diff --git a/docs/DOCSMAIN.md b/docs/DOCSMAIN.md index 0961a34..1b6681a 100644 --- a/docs/DOCSMAIN.md +++ b/docs/DOCSMAIN.md @@ -33,3 +33,34 @@ outrank --help * A minimal showcase of performing feature ranking on a generic CSV is demonstrated with [this example](https://github.com/outbrain/outrank/tree/main/scripts/run_minimal.sh). * [More examples](https://github.com/outbrain/outrank/tree/main/examples) demonstrating OutRank's capabilities are also available. + + +# OutRank as a Python library +Once installed, _OutRank_ can be used as any other Python library. For example, generic feature ranking algorithms can be accessed as + +```python +from outrank.algorithms.feature_ranking.ranking_mi_numba import ( + mutual_info_estimator_numba, +) + +# Some synthetic minimal data (Numpy vectors) +a = np.array([1, 0, 0, 0, 1, 1, 1, 0], dtype=np.int32) + +lowest = np.array(np.random.permutation(a), dtype=np.int32) +medium = np.array([1, 1, 0, 0, 1, 1, 1, 1], dtype=np.int32) +high = np.array([1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int32) + +lowest_score = mutual_info_estimator_numba( + a, lowest, np.float32(1.0), False, +) +medium_score = mutual_info_estimator_numba( + a, medium, np.float32(1.0), False, +) +high_score = mutual_info_estimator_numba( + a, high, np.float32(1.0), False, +) + +scores = [lowest_score, medium_score, high_score] +sorted_score_indices = np.argsort(scores) +assert np.sum(np.array([0, 1, 2]) - sorted_score_indices) == 0 +``` diff --git a/docs/outrank.html b/docs/outrank.html index c1c4414..461d09d 100644 --- a/docs/outrank.html +++ b/docs/outrank.html @@ -26,6 +26,7 @@

Contents

  • Welcome to OutRank's documentation!
  • Setup
  • Example use cases
    • +
  • OutRank as a Python library
    • @@ -96,6 +97,38 @@

    Example use cases

  • A minimal showcase of performing feature ranking on a generic CSV is demonstrated with this example.

  • More examples demonstrating OutRank's capabilities are also available.

    • + +

    OutRank as a Python library

    + +

    Once installed, _OutRank_ can be used as any other Python library. For example, generic feature ranking algorithms can be accessed as

    + +
    +
    from outrank.algorithms.feature_ranking.ranking_mi_numba import (
+    mutual_info_estimator_numba,
+)
+
+# Some synthetic minimal data (Numpy vectors)
+a = np.array([1, 0, 0, 0, 1, 1, 1, 0], dtype=np.int32)
+
+lowest = np.array(np.random.permutation(a), dtype=np.int32)
+medium = np.array([1, 1, 0, 0, 1, 1, 1, 1], dtype=np.int32)
+high = np.array([1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int32)
+
+lowest_score = mutual_info_estimator_numba(
+    a, lowest, np.float32(1.0), False,
+)
+medium_score = mutual_info_estimator_numba(
+    a, medium, np.float32(1.0), False,
+)
+high_score = mutual_info_estimator_numba(
+    a, high, np.float32(1.0), False,
+)
+
+scores = [lowest_score, medium_score, high_score]
+sorted_score_indices = np.argsort(scores)
+assert np.sum(np.array([0, 1, 2]) - sorted_score_indices) ==  0
+
    +
    diff --git a/docs/outrank/algorithms/feature_ranking/ranking_mi_numba.html b/docs/outrank/algorithms/feature_ranking/ranking_mi_numba.html index af09096..08c2ab4 100644 --- a/docs/outrank/algorithms/feature_ranking/ranking_mi_numba.html +++ b/docs/outrank/algorithms/feature_ranking/ranking_mi_numba.html @@ -94,7 +94,7 @@

    29 30 31@njit( - 32 'float32(int32[:], int32[:], int32, float32, int32[:])', + 32 'float32(uint32[:], int32[:], int32, float32, uint32[:])', 33 cache=True, 34 fastmath=True, 35 error_model='numpy', @@ -147,114 +147,120 @@

