    data_path = os.path.join(data_path, 'data.csv')
+377    encoding = 'latin1'
+378    return DatasetInformationStorage(
+379        data_path, column_names, column_types, col_delimiter, encoding, None,
+380    )
 

 
 
@@ -1739,18 +1747,18 @@
- 
379def parse_csv_raw(data_path) -> DatasetInformationStorage:
-380    column_types: set[str] = set()
-381
-382    data_path = os.path.join(data_path, 'data.csv')
-383    with open(data_path) as inp_data:
-384        header = inp_data.readline()
-385    col_delimiter = ','
-386    column_names = header.strip().split(col_delimiter)
-387    encoding = 'latin1'
-388    return DatasetInformationStorage(
-389        data_path, column_names, column_types, col_delimiter, encoding, None,
-390    )
+            
383def parse_csv_raw(data_path) -> DatasetInformationStorage:
+384    column_types: set[str] = set()
+385
+386    data_path = os.path.join(data_path, 'data.csv')
+387    with open(data_path) as inp_data:
+388        header = inp_data.readline()
+389    col_delimiter = ','
+390    column_names = header.strip().split(col_delimiter)
+391    encoding = 'latin1'
+392    return DatasetInformationStorage(
+393        data_path, column_names, column_types, col_delimiter, encoding, None,
+394    )
 

 
 
@@ -1768,22 +1776,22 @@
- 
393def extract_features_from_reference_JSON(json_path: str) -> set[Any]:
-394    """Given a model's JSON, extract unique features"""
-395
-396    with open(json_path) as jp:
-397        content = json.load(jp)
-398
-399    unique_features = set()
-400    feature_space = content['desc'].get('features', [])
-401    fields_space = content['desc'].get('fields', [])
-402    joint_space = feature_space + fields_space
-403
-404    for feature_tuple in joint_space:
-405        for individual_feature in feature_tuple.split(','):
-406            unique_features.add(individual_feature)
+            
397def extract_features_from_reference_JSON(json_path: str) -> set[Any]:
+398    """Given a model's JSON, extract unique features"""
+399
+400    with open(json_path) as jp:
+401        content = json.load(jp)
+402
+403    unique_features = set()
+404    feature_space = content['desc'].get('features', [])
+405    fields_space = content['desc'].get('fields', [])
+406    joint_space = feature_space + fields_space
 407
-408    return unique_features
+408    for feature_tuple in joint_space:
+409        for individual_feature in feature_tuple.split(','):
+410            unique_features.add(individual_feature)
+411
+412    return unique_features
 

 
 
@@ -1803,178 +1811,178 @@
- 
411def summarize_feature_bounds_for_transformers(
-412    bounds_object_storage: Any,
-413    feature_types: list[str],
-414    task_name: str,
-415    label_name: str,
-416    granularity: int = 15,
-417    output_summary_table_only: bool = False,
-418):
-419    """summarization auxilliary method for generating JSON-based specs"""
-420
-421    if bounds_object_storage is None:
-422        logging.info('Bounds storage object is empty.')
-423        exit()
+            
415def summarize_feature_bounds_for_transformers(
+416    bounds_object_storage: Any,
+417    feature_types: list[str],
+418    task_name: str,
+419    label_name: str,
+420    granularity: int = 15,
+421    output_summary_table_only: bool = False,
+422):
+423    """summarization auxilliary method for generating JSON-based specs"""
 424
-425    final_storage = defaultdict(list)
-426    for el in bounds_object_storage:
-427        if isinstance(el, dict):
-428            for k, v in el.items():
-429                final_storage[k].append(v)
-430
-431    summary_table_rows = []
-432    for k, v in final_storage.items():
-433        # Conduct local aggregation + bound changes
-434        if k in feature_types and k != label_name:
-435            minima, maxima, medians, uniques = [], [], [], []
-436            for feature_summary in v:
-437                minima.append(feature_summary.minimum)
-438                maxima.append(feature_summary.maximum)
-439                medians.append(feature_summary.median)
-440                uniques.append(feature_summary.num_unique)
-441            summary_table_rows.append(
-442                [
-443                    k,
-444                    round(np.min(minima), 2),
-445                    round(np.max(maxima), 2),
-446                    round(np.median(medians), 2),
-447                    int(np.mean(uniques)),
-448                ],
-449            )
-450
-451    if len(summary_table_rows) == 0:
-452        logging.info('No numeric features to summarize.')
-453        return None
+425    if bounds_object_storage is None:
+426        logging.info('Bounds storage object is empty.')
+427        exit()
+428
+429    final_storage = defaultdict(list)
+430    for el in bounds_object_storage:
+431        if isinstance(el, dict):
+432            for k, v in el.items():
+433                final_storage[k].append(v)
+434
+435    summary_table_rows = []
+436    for k, v in final_storage.items():
+437        # Conduct local aggregation + bound changes
+438        if k in feature_types and k != label_name:
+439            minima, maxima, medians, uniques = [], [], [], []
+440            for feature_summary in v:
+441                minima.append(feature_summary.minimum)
+442                maxima.append(feature_summary.maximum)
+443                medians.append(feature_summary.median)
+444                uniques.append(feature_summary.num_unique)
+445            summary_table_rows.append(
+446                [
+447                    k,
+448                    round(np.min(minima), 2),
+449                    round(np.max(maxima), 2),
+450                    round(np.median(medians), 2),
+451                    int(np.mean(uniques)),
+452                ],
+453            )
 454
-455    summary_table: pd.Dataframe = pd.DataFrame(summary_table_rows)
-456    summary_table.columns = [
-457        'Feature',
-458        'Minimum',
-459        'Maximum',
-460        'Median',
-461        'Num avg. unique (batch)',
-462    ]
-463
-464    if output_summary_table_only:
-465        return summary_table
-466
-467    if len(summary_table) == 0:
-468        logging.info('Summary table empty, skipping transformer generation ..')
-469        return
+455    if len(summary_table_rows) == 0:
+456        logging.info('No numeric features to summarize.')
+457        return None
+458
+459    summary_table: pd.Dataframe = pd.DataFrame(summary_table_rows)
+460    summary_table.columns = [
+461        'Feature',
+462        'Minimum',
+463        'Maximum',
+464        'Median',
+465        'Num avg. unique (batch)',
+466    ]
+467
+468    if output_summary_table_only:
+469        return summary_table
 470
-471    if task_name == 'feature_summary_transformers':
-472        transformers_per_feature = defaultdict(list)
-473
-474        # Take care of weights first -> range is pre-defined
-475        for k, v in final_storage.items():
-476            if label_name in k or 'dummy' in k:
-477                continue
-478
-479            weight_template = {
-480                'feature': k,
-481                'src_features': [k],
-482                'transformations': ['Weight'],
-483                'weights': [0, 0.5, 1.5, 2, 3, 10],
-484            }
-485            transformers_per_feature[k].append(weight_template)
-486
-487        # Consider numeric transformations - pairs and single ones
-488        for enx, row in summary_table.iterrows():
-489            if row.Feature == 'dummy':
-490                continue
-491            try:
-492                actual_range = (
-493                    np.arange(
-494                        row['Minimum'],
-495                        row['Maximum'],
-496                        (row['Maximum'] - row['Minimum']) / granularity,
-497                    )
-498                    .round(2)
-499                    .tolist()
-500                )
-501                binner_template = {
-502                    'feature': f'{row.Feature}',
-503                    'src_features': [row.Feature],
-504                    'transformations': [
-505                        'BinnerSqrt',
-506                        'BinnerLog',
-507                        'BinnerSqrtPlain',
-508                        'BinnerLogPlain',
-509                    ],
-510                    'n': actual_range,
-511                    'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
-512                }
-513
-514            except Exception as es:
-515                logging.info(
-516                    f'\U0001F631 Encountered {es}. The problematic feature is: {row}, skipping transformer for this feature ..',
-517                )
-518
-519            transformers_per_feature[row.Feature].append(binner_template)
-520
-521            # We want the full loop here, due to asymmetry of transformation(s)
-522            for enx_second, row_second in summary_table.iterrows():
-523                if enx_second < enx:
-524                    continue
-525
-526                # The n values are defined based on maxima of the second feature
-527                if row_second.Feature != row.Feature:
-528                    n_bound = round(row_second['Median'] + row['Median'], 2)
-529                    max_bound = round(
-530                        min(row_second['Maximum'], row['Maximum']), 2,
-531                    )
-532                    min_bound = round(
-533                        row_second['Minimum'] + row['Minimum'], 2,
-534                    )
-535                    range_spectrum = sorted(
-536                        list(
-537                            {
-538                                0.0,
-539                                min_bound,
-540                                n_bound / 10,
-541                                n_bound / 5,
-542                                n_bound,
-543                                max_bound,
-544                            },
-545                        ),
-546                    )
-547
-548                    range_spectrum = [x for x in range_spectrum if x >= 0]
-549                    binner_pair_template = {
-550                        'feature': f'{row.Feature}Ratio{row_second.Feature}',
-551                        'src_features': [row.Feature, row_second.Feature],
-552                        'transformations': ['BinnerLogRatioPlain', 'BinnerLogRatio'],
-553                        'n': range_spectrum,
-554                        'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
-555                    }
-556
-557                    binner_pair_template_second = {
-558                        'feature': f'{row_second.Feature}Ratio{row.Feature}',
-559                        'src_features': [row_second.Feature, row.Feature],
-560                        'transformations': ['BinnerLogRatioPlain', 'BinnerLogRatio'],
-561                        'n': range_spectrum,
-562                        'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
-563                    }
-564
-565                    transformers_per_feature[row.Feature].append(
-566                        binner_pair_template,
-567                    )
-568                    transformers_per_feature[row.Feature].append(
-569                        binner_pair_template_second,
-570                    )
-571
-572        binner_templates = []
-573        for k, v in transformers_per_feature.items():
-574            for transformer_struct in v:
-575                binner_templates.append(transformer_struct)
-576
-577        logging.info(
-578            f'Generated {len(binner_templates)} transformation search specifications.\n',
-579        )
-580        namespace_full = f'"random_grid_feature_transform": {json.dumps(binner_templates)}, "random_grid_epochs": 512'
-581        logging.info('Generated transformations below:\n')
-582        print(namespace_full)
+471    if len(summary_table) == 0:
+472        logging.info('Summary table empty, skipping transformer generation ..')
+473        return
+474
+475    if task_name == 'feature_summary_transformers':
+476        transformers_per_feature = defaultdict(list)
+477
+478        # Take care of weights first -> range is pre-defined
+479        for k, v in final_storage.items():
+480            if label_name in k or 'dummy' in k:
+481                continue
+482
+483            weight_template = {
+484                'feature': k,
+485                'src_features': [k],
+486                'transformations': ['Weight'],
+487                'weights': [0, 0.5, 1.5, 2, 3, 10],
+488            }
+489            transformers_per_feature[k].append(weight_template)
+490
+491        # Consider numeric transformations - pairs and single ones
+492        for enx, row in summary_table.iterrows():
+493            if row.Feature == 'dummy':
+494                continue
+495            try:
+496                actual_range = (
+497                    np.arange(
+498                        row['Minimum'],
+499                        row['Maximum'],
+500                        (row['Maximum'] - row['Minimum']) / granularity,
+501                    )
+502                    .round(2)
+503                    .tolist()
+504                )
+505                binner_template = {
+506                    'feature': f'{row.Feature}',
+507                    'src_features': [row.Feature],
+508                    'transformations': [
+509                        'BinnerSqrt',
+510                        'BinnerLog',
+511                        'BinnerSqrtPlain',
+512                        'BinnerLogPlain',
+513                    ],
+514                    'n': actual_range,
+515                    'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
+516                }
+517
+518            except Exception as es:
+519                logging.info(
+520                    f'\U0001F631 Encountered {es}. The problematic feature is: {row}, skipping transformer for this feature ..',
+521                )
+522
+523            transformers_per_feature[row.Feature].append(binner_template)
+524
+525            # We want the full loop here, due to asymmetry of transformation(s)
+526            for enx_second, row_second in summary_table.iterrows():
+527                if enx_second < enx:
+528                    continue
+529
+530                # The n values are defined based on maxima of the second feature
+531                if row_second.Feature != row.Feature:
+532                    n_bound = round(row_second['Median'] + row['Median'], 2)
+533                    max_bound = round(
+534                        min(row_second['Maximum'], row['Maximum']), 2,
+535                    )
+536                    min_bound = round(
+537                        row_second['Minimum'] + row['Minimum'], 2,
+538                    )
+539                    range_spectrum = sorted(
+540                        list(
+541                            {
+542                                0.0,
+543                                min_bound,
+544                                n_bound / 10,
+545                                n_bound / 5,
+546                                n_bound,
+547                                max_bound,
+548                            },
+549                        ),
+550                    )
+551
+552                    range_spectrum = [x for x in range_spectrum if x >= 0]
+553                    binner_pair_template = {
+554                        'feature': f'{row.Feature}Ratio{row_second.Feature}',
+555                        'src_features': [row.Feature, row_second.Feature],
+556                        'transformations': ['BinnerLogRatioPlain', 'BinnerLogRatio'],
+557                        'n': range_spectrum,
+558                        'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
+559                    }
+560
+561                    binner_pair_template_second = {
+562                        'feature': f'{row_second.Feature}Ratio{row.Feature}',
+563                        'src_features': [row_second.Feature, row.Feature],
+564                        'transformations': ['BinnerLogRatioPlain', 'BinnerLogRatio'],
+565                        'n': range_spectrum,
+566                        'resolutions': [0.1, 2, 4, 8, 16, 32, 64, 128],
+567                    }
+568
+569                    transformers_per_feature[row.Feature].append(
+570                        binner_pair_template,
+571                    )
+572                    transformers_per_feature[row.Feature].append(
+573                        binner_pair_template_second,
+574                    )
+575
+576        binner_templates = []
+577        for k, v in transformers_per_feature.items():
+578            for transformer_struct in v:
+579                binner_templates.append(transformer_struct)
+580
+581        logging.info(
+582            f'Generated {len(binner_templates)} transformation search specifications.\n',
+583        )
+584        namespace_full = f'"random_grid_feature_transform": {json.dumps(binner_templates)}, "random_grid_epochs": 512'
+585        logging.info('Generated transformations below:\n')
+586        print(namespace_full)
 

 
 
@@ -1994,59 +2002,59 @@
- 
585def summarize_rare_counts(
-586    term_counter: Any,
-587    args: Any,
-588    cardinality_object: Any,
-589    object_info: DatasetInformationStorage,
-590) -> None:
-591    """Write rare values"""
-592
-593    out_df_rows = []
-594    logging.info(
-595        f'Rare value summary (freq <= {args.rare_value_count_upper_bound}) follows ..',
-596    )
-597
-598    for namespace_tuple, count in term_counter.items():
-599        namespace, value = namespace_tuple
-600        out_df_rows.append([namespace, value, count])
-601    out_df: pd.DataFrame = pd.DataFrame(out_df_rows)
-602    out_df.columns = ['Namespace', 'value', 'Count']
-603    out_df.to_csv(
-604        os.path.join(args.output_folder, 'rare_values.tsv'), sep='\t', index=False,
-605    )
-606    logging.info(f'Wrote rare values to {args.output_folder}/rare_values.tsv')
-607
-608    overall_rare_counts = Counter(out_df.Namespace.values)
-609    sorted_counts = sorted(
-610        overall_rare_counts.items(), key=lambda pair: pair[1], reverse=True,
-611    )
-612    for k, v in sorted_counts:
-613        logging.info(f'Namespace: {k} ---- Rare values observed: {v}')
-614
-615    final_df_rows = []
+            
589def summarize_rare_counts(
+590    term_counter: Any,
+591    args: Any,
+592    cardinality_object: Any,
+593    object_info: DatasetInformationStorage,
+594) -> None:
+595    """Write rare values"""
+596
+597    out_df_rows = []
+598    logging.info(
+599        f'Rare value summary (freq <= {args.rare_value_count_upper_bound}) follows ..',
+600    )
+601
+602    for namespace_tuple, count in term_counter.items():
+603        namespace, value = namespace_tuple
+604        out_df_rows.append([namespace, value, count])
+605    out_df: pd.DataFrame = pd.DataFrame(out_df_rows)
+606    out_df.columns = ['Namespace', 'value', 'Count']
+607    out_df.to_csv(
+608        os.path.join(args.output_folder, 'rare_values.tsv'), sep='\t', index=False,
+609    )
+610    logging.info(f'Wrote rare values to {args.output_folder}/rare_values.tsv')
+611
+612    overall_rare_counts = Counter(out_df.Namespace.values)
+613    sorted_counts = sorted(
+614        overall_rare_counts.items(), key=lambda pair: pair[1], reverse=True,
+615    )
 616    for k, v in sorted_counts:
-617        cardinality = len(cardinality_object[k])
-618        rare_proportion = np.round(100 * (v / cardinality), 2)
-619        col_type = 'nominal'
-620        if k in object_info.column_types:
-621            col_type = 'numeric'
-622        final_df_rows.append(
-623            {
-624                'rare_proportion': rare_proportion,
-625                'feature_type': col_type,
-626                'feature_name': k,
-627            },
-628        )
-629
-630    final_df: pd.DataFrame = pd.DataFrame(final_df_rows)
-631    final_df = final_df.sort_values(by=['rare_proportion'])
-632    logging.info(
-633        f'Wrote feature sparsity summary to {args.output_folder}/feature_sparsity_summary.tsv',
-634    )
-635    final_df.to_csv(
-636        f'{args.output_folder}/feature_sparsity_summary.tsv', index=False, sep='\t',
-637    )
+617        logging.info(f'Namespace: {k} ---- Rare values observed: {v}')
+618
+619    final_df_rows = []
+620    for k, v in sorted_counts:
+621        cardinality = len(cardinality_object[k])
+622        rare_proportion = np.round(100 * (v / cardinality), 2)
+623        col_type = 'nominal'
+624        if k in object_info.column_types:
+625            col_type = 'numeric'
+626        final_df_rows.append(
+627            {
+628                'rare_proportion': rare_proportion,
+629                'feature_type': col_type,
+630                'feature_name': k,
+631            },
+632        )
+633
+634    final_df: pd.DataFrame = pd.DataFrame(final_df_rows)
+635    final_df = final_df.sort_values(by=['rare_proportion'])
+636    logging.info(
+637        f'Wrote feature sparsity summary to {args.output_folder}/feature_sparsity_summary.tsv',
+638    )
+639    final_df.to_csv(
+640        f'{args.output_folder}/feature_sparsity_summary.tsv', index=False, sep='\t',
+641    )
 

 
 
diff --git a/docs/outrank/task_ranking.html b/docs/outrank/task_ranking.html
index 844454d..3bff0db 100644
--- a/docs/outrank/task_ranking.html
+++ b/docs/outrank/task_ranking.html
@@ -94,253 +94,254 @@ 

  38    if args.task in ['identify_rare_values', 'feature_summary_transformers']:
  39        args.heuristic = 'Constant'
  40
- 41    display_tool_name()
- 42    display_random_tip()
- 43
- 44    dataset_info = get_dataset_info(args)
- 45
- 46    for arg in vars(args):
- 47        logging.info(f'{arg} set to: {getattr(args, arg)}')
- 48
- 49    # Generate output folders (if not present)
- 50    output_dir = os.path.dirname(
- 51        os.path.join(
- 52            args.output_folder, 'pairwise_ranks.tsv',
- 53        ),
- 54    )
- 55    if not os.path.exists(output_dir):
- 56        os.makedirs(output_dir)
- 57
- 58    # Initialize the global pool
- 59    GLOBAL_CPU_POOL = Pool(args.num_threads)
- 60    global_mutual_information_estimates = []
- 61    global_bounds_storage = []
- 62    global_memory_storage = []
- 63    all_timings = []
- 64    # Traverse the batches
- 65    for raw_dump in glob.glob(dataset_info.data_path):
- 66
- 67        if (
- 68            args.data_source == 'ob-vw'
- 69            or args.data_source == 'ob-csv'
- 70            or args.data_source == 'csv-raw'
- 71            or args.data_source == 'ob-raw-dump'
- 72        ):
- 73            all_subfiles = [raw_dump]
- 74
- 75        for partial_data in all_subfiles:
- 76            cmd_arguments = {
- 77                'input_file': partial_data,
- 78                'fw_col_mapping': dataset_info.fw_map,
- 79                'column_descriptions': dataset_info.column_names,
- 80                'numeric_column_types': dataset_info.column_types,
- 81                'args': args,
- 82                'data_encoding': dataset_info.encoding,
- 83                'cpu_pool': GLOBAL_CPU_POOL,
- 84                'delimiter': dataset_info.col_delimiter,
- 85                'logger': logging,
- 86            }
- 87
- 88            if (
- 89                args.data_source == 'ob-csv'
- 90                or args.data_source == 'ob-vw'
- 91                or args.data_source == 'csv-raw'
- 92                or args.data_source == 'ob-raw-dump'
- 93            ):
- 94                (
- 95                    checkpoint_timings,
- 96                    mutual_information_estimates,
- 97                    cardinality_object,
- 98                    bounds_object_storage,
- 99                    memory_object_storage,
-100                    coverage_object,
-101                    RARE_VALUE_STORAGE,
-102                ) = estimate_importances_minibatches(**cmd_arguments)
-103
-104            global_bounds_storage += bounds_object_storage
-105            global_memory_storage += memory_object_storage
-106            all_timings += checkpoint_timings
-107
-108            if cardinality_object is None:
-109                continue
-110
-111            if coverage_object is None:
-112                continue
-113
-114            if mutual_information_estimates is not None:
-115                global_mutual_information_estimates.append(
-116                    mutual_information_estimates,
-117                )
-118
-119    if args.task == 'identify_rare_values':
-120        logging.info('Summarizing rare values ..')
-121        summarize_rare_counts(
-122            RARE_VALUE_STORAGE, args, cardinality_object, dataset_info,
-123        )
-124        exit()
-125
-126    if args.task == 'feature_summary_transformers':
-127        summarize_feature_bounds_for_transformers(
-128            bounds_object_storage,
-129            dataset_info.column_types,
-130            args.task,
-131            args.label_column,
-132        )
-133        exit()
-134    else:
-135        summary_of_numeric_features = summarize_feature_bounds_for_transformers(
-136            bounds_object_storage,
-137            dataset_info.column_types,
-138            args.task,
-139            args.label_column,
-140            output_summary_table_only=True,
-141        )
-142        if summary_of_numeric_features is not None:
-143            num_out = os.path.join(
-144                args.output_folder, 'numeric_feature_statistics.tsv',
-145            )
-146            summary_of_numeric_features.to_csv(num_out, sep='\t', index=False)
-147            logging.info(
-148                f'Stored statistics of numeric features to {num_out} ..',
-149            )
-150
-151    # Just in case.
-152    GLOBAL_CPU_POOL.close()
-153    GLOBAL_CPU_POOL.join()
-154
-155    if len(global_mutual_information_estimates) == 0:
-156        logging.info('No rankings were obtained, exiting ..')
-157        exit()
-158
-159    # Compute median imps across batches
-160    triplets = pd.concat(global_mutual_information_estimates, axis=0)
-161    triplets.columns = ['FeatureA', 'FeatureB', 'Score']
-162
-163    if '3mr' in args.heuristic:
-164        # relevance include MI-scores of features w.r.t. labels
-165        relevance_df = triplets[triplets.FeatureB == args.label_column].copy()
-166        relevance_df = relevance_df[
-167            relevance_df.FeatureA.map(lambda x: ' AND_REL ' not in x)
-168        ][['FeatureA', 'Score']]
-169        relevance_df = relevance_df[relevance_df.FeatureA != args.label_column]
-170
-171        # relations include MI-scores of combinations w.r.t. label
-172        relations_df = triplets[triplets.FeatureB == args.label_column][
-173            ['FeatureA', 'Score']
-174        ].copy()
-175        relations_df = relations_df[
-176            relations_df.FeatureA.map(lambda x: ' AND_REL ' in x)
-177        ]
-178        relations_df['FeatureB'] = relations_df.FeatureA.map(
-179            lambda x: x.split(' AND_REL ')[1],
-180        )
-181        relations_df['FeatureA'] = relations_df.FeatureA.map(
-182            lambda x: x.split(' AND_REL ')[0],
-183        )
-184
-185        # redundancies include MI-scores of features w.r.t. non-label features
-186        redundancies_df = triplets[(
-187            triplets.FeatureB != args.label_column
-188        )].copy()
-189        redundancies_df = redundancies_df[
-190            redundancies_df.FeatureA !=
-191            args.label_column
-192        ]
-193        redundancies_df = redundancies_df[
-194            redundancies_df.apply(
-195                lambda x: (' AND_REL ' not in x.FeatureA)
-196                and (' AND_REL ' not in x.FeatureB),
-197                axis=1,
-198            )
-199        ]
-200
-201        # normalize
-202        relevance_df['score'] = (relevance_df.Score - relevance_df.Score.min()) / (
-203            relevance_df.Score.max() - relevance_df.Score.min()
-204        )
-205        relations_df['score'] = (relations_df.Score - relations_df.Score.min()) / (
-206            relations_df.Score.max() - relations_df.Score.min()
-207        )
-208        redundancies_df['score'] = (
-209            redundancies_df.Score - redundancies_df.Score.min()
-210        ) / (redundancies_df.Score.max() - redundancies_df.Score.min())
-211
-212        # create dicts
-213        relevance_dict = {
-214            row.FeatureA: row.score for _,
-215            row in relevance_df.iterrows()
-216        }
-217        relations_dict = {
-218            (row.FeatureA, row.FeatureB): row.score
-219            for _, row in relations_df.iterrows()
-220        }
-221        relations_dict.update(
-222            {
-223                (row.FeatureB, row.FeatureA): row.score
-224                for _, row in relations_df.iterrows()
-225            },
-226        )
-227        redundancy_dict = {
-228            (row.FeatureA, row.FeatureB): row.score
-229            for _, row in redundancies_df.iterrows()
-230        }
-231
-232        # compute 3mr ranks
-233        mrmrmr_ranking = rank_features_3MR(
-234            relevance_dict, redundancy_dict, relations_dict,
-235        )
-236        mrmrmr_ranking.to_csv(
-237            os.path.join(args.output_folder, '3mr_ranks.tsv'), sep='\t', index=False,
-238        )
-239
-240    feature_first_modified = []
-241    feature_second_modified = []
-242
-243    if args.include_cardinality_in_feature_names == 'True':
-244        for enx in range(triplets.shape[0]):
-245            feature_first = triplets.iloc[enx]['FeatureA']
-246            feature_second = triplets.iloc[enx]['FeatureB']
-247            card_first = str(len(cardinality_object[feature_first]))
-248            card_second = str(len(cardinality_object[feature_second]))
-249            cov_first = int(
-250                round((np.mean(np.array(coverage_object[feature_first]))), 1),
-251            )
-252            cov_second = int(
-253                round(np.mean(np.array(coverage_object[feature_second])), 1),
-254            )
-255
-256            feature_first_modified.append(
-257                feature_first + f'-({card_first}; {cov_first})',
-258            )
-259            feature_second_modified.append(
-260                feature_second + f'-({card_second}; {cov_second})',
-261            )
-262
-263        triplets['FeatureA'] = feature_first_modified
-264        triplets['FeatureB'] = feature_second_modified
-265
-266    feature_memory_df = pd.DataFrame(global_memory_storage).mean()
-267    feature_memory_df.columns = ['NormalizedSize']
-268    feature_memory_df.to_csv(
-269        f'{args.output_folder}/memory.tsv', sep='\t', index=True,
-270    )
-271
-272    triplets = triplets.sort_values(by=['Score'])
-273
-274    triplets.to_csv(
-275        os.path.join(args.output_folder, 'pairwise_ranks.tsv'), sep='\t', index=False,
-276    )
-277
-278    # Write timings and config for replicability
-279    dfx = pd.DataFrame(all_timings)
-280    dfx.to_json(f'{args.output_folder}/timings.json')
-281    write_json_dump_to_file(args, f'{args.output_folder}/arguments.json')
-282
-283    logging.info(
-284        f'Finished with ranking! Result stored as: {args.output_folder}/pairwise_ranks.tsv. Cleaning up tmp files ..',
-285    )
-286
-287    os.remove('ranking_checkpoint_tmp.tsv')
+ 41    if args.silent != 'True':
+ 42        display_tool_name()
+ 43        display_random_tip()
+ 44
+ 45    dataset_info = get_dataset_info(args)
+ 46
+ 47    for arg in vars(args):
+ 48        logging.info(f'{arg} set to: {getattr(args, arg)}')
+ 49
+ 50    # Generate output folders (if not present)
+ 51    output_dir = os.path.dirname(
+ 52        os.path.join(
+ 53            args.output_folder, 'pairwise_ranks.tsv',
+ 54        ),
+ 55    )
+ 56    if not os.path.exists(output_dir):
+ 57        os.makedirs(output_dir)
+ 58
+ 59    # Initialize the global pool
+ 60    GLOBAL_CPU_POOL = Pool(args.num_threads)
+ 61    global_mutual_information_estimates = []
+ 62    global_bounds_storage = []
+ 63    global_memory_storage = []
+ 64    all_timings = []
+ 65    # Traverse the batches
+ 66    for raw_dump in glob.glob(dataset_info.data_path):
+ 67
+ 68        if (
+ 69            args.data_source == 'ob-vw'
+ 70            or args.data_source == 'ob-csv'
+ 71            or args.data_source == 'csv-raw'
+ 72            or args.data_source == 'ob-raw-dump'
+ 73        ):
+ 74            all_subfiles = [raw_dump]
+ 75
+ 76        for partial_data in all_subfiles:
+ 77            cmd_arguments = {
+ 78                'input_file': partial_data,
+ 79                'fw_col_mapping': dataset_info.fw_map,
+ 80                'column_descriptions': dataset_info.column_names,
+ 81                'numeric_column_types': dataset_info.column_types,
+ 82                'args': args,
+ 83                'data_encoding': dataset_info.encoding,
+ 84                'cpu_pool': GLOBAL_CPU_POOL,
+ 85                'delimiter': dataset_info.col_delimiter,
+ 86                'logger': logging,
+ 87            }
+ 88
+ 89            if (
+ 90                args.data_source == 'ob-csv'
+ 91                or args.data_source == 'ob-vw'
+ 92                or args.data_source == 'csv-raw'
+ 93                or args.data_source == 'ob-raw-dump'
+ 94            ):
+ 95                (
+ 96                    checkpoint_timings,
+ 97                    mutual_information_estimates,
+ 98                    cardinality_object,
+ 99                    bounds_object_storage,
+100                    memory_object_storage,
+101                    coverage_object,
+102                    RARE_VALUE_STORAGE,
+103                ) = estimate_importances_minibatches(**cmd_arguments)
+104
+105            global_bounds_storage += bounds_object_storage
+106            global_memory_storage += memory_object_storage
+107            all_timings += checkpoint_timings
+108
+109            if cardinality_object is None:
+110                continue
+111
+112            if coverage_object is None:
+113                continue
+114
+115            if mutual_information_estimates is not None:
+116                global_mutual_information_estimates.append(
+117                    mutual_information_estimates,
+118                )
+119
+120    if args.task == 'identify_rare_values':
+121        logging.info('Summarizing rare values ..')
+122        summarize_rare_counts(
+123            RARE_VALUE_STORAGE, args, cardinality_object, dataset_info,
+124        )
+125        exit()
+126
+127    if args.task == 'feature_summary_transformers':
+128        summarize_feature_bounds_for_transformers(
+129            bounds_object_storage,
+130            dataset_info.column_types,
+131            args.task,
+132            args.label_column,
+133        )
+134        exit()
+135    else:
+136        summary_of_numeric_features = summarize_feature_bounds_for_transformers(
+137            bounds_object_storage,
+138            dataset_info.column_types,
+139            args.task,
+140            args.label_column,
+141            output_summary_table_only=True,
+142        )
+143        if summary_of_numeric_features is not None:
+144            num_out = os.path.join(
+145                args.output_folder, 'numeric_feature_statistics.tsv',
+146            )
+147            summary_of_numeric_features.to_csv(num_out, sep='\t', index=False)
+148            logging.info(
+149                f'Stored statistics of numeric features to {num_out} ..',
+150            )
+151
+152    # Just in case.
+153    GLOBAL_CPU_POOL.close()
+154    GLOBAL_CPU_POOL.join()
+155
+156    if len(global_mutual_information_estimates) == 0:
+157        logging.info('No rankings were obtained, exiting ..')
+158        exit()
+159
+160    # Compute median imps across batches
+161    triplets = pd.concat(global_mutual_information_estimates, axis=0)
+162    triplets.columns = ['FeatureA', 'FeatureB', 'Score']
+163
+164    if '3mr' in args.heuristic:
+165        # relevance include MI-scores of features w.r.t. labels
+166        relevance_df = triplets[triplets.FeatureB == args.label_column].copy()
+167        relevance_df = relevance_df[
+168            relevance_df.FeatureA.map(lambda x: ' AND_REL ' not in x)
+169        ][['FeatureA', 'Score']]
+170        relevance_df = relevance_df[relevance_df.FeatureA != args.label_column]
+171
+172        # relations include MI-scores of combinations w.r.t. label
+173        relations_df = triplets[triplets.FeatureB == args.label_column][
+174            ['FeatureA', 'Score']
+175        ].copy()
+176        relations_df = relations_df[
+177            relations_df.FeatureA.map(lambda x: ' AND_REL ' in x)
+178        ]
+179        relations_df['FeatureB'] = relations_df.FeatureA.map(
+180            lambda x: x.split(' AND_REL ')[1],
+181        )
+182        relations_df['FeatureA'] = relations_df.FeatureA.map(
+183            lambda x: x.split(' AND_REL ')[0],
+184        )
+185
+186        # redundancies include MI-scores of features w.r.t. non-label features
+187        redundancies_df = triplets[(
+188            triplets.FeatureB != args.label_column
+189        )].copy()
+190        redundancies_df = redundancies_df[
+191            redundancies_df.FeatureA !=
+192            args.label_column
+193        ]
+194        redundancies_df = redundancies_df[
+195            redundancies_df.apply(
+196                lambda x: (' AND_REL ' not in x.FeatureA)
+197                and (' AND_REL ' not in x.FeatureB),
+198                axis=1,
+199            )
+200        ]
+201
+202        # normalize
+203        relevance_df['score'] = (relevance_df.Score - relevance_df.Score.min()) / (
+204            relevance_df.Score.max() - relevance_df.Score.min()
+205        )
+206        relations_df['score'] = (relations_df.Score - relations_df.Score.min()) / (
+207            relations_df.Score.max() - relations_df.Score.min()
+208        )
+209        redundancies_df['score'] = (
+210            redundancies_df.Score - redundancies_df.Score.min()
+211        ) / (redundancies_df.Score.max() - redundancies_df.Score.min())
+212
+213        # create dicts
+214        relevance_dict = {
+215            row.FeatureA: row.score for _,
+216            row in relevance_df.iterrows()
+217        }
+218        relations_dict = {
+219            (row.FeatureA, row.FeatureB): row.score
+220            for _, row in relations_df.iterrows()
+221        }
+222        relations_dict.update(
+223            {
+224                (row.FeatureB, row.FeatureA): row.score
+225                for _, row in relations_df.iterrows()
+226            },
+227        )
+228        redundancy_dict = {
+229            (row.FeatureA, row.FeatureB): row.score
+230            for _, row in redundancies_df.iterrows()
+231        }
+232
+233        # compute 3mr ranks
+234        mrmrmr_ranking = rank_features_3MR(
+235            relevance_dict, redundancy_dict, relations_dict,
+236        )
+237        mrmrmr_ranking.to_csv(
+238            os.path.join(args.output_folder, '3mr_ranks.tsv'), sep='\t', index=False,
+239        )
+240
+241    feature_first_modified = []
+242    feature_second_modified = []
+243
+244    if args.include_cardinality_in_feature_names == 'True':
+245        for enx in range(triplets.shape[0]):
+246            feature_first = triplets.iloc[enx]['FeatureA']
+247            feature_second = triplets.iloc[enx]['FeatureB']
+248            card_first = str(len(cardinality_object[feature_first]))
+249            card_second = str(len(cardinality_object[feature_second]))
+250            cov_first = int(
+251                round((np.mean(np.array(coverage_object[feature_first]))), 1),
+252            )
+253            cov_second = int(
+254                round(np.mean(np.array(coverage_object[feature_second])), 1),
+255            )
+256
+257            feature_first_modified.append(
+258                feature_first + f'-({card_first}; {cov_first})',
+259            )
+260            feature_second_modified.append(
+261                feature_second + f'-({card_second}; {cov_second})',
+262            )
+263
+264        triplets['FeatureA'] = feature_first_modified
+265        triplets['FeatureB'] = feature_second_modified
+266
+267    feature_memory_df = pd.DataFrame(global_memory_storage).mean()
+268    feature_memory_df.columns = ['NormalizedSize']
+269    feature_memory_df.to_csv(
+270        f'{args.output_folder}/memory.tsv', sep='\t', index=True,
+271    )
+272
+273    triplets = triplets.sort_values(by=['Score'])
+274
+275    triplets.to_csv(
+276        os.path.join(args.output_folder, 'pairwise_ranks.tsv'), sep='\t', index=False,
+277    )
+278
+279    # Write timings and config for replicability
+280    dfx = pd.DataFrame(all_timings)
+281    dfx.to_json(f'{args.output_folder}/timings.json')
+282    write_json_dump_to_file(args, f'{args.output_folder}/arguments.json')
+283
+284    logging.info(
+285        f'Finished with ranking! Result stored as: {args.output_folder}/pairwise_ranks.tsv. Cleaning up tmp files ..',
+286    )
+287
+288    os.remove('ranking_checkpoint_tmp.tsv')
@@ -363,253 +364,254 @@