    82 initial_prob = _f_value_counts / all_events 83 x_value_subspace = np.where(X == f_values[f_index]) 84 - 85 Y_classes = Y[x_value_subspace] - 86 Y_classes_spoofed = np.roll(Y, _f_value_counts)[x_value_subspace] + 85 Y_classes = Y[x_value_subspace].astype(np.uint32) + 86 subspace_size = x_value_subspace[0].size 87 - 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 conditional_entropy += compute_conditional_entropy( - 97 Y_classes, class_values, _f_value_counts, initial_prob, nonzero_class_counts, - 98 ) - 99 -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 + 88 # Right-shift to simulate noise + 89 Y_classes_spoofed = np.zeros(subspace_size, dtype=np.uint32) + 90 for enx, el in enumerate(x_value_subspace[0]): + 91 index = (el + _f_value_counts) % len(Y) + 92 Y_classes_spoofed[enx] = Y[index] + 93 + 94 nonzero_class_counts = np.zeros(len(class_values), dtype=np.uint32) + 95 nonzero_class_counts_spoofed = np.zeros(len(class_values), dtype=np.uint32) + 96 + 97 # Cache nonzero counts + 98 for index, c in enumerate(class_values): + 99 nonzero_class_counts[index] = np.count_nonzero(Y_classes == c) +100 nonzero_class_counts_spoofed[index] = np.count_nonzero(Y_classes_spoofed == c) +101 +102 conditional_entropy += compute_conditional_entropy( +103 Y_classes, class_values, _f_value_counts, initial_prob, nonzero_class_counts, +104 ) +105 +106 if cardinality_correction: +107 background_cond_entropy += compute_conditional_entropy( +108 Y_classes_spoofed, class_values, _f_value_counts, initial_prob, nonzero_class_counts_spoofed, +109 ) +110 +111 if not cardinality_correction: +112 return full_entropy - conditional_entropy 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 +114 else: +115 # note: full entropy falls out during derivation of final term +116 core_joint_entropy = -conditional_entropy + background_cond_entropy +117 return core_joint_entropy +118 +119 +120@njit( +121 'float32(int32[:], int32[:], float32, b1)', +122 cache=True, +123 fastmath=True, +124 error_model='numpy', +125 boundscheck=True, +126) +127def mutual_info_estimator_numba( +128 Y, X, approximation_factor=1, cardinality_correction=False, +129): +130 """Core estimator logic. Compute unique elements, subset if required""" +131 +132 all_events = len(X) +133 f_values, f_value_counts = numba_unique(X) +134 +135 # Diagonal entries +136 if np.sum(X - Y) == 0: +137 cardinality_correction = False +138 +139 if approximation_factor < 1: +140 subspace_size = int(approximation_factor * all_events) +141 if subspace_size != 0: +142 subspace = np.random.randint(0, all_events, size=subspace_size) +143 X = X[subspace] +144 Y = Y[subspace] 145 -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) +146 joint_entropy_core = compute_entropies( +147 X, Y, all_events, f_values, f_value_counts, cardinality_correction, +148 ) +149 +150 return approximation_factor * joint_entropy_core +151 +152 +153if __name__ == '__main__': +154 import pandas as pd +155 from sklearn.feature_selection import mutual_info_classif +156 +157 np.random.seed(123) +158 import time +159 +160 final_times = [] +161 for algo in ['MI-numba-randomized']: +162 for order in range(20, 21): +163 for j in range(1): +164 start = time.time() +165 a = np.random.randint(1000, size=2**order).astype(np.int32) +166 b = np.random.randint(1000, size=2**order).astype(np.int32) +167 if algo == 'MI': +168 final_score = mutual_info_classif( +169 a.reshape(-1, 1), b.reshape(-1), discrete_features=True, +170 ) +171 elif algo == 'MI-numba-randomized': +172 final_score = mutual_info_estimator_numba( +173 a, b, np.float32(1.0), True, +174 ) +175 elif algo == 'MI-numba': +176 final_score = mutual_info_estimator_numba( +177 a, b, np.float32(1.0), False, +178 ) +179 elif algo == 'MI-numba-randomized-ap': +180 final_score = mutual_info_estimator_numba( +181 a, b, np.float32(0.3), True, +182 ) +183 elif algo == 'MI-numba-ap': +184 final_score = mutual_info_estimator_numba( +185 a, b, np.float32(0.3), False, +186 ) +187 +188 end = time.time() +189 tdiff = end - start +190 instance = { +191 'time': tdiff, +192 'samples 2e': order, 'algorithm': algo, +193 } +194 final_times.append(instance) +195 print(instance) +196 dfx = pd.DataFrame(final_times) +197 dfx = dfx.sort_values(by=['samples 2e']) +198 print(dfx) @@ -300,7 +306,7 @@