39 if args.task in ['identify_rare_values', 'feature_summary_transformers']: 40 args.heuristic = 'Constant' 41 - 42 display_tool_name() - 43 display_random_tip() - 44 - 45 dataset_info = get_dataset_info(args) - 46 - 47 for arg in vars(args): - 48 logging.info(f'{arg} set to: {getattr(args, arg)}') - 49 - 50 # Generate output folders (if not present) - 51 output_dir = os.path.dirname( - 52 os.path.join( - 53 args.output_folder, 'pairwise_ranks.tsv', - 54 ), - 55 ) - 56 if not os.path.exists(output_dir): - 57 os.makedirs(output_dir) - 58 - 59 # Initialize the global pool - 60 GLOBAL_CPU_POOL = Pool(args.num_threads) - 61 global_mutual_information_estimates = [] - 62 global_bounds_storage = [] - 63 global_memory_storage = [] - 64 all_timings = [] - 65 # Traverse the batches - 66 for raw_dump in glob.glob(dataset_info.data_path): - 67 - 68 if ( - 69 args.data_source == 'ob-vw' - 70 or args.data_source == 'ob-csv' - 71 or args.data_source == 'csv-raw' - 72 or args.data_source == 'ob-raw-dump' - 73 ): - 74 all_subfiles = [raw_dump] - 75 - 76 for partial_data in all_subfiles: - 77 cmd_arguments = { - 78 'input_file': partial_data, - 79 'fw_col_mapping': dataset_info.fw_map, - 80 'column_descriptions': dataset_info.column_names, - 81 'numeric_column_types': dataset_info.column_types, - 82 'args': args, - 83 'data_encoding': dataset_info.encoding, - 84 'cpu_pool': GLOBAL_CPU_POOL, - 85 'delimiter': dataset_info.col_delimiter, - 86 'logger': logging, - 87 } - 88 - 89 if ( - 90 args.data_source == 'ob-csv' - 91 or args.data_source == 'ob-vw' - 92 or args.data_source == 'csv-raw' - 93 or args.data_source == 'ob-raw-dump' - 94 ): - 95 ( - 96 checkpoint_timings, - 97 mutual_information_estimates, - 98 cardinality_object, - 99 bounds_object_storage, -100 memory_object_storage, -101 coverage_object, -102 RARE_VALUE_STORAGE, -103 ) = estimate_importances_minibatches(**cmd_arguments) -104 -105 global_bounds_storage += bounds_object_storage -106 global_memory_storage += memory_object_storage -107 all_timings += checkpoint_timings -108 -109 if cardinality_object is None: -110 continue -111 -112 if coverage_object is None: -113 continue -114 -115 if mutual_information_estimates is not None: -116 global_mutual_information_estimates.append( -117 mutual_information_estimates, -118 ) -119 -120 if args.task == 'identify_rare_values': -121 logging.info('Summarizing rare values ..') -122 summarize_rare_counts( -123 RARE_VALUE_STORAGE, args, cardinality_object, dataset_info, -124 ) -125 exit() -126 -127 if args.task == 'feature_summary_transformers': -128 summarize_feature_bounds_for_transformers( -129 bounds_object_storage, -130 dataset_info.column_types, -131 args.task, -132 args.label_column, -133 ) -134 exit() -135 else: -136 summary_of_numeric_features = summarize_feature_bounds_for_transformers( -137 bounds_object_storage, -138 dataset_info.column_types, -139 args.task, -140 args.label_column, -141 output_summary_table_only=True, -142 ) -143 if summary_of_numeric_features is not None: -144 num_out = os.path.join( -145 args.output_folder, 'numeric_feature_statistics.tsv', -146 ) -147 summary_of_numeric_features.to_csv(num_out, sep='\t', index=False) -148 logging.info( -149 f'Stored statistics of numeric features to {num_out} ..', -150 ) -151 -152 # Just in case. -153 GLOBAL_CPU_POOL.close() -154 GLOBAL_CPU_POOL.join() -155 -156 if len(global_mutual_information_estimates) == 0: -157 logging.info('No rankings were obtained, exiting ..') -158 exit() -159 -160 # Compute median imps across batches -161 triplets = pd.concat(global_mutual_information_estimates, axis=0) -162 triplets.columns = ['FeatureA', 'FeatureB', 'Score'] -163 -164 if '3mr' in args.heuristic: -165 # relevance include MI-scores of features w.r.t. labels -166 relevance_df = triplets[triplets.FeatureB == args.label_column].copy() -167 relevance_df = relevance_df[ -168 relevance_df.FeatureA.map(lambda x: ' AND_REL ' not in x) -169 ][['FeatureA', 'Score']] -170 relevance_df = relevance_df[relevance_df.FeatureA != args.label_column] -171 -172 # relations include MI-scores of combinations w.r.t. label -173 relations_df = triplets[triplets.FeatureB == args.label_column][ -174 ['FeatureA', 'Score'] -175 ].copy() -176 relations_df = relations_df[ -177 relations_df.FeatureA.map(lambda x: ' AND_REL ' in x) -178 ] -179 relations_df['FeatureB'] = relations_df.FeatureA.map( -180 lambda x: x.split(' AND_REL ')[1], -181 ) -182 relations_df['FeatureA'] = relations_df.FeatureA.map( -183 lambda x: x.split(' AND_REL ')[0], -184 ) -185 -186 # redundancies include MI-scores of features w.r.t. non-label features -187 redundancies_df = triplets[( -188 triplets.FeatureB != args.label_column -189 )].copy() -190 redundancies_df = redundancies_df[ -191 redundancies_df.FeatureA != -192 args.label_column -193 ] -194 redundancies_df = redundancies_df[ -195 redundancies_df.apply( -196 lambda x: (' AND_REL ' not in x.FeatureA) -197 and (' AND_REL ' not in x.FeatureB), -198 axis=1, -199 ) -200 ] -201 -202 # normalize -203 relevance_df['score'] = (relevance_df.Score - relevance_df.Score.min()) / ( -204 relevance_df.Score.max() - relevance_df.Score.min() -205 ) -206 relations_df['score'] = (relations_df.Score - relations_df.Score.min()) / ( -207 relations_df.Score.max() - relations_df.Score.min() -208 ) -209 redundancies_df['score'] = ( -210 redundancies_df.Score - redundancies_df.Score.min() -211 ) / (redundancies_df.Score.max() - redundancies_df.Score.min()) -212 -213 # create dicts -214 relevance_dict = { -215 row.FeatureA: row.score for _, -216 row in relevance_df.iterrows() -217 } -218 relations_dict = { -219 (row.FeatureA, row.FeatureB): row.score -220 for _, row in relations_df.iterrows() -221 } -222 relations_dict.update( -223 { -224 (row.FeatureB, row.FeatureA): row.score -225 for _, row in relations_df.iterrows() -226 }, -227 ) -228 redundancy_dict = { -229 (row.FeatureA, row.FeatureB): row.score -230 for _, row in redundancies_df.iterrows() -231 } -232 -233 # compute 3mr ranks -234 mrmrmr_ranking = rank_features_3MR( -235 relevance_dict, redundancy_dict, relations_dict, -236 ) -237 mrmrmr_ranking.to_csv( -238 os.path.join(args.output_folder, '3mr_ranks.tsv'), sep='\t', index=False, -239 ) -240 -241 feature_first_modified = [] -242 feature_second_modified = [] -243 -244 if args.include_cardinality_in_feature_names == 'True': -245 for enx in range(triplets.shape[0]): -246 feature_first = triplets.iloc[enx]['FeatureA'] -247 feature_second = triplets.iloc[enx]['FeatureB'] -248 card_first = str(len(cardinality_object[feature_first])) -249 card_second = str(len(cardinality_object[feature_second])) -250 cov_first = int( -251 round((np.mean(np.array(coverage_object[feature_first]))), 1), -252 ) -253 cov_second = int( -254 round(np.mean(np.array(coverage_object[feature_second])), 1), -255 ) -256 -257 feature_first_modified.append( -258 feature_first + f'-({card_first}; {cov_first})', -259 ) -260 feature_second_modified.append( -261 feature_second + f'-({card_second}; {cov_second})', -262 ) -263 -264 triplets['FeatureA'] = feature_first_modified -265 triplets['FeatureB'] = feature_second_modified -266 -267 feature_memory_df = pd.DataFrame(global_memory_storage).mean() -268 feature_memory_df.columns = ['NormalizedSize'] -269 feature_memory_df.to_csv( -270 f'{args.output_folder}/memory.tsv', sep='\t', index=True, -271 ) -272 -273 triplets = triplets.sort_values(by=['Score']) -274 -275 triplets.to_csv( -276 os.path.join(args.output_folder, 'pairwise_ranks.tsv'), sep='\t', index=False, -277 ) -278 -279 # Write timings and config for replicability -280 dfx = pd.DataFrame(all_timings) -281 dfx.to_json(f'{args.output_folder}/timings.json') -282 write_json_dump_to_file(args, f'{args.output_folder}/arguments.json') -283 -284 logging.info( -285 f'Finished with ranking! Result stored as: {args.output_folder}/pairwise_ranks.tsv. Cleaning up tmp files ..', -286 ) -287 -288 os.remove('ranking_checkpoint_tmp.tsv') + 42 if args.silent != 'True': + 43 display_tool_name() + 44 display_random_tip() + 45 + 46 dataset_info = get_dataset_info(args) + 47 + 48 for arg in vars(args): + 49 logging.info(f'{arg} set to: {getattr(args, arg)}') + 50 + 51 # Generate output folders (if not present) + 52 output_dir = os.path.dirname( + 53 os.path.join( + 54 args.output_folder, 'pairwise_ranks.tsv', + 55 ), + 56 ) + 57 if not os.path.exists(output_dir): + 58 os.makedirs(output_dir) + 59 + 60 # Initialize the global pool + 61 GLOBAL_CPU_POOL = Pool(args.num_threads) + 62 global_mutual_information_estimates = [] + 63 global_bounds_storage = [] + 64 global_memory_storage = [] + 65 all_timings = [] + 66 # Traverse the batches + 67 for raw_dump in glob.glob(dataset_info.data_path): + 68 + 69 if ( + 70 args.data_source == 'ob-vw' + 71 or args.data_source == 'ob-csv' + 72 or args.data_source == 'csv-raw' + 73 or args.data_source == 'ob-raw-dump' + 74 ): + 75 all_subfiles = [raw_dump] + 76 + 77 for partial_data in all_subfiles: + 78 cmd_arguments = { + 79 'input_file': partial_data, + 80 'fw_col_mapping': dataset_info.fw_map, + 81 'column_descriptions': dataset_info.column_names, + 82 'numeric_column_types': dataset_info.column_types, + 83 'args': args, + 84 'data_encoding': dataset_info.encoding, + 85 'cpu_pool': GLOBAL_CPU_POOL, + 86 'delimiter': dataset_info.col_delimiter, + 87 'logger': logging, + 88 } + 89 + 90 if ( + 91 args.data_source == 'ob-csv' + 92 or args.data_source == 'ob-vw' + 93 or args.data_source == 'csv-raw' + 94 or args.data_source == 'ob-raw-dump' + 95 ): + 96 ( + 97 checkpoint_timings, + 98 mutual_information_estimates, + 99 cardinality_object, +100 bounds_object_storage, +101 memory_object_storage, +102 coverage_object, +103 RARE_VALUE_STORAGE, +104 ) = estimate_importances_minibatches(**cmd_arguments) +105 +106 global_bounds_storage += bounds_object_storage +107 global_memory_storage += memory_object_storage +108 all_timings += checkpoint_timings +109 +110 if cardinality_object is None: +111 continue +112 +113 if coverage_object is None: +114 continue +115 +116 if mutual_information_estimates is not None: +117 global_mutual_information_estimates.append( +118 mutual_information_estimates, +119 ) +120 +121 if args.task == 'identify_rare_values': +122 logging.info('Summarizing rare values ..') +123 summarize_rare_counts( +124 RARE_VALUE_STORAGE, args, cardinality_object, dataset_info, +125 ) +126 exit() +127 +128 if args.task == 'feature_summary_transformers': +129 summarize_feature_bounds_for_transformers( +130 bounds_object_storage, +131 dataset_info.column_types, +132 args.task, +133 args.label_column, +134 ) +135 exit() +136 else: +137 summary_of_numeric_features = summarize_feature_bounds_for_transformers( +138 bounds_object_storage, +139 dataset_info.column_types, +140 args.task, +141 args.label_column, +142 output_summary_table_only=True, +143 ) +144 if summary_of_numeric_features is not None: +145 num_out = os.path.join( +146 args.output_folder, 'numeric_feature_statistics.tsv', +147 ) +148 summary_of_numeric_features.to_csv(num_out, sep='\t', index=False) +149 logging.info( +150 f'Stored statistics of numeric features to {num_out} ..', +151 ) +152 +153 # Just in case. +154 GLOBAL_CPU_POOL.close() +155 GLOBAL_CPU_POOL.join() +156 +157 if len(global_mutual_information_estimates) == 0: +158 logging.info('No rankings were obtained, exiting ..') +159 exit() +160 +161 # Compute median imps across batches +162 triplets = pd.concat(global_mutual_information_estimates, axis=0) +163 triplets.columns = ['FeatureA', 'FeatureB', 'Score'] +164 +165 if '3mr' in args.heuristic: +166 # relevance include MI-scores of features w.r.t. labels +167 relevance_df = triplets[triplets.FeatureB == args.label_column].copy() +168 relevance_df = relevance_df[ +169 relevance_df.FeatureA.map(lambda x: ' AND_REL ' not in x) +170 ][['FeatureA', 'Score']] +171 relevance_df = relevance_df[relevance_df.FeatureA != args.label_column] +172 +173 # relations include MI-scores of combinations w.r.t. label +174 relations_df = triplets[triplets.FeatureB == args.label_column][ +175 ['FeatureA', 'Score'] +176 ].copy() +177 relations_df = relations_df[ +178 relations_df.FeatureA.map(lambda x: ' AND_REL ' in x) +179 ] +180 relations_df['FeatureB'] = relations_df.FeatureA.map( +181 lambda x: x.split(' AND_REL ')[1], +182 ) +183 relations_df['FeatureA'] = relations_df.FeatureA.map( +184 lambda x: x.split(' AND_REL ')[0], +185 ) +186 +187 # redundancies include MI-scores of features w.r.t. non-label features +188 redundancies_df = triplets[( +189 triplets.FeatureB != args.label_column +190 )].copy() +191 redundancies_df = redundancies_df[ +192 redundancies_df.FeatureA != +193 args.label_column +194 ] +195 redundancies_df = redundancies_df[ +196 redundancies_df.apply( +197 lambda x: (' AND_REL ' not in x.FeatureA) +198 and (' AND_REL ' not in x.FeatureB), +199 axis=1, +200 ) +201 ] +202 +203 # normalize +204 relevance_df['score'] = (relevance_df.Score - relevance_df.Score.min()) / ( +205 relevance_df.Score.max() - relevance_df.Score.min() +206 ) +207 relations_df['score'] = (relations_df.Score - relations_df.Score.min()) / ( +208 relations_df.Score.max() - relations_df.Score.min() +209 ) +210 redundancies_df['score'] = ( +211 redundancies_df.Score - redundancies_df.Score.min() +212 ) / (redundancies_df.Score.max() - redundancies_df.Score.min()) +213 +214 # create dicts +215 relevance_dict = { +216 row.FeatureA: row.score for _, +217 row in relevance_df.iterrows() +218 } +219 relations_dict = { +220 (row.FeatureA, row.FeatureB): row.score +221 for _, row in relations_df.iterrows() +222 } +223 relations_dict.update( +224 { +225 (row.FeatureB, row.FeatureA): row.score +226 for _, row in relations_df.iterrows() +227 }, +228 ) +229 redundancy_dict = { +230 (row.FeatureA, row.FeatureB): row.score +231 for _, row in redundancies_df.iterrows() +232 } +233 +234 # compute 3mr ranks +235 mrmrmr_ranking = rank_features_3MR( +236 relevance_dict, redundancy_dict, relations_dict, +237 ) +238 mrmrmr_ranking.to_csv( +239 os.path.join(args.output_folder, '3mr_ranks.tsv'), sep='\t', index=False, +240 ) +241 +242 feature_first_modified = [] +243 feature_second_modified = [] +244 +245 if args.include_cardinality_in_feature_names == 'True': +246 for enx in range(triplets.shape[0]): +247 feature_first = triplets.iloc[enx]['FeatureA'] +248 feature_second = triplets.iloc[enx]['FeatureB'] +249 card_first = str(len(cardinality_object[feature_first])) +250 card_second = str(len(cardinality_object[feature_second])) +251 cov_first = int( +252 round((np.mean(np.array(coverage_object[feature_first]))), 1), +253 ) +254 cov_second = int( +255 round(np.mean(np.array(coverage_object[feature_second])), 1), +256 ) +257 +258 feature_first_modified.append( +259 feature_first + f'-({card_first}; {cov_first})', +260 ) +261 feature_second_modified.append( +262 feature_second + f'-({card_second}; {cov_second})', +263 ) +264 +265 triplets['FeatureA'] = feature_first_modified +266 triplets['FeatureB'] = feature_second_modified +267 +268 feature_memory_df = pd.DataFrame(global_memory_storage).mean() +269 feature_memory_df.columns = ['NormalizedSize'] +270 feature_memory_df.to_csv( +271 f'{args.output_folder}/memory.tsv', sep='\t', index=True, +272 ) +273 +274 triplets = triplets.sort_values(by=['Score']) +275 +276 triplets.to_csv( +277 os.path.join(args.output_folder, 'pairwise_ranks.tsv'), sep='\t', index=False, +278 ) +279 +280 # Write timings and config for replicability +281 dfx = pd.DataFrame(all_timings) +282 dfx.to_json(f'{args.output_folder}/timings.json') +283 write_json_dump_to_file(args, f'{args.output_folder}/arguments.json') +284 +285 logging.info( +286 f'Finished with ranking! Result stored as: {args.output_folder}/pairwise_ranks.tsv. Cleaning up tmp files ..', +287 ) +288 +289 os.remove('ranking_checkpoint_tmp.tsv') diff --git a/docs/search.js b/docs/search.js index 8adf396..e292afb 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oWelcome to OutRank's documentation!

\n\n

All functions/methods can be searched-for (search bar on the left).

\n\n

This tool enables fast screening of feature-feature interactions. Its purpose is to give the user fast insight into potential redundancies/anomalies in the data.\nIt is implemented to operate in _mini batches_, it traverses the raw data incrementally, refining the rankings as it goes along. The core operation, interaction ranking, outputs triplets which look as follows:

\n\n
featureA    featureB    0.512\nfeatureA    featureC    0.125\n
\n\n

Setup

\n\n
\n
pip install outrank\n
\n
\n\n

and test a minimal cycle with

\n\n
\n
outrank --task selftest\n
\n
\n\n

if this passes, you can be pretty certain OutRank will perform as intended. OutRank's primary use case is as a CLI tool, begin exploring with

\n\n
\n
outrank --help\n
\n
\n\n

Example use cases

\n\n\n"}, "outrank.algorithms": {"fullname": "outrank.algorithms", "modulename": "outrank.algorithms", "kind": "module", "doc": "

\n"}, "outrank.algorithms.feature_ranking": {"fullname": "outrank.algorithms.feature_ranking", "modulename": "outrank.algorithms.feature_ranking", "kind": "module", "doc": "

\n"}, "outrank.algorithms.feature_ranking.ranking_mi_numba": {"fullname": "outrank.algorithms.feature_ranking.ranking_mi_numba", "modulename": "outrank.algorithms.feature_ranking.ranking_mi_numba", "kind": "module", "doc": "

\n"}, "outrank.algorithms.feature_ranking.ranking_mi_numba.numba_unique": {"fullname": "outrank.algorithms.feature_ranking.ranking_mi_numba.numba_unique", "modulename": "outrank.algorithms.feature_ranking.ranking_mi_numba", "qualname": "numba_unique", "kind": "function", "doc": "

Identify unique elements in an array, fast

\n", "signature": "(a):", "funcdef": "def"}, "outrank.algorithms.feature_ranking.ranking_mi_numba.compute_conditional_entropy": {"fullname": "outrank.algorithms.feature_ranking.ranking_mi_numba.compute_conditional_entropy", "modulename": "outrank.algorithms.feature_ranking.ranking_mi_numba", "qualname": "compute_conditional_entropy", "kind": "function", "doc": "

\n", "signature": "(\tY_classes,\tclass_values,\tclass_var_shape,\tinitial_prob,\tnonzero_counts):", "funcdef": "def"}, "outrank.algorithms.feature_ranking.ranking_mi_numba.compute_entropies": {"fullname": "outrank.algorithms.feature_ranking.ranking_mi_numba.compute_entropies", "modulename": "outrank.algorithms.feature_ranking.ranking_mi_numba", "qualname": "compute_entropies", "kind": "function", "doc": "

Core entropy computation function

\n", "signature": "(X, Y, all_events, f_values, f_value_counts, cardinality_correction):", "funcdef": "def"}, "outrank.algorithms.feature_ranking.ranking_mi_numba.mutual_info_estimator_numba": {"fullname": "outrank.algorithms.feature_ranking.ranking_mi_numba.mutual_info_estimator_numba", "modulename": "outrank.algorithms.feature_ranking.ranking_mi_numba", "qualname": "mutual_info_estimator_numba", "kind": "function", "doc": "

Core estimator logic. Compute unique elements, subset if required

\n", "signature": "(Y, X, approximation_factor=1, cardinality_correction=False):", "funcdef": "def"}, "outrank.algorithms.importance_estimator": {"fullname": "outrank.algorithms.importance_estimator", "modulename": "outrank.algorithms.importance_estimator", "kind": "module", "doc": "

\n"}, "outrank.algorithms.importance_estimator.sklearn_MI": {"fullname": "outrank.algorithms.importance_estimator.sklearn_MI", "modulename": "outrank.algorithms.importance_estimator", "qualname": "sklearn_MI", "kind": "function", "doc": "

\n", "signature": "(vector_first: Any, vector_second: Any) -> float:", "funcdef": "def"}, "outrank.algorithms.importance_estimator.sklearn_surrogate": {"fullname": "outrank.algorithms.importance_estimator.sklearn_surrogate", "modulename": "outrank.algorithms.importance_estimator", "qualname": "sklearn_surrogate", "kind": "function", "doc": "

\n", "signature": "(vector_first: Any, vector_second: Any, surrogate_model: str) -> float:", "funcdef": "def"}, "outrank.algorithms.importance_estimator.numba_mi": {"fullname": "outrank.algorithms.importance_estimator.numba_mi", "modulename": "outrank.algorithms.importance_estimator", "qualname": "numba_mi", "kind": "function", "doc": "

\n", "signature": "(vector_first, vector_second, heuristic):", "funcdef": "def"}, "outrank.algorithms.importance_estimator.sklearn_mi_adj": {"fullname": "outrank.algorithms.importance_estimator.sklearn_mi_adj", "modulename": "outrank.algorithms.importance_estimator", "qualname": "sklearn_mi_adj", "kind": "function", "doc": "

\n", "signature": "(vector_first, vector_second):", "funcdef": "def"}, "outrank.algorithms.importance_estimator.get_importances_estimate_pairwise": {"fullname": "outrank.algorithms.importance_estimator.get_importances_estimate_pairwise", "modulename": "outrank.algorithms.importance_estimator", "qualname": "get_importances_estimate_pairwise", "kind": "function", "doc": "

A method for parallel importances estimation. As interaction scoring is independent, individual scores can be computed in parallel.

\n", "signature": "(combination, args, tmp_df):", "funcdef": "def"}, "outrank.algorithms.importance_estimator.rank_features_3MR": {"fullname": "outrank.algorithms.importance_estimator.rank_features_3MR", "modulename": "outrank.algorithms.importance_estimator", "qualname": "rank_features_3MR", "kind": "function", "doc": "

\n", "signature": "(\trelevance_dict: dict[str, float],\tredundancy_dict: dict[tuple[typing.Any, typing.Any], typing.Any],\trelational_dict: dict[tuple[typing.Any, typing.Any], typing.Any],\tstrategy: str = 'median',\talpha: float = 1,\tbeta: float = 1) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.algorithms.importance_estimator.get_importances_estimate_nonmyopic": {"fullname": "outrank.algorithms.importance_estimator.get_importances_estimate_nonmyopic", "modulename": "outrank.algorithms.importance_estimator", "qualname": "get_importances_estimate_nonmyopic", "kind": "function", "doc": "

\n", "signature": "(args: Any, tmp_df: pandas.core.frame.DataFrame):", "funcdef": "def"}, "outrank.algorithms.sketches": {"fullname": "outrank.algorithms.sketches", "modulename": "outrank.algorithms.sketches", "kind": "module", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "kind": "module", "doc": "

This module implements probabilistic data structure which is able to calculate the cardinality of large multisets in a single pass using little auxiliary memory

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache", "kind": "class", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.__init__": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.__init__", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.__init__", "kind": "function", "doc": "

\n", "signature": "(error_rate=0.005)"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.p": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.p", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.p", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.m": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.m", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.m", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_set": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_set", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.warmup_set", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_size": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_size", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.warmup_size", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.width": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.width", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.width", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.hll_flag": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.hll_flag", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.hll_flag", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.add": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.add", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.add", "kind": "function", "doc": "

\n", "signature": "(self, value):", "funcdef": "def"}, "outrank.algorithms.synthetic_data_generators": {"fullname": "outrank.algorithms.synthetic_data_generators", "modulename": "outrank.algorithms.synthetic_data_generators", "kind": "module", "doc": "

\n"}, "outrank.algorithms.synthetic_data_generators.generator_naive": {"fullname": "outrank.algorithms.synthetic_data_generators.generator_naive", "modulename": "outrank.algorithms.synthetic_data_generators.generator_naive", "kind": "module", "doc": "

\n"}, "outrank.algorithms.synthetic_data_generators.generator_naive.generate_random_matrix": {"fullname": "outrank.algorithms.synthetic_data_generators.generator_naive.generate_random_matrix", "modulename": "outrank.algorithms.synthetic_data_generators.generator_naive", "qualname": "generate_random_matrix", "kind": "function", "doc": "

\n", "signature": "(num_features=100, size=20000):", "funcdef": "def"}, "outrank.core_ranking": {"fullname": "outrank.core_ranking", "modulename": "outrank.core_ranking", "kind": "module", "doc": "

\n"}, "outrank.core_ranking.logger": {"fullname": "outrank.core_ranking.logger", "modulename": "outrank.core_ranking", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger syn-logger (DEBUG)>"}, "outrank.core_ranking.GLOBAL_CARDINALITY_STORAGE": {"fullname": "outrank.core_ranking.GLOBAL_CARDINALITY_STORAGE", "modulename": "outrank.core_ranking", "qualname": "GLOBAL_CARDINALITY_STORAGE", "kind": "variable", "doc": "

\n", "annotation": ": dict[typing.Any, typing.Any]", "default_value": "{}"}, "outrank.core_ranking.GLOBAL_RARE_VALUE_STORAGE": {"fullname": "outrank.core_ranking.GLOBAL_RARE_VALUE_STORAGE", "modulename": "outrank.core_ranking", "qualname": "GLOBAL_RARE_VALUE_STORAGE", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, typing.Any]", "default_value": "Counter()"}, "outrank.core_ranking.GLOBAL_PRIOR_COMB_COUNTS": {"fullname": "outrank.core_ranking.GLOBAL_PRIOR_COMB_COUNTS", "modulename": "outrank.core_ranking", "qualname": "GLOBAL_PRIOR_COMB_COUNTS", "kind": "variable", "doc": "

\n", "annotation": ": dict[typing.Any, int]", "default_value": "Counter()"}, "outrank.core_ranking.IGNORED_VALUES": {"fullname": "outrank.core_ranking.IGNORED_VALUES", "modulename": "outrank.core_ranking", "qualname": "IGNORED_VALUES", "kind": "variable", "doc": "

\n", "default_value": "set()"}, "outrank.core_ranking.HYPERLL_ERROR_BOUND": {"fullname": "outrank.core_ranking.HYPERLL_ERROR_BOUND", "modulename": "outrank.core_ranking", "qualname": "HYPERLL_ERROR_BOUND", "kind": "variable", "doc": "

\n", "default_value": "0.02"}, "outrank.core_ranking.prior_combinations_sample": {"fullname": "outrank.core_ranking.prior_combinations_sample", "modulename": "outrank.core_ranking", "qualname": "prior_combinations_sample", "kind": "function", "doc": "

Make sure only relevant subspace of combinations is selected based on prior counts

\n", "signature": "(\tcombinations: list[tuple[typing.Any, ...]],\targs: Any) -> list[tuple[typing.Any, ...]]:", "funcdef": "def"}, "outrank.core_ranking.mixed_rank_graph": {"fullname": "outrank.core_ranking.mixed_rank_graph", "modulename": "outrank.core_ranking", "qualname": "mixed_rank_graph", "kind": "function", "doc": "

Compute the full mixed rank graph corresponding to all pairwise feature interactions based on the selected heuristic

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any,\tcpu_pool: Any,\tpbar: Any) -> outrank.core_utils.BatchRankingSummary:", "funcdef": "def"}, "outrank.core_ranking.enrich_with_transformations": {"fullname": "outrank.core_ranking.enrich_with_transformations", "modulename": "outrank.core_ranking", "qualname": "enrich_with_transformations", "kind": "function", "doc": "

Construct a collection of new features based on pre-defined transformations/rules

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tnum_col_types: set[str],\tlogger: Any,\targs: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_combined_features": {"fullname": "outrank.core_ranking.compute_combined_features", "modulename": "outrank.core_ranking", "qualname": "compute_combined_features", "kind": "function", "doc": "

Compute higher order features via xxhash-based trick.