    -
    @njit('float32(int32[:], int32[:], int32, float32, int32[:])', cache=True, fastmath=True, error_model='numpy', boundscheck=True)
    +
    @njit('float32(uint32[:], int32[:], int32, float32, uint32[:])', cache=True, fastmath=True, error_model='numpy', boundscheck=True)
    def compute_conditional_entropy( Y_classes, class_values, class_var_shape, initial_prob, nonzero_counts): @@ -310,7 +316,7 @@ 

    32@njit(
-33    'float32(int32[:], int32[:], int32, float32, int32[:])',
+33    'float32(uint32[:], int32[:], int32, float32, uint32[:])',
 34    cache=True,
 35    fastmath=True,
 36    error_model='numpy',
@@ -379,33 +385,39 @@ 
    
  83        initial_prob = _f_value_counts / all_events
  84        x_value_subspace = np.where(X == f_values[f_index])
  85
- 86        Y_classes = Y[x_value_subspace]
- 87        Y_classes_spoofed = np.roll(Y, _f_value_counts)[x_value_subspace]
+ 86        Y_classes = Y[x_value_subspace].astype(np.uint32)
+ 87        subspace_size = x_value_subspace[0].size
  88
- 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        conditional_entropy += compute_conditional_entropy(
- 98            Y_classes, class_values, _f_value_counts, initial_prob, nonzero_class_counts,
- 99        )
-100
-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
+ 89        # Right-shift to simulate noise
+ 90        Y_classes_spoofed = np.zeros(subspace_size, dtype=np.uint32)
+ 91        for enx, el in enumerate(x_value_subspace[0]):
+ 92            index = (el + _f_value_counts) % len(Y)
+ 93            Y_classes_spoofed[enx] = Y[index]
+ 94
+ 95        nonzero_class_counts = np.zeros(len(class_values), dtype=np.uint32)
+ 96        nonzero_class_counts_spoofed = np.zeros(len(class_values), dtype=np.uint32)
+ 97
+ 98        # Cache nonzero counts
+ 99        for index, c in enumerate(class_values):
+100            nonzero_class_counts[index] = np.count_nonzero(Y_classes == c)
+101            nonzero_class_counts_spoofed[index] = np.count_nonzero(Y_classes_spoofed == c)
+102
+103        conditional_entropy += compute_conditional_entropy(
+104            Y_classes, class_values, _f_value_counts, initial_prob, nonzero_class_counts,
+105        )
+106
+107        if cardinality_correction:
+108            background_cond_entropy += compute_conditional_entropy(
+109                Y_classes_spoofed, class_values, _f_value_counts, initial_prob, nonzero_class_counts_spoofed,
+110            )
+111
+112    if not cardinality_correction:
+113        return full_entropy - conditional_entropy
+114
+115    else:
+116        # note: full entropy falls out during derivation of final term
+117        core_joint_entropy = -conditional_entropy + background_cond_entropy
+118        return core_joint_entropy
    @@ -426,37 +438,37 @@

    - 
    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
+            
    121@njit(
+122    'float32(int32[:], int32[:], float32, b1)',
+123    cache=True,
+124    fastmath=True,
+125    error_model='numpy',
+126    boundscheck=True,
+127)
+128def mutual_info_estimator_numba(
+129    Y, X, approximation_factor=1, cardinality_correction=False,
+130):
+131    """Core estimator logic. Compute unique elements, subset if required"""
+132
+133    all_events = len(X)
+134    f_values, f_value_counts = numba_unique(X)
+135
+136    # Diagonal entries
+137    if np.sum(X - Y) == 0:
+138        cardinality_correction = False
+139
+140    if approximation_factor < 1:
+141        subspace_size = int(approximation_factor * all_events)
+142        if subspace_size != 0:
+143            subspace = np.random.randint(0, all_events, size=subspace_size)
+144            X = X[subspace]
+145            Y = Y[subspace]
+146
+147    joint_entropy_core = compute_entropies(
+148        X, Y, all_events, f_values, f_value_counts, cardinality_correction,
+149    )
+150
+151    return approximation_factor * joint_entropy_core
 