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any,\tpbar: Any,\tis_3mr: bool = False) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_expanded_multivalue_features": {"fullname": "outrank.core_ranking.compute_expanded_multivalue_features", "modulename": "outrank.core_ranking", "qualname": "compute_expanded_multivalue_features", "kind": "function", "doc": "

Compute one-hot encoded feature space based on each designated multivalue feature. E.g., feature with value \"a,b,c\" becomes three features, values of which are presence of a given value in a mutlivalue feature of choice.

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any,\tpbar: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_subfeatures": {"fullname": "outrank.core_ranking.compute_subfeatures", "modulename": "outrank.core_ranking", "qualname": "compute_subfeatures", "kind": "function", "doc": "

Compute derived features that are more fine-grained. Implements logic around two operators that govern feature construction.\n->: One sided construction - every value from left side is fine, separate ones from the right side feature will be considered.\n<->: Two sided construction - two-sided values present. This means that each value from a is combined with each from b, forming |A|*|B| new features (one-hot encoded)

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any,\tpbar: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.include_noisy_features": {"fullname": "outrank.core_ranking.include_noisy_features", "modulename": "outrank.core_ranking", "qualname": "include_noisy_features", "kind": "function", "doc": "

Add randomized features that serve as a sanity check

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_coverage": {"fullname": "outrank.core_ranking.compute_coverage", "modulename": "outrank.core_ranking", "qualname": "compute_coverage", "kind": "function", "doc": "

Compute coverage of features, incrementally

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any) -> dict[str, set[str]]:", "funcdef": "def"}, "outrank.core_ranking.compute_feature_memory_consumption": {"fullname": "outrank.core_ranking.compute_feature_memory_consumption", "modulename": "outrank.core_ranking", "qualname": "compute_feature_memory_consumption", "kind": "function", "doc": "

An approximation of how much feature take up

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any) -> dict[str, set[str]]:", "funcdef": "def"}, "outrank.core_ranking.compute_value_counts": {"fullname": "outrank.core_ranking.compute_value_counts", "modulename": "outrank.core_ranking", "qualname": "compute_value_counts", "kind": "function", "doc": "

Update the count structure

\n", "signature": "(input_dataframe: pandas.core.frame.DataFrame, args: Any):", "funcdef": "def"}, "outrank.core_ranking.compute_cardinalities": {"fullname": "outrank.core_ranking.compute_cardinalities", "modulename": "outrank.core_ranking", "qualname": "compute_cardinalities", "kind": "function", "doc": "

Compute cardinalities of features, incrementally

\n", "signature": "(input_dataframe: pandas.core.frame.DataFrame, pbar: Any) -> None:", "funcdef": "def"}, "outrank.core_ranking.compute_bounds_increment": {"fullname": "outrank.core_ranking.compute_bounds_increment", "modulename": "outrank.core_ranking", "qualname": "compute_bounds_increment", "kind": "function", "doc": "

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tnumeric_column_types: set[str]) -> dict[str, typing.Any]:", "funcdef": "def"}, "outrank.core_ranking.compute_batch_ranking": {"fullname": "outrank.core_ranking.compute_batch_ranking", "modulename": "outrank.core_ranking", "qualname": "compute_batch_ranking", "kind": "function", "doc": "

Enrich the feature space and compute the batch importances

\n", "signature": "(\tline_tmp_storage: list[list[typing.Any]],\tnumeric_column_types: set[str],\targs: Any,\tcpu_pool: Any,\tcolumn_descriptions: list[str],\tlogger: Any,\tpbar: Any) -> tuple[outrank.core_utils.BatchRankingSummary, dict[str, typing.Any], dict[str, set[str]], dict[str, set[str]]]:", "funcdef": "def"}, "outrank.core_ranking.get_num_of_instances": {"fullname": "outrank.core_ranking.get_num_of_instances", "modulename": "outrank.core_ranking", "qualname": "get_num_of_instances", "kind": "function", "doc": "

Count the number of lines in a file, fast - useful for progress logging

\n", "signature": "(fname: str) -> int:", "funcdef": "def"}, "outrank.core_ranking.get_grouped_df": {"fullname": "outrank.core_ranking.get_grouped_df", "modulename": "outrank.core_ranking", "qualname": "get_grouped_df", "kind": "function", "doc": "

A helper method that enables median-based aggregation after processing

\n", "signature": "(\timportances_df_list: list[tuple[str, str, float]]) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.checkpoint_importances_df": {"fullname": "outrank.core_ranking.checkpoint_importances_df", "modulename": "outrank.core_ranking", "qualname": "checkpoint_importances_df", "kind": "function", "doc": "

A helper which stores intermediary state - useful for longer runs

\n", "signature": "(importances_batch: list[tuple[str, str, float]]) -> None:", "funcdef": "def"}, "outrank.core_ranking.estimate_importances_minibatches": {"fullname": "outrank.core_ranking.estimate_importances_minibatches", "modulename": "outrank.core_ranking", "qualname": "estimate_importances_minibatches", "kind": "function", "doc": "

Interaction score estimator - suitable for example for csv-like input data types.\nThis type of data is normally a single large csv, meaning that minibatch processing needs to\nhappen during incremental handling of the file (that\"s not the case for pre-separated ob data)

\n", "signature": "(\tinput_file: str,\tcolumn_descriptions: list,\tfw_col_mapping: dict[str, str],\tnumeric_column_types: set,\tbatch_size: int = 100000,\targs: Any = None,\tdata_encoding: str = 'utf-8',\tcpu_pool: Any = None,\tdelimiter: str = '\\t',\tfeature_construction_mode: bool = False,\tlogger: Any = None) -> tuple[list[dict[str, typing.Any]], typing.Any, dict[typing.Any, typing.Any], list[dict[str, typing.Any]], list[dict[str, set[str]]], collections.defaultdict[str, list[set[str]]], dict[str, typing.Any]]:", "funcdef": "def"}, "outrank.core_selftest": {"fullname": "outrank.core_selftest", "modulename": "outrank.core_selftest", "kind": "module", "doc": "

\n"}, "outrank.core_utils": {"fullname": "outrank.core_utils", "modulename": "outrank.core_utils", "kind": "module", "doc": "

\n"}, "outrank.core_utils.pro_tips": {"fullname": "outrank.core_utils.pro_tips", "modulename": "outrank.core_utils", "qualname": "pro_tips", "kind": "variable", "doc": "

\n", "default_value": "['OutRank can construct subfeatures; features based on subspaces. Example command argument is: --subfeature_mapping "feature_a->feature_b;feature_c<->feature_d;feature_c<->feature_e"', 'Heuristic MI-numba-randomized seems like the best of both worlds! (speed + performance).', 'Heuristic surrogate-lr performs cross-validation (internally), keep that in mind!', 'Consider running OutRank on a smaller data sample first, might be enough (--subsampling = a lot).', 'There are two types of combinations supported; unsupervised pairwise ranking (redundancies- --target_ranking_only=False), and supervised combinations - (--interaction_order > 1)', 'Visualization part also includes clustering - this might be very insightful!', 'By default OutRank includes feature cardinality and coverage in feature names (card; cov)', 'Intermediary checkpoints (tmp_checkpoint.tsv) might already give you insights during longer runs.', 'In theory, you can rank redundancies of combined features (--interaction_order AND --target_ranking_only=False).', 'Give it as many threads as physically possible (--num_threads).', 'You can speed up ranking by diminishing feature buffer size (--combination_number_upper_bound determines how many ranking computations per batch will be considered). This, and --subsampling are very powerful together.', 'Want to rank feature transformations, but not sure which ones to choose? --transformers=default should serve as a solid baseline (common DS transformations included).', 'Your target can be any feature! (explaining one feature with others)', 'OutRank uses HyperLogLog for cardinality estimation - this is also a potential usecase (understanding cardinalities across different data sets).', 'Each feature is named as featureName(cardinality, coverage in percents) in the final files.', 'You can generate candidate feature transformation ranges (fw) by using --task=feature_summary_transformers.']"}, "outrank.core_utils.write_json_dump_to_file": {"fullname": "outrank.core_utils.write_json_dump_to_file", "modulename": "outrank.core_utils", "qualname": "write_json_dump_to_file", "kind": "function", "doc": "

\n", "signature": "(args: Any, config_name: str) -> None:", "funcdef": "def"}, "outrank.core_utils.internal_hash": {"fullname": "outrank.core_utils.internal_hash", "modulename": "outrank.core_utils", "qualname": "internal_hash", "kind": "function", "doc": "

A generic internal hash used throughout ranking procedure - let's hardcode seed here for sure

\n", "signature": "(input_obj: str) -> str:", "funcdef": "def"}, "outrank.core_utils.DatasetInformationStorage": {"fullname": "outrank.core_utils.DatasetInformationStorage", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage", "kind": "class", "doc": "

A generic class for holding properties of a given type of dataset

\n"}, "outrank.core_utils.DatasetInformationStorage.__init__": {"fullname": "outrank.core_utils.DatasetInformationStorage.__init__", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.__init__", "kind": "function", "doc": "

\n", "signature": "(\tdata_path: str,\tcolumn_names: list[str],\tcolumn_types: set[str],\tcol_delimiter: str | None,\tencoding: str,\tfw_map: dict[str, str] | None)"}, "outrank.core_utils.DatasetInformationStorage.data_path": {"fullname": "outrank.core_utils.DatasetInformationStorage.data_path", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.data_path", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.DatasetInformationStorage.column_names": {"fullname": "outrank.core_utils.DatasetInformationStorage.column_names", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.column_names", "kind": "variable", "doc": "

\n", "annotation": ": list[str]"}, "outrank.core_utils.DatasetInformationStorage.column_types": {"fullname": "outrank.core_utils.DatasetInformationStorage.column_types", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.column_types", "kind": "variable", "doc": "

\n", "annotation": ": set[str]"}, "outrank.core_utils.DatasetInformationStorage.col_delimiter": {"fullname": "outrank.core_utils.DatasetInformationStorage.col_delimiter", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.col_delimiter", "kind": "variable", "doc": "

\n", "annotation": ": str | None"}, "outrank.core_utils.DatasetInformationStorage.encoding": {"fullname": "outrank.core_utils.DatasetInformationStorage.encoding", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.encoding", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.DatasetInformationStorage.fw_map": {"fullname": "outrank.core_utils.DatasetInformationStorage.fw_map", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.fw_map", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, str] | None"}, "outrank.core_utils.NumericFeatureSummary": {"fullname": "outrank.core_utils.NumericFeatureSummary", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary", "kind": "class", "doc": "

A generic class storing numeric feature statistics

\n"}, "outrank.core_utils.NumericFeatureSummary.__init__": {"fullname": "outrank.core_utils.NumericFeatureSummary.__init__", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(\tfeature_name: str,\tminimum: float,\tmaximum: float,\tmedian: float,\tnum_unique: int)"}, "outrank.core_utils.NumericFeatureSummary.feature_name": {"fullname": "outrank.core_utils.NumericFeatureSummary.feature_name", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.feature_name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.NumericFeatureSummary.minimum": {"fullname": "outrank.core_utils.NumericFeatureSummary.minimum", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.minimum", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.maximum": {"fullname": "outrank.core_utils.NumericFeatureSummary.maximum", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.maximum", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.median": {"fullname": "outrank.core_utils.NumericFeatureSummary.median", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.median", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.num_unique": {"fullname": "outrank.core_utils.NumericFeatureSummary.num_unique", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.num_unique", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "outrank.core_utils.NominalFeatureSummary": {"fullname": "outrank.core_utils.NominalFeatureSummary", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary", "kind": "class", "doc": "

A generic class storing numeric feature statistics

\n"}, "outrank.core_utils.NominalFeatureSummary.__init__": {"fullname": "outrank.core_utils.NominalFeatureSummary.__init__", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(feature_name: str, num_unique: int)"}, "outrank.core_utils.NominalFeatureSummary.feature_name": {"fullname": "outrank.core_utils.NominalFeatureSummary.feature_name", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.feature_name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.NominalFeatureSummary.num_unique": {"fullname": "outrank.core_utils.NominalFeatureSummary.num_unique", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.num_unique", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "outrank.core_utils.BatchRankingSummary": {"fullname": "outrank.core_utils.BatchRankingSummary", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary", "kind": "class", "doc": "

A generic class representing batched ranking results

\n"}, "outrank.core_utils.BatchRankingSummary.__init__": {"fullname": "outrank.core_utils.BatchRankingSummary.__init__", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(\ttriplet_scores: list[tuple[str, str, float]],\tstep_times: dict[str, typing.Any])"}, "outrank.core_utils.BatchRankingSummary.triplet_scores": {"fullname": "outrank.core_utils.BatchRankingSummary.triplet_scores", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.triplet_scores", "kind": "variable", "doc": "

\n", "annotation": ": list[tuple[str, str, float]]"}, "outrank.core_utils.BatchRankingSummary.step_times": {"fullname": "outrank.core_utils.BatchRankingSummary.step_times", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.step_times", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, typing.Any]"}, "outrank.core_utils.display_random_tip": {"fullname": "outrank.core_utils.display_random_tip", "modulename": "outrank.core_utils", "qualname": "display_random_tip", "kind": "function", "doc": "

\n", "signature": "() -> None:", "funcdef": "def"}, "outrank.core_utils.get_dataset_info": {"fullname": "outrank.core_utils.get_dataset_info", "modulename": "outrank.core_utils", "qualname": "get_dataset_info", "kind": "function", "doc": "

\n", "signature": "(args: Any):", "funcdef": "def"}, "outrank.core_utils.display_tool_name": {"fullname": "outrank.core_utils.display_tool_name", "modulename": "outrank.core_utils", "qualname": "display_tool_name", "kind": "function", "doc": "

\n", "signature": "() -> None:", "funcdef": "def"}, "outrank.core_utils.parse_ob_line": {"fullname": "outrank.core_utils.parse_ob_line", "modulename": "outrank.core_utils", "qualname": "parse_ob_line", "kind": "function", "doc": "

Outbrain line parsing - generic TSVs

\n", "signature": "(line_string: str, delimiter: str = '\\t', args: Any = None) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_line_vw": {"fullname": "outrank.core_utils.parse_ob_line_vw", "modulename": "outrank.core_utils", "qualname": "parse_ob_line_vw", "kind": "function", "doc": "

Parse a sparse vw line into a pandas df with pre-defined namespace

\n", "signature": "(\tline_string: str,\tdelimiter: str,\targs: Any = None,\tfw_col_mapping=None,\ttable_header=None,\tinclude_namespace_info=False) -> list[str | None]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_csv_line": {"fullname": "outrank.core_utils.parse_ob_csv_line", "modulename": "outrank.core_utils", "qualname": "parse_ob_csv_line", "kind": "function", "doc": "

Data can have commas within JSON field dumps

\n", "signature": "(line_string: str, delimiter: str = ',', args: Any = None) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.generic_line_parser": {"fullname": "outrank.core_utils.generic_line_parser", "modulename": "outrank.core_utils", "qualname": "generic_line_parser", "kind": "function", "doc": "

A generic method aimed to parse data from different sources.