    
 
 
diff --git a/docs/outrank/core_ranking.html b/docs/outrank/core_ranking.html
index 35caebf..3daf898 100644
--- a/docs/outrank/core_ranking.html
+++ b/docs/outrank/core_ranking.html
@@ -51,6 +51,9 @@ 
    API Documentation
    
             
  • 
                     prior_combinations_sample
             
    • 
+            
  • 
+                    get_combinations_from_columns
+            
    • 
             
  • 
                     mixed_rank_graph
             
    • 
@@ -165,694 +168,698 @@ 
    
  43IGNORED_VALUES = set()
  44HYPERLL_ERROR_BOUND = 0.02
  45
- 46def prior_combinations_sample(combinations: list[tuple[Any, ...]], args: Any) -> list[tuple[Any, ...]]:
- 47    """Make sure only relevant subspace of combinations is selected based on prior counts"""
- 48
- 49    if len(GLOBAL_PRIOR_COMB_COUNTS) == 0:
- 50        for combination in combinations:
- 51            GLOBAL_PRIOR_COMB_COUNTS[combination] += 1
- 52        tmp = combinations[:args.combination_number_upper_bound]
- 53    else:
- 54        tmp = list(x[0] for x in sorted(GLOBAL_PRIOR_COMB_COUNTS.items(), key=lambda x:x[1], reverse=False))[:args.combination_number_upper_bound]
- 55
- 56    for combination in tmp:
- 57        GLOBAL_PRIOR_COMB_COUNTS[combination] += 1
- 58
- 59    return tmp
- 60
+ 46
+ 47def prior_combinations_sample(combinations: list[tuple[Any, ...]], args: Any) -> list[tuple[Any, ...]]:
+ 48    """Make sure only relevant subspace of combinations is selected based on prior counts"""
+ 49
+ 50    if len(GLOBAL_PRIOR_COMB_COUNTS) == 0:
+ 51        for combination in combinations:
+ 52            GLOBAL_PRIOR_COMB_COUNTS[combination] += 1
+ 53        tmp = combinations[:args.combination_number_upper_bound]
+ 54    else:
+ 55        tmp = list(x[0] for x in sorted(GLOBAL_PRIOR_COMB_COUNTS.items(), key=lambda x:x[1], reverse=False))[:args.combination_number_upper_bound]
+ 56
+ 57    for combination in tmp:
+ 58        GLOBAL_PRIOR_COMB_COUNTS[combination] += 1
+ 59
+ 60    return tmp
  61
- 62def mixed_rank_graph(
- 63    input_dataframe: pd.DataFrame, args: Any, cpu_pool: Any, pbar: Any,
- 64) -> BatchRankingSummary:
- 65    """Compute the full mixed rank graph corresponding to all pairwise feature interactions based on the selected heuristic"""
- 66
- 67    all_columns = input_dataframe.columns
- 68
- 69    triplets = []
- 70    tmp_df = input_dataframe.copy().astype('category')
- 71    out_time_struct = {}
- 72
- 73    # Handle cont. types prior to interaction evaluation
- 74    pbar.set_description('Encoding 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
- 81    # Helper method for parallel estimation
- 82    combinations = list(
- 83        itertools.combinations_with_replacement(all_columns, 2),
- 84    )
- 85
- 86    if '3mr' in args.heuristic:
- 87        rel_columns = [
- 88            column for column in all_columns if ' AND_REL ' in column
+ 62
+ 63def get_combinations_from_columns(all_columns: pd.Index, args: Any) -> list[tuple[Any, ...]]:
+ 64    """Return feature-feature & feature-label combinations, depending on the heuristic and ranking scope"""
+ 65
+ 66    if '3mr' in args.heuristic:
+ 67        rel_columns = [column for column in all_columns if ' AND_REL ' in column]
+ 68        non_rel_columns = sorted(set(all_columns) - set(rel_columns))
+ 69
+ 70        combinations = list(
+ 71            itertools.combinations_with_replacement(non_rel_columns, 2),
+ 72        )
+ 73        combinations += [(column, args.label_column) for column in rel_columns]
+ 74    else:
+ 75        _combinations = itertools.combinations_with_replacement(all_columns, 2)
+ 76
+ 77        # Some applications do not require the full feature-feature triangular matrix
+ 78        if args.target_ranking_only == 'True':
+ 79            combinations = [x for x in _combinations if args.label_column in x]
+ 80        else:
+ 81            combinations = list(_combinations)
+ 82
+ 83    if args.target_ranking_only != 'True':
+ 84        # Diagonal elements (non-label)
+ 85        combinations += [
+ 86            (individual_column, individual_column)
+ 87            for individual_column in all_columns
+ 88            if individual_column != args.label_column
  89        ]
- 90        non_rel_columns = list(set(all_columns) - set(rel_columns))
- 91        combinations = list(
- 92            itertools.combinations_with_replacement(non_rel_columns, 2),
- 93        )
- 94        combinations += [(column, args.label_column) for column in rel_columns]
- 95    else:
- 96        combinations = list(
- 97            itertools.combinations_with_replacement(all_columns, 2),
- 98        )
+ 90    return combinations
+ 91
+ 92
+ 93def mixed_rank_graph(
+ 94    input_dataframe: pd.DataFrame, args: Any, cpu_pool: Any, pbar: Any,
+ 95) -> BatchRankingSummary:
+ 96    """Compute the full mixed rank graph corresponding to all pairwise feature interactions based on the selected heuristic"""
+ 97
+ 98    all_columns = input_dataframe.columns
  99
-100    # Diagonal elements
-101    for individual_column in all_columns:
-102        if individual_column != args.label_column:
-103            combinations += [(individual_column, individual_column)]
-104
-105    # Some applications do not require the full feature-feature triangular matrix
-106    if (args.target_ranking_only == 'True') and ('3mr' not in args.heuristic):
-107        combinations = [x for x in combinations if args.label_column in x]
+100    triplets = []
+101    tmp_df = input_dataframe.copy().astype('category')
+102    out_time_struct = {}
+103
+104    # Handle cont. types prior to interaction evaluation
+105    pbar.set_description('Encoding columns')
+106    start_enc_timer = timer()
+107    tmp_df = pd.DataFrame({k : tmp_df[k].cat.codes for k in all_columns})
 108
-109    combinations = prior_combinations_sample(combinations, args)
-110    random.shuffle(combinations)
+109    end_enc_timer = timer()
+110    out_time_struct['encoding_columns'] = end_enc_timer - start_enc_timer
 111
-112    if args.heuristic == 'Constant':
-113        final_constant_imp = []
-114        for c1, c2 in combinations:
-115            final_constant_imp.append((c1, c2, 0.0))
-116
-117        out_time_struct['feature_score_computation'] = end_enc_timer - \
-118            start_enc_timer
-119        return BatchRankingSummary(final_constant_imp, out_time_struct)
+112    combinations = get_combinations_from_columns(all_columns, args)
+113    combinations = prior_combinations_sample(combinations, args)
+114    random.shuffle(combinations)
+115
+116    if args.heuristic == 'Constant':
+117        final_constant_imp = []
+118        for c1, c2 in combinations:
+119            final_constant_imp.append((c1, c2, 0.0))
 120
-121    # Map the scoring calls to the worker pool
-122    pbar.set_description('Allocating thread pool')
-123
-124    # starmap is an alternative that is slower unfortunately (but nicer)
-125    def get_grounded_importances_estimate(combination: tuple[str]) -> Any:
-126        return get_importances_estimate_pairwise(combination, args, tmp_df=tmp_df)
+121        out_time_struct['feature_score_computation'] = end_enc_timer - \
+122            start_enc_timer
+123        return BatchRankingSummary(final_constant_imp, out_time_struct)
+124
+125    # Map the scoring calls to the worker pool
+126    pbar.set_description('Allocating thread pool')
 127
-128    start_enc_timer = timer()
-129    with cpu_pool as p:
-130        pbar.set_description(f'Computing (#ftr={len(combinations)})')
-131        results = p.amap(get_grounded_importances_estimate, combinations)
-132        while not results.ready():