\n", "signature": "(\tline_string: str,\tdelimiter: str,\targs: Any = None,\tfw_col_mapping: Any = None,\ttable_header: Any = None) -> list[typing.Any]:", "funcdef": "def"}, "outrank.core_utils.read_reference_json": {"fullname": "outrank.core_utils.read_reference_json", "modulename": "outrank.core_utils", "qualname": "read_reference_json", "kind": "function", "doc": "

A helper method for reading a JSON

\n", "signature": "(json_path) -> dict[str, dict]:", "funcdef": "def"}, "outrank.core_utils.parse_namespace": {"fullname": "outrank.core_utils.parse_namespace", "modulename": "outrank.core_utils", "qualname": "parse_namespace", "kind": "function", "doc": "

Parse the feature namespace for type awareness

\n", "signature": "(namespace_path: str) -> tuple[set[str], dict[str, str]]:", "funcdef": "def"}, "outrank.core_utils.read_column_names": {"fullname": "outrank.core_utils.read_column_names", "modulename": "outrank.core_utils", "qualname": "read_column_names", "kind": "function", "doc": "

Read the col. header

\n", "signature": "(mapping_file: str) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_vw_feature_information": {"fullname": "outrank.core_utils.parse_ob_vw_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_vw_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_ob_raw_feature_information": {"fullname": "outrank.core_utils.parse_ob_raw_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_raw_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_ob_feature_information": {"fullname": "outrank.core_utils.parse_ob_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_csv_with_description_information": {"fullname": "outrank.core_utils.parse_csv_with_description_information", "modulename": "outrank.core_utils", "qualname": "parse_csv_with_description_information", "kind": "function", "doc": "

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_csv_raw": {"fullname": "outrank.core_utils.parse_csv_raw", "modulename": "outrank.core_utils", "qualname": "parse_csv_raw", "kind": "function", "doc": "

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.extract_features_from_reference_JSON": {"fullname": "outrank.core_utils.extract_features_from_reference_JSON", "modulename": "outrank.core_utils", "qualname": "extract_features_from_reference_JSON", "kind": "function", "doc": "

Given a model's JSON, extract unique features

\n", "signature": "(json_path: str) -> set[typing.Any]:", "funcdef": "def"}, "outrank.core_utils.summarize_feature_bounds_for_transformers": {"fullname": "outrank.core_utils.summarize_feature_bounds_for_transformers", "modulename": "outrank.core_utils", "qualname": "summarize_feature_bounds_for_transformers", "kind": "function", "doc": "

summarization auxilliary method for generating JSON-based specs

\n", "signature": "(\tbounds_object_storage: Any,\tfeature_types: list[str],\ttask_name: str,\tlabel_name: str,\tgranularity: int = 15,\toutput_summary_table_only: bool = False):", "funcdef": "def"}, "outrank.core_utils.summarize_rare_counts": {"fullname": "outrank.core_utils.summarize_rare_counts", "modulename": "outrank.core_utils", "qualname": "summarize_rare_counts", "kind": "function", "doc": "

Write rare values

\n", "signature": "(\tterm_counter: Any,\targs: Any,\tcardinality_object: Any,\tobject_info: outrank.core_utils.DatasetInformationStorage) -> None:", "funcdef": "def"}, "outrank.feature_transformations": {"fullname": "outrank.feature_transformations", "modulename": "outrank.feature_transformations", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault": {"fullname": "outrank.feature_transformations.feature_transformer_vault", "modulename": "outrank.feature_transformations.feature_transformer_vault", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers.MINIMAL_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers.MINIMAL_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "qualname": "MINIMAL_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)'}"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers.DEFAULT_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers.DEFAULT_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "qualname": "DEFAULT_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)', '_tr_div(x,abs(x))*log(abs(x))': 'np.divide(X, np.abs(X)) * np.log(np.abs(X))', '_tr_log(x + sqrt(pow(x,2), 1)': 'np.log(X + np.sqrt(np.power(X, 2) + 1))', '_tr_log*sqrt': 'np.log(X + 1) * np.sqrt(X)', '_tr_log*100': 'np.round(np.log(X + 1) * 100, 0)', '_tr_nonzero': 'np.where(X != 0, 1, 0)', '_tr_round(div(x,max))': 'np.round(np.divide(X, np.max(X)), 0)'}"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.FW_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.FW_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "FW_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)', '_tr_div(x,abs(x))*log(abs(x))': 'np.divide(X, np.abs(X)) * np.log(np.abs(X))', '_tr_log(x + sqrt(pow(x,2), 1)': 'np.log(X + np.sqrt(np.power(X, 2) + 1))', '_tr_log*sqrt': 'np.log(X + 1) * np.sqrt(X)', '_tr_log*100': 'np.round(np.log(X + 1) * 100, 0)', '_tr_nonzero': 'np.where(X != 0, 1, 0)', '_tr_round(div(x,max))': 'np.round(np.divide(X, np.max(X)), 0)', '_tr_fw_sqrt_res_1_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*1,0), 0))', '_tr_fw_log_res_1_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*1,0), 0))', '_tr_fw_log_res_1_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*1,0), 0))', '_tr_fw_log_res_1_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*1,0), 0))', '_tr_fw_log_res_1_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*1,0), 0))', '_tr_fw_log_res_1_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*1,0), 0))', '_tr_fw_log_res_1_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*1,0), 0))', '_tr_fw_log_res_1_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*1,0), 0))', '_tr_fw_log_res_1_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*1,0), 0))', '_tr_fw_sqrt_res_10_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*10,0), 0))', '_tr_fw_log_res_10_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*10,0), 0))', '_tr_fw_log_res_10_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*10,0), 0))', '_tr_fw_log_res_10_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*10,0), 0))', '_tr_fw_log_res_10_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*10,0), 0))', '_tr_fw_log_res_10_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*10,0), 0))', '_tr_fw_log_res_10_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*10,0), 0))', '_tr_fw_log_res_10_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*10,0), 0))', '_tr_fw_log_res_10_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*10,0), 0))', '_tr_fw_sqrt_res_50_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*50,0), 0))', '_tr_fw_log_res_50_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*50,0), 0))', '_tr_fw_log_res_50_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*50,0), 0))', '_tr_fw_log_res_50_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*50,0), 0))', '_tr_fw_log_res_50_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*50,0), 0))', '_tr_fw_log_res_50_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*50,0), 0))', '_tr_fw_log_res_50_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*50,0), 0))', '_tr_fw_log_res_50_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*50,0), 0))', '_tr_fw_log_res_50_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*50,0), 0))', '_tr_fw_sqrt_res_100_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*100,0), 0))', '_tr_fw_log_res_100_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*100,0), 0))', '_tr_fw_log_res_100_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*100,0), 0))', '_tr_fw_log_res_100_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*100,0), 0))', '_tr_fw_log_res_100_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*100,0), 0))', '_tr_fw_log_res_100_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*100,0), 0))', '_tr_fw_log_res_100_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*100,0), 0))', '_tr_fw_log_res_100_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*100,0), 0))', '_tr_fw_log_res_100_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*100,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*1,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*10,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*50,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*100,0), 0))'}"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.resolution_range": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.resolution_range", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "resolution_range", "kind": "variable", "doc": "

\n", "default_value": "[1, 10, 50, 100]"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.greater_than_range": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.greater_than_range", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "greater_than_range", "kind": "variable", "doc": "

\n", "default_value": "[1, 2, 4, 8, 16, 32, 64, 96]"}, "outrank.feature_transformations.ranking_transformers": {"fullname": "outrank.feature_transformations.ranking_transformers", "modulename": "outrank.feature_transformations.ranking_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerNoise", "kind": "class", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.noise_preset": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.noise_preset", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerNoise.noise_preset", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.construct_new_features": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.construct_new_features", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerNoise.construct_new_features", "kind": "function", "doc": "

Generate a few standard noise distributions

\n", "signature": "(self, dataframe: pandas.core.frame.DataFrame, label_column=None):", "funcdef": "def"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric", "kind": "class", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.__init__": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.__init__", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.__init__", "kind": "function", "doc": "

\n", "signature": "(numeric_column_names: set[str], preset: str = 'default')"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.numeric_column_names": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.numeric_column_names", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.numeric_column_names", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.constructed_feature_names": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.constructed_feature_names", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.constructed_feature_names", "kind": "variable", "doc": "

\n", "annotation": ": set[str]"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.max_maj_support": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.max_maj_support", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.max_maj_support", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.nan_prop_support": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.nan_prop_support", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.nan_prop_support", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.get_vals": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.get_vals", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.get_vals", "kind": "function", "doc": "

\n", "signature": "(self, tmp_df: pandas.core.frame.DataFrame, col_name: str) -> Any:", "funcdef": "def"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.construct_baseline_features": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.construct_baseline_features", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.construct_baseline_features", "kind": "function", "doc": "

\n", "signature": "(self, dataframe: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.construct_new_features": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.construct_new_features", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.construct_new_features", "kind": "function", "doc": "

\n", "signature": "(self, dataframe: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.task_generators": {"fullname": "outrank.task_generators", "modulename": "outrank.task_generators", "kind": "module", "doc": "

\n"}, "outrank.task_generators.logger": {"fullname": "outrank.task_generators.logger", "modulename": "outrank.task_generators", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger syn-logger (DEBUG)>"}, "outrank.task_generators.outrank_task_generate_data_set": {"fullname": "outrank.task_generators.outrank_task_generate_data_set", "modulename": "outrank.task_generators", "qualname": "outrank_task_generate_data_set", "kind": "function", "doc": "

Core method for generating data sets

\n", "signature": "(args):", "funcdef": "def"}, "outrank.task_ranking": {"fullname": "outrank.task_ranking", "modulename": "outrank.task_ranking", "kind": "module", "doc": "

\n"}, "outrank.task_ranking.outrank_task_conduct_ranking": {"fullname": "outrank.task_ranking.outrank_task_conduct_ranking", "modulename": "outrank.task_ranking", "qualname": "outrank_task_conduct_ranking", "kind": "function", "doc": "

\n", "signature": "(args: Any):", "funcdef": "def"}, "outrank.task_selftest": {"fullname": "outrank.task_selftest", "modulename": "outrank.task_selftest", "kind": "module", "doc": "

\n"}, "outrank.task_selftest.logger": {"fullname": "outrank.task_selftest.logger", "modulename": "outrank.task_selftest", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger syn-logger (DEBUG)>"}, "outrank.task_selftest.conduct_self_test": {"fullname": "outrank.task_selftest.conduct_self_test", "modulename": "outrank.task_selftest", "qualname": "conduct_self_test", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "outrank.task_summary": {"fullname": "outrank.task_summary", "modulename": "outrank.task_summary", "kind": "module", "doc": "

\n"}, "outrank.task_summary.outrank_task_result_summary": {"fullname": "outrank.task_summary.outrank_task_result_summary", "modulename": "outrank.task_summary", "qualname": "outrank_task_result_summary", "kind": "function", "doc": "

\n", "signature": "(args):", "funcdef": "def"}, "outrank.task_visualization": {"fullname": "outrank.task_visualization", "modulename": "outrank.task_visualization", "kind": "module", "doc": "

\n"}, "outrank.task_visualization.outrank_task_visualize_results": {"fullname": "outrank.task_visualization.outrank_task_visualize_results", "modulename": "outrank.task_visualization", "qualname": "outrank_task_visualize_results", "kind": "function", "doc": "

\n", "signature": "(args):", "funcdef": "def"}, "outrank.visualizations": {"fullname": "outrank.visualizations", "modulename": "outrank.visualizations", "kind": "module", "doc": "

\n"}, "outrank.visualizations.ranking_visualization": {"fullname": "outrank.visualizations.ranking_visualization", "modulename": "outrank.visualizations.ranking_visualization", "kind": "module", "doc": "

\n"}, "outrank.visualizations.ranking_visualization.visualize_hierarchical_clusters": {"fullname": "outrank.visualizations.ranking_visualization.visualize_hierarchical_clusters", "modulename": "outrank.visualizations.ranking_visualization", "qualname": "visualize_hierarchical_clusters", "kind": "function", "doc": "

A method for visualization of hierarchical clusters w.r.t. different linkage functions

\n", "signature": "(\ttriplet_dataframe: pandas.core.frame.DataFrame,\toutput_folder: str,\timage_format: str = 'png',\tmax_num_clusters: int = 100) -> None:", "funcdef": "def"}, "outrank.visualizations.ranking_visualization.visualize_heatmap": {"fullname": "outrank.visualizations.ranking_visualization.visualize_heatmap", "modulename": "outrank.visualizations.ranking_visualization", "qualname": "visualize_heatmap", "kind": "function", "doc": "

\n", "signature": "(\ttriplets: pandas.core.frame.DataFrame,\toutput_folder: str,\timage_format: str) -> None:", "funcdef": "def"}, "outrank.visualizations.ranking_visualization.visualize_barplots": {"fullname": "outrank.visualizations.ranking_visualization.visualize_barplots", "modulename": "outrank.visualizations.ranking_visualization", "qualname": "visualize_barplots", "kind": "function", "doc": "

\n", "signature": "(\ttriplets: pandas.core.frame.DataFrame,\toutput_folder: str,\treference_json: str,\timage_format: str,\tlabel: str,\theuristic: str) -> None:", "funcdef": "def"}, "outrank.visualizations.ranking_visualization.visualize_all": {"fullname": "outrank.visualizations.ranking_visualization.visualize_all", "modulename": "outrank.visualizations.ranking_visualization", "qualname": "visualize_all", "kind": "function", "doc": "

A method for visualization of the obtained feature interaction maps.

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Welcome to OutRank's documentation!

\n\n

All functions/methods can be searched-for (search bar on the left).

\n\n

This tool enables fast screening of feature-feature interactions. Its purpose is to give the user fast insight into potential redundancies/anomalies in the data.\nIt is implemented to operate in _mini batches_, it traverses the raw data incrementally, refining the rankings as it goes along. The core operation, interaction ranking, outputs triplets which look as follows:

\n\n
featureA    featureB    0.512\nfeatureA    featureC    0.125\n
\n\n

Setup

\n\n
\n
pip install outrank\n
\n
\n\n

and test a minimal cycle with

\n\n
\n
outrank --task selftest\n
\n
\n\n

if this passes, you can be pretty certain OutRank will perform as intended. OutRank's primary use case is as a CLI tool, begin exploring with

\n\n
\n
outrank --help\n
\n
\n\n

Example use cases

\n\n\n"}, "outrank.algorithms": {"fullname": "outrank.algorithms", "modulename": "outrank.algorithms", "kind": "module", "doc": "

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Identify unique elements in an array, fast

\n", "signature": "(a):", "funcdef": "def"}, "outrank.algorithms.feature_ranking.ranking_mi_numba.compute_conditional_entropy": {"fullname": "outrank.algorithms.feature_ranking.ranking_mi_numba.compute_conditional_entropy", "modulename": "outrank.algorithms.feature_ranking.ranking_mi_numba", "qualname": "compute_conditional_entropy", "kind": "function", "doc": "

\n", "signature": "(\tY_classes,\tclass_values,\tclass_var_shape,\tinitial_prob,\tnonzero_counts):", "funcdef": "def"}, "outrank.algorithms.feature_ranking.ranking_mi_numba.compute_entropies": {"fullname": "outrank.algorithms.feature_ranking.ranking_mi_numba.compute_entropies", "modulename": "outrank.algorithms.feature_ranking.ranking_mi_numba", "qualname": "compute_entropies", "kind": "function", "doc": "

Core entropy computation function

\n", "signature": "(X, Y, all_events, f_values, f_value_counts, cardinality_correction):", "funcdef": "def"}, "outrank.algorithms.feature_ranking.ranking_mi_numba.mutual_info_estimator_numba": {"fullname": "outrank.algorithms.feature_ranking.ranking_mi_numba.mutual_info_estimator_numba", "modulename": "outrank.algorithms.feature_ranking.ranking_mi_numba", "qualname": "mutual_info_estimator_numba", "kind": "function", "doc": "

Core estimator logic. Compute unique elements, subset if required

\n", "signature": "(Y, X, approximation_factor=1, cardinality_correction=False):", "funcdef": "def"}, "outrank.algorithms.importance_estimator": {"fullname": "outrank.algorithms.importance_estimator", "modulename": "outrank.algorithms.importance_estimator", "kind": "module", "doc": "

\n"}, "outrank.algorithms.importance_estimator.logger": {"fullname": "outrank.algorithms.importance_estimator.logger", "modulename": "outrank.algorithms.importance_estimator", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger syn-logger (DEBUG)>"}, "outrank.algorithms.importance_estimator.sklearn_MI": {"fullname": "outrank.algorithms.importance_estimator.sklearn_MI", "modulename": "outrank.algorithms.importance_estimator", "qualname": "sklearn_MI", "kind": "function", "doc": "

\n", "signature": "(vector_first: Any, vector_second: Any) -> float:", "funcdef": "def"}, "outrank.algorithms.importance_estimator.sklearn_surrogate": {"fullname": "outrank.algorithms.importance_estimator.sklearn_surrogate", "modulename": "outrank.algorithms.importance_estimator", "qualname": "sklearn_surrogate", "kind": "function", "doc": "

\n", "signature": "(vector_first: Any, vector_second: Any, surrogate_model: str) -> float:", "funcdef": "def"}, "outrank.algorithms.importance_estimator.numba_mi": {"fullname": "outrank.algorithms.importance_estimator.numba_mi", "modulename": "outrank.algorithms.importance_estimator", "qualname": "numba_mi", "kind": "function", "doc": "

\n", "signature": "(vector_first, vector_second, heuristic):", "funcdef": "def"}, "outrank.algorithms.importance_estimator.sklearn_mi_adj": {"fullname": "outrank.algorithms.importance_estimator.sklearn_mi_adj", "modulename": "outrank.algorithms.importance_estimator", "qualname": "sklearn_mi_adj", "kind": "function", "doc": "

\n", "signature": "(vector_first, vector_second):", "funcdef": "def"}, "outrank.algorithms.importance_estimator.get_importances_estimate_pairwise": {"fullname": "outrank.algorithms.importance_estimator.get_importances_estimate_pairwise", "modulename": "outrank.algorithms.importance_estimator", "qualname": "get_importances_estimate_pairwise", "kind": "function", "doc": "

A method for parallel importances estimation. As interaction scoring is independent, individual scores can be computed in parallel.

\n", "signature": "(combination, args, tmp_df):", "funcdef": "def"}, "outrank.algorithms.importance_estimator.rank_features_3MR": {"fullname": "outrank.algorithms.importance_estimator.rank_features_3MR", "modulename": "outrank.algorithms.importance_estimator", "qualname": "rank_features_3MR", "kind": "function", "doc": "

\n", "signature": "(\trelevance_dict: dict[str, float],\tredundancy_dict: dict[tuple[typing.Any, typing.Any], typing.Any],\trelational_dict: dict[tuple[typing.Any, typing.Any], typing.Any],\tstrategy: str = 'median',\talpha: float = 1,\tbeta: float = 1) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.algorithms.importance_estimator.get_importances_estimate_nonmyopic": {"fullname": "outrank.algorithms.importance_estimator.get_importances_estimate_nonmyopic", "modulename": "outrank.algorithms.importance_estimator", "qualname": "get_importances_estimate_nonmyopic", "kind": "function", "doc": "

\n", "signature": "(args: Any, tmp_df: pandas.core.frame.DataFrame):", "funcdef": "def"}, "outrank.algorithms.sketches": {"fullname": "outrank.algorithms.sketches", "modulename": "outrank.algorithms.sketches", "kind": "module", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "kind": "module", "doc": "

This module implements probabilistic data structure which is able to calculate the cardinality of large multisets in a single pass using little auxiliary memory

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache", "kind": "class", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.__init__": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.__init__", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.__init__", "kind": "function", "doc": "

\n", "signature": "(error_rate=0.005)"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.p": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.p", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.p", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.m": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.m", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.m", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_set": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_set", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.warmup_set", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_size": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_size", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.warmup_size", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.width": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.width", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.width", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.hll_flag": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.hll_flag", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.hll_flag", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.add": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.add", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.add", "kind": "function", "doc": "

\n", "signature": "(self, value):", "funcdef": "def"}, "outrank.algorithms.synthetic_data_generators": {"fullname": "outrank.algorithms.synthetic_data_generators", "modulename": "outrank.algorithms.synthetic_data_generators", "kind": "module", "doc": "

\n"}, "outrank.algorithms.synthetic_data_generators.generator_naive": {"fullname": "outrank.algorithms.synthetic_data_generators.generator_naive", "modulename": "outrank.algorithms.synthetic_data_generators.generator_naive", "kind": "module", "doc": "

\n"}, "outrank.algorithms.synthetic_data_generators.generator_naive.generate_random_matrix": {"fullname": "outrank.algorithms.synthetic_data_generators.generator_naive.generate_random_matrix", "modulename": "outrank.algorithms.synthetic_data_generators.generator_naive", "qualname": "generate_random_matrix", "kind": "function", "doc": "

\n", "signature": "(num_features=100, size=20000):", "funcdef": "def"}, "outrank.core_ranking": {"fullname": "outrank.core_ranking", "modulename": "outrank.core_ranking", "kind": "module", "doc": "

\n"}, "outrank.core_ranking.logger": {"fullname": "outrank.core_ranking.logger", "modulename": "outrank.core_ranking", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger syn-logger (DEBUG)>"}, "outrank.core_ranking.GLOBAL_CARDINALITY_STORAGE": {"fullname": "outrank.core_ranking.GLOBAL_CARDINALITY_STORAGE", "modulename": "outrank.core_ranking", "qualname": "GLOBAL_CARDINALITY_STORAGE", "kind": "variable", "doc": "

\n", "annotation": ": dict[typing.Any, typing.Any]", "default_value": "{}"}, "outrank.core_ranking.GLOBAL_RARE_VALUE_STORAGE": {"fullname": "outrank.core_ranking.GLOBAL_RARE_VALUE_STORAGE", "modulename": "outrank.core_ranking", "qualname": "GLOBAL_RARE_VALUE_STORAGE", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, typing.Any]", "default_value": "Counter()"}, "outrank.core_ranking.GLOBAL_PRIOR_COMB_COUNTS": {"fullname": "outrank.core_ranking.GLOBAL_PRIOR_COMB_COUNTS", "modulename": "outrank.core_ranking", "qualname": "GLOBAL_PRIOR_COMB_COUNTS", "kind": "variable", "doc": "

\n", "annotation": ": dict[typing.Any, int]", "default_value": "Counter()"}, "outrank.core_ranking.IGNORED_VALUES": {"fullname": "outrank.core_ranking.IGNORED_VALUES", "modulename": "outrank.core_ranking", "qualname": "IGNORED_VALUES", "kind": "variable", "doc": "

\n", "default_value": "set()"}, "outrank.core_ranking.HYPERLL_ERROR_BOUND": {"fullname": "outrank.core_ranking.HYPERLL_ERROR_BOUND", "modulename": "outrank.core_ranking", "qualname": "HYPERLL_ERROR_BOUND", "kind": "variable", "doc": "

\n", "default_value": "0.02"}, "outrank.core_ranking.prior_combinations_sample": {"fullname": "outrank.core_ranking.prior_combinations_sample", "modulename": "outrank.core_ranking", "qualname": "prior_combinations_sample", "kind": "function", "doc": "

Make sure only relevant subspace of combinations is selected based on prior counts

\n", "signature": "(\tcombinations: list[tuple[typing.Any, ...]],\targs: Any) -> list[tuple[typing.Any, ...]]:", "funcdef": "def"}, "outrank.core_ranking.mixed_rank_graph": {"fullname": "outrank.core_ranking.mixed_rank_graph", "modulename": "outrank.core_ranking", "qualname": "mixed_rank_graph", "kind": "function", "doc": "

Compute the full mixed rank graph corresponding to all pairwise feature interactions based on the selected heuristic

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any,\tcpu_pool: Any,\tpbar: Any) -> outrank.core_utils.BatchRankingSummary:", "funcdef": "def"}, "outrank.core_ranking.enrich_with_transformations": {"fullname": "outrank.core_ranking.enrich_with_transformations", "modulename": "outrank.core_ranking", "qualname": "enrich_with_transformations", "kind": "function", "doc": "

Construct a collection of new features based on pre-defined transformations/rules

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tnum_col_types: set[str],\tlogger: Any,\targs: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_combined_features": {"fullname": "outrank.core_ranking.compute_combined_features", "modulename": "outrank.core_ranking", "qualname": "compute_combined_features", "kind": "function", "doc": "

Compute higher order features via xxhash-based trick.

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any,\tpbar: Any,\tis_3mr: bool = False) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_expanded_multivalue_features": {"fullname": "outrank.core_ranking.compute_expanded_multivalue_features", "modulename": "outrank.core_ranking", "qualname": "compute_expanded_multivalue_features", "kind": "function", "doc": "

Compute one-hot encoded feature space based on each designated multivalue feature. E.g., feature with value \"a,b,c\" becomes three features, values of which are presence of a given value in a mutlivalue feature of choice.

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any,\tpbar: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_subfeatures": {"fullname": "outrank.core_ranking.compute_subfeatures", "modulename": "outrank.core_ranking", "qualname": "compute_subfeatures", "kind": "function", "doc": "

Compute derived features that are more fine-grained. Implements logic around two operators that govern feature construction.\n->: One sided construction - every value from left side is fine, separate ones from the right side feature will be considered.\n<->: Two sided construction - two-sided values present. This means that each value from a is combined with each from b, forming |A|*|B| new features (one-hot encoded)

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any,\tpbar: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.include_noisy_features": {"fullname": "outrank.core_ranking.include_noisy_features", "modulename": "outrank.core_ranking", "qualname": "include_noisy_features", "kind": "function", "doc": "

Add randomized features that serve as a sanity check

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_coverage": {"fullname": "outrank.core_ranking.compute_coverage", "modulename": "outrank.core_ranking", "qualname": "compute_coverage", "kind": "function", "doc": "

Compute coverage of features, incrementally

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any) -> dict[str, set[str]]:", "funcdef": "def"}, "outrank.core_ranking.compute_feature_memory_consumption": {"fullname": "outrank.core_ranking.compute_feature_memory_consumption", "modulename": "outrank.core_ranking", "qualname": "compute_feature_memory_consumption", "kind": "function", "doc": "

An approximation of how much feature take up

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any) -> dict[str, set[str]]:", "funcdef": "def"}, "outrank.core_ranking.compute_value_counts": {"fullname": "outrank.core_ranking.compute_value_counts", "modulename": "outrank.core_ranking", "qualname": "compute_value_counts", "kind": "function", "doc": "

Update the count structure

\n", "signature": "(input_dataframe: pandas.core.frame.DataFrame, args: Any):", "funcdef": "def"}, "outrank.core_ranking.compute_cardinalities": {"fullname": "outrank.core_ranking.compute_cardinalities", "modulename": "outrank.core_ranking", "qualname": "compute_cardinalities", "kind": "function", "doc": "

Compute cardinalities of features, incrementally

\n", "signature": "(input_dataframe: pandas.core.frame.DataFrame, pbar: Any) -> None:", "funcdef": "def"}, "outrank.core_ranking.compute_bounds_increment": {"fullname": "outrank.core_ranking.compute_bounds_increment", "modulename": "outrank.core_ranking", "qualname": "compute_bounds_increment", "kind": "function", "doc": "

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tnumeric_column_types: set[str]) -> dict[str, typing.Any]:", "funcdef": "def"}, "outrank.core_ranking.compute_batch_ranking": {"fullname": "outrank.core_ranking.compute_batch_ranking", "modulename": "outrank.core_ranking", "qualname": "compute_batch_ranking", "kind": "function", "doc": "

Enrich the feature space and compute the batch importances

\n", "signature": "(\tline_tmp_storage: list[list[typing.Any]],\tnumeric_column_types: set[str],\targs: Any,\tcpu_pool: Any,\tcolumn_descriptions: list[str],\tlogger: Any,\tpbar: Any) -> tuple[outrank.core_utils.BatchRankingSummary, dict[str, typing.Any], dict[str, set[str]], dict[str, set[str]]]:", "funcdef": "def"}, "outrank.core_ranking.get_num_of_instances": {"fullname": "outrank.core_ranking.get_num_of_instances", "modulename": "outrank.core_ranking", "qualname": "get_num_of_instances", "kind": "function", "doc": "

Count the number of lines in a file, fast - useful for progress logging

\n", "signature": "(fname: str) -> int:", "funcdef": "def"}, "outrank.core_ranking.get_grouped_df": {"fullname": "outrank.core_ranking.get_grouped_df", "modulename": "outrank.core_ranking", "qualname": "get_grouped_df", "kind": "function", "doc": "

A helper method that enables median-based aggregation after processing

\n", "signature": "(\timportances_df_list: list[tuple[str, str, float]]) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.checkpoint_importances_df": {"fullname": "outrank.core_ranking.checkpoint_importances_df", "modulename": "outrank.core_ranking", "qualname": "checkpoint_importances_df", "kind": "function", "doc": "

A helper which stores intermediary state - useful for longer runs

\n", "signature": "(importances_batch: list[tuple[str, str, float]]) -> None:", "funcdef": "def"}, "outrank.core_ranking.estimate_importances_minibatches": {"fullname": "outrank.core_ranking.estimate_importances_minibatches", "modulename": "outrank.core_ranking", "qualname": "estimate_importances_minibatches", "kind": "function", "doc": "

Interaction score estimator - suitable for example for csv-like input data types.\nThis type of data is normally a single large csv, meaning that minibatch processing needs to\nhappen during incremental handling of the file (that\"s not the case for pre-separated ob data)

\n", "signature": "(\tinput_file: str,\tcolumn_descriptions: list,\tfw_col_mapping: dict[str, str],\tnumeric_column_types: set,\tbatch_size: int = 100000,\targs: Any = None,\tdata_encoding: str = 'utf-8',\tcpu_pool: Any = None,\tdelimiter: str = '\\t',\tfeature_construction_mode: bool = False,\tlogger: Any = None) -> tuple[list[dict[str, typing.Any]], typing.Any, dict[typing.Any, typing.Any], list[dict[str, typing.Any]], list[dict[str, set[str]]], collections.defaultdict[str, list[set[str]]], dict[str, typing.Any]]:", "funcdef": "def"}, "outrank.core_selftest": {"fullname": "outrank.core_selftest", "modulename": "outrank.core_selftest", "kind": "module", "doc": "

\n"}, "outrank.core_utils": {"fullname": "outrank.core_utils", "modulename": "outrank.core_utils", "kind": "module", "doc": "

\n"}, "outrank.core_utils.pro_tips": {"fullname": "outrank.core_utils.pro_tips", "modulename": "outrank.core_utils", "qualname": "pro_tips", "kind": "variable", "doc": "

\n", "default_value": "['OutRank can construct subfeatures; features based on subspaces. Example command argument is: --subfeature_mapping "feature_a->feature_b;feature_c<->feature_d;feature_c<->feature_e"', 'Heuristic MI-numba-randomized seems like the best of both worlds! (speed + performance).', 'Heuristic surrogate-lr performs cross-validation (internally), keep that in mind!', 'Consider running OutRank on a smaller data sample first, might be enough (--subsampling = a lot).', 'There are two types of combinations supported; unsupervised pairwise ranking (redundancies- --target_ranking_only=False), and supervised combinations - (--interaction_order > 1)', 'Visualization part also includes clustering - this might be very insightful!', 'By default OutRank includes feature cardinality and coverage in feature names (card; cov)', 'Intermediary checkpoints (tmp_checkpoint.tsv) might already give you insights during longer runs.', 'In theory, you can rank redundancies of combined features (--interaction_order AND --target_ranking_only=False).', 'Give it as many threads as physically possible (--num_threads).', 'You can speed up ranking by diminishing feature buffer size (--combination_number_upper_bound determines how many ranking computations per batch will be considered). This, and --subsampling are very powerful together.', 'Want to rank feature transformations, but not sure which ones to choose? --transformers=default should serve as a solid baseline (common DS transformations included).', 'Your target can be any feature! (explaining one feature with others)', 'OutRank uses HyperLogLog for cardinality estimation - this is also a potential usecase (understanding cardinalities across different data sets).', 'Each feature is named as featureName(cardinality, coverage in percents) in the final files.', 'You can generate candidate feature transformation ranges (fw) by using --task=feature_summary_transformers.']"}, "outrank.core_utils.write_json_dump_to_file": {"fullname": "outrank.core_utils.write_json_dump_to_file", "modulename": "outrank.core_utils", "qualname": "write_json_dump_to_file", "kind": "function", "doc": "

\n", "signature": "(args: Any, config_name: str) -> None:", "funcdef": "def"}, "outrank.core_utils.internal_hash": {"fullname": "outrank.core_utils.internal_hash", "modulename": "outrank.core_utils", "qualname": "internal_hash", "kind": "function", "doc": "

A generic internal hash used throughout ranking procedure - let's hardcode seed here for sure

\n", "signature": "(input_obj: str) -> str:", "funcdef": "def"}, "outrank.core_utils.DatasetInformationStorage": {"fullname": "outrank.core_utils.DatasetInformationStorage", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage", "kind": "class", "doc": "

A generic class for holding properties of a given type of dataset

\n"}, "outrank.core_utils.DatasetInformationStorage.__init__": {"fullname": "outrank.core_utils.DatasetInformationStorage.__init__", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.__init__", "kind": "function", "doc": "

\n", "signature": "(\tdata_path: str,\tcolumn_names: list[str],\tcolumn_types: set[str],\tcol_delimiter: str | None,\tencoding: str,\tfw_map: dict[str, str] | None)"}, "outrank.core_utils.DatasetInformationStorage.data_path": {"fullname": "outrank.core_utils.DatasetInformationStorage.data_path", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.data_path", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.DatasetInformationStorage.column_names": {"fullname": "outrank.core_utils.DatasetInformationStorage.column_names", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.column_names", "kind": "variable", "doc": "

\n", "annotation": ": list[str]"}, "outrank.core_utils.DatasetInformationStorage.column_types": {"fullname": "outrank.core_utils.DatasetInformationStorage.column_types", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.column_types", "kind": "variable", "doc": "

\n", "annotation": ": set[str]"}, "outrank.core_utils.DatasetInformationStorage.col_delimiter": {"fullname": "outrank.core_utils.DatasetInformationStorage.col_delimiter", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.col_delimiter", "kind": "variable", "doc": "

\n", "annotation": ": str | None"}, "outrank.core_utils.DatasetInformationStorage.encoding": {"fullname": "outrank.core_utils.DatasetInformationStorage.encoding", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.encoding", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.DatasetInformationStorage.fw_map": {"fullname": "outrank.core_utils.DatasetInformationStorage.fw_map", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.fw_map", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, str] | None"}, "outrank.core_utils.NumericFeatureSummary": {"fullname": "outrank.core_utils.NumericFeatureSummary", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary", "kind": "class", "doc": "

A generic class storing numeric feature statistics

\n"}, "outrank.core_utils.NumericFeatureSummary.__init__": {"fullname": "outrank.core_utils.NumericFeatureSummary.__init__", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(\tfeature_name: str,\tminimum: float,\tmaximum: float,\tmedian: float,\tnum_unique: int)"}, "outrank.core_utils.NumericFeatureSummary.feature_name": {"fullname": "outrank.core_utils.NumericFeatureSummary.feature_name", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.feature_name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.NumericFeatureSummary.minimum": {"fullname": "outrank.core_utils.NumericFeatureSummary.minimum", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.minimum", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.maximum": {"fullname": "outrank.core_utils.NumericFeatureSummary.maximum", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.maximum", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.median": {"fullname": "outrank.core_utils.NumericFeatureSummary.median", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.median", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.num_unique": {"fullname": "outrank.core_utils.NumericFeatureSummary.num_unique", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.num_unique", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "outrank.core_utils.NominalFeatureSummary": {"fullname": "outrank.core_utils.NominalFeatureSummary", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary", "kind": "class", "doc": "

A generic class storing numeric feature statistics

\n"}, "outrank.core_utils.NominalFeatureSummary.__init__": {"fullname": "outrank.core_utils.NominalFeatureSummary.__init__", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(feature_name: str, num_unique: int)"}, "outrank.core_utils.NominalFeatureSummary.feature_name": {"fullname": "outrank.core_utils.NominalFeatureSummary.feature_name", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.feature_name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.NominalFeatureSummary.num_unique": {"fullname": "outrank.core_utils.NominalFeatureSummary.num_unique", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.num_unique", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "outrank.core_utils.BatchRankingSummary": {"fullname": "outrank.core_utils.BatchRankingSummary", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary", "kind": "class", "doc": "

A generic class representing batched ranking results

\n"}, "outrank.core_utils.BatchRankingSummary.__init__": {"fullname": "outrank.core_utils.BatchRankingSummary.__init__", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(\ttriplet_scores: list[tuple[str, str, float]],\tstep_times: dict[str, typing.Any])"}, "outrank.core_utils.BatchRankingSummary.triplet_scores": {"fullname": "outrank.core_utils.BatchRankingSummary.triplet_scores", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.triplet_scores", "kind": "variable", "doc": "

\n", "annotation": ": list[tuple[str, str, float]]"}, "outrank.core_utils.BatchRankingSummary.step_times": {"fullname": "outrank.core_utils.BatchRankingSummary.step_times", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.step_times", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, typing.Any]"}, "outrank.core_utils.display_random_tip": {"fullname": "outrank.core_utils.display_random_tip", "modulename": "outrank.core_utils", "qualname": "display_random_tip", "kind": "function", "doc": "

\n", "signature": "() -> None:", "funcdef": "def"}, "outrank.core_utils.get_dataset_info": {"fullname": "outrank.core_utils.get_dataset_info", "modulename": "outrank.core_utils", "qualname": "get_dataset_info", "kind": "function", "doc": "

\n", "signature": "(args: Any):", "funcdef": "def"}, "outrank.core_utils.display_tool_name": {"fullname": "outrank.core_utils.display_tool_name", "modulename": "outrank.core_utils", "qualname": "display_tool_name", "kind": "function", "doc": "

\n", "signature": "() -> None:", "funcdef": "def"}, "outrank.core_utils.parse_ob_line": {"fullname": "outrank.core_utils.parse_ob_line", "modulename": "outrank.core_utils", "qualname": "parse_ob_line", "kind": "function", "doc": "

Outbrain line parsing - generic TSVs

\n", "signature": "(line_string: str, delimiter: str = '\\t', args: Any = None) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_line_vw": {"fullname": "outrank.core_utils.parse_ob_line_vw", "modulename": "outrank.core_utils", "qualname": "parse_ob_line_vw", "kind": "function", "doc": "

Parse a sparse vw line into a pandas df with pre-defined namespace

\n", "signature": "(\tline_string: str,\tdelimiter: str,\targs: Any = None,\tfw_col_mapping=None,\ttable_header=None,\tinclude_namespace_info=False) -> list[str | None]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_csv_line": {"fullname": "outrank.core_utils.parse_ob_csv_line", "modulename": "outrank.core_utils", "qualname": "parse_ob_csv_line", "kind": "function", "doc": "

Data can have commas within JSON field dumps

\n", "signature": "(line_string: str, delimiter: str = ',', args: Any = None) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.generic_line_parser": {"fullname": "outrank.core_utils.generic_line_parser", "modulename": "outrank.core_utils", "qualname": "generic_line_parser", "kind": "function", "doc": "

A generic method aimed to parse data from different sources.

\n", "signature": "(\tline_string: str,\tdelimiter: str,\targs: Any = None,\tfw_col_mapping: Any = None,\ttable_header: Any = None) -> list[typing.Any]:", "funcdef": "def"}, "outrank.core_utils.read_reference_json": {"fullname": "outrank.core_utils.read_reference_json", "modulename": "outrank.core_utils", "qualname": "read_reference_json", "kind": "function", "doc": "

A helper method for reading a JSON

\n", "signature": "(json_path) -> dict[str, dict]:", "funcdef": "def"}, "outrank.core_utils.parse_namespace": {"fullname": "outrank.core_utils.parse_namespace", "modulename": "outrank.core_utils", "qualname": "parse_namespace", "kind": "function", "doc": "

Parse the feature namespace for type awareness

\n", "signature": "(namespace_path: str) -> tuple[set[str], dict[str, str]]:", "funcdef": "def"}, "outrank.core_utils.read_column_names": {"fullname": "outrank.core_utils.read_column_names", "modulename": "outrank.core_utils", "qualname": "read_column_names", "kind": "function", "doc": "

Read the col. header

\n", "signature": "(mapping_file: str) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_vw_feature_information": {"fullname": "outrank.core_utils.parse_ob_vw_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_vw_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_ob_raw_feature_information": {"fullname": "outrank.core_utils.parse_ob_raw_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_raw_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_ob_feature_information": {"fullname": "outrank.core_utils.parse_ob_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_csv_with_description_information": {"fullname": "outrank.core_utils.parse_csv_with_description_information", "modulename": "outrank.core_utils", "qualname": "parse_csv_with_description_information", "kind": "function", "doc": "

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_csv_raw": {"fullname": "outrank.core_utils.parse_csv_raw", "modulename": "outrank.core_utils", "qualname": "parse_csv_raw", "kind": "function", "doc": "

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.extract_features_from_reference_JSON": {"fullname": "outrank.core_utils.extract_features_from_reference_JSON", "modulename": "outrank.core_utils", "qualname": "extract_features_from_reference_JSON", "kind": "function", "doc": "

Given a model's JSON, extract unique features

\n", "signature": "(json_path: str) -> set[typing.Any]:", "funcdef": "def"}, "outrank.core_utils.summarize_feature_bounds_for_transformers": {"fullname": "outrank.core_utils.summarize_feature_bounds_for_transformers", "modulename": "outrank.core_utils", "qualname": "summarize_feature_bounds_for_transformers", "kind": "function", "doc": "

summarization auxilliary method for generating JSON-based specs

\n", "signature": "(\tbounds_object_storage: Any,\tfeature_types: list[str],\ttask_name: str,\tlabel_name: str,\tgranularity: int = 15,\toutput_summary_table_only: bool = False):", "funcdef": "def"}, "outrank.core_utils.summarize_rare_counts": {"fullname": "outrank.core_utils.summarize_rare_counts", "modulename": "outrank.core_utils", "qualname": "summarize_rare_counts", "kind": "function", "doc": "

Write rare values

\n", "signature": "(\tterm_counter: Any,\targs: Any,\tcardinality_object: Any,\tobject_info: outrank.core_utils.DatasetInformationStorage) -> None:", "funcdef": "def"}, "outrank.feature_transformations": {"fullname": "outrank.feature_transformations", "modulename": "outrank.feature_transformations", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault": {"fullname": "outrank.feature_transformations.feature_transformer_vault", "modulename": "outrank.feature_transformations.feature_transformer_vault", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers.MINIMAL_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers.MINIMAL_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "qualname": "MINIMAL_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)'}"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers.DEFAULT_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers.DEFAULT_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "qualname": "DEFAULT_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)', '_tr_div(x,abs(x))*log(abs(x))': 'np.divide(X, np.abs(X)) * np.log(np.abs(X))', '_tr_log(x + sqrt(pow(x,2), 1)': 'np.log(X + np.sqrt(np.power(X, 2) + 1))', '_tr_log*sqrt': 'np.log(X + 1) * np.sqrt(X)', '_tr_log*100': 'np.round(np.log(X + 1) * 100, 0)', '_tr_nonzero': 'np.where(X != 0, 1, 0)', '_tr_round(div(x,max))': 'np.round(np.divide(X, np.max(X)), 0)'}"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.FW_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.FW_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "FW_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)', '_tr_div(x,abs(x))*log(abs(x))': 'np.divide(X, np.abs(X)) * np.log(np.abs(X))', '_tr_log(x + sqrt(pow(x,2), 1)': 'np.log(X + np.sqrt(np.power(X, 2) + 1))', '_tr_log*sqrt': 'np.log(X + 1) * np.sqrt(X)', '_tr_log*100': 'np.round(np.log(X + 1) * 100, 0)', '_tr_nonzero': 'np.where(X != 0, 1, 0)', '_tr_round(div(x,max))': 'np.round(np.divide(X, np.max(X)), 0)', '_tr_fw_sqrt_res_1_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*1,0), 0))', '_tr_fw_log_res_1_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*1,0), 0))', '_tr_fw_log_res_1_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*1,0), 0))', '_tr_fw_log_res_1_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*1,0), 0))', '_tr_fw_log_res_1_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*1,0), 0))', '_tr_fw_log_res_1_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*1,0), 0))', '_tr_fw_log_res_1_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*1,0), 0))', '_tr_fw_log_res_1_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*1,0), 0))', '_tr_fw_log_res_1_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*1,0), 0))', '_tr_fw_sqrt_res_10_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*10,0), 0))', '_tr_fw_log_res_10_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*10,0), 0))', '_tr_fw_log_res_10_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*10,0), 0))', '_tr_fw_log_res_10_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*10,0), 0))', '_tr_fw_log_res_10_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*10,0), 0))', '_tr_fw_log_res_10_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*10,0), 0))', '_tr_fw_log_res_10_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*10,0), 0))', '_tr_fw_log_res_10_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*10,0), 0))', '_tr_fw_log_res_10_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*10,0), 0))', '_tr_fw_sqrt_res_50_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*50,0), 0))', '_tr_fw_log_res_50_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*50,0), 0))', '_tr_fw_log_res_50_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*50,0), 0))', '_tr_fw_log_res_50_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*50,0), 0))', '_tr_fw_log_res_50_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*50,0), 0))', '_tr_fw_log_res_50_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*50,0), 0))', '_tr_fw_log_res_50_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*50,0), 0))', '_tr_fw_log_res_50_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*50,0), 0))', '_tr_fw_log_res_50_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*50,0), 0))', '_tr_fw_sqrt_res_100_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*100,0), 0))', '_tr_fw_log_res_100_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*100,0), 0))', '_tr_fw_log_res_100_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*100,0), 0))', '_tr_fw_log_res_100_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*100,0), 0))', '_tr_fw_log_res_100_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*100,0), 0))', '_tr_fw_log_res_100_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*100,0), 0))', '_tr_fw_log_res_100_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*100,0), 0))', '_tr_fw_log_res_100_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*100,0), 0))', '_tr_fw_log_res_100_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*100,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*1,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*10,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*50,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*100,0), 0))'}"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.resolution_range": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.resolution_range", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "resolution_range", "kind": "variable", "doc": "

\n", "default_value": "[1, 10, 50, 100]"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.greater_than_range": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.greater_than_range", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "greater_than_range", "kind": "variable", "doc": "

\n", "default_value": "[1, 2, 4, 8, 16, 32, 64, 96]"}, "outrank.feature_transformations.ranking_transformers": {"fullname": "outrank.feature_transformations.ranking_transformers", "modulename": "outrank.feature_transformations.ranking_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerNoise", "kind": "class", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.noise_preset": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.noise_preset", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerNoise.noise_preset", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.construct_new_features": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.construct_new_features", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerNoise.construct_new_features", "kind": "function", "doc": "

Generate a few standard noise distributions

\n", "signature": "(self, dataframe: pandas.core.frame.DataFrame, label_column=None):", "funcdef": "def"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric", "kind": "class", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.__init__": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.__init__", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.__init__", "kind": "function", "doc": "

\n", "signature": "(numeric_column_names: set[str], preset: str = 'default')"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.numeric_column_names": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.numeric_column_names", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.numeric_column_names", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.constructed_feature_names": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.constructed_feature_names", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.constructed_feature_names", "kind": "variable", "doc": "

\n", "annotation": ": set[str]"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.max_maj_support": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.max_maj_support", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.max_maj_support", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.nan_prop_support": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.nan_prop_support", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.nan_prop_support", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.get_vals": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.get_vals", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.get_vals", "kind": "function", "doc": "

\n", "signature": "(self, tmp_df: pandas.core.frame.DataFrame, col_name: str) -> Any:", "funcdef": "def"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.construct_baseline_features": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.construct_baseline_features", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.construct_baseline_features", "kind": "function", "doc": "

\n", "signature": "(self, dataframe: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.construct_new_features": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.construct_new_features", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.construct_new_features", "kind": "function", "doc": "

\n", "signature": "(self, dataframe: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.task_generators": {"fullname": "outrank.task_generators", "modulename": "outrank.task_generators", "kind": "module", "doc": "

\n"}, "outrank.task_generators.logger": {"fullname": "outrank.task_generators.logger", "modulename": "outrank.task_generators", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger syn-logger (DEBUG)>"}, "outrank.task_generators.outrank_task_generate_data_set": {"fullname": "outrank.task_generators.outrank_task_generate_data_set", "modulename": "outrank.task_generators", "qualname": "outrank_task_generate_data_set", "kind": "function", "doc": "

Core method for generating data sets

\n", "signature": "(args):", "funcdef": "def"}, "outrank.task_ranking": {"fullname": "outrank.task_ranking", "modulename": "outrank.task_ranking", "kind": "module", "doc": "

\n"}, "outrank.task_ranking.outrank_task_conduct_ranking": {"fullname": "outrank.task_ranking.outrank_task_conduct_ranking", "modulename": "outrank.task_ranking", "qualname": "outrank_task_conduct_ranking", "kind": "function", "doc": "

\n", "signature": "(args: Any):", "funcdef": "def"}, "outrank.task_selftest": {"fullname": "outrank.task_selftest", "modulename": "outrank.task_selftest", "kind": "module", "doc": "

\n"}, "outrank.task_selftest.logger": {"fullname": "outrank.task_selftest.logger", "modulename": "outrank.task_selftest", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger syn-logger (DEBUG)>"}, "outrank.task_selftest.conduct_self_test": {"fullname": "outrank.task_selftest.conduct_self_test", "modulename": "outrank.task_selftest", "qualname": "conduct_self_test", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "outrank.task_summary": {"fullname": "outrank.task_summary", "modulename": "outrank.task_summary", "kind": "module", "doc": "

\n"}, "outrank.task_summary.outrank_task_result_summary": {"fullname": "outrank.task_summary.outrank_task_result_summary", "modulename": "outrank.task_summary", "qualname": "outrank_task_result_summary", "kind": "function", "doc": "

\n", "signature": "(args):", "funcdef": "def"}, "outrank.task_visualization": {"fullname": "outrank.task_visualization", "modulename": "outrank.task_visualization", "kind": "module", "doc": "

\n"}, "outrank.task_visualization.outrank_task_visualize_results": {"fullname": "outrank.task_visualization.outrank_task_visualize_results", "modulename": "outrank.task_visualization", "qualname": "outrank_task_visualize_results", "kind": "function", "doc": "

\n", "signature": "(args):", "funcdef": "def"}, "outrank.visualizations": {"fullname": "outrank.visualizations", "modulename": "outrank.visualizations", "kind": "module", "doc": "

\n"}, "outrank.visualizations.ranking_visualization": {"fullname": "outrank.visualizations.ranking_visualization", "modulename": "outrank.visualizations.ranking_visualization", "kind": "module", "doc": "

\n"}, "outrank.visualizations.ranking_visualization.visualize_hierarchical_clusters": {"fullname": "outrank.visualizations.ranking_visualization.visualize_hierarchical_clusters", "modulename": "outrank.visualizations.ranking_visualization", "qualname": "visualize_hierarchical_clusters", "kind": "function", "doc": "

A method for visualization of hierarchical clusters w.r.t. different linkage functions

\n", "signature": "(\ttriplet_dataframe: pandas.core.frame.DataFrame,\toutput_folder: str,\timage_format: str = 'png',\tmax_num_clusters: int = 100) -> None:", "funcdef": "def"}, "outrank.visualizations.ranking_visualization.visualize_heatmap": {"fullname": "outrank.visualizations.ranking_visualization.visualize_heatmap", "modulename": "outrank.visualizations.ranking_visualization", "qualname": "visualize_heatmap", "kind": "function", "doc": "

\n", "signature": "(\ttriplets: pandas.core.frame.DataFrame,\toutput_folder: str,\timage_format: str) -> None:", "funcdef": "def"}, "outrank.visualizations.ranking_visualization.visualize_barplots": {"fullname": "outrank.visualizations.ranking_visualization.visualize_barplots", "modulename": "outrank.visualizations.ranking_visualization", "qualname": "visualize_barplots", "kind": "function", "doc": "

\n", "signature": "(\ttriplets: pandas.core.frame.DataFrame,\toutput_folder: str,\treference_json: str,\timage_format: str,\tlabel: str,\theuristic: str) -> None:", "funcdef": "def"}, "outrank.visualizations.ranking_visualization.visualize_all": {"fullname": "outrank.visualizations.ranking_visualization.visualize_all", "modulename": "outrank.visualizations.ranking_visualization", "qualname": "visualize_all", "kind": "function", "doc": "

A method for visualization of the obtained feature interaction maps.

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long_description=_read_description(), long_description_content_type='text/markdown',