-133            time.sleep(4)
-134        triplets = results.get()
-135    end_enc_timer = timer()
-136    out_time_struct['feature_score_computation'] = end_enc_timer - \
-137        start_enc_timer
-138
-139    # Gather the final triplets
-140    pbar.set_description('Aggregation of ranking results')
-141    final_triplets = []
-142    for triplet in triplets:
-143        inv = (triplet[1], triplet[0], triplet[2])
-144        final_triplets.append(inv)
-145        final_triplets.append(triplet)
-146        triplets = final_triplets
-147
-148    pbar.set_description('Proceeding to the next batch of data')
-149    return BatchRankingSummary(triplets, out_time_struct)
-150
+128    # starmap is an alternative that is slower unfortunately (but nicer)
+129    def get_grounded_importances_estimate(combination: tuple[str]) -> Any:
+130        return get_importances_estimate_pairwise(combination, args, tmp_df=tmp_df)
+131
+132    start_enc_timer = timer()
+133    with cpu_pool as p:
+134        pbar.set_description(f'Computing (#ftr={len(combinations)})')
+135        results = p.amap(get_grounded_importances_estimate, combinations)
+136        while not results.ready():
+137            time.sleep(4)
+138        triplets = results.get()
+139    end_enc_timer = timer()
+140    out_time_struct['feature_score_computation'] = end_enc_timer - \
+141        start_enc_timer
+142
+143    # Gather the final triplets
+144    pbar.set_description('Aggregation of ranking results')
+145    final_triplets = []
+146    for triplet in triplets:
+147        inv = (triplet[1], triplet[0], triplet[2])
+148        final_triplets.append(inv)
+149        final_triplets.append(triplet)
+150        triplets = final_triplets
 151
-152def enrich_with_transformations(
-153    input_dataframe: pd.DataFrame, num_col_types: set[str], logger: Any, args: Any,
-154) -> pd.DataFrame:
-155    """Construct a collection of new features based on pre-defined transformations/rules"""
-156
-157    transformer = FeatureTransformerGeneric(
-158        num_col_types, preset=args.transformers,
-159    )
-160    transformed_df = transformer.construct_new_features(input_dataframe)
-161    logger.info(
-162        f'Constructed {len(transformer.constructed_feature_names)} new features ..',
+152    pbar.set_description('Proceeding to the next batch of data')
+153    return BatchRankingSummary(triplets, out_time_struct)
+154
+155
+156def enrich_with_transformations(
+157    input_dataframe: pd.DataFrame, num_col_types: set[str], logger: Any, args: Any,
+158) -> pd.DataFrame:
+159    """Construct a collection of new features based on pre-defined transformations/rules"""
+160
+161    transformer = FeatureTransformerGeneric(
+162        num_col_types, preset=args.transformers,
 163    )
-164
-165    return transformed_df
-166
-167
-168def compute_combined_features(
-169    input_dataframe: pd.DataFrame,
-170    logger: Any,
-171    args: Any,
-172    pbar: Any,
-173    is_3mr: bool = False,
-174) -> pd.DataFrame:
-175    """Compute higher order features via xxhash-based trick."""
-176
-177    all_columns = [
-178        x for x in input_dataframe.columns if x != args.label_column
-179    ]
-180    join_string = ' AND_REL ' if is_3mr else ' AND '
-181    interaction_order = 2 if is_3mr else args.interaction_order
-182
-183    full_combination_space = list(
-184        itertools.combinations(all_columns, interaction_order),
-185    )
+164    transformed_df = transformer.construct_new_features(input_dataframe)
+165    logger.info(
+166        f'Constructed {len(transformer.constructed_feature_names)} new features ..',
+167    )
+168
+169    return transformed_df
+170
+171
+172def compute_combined_features(
+173    input_dataframe: pd.DataFrame,
+174    logger: Any,
+175    args: Any,
+176    pbar: Any,
+177    is_3mr: bool = False,
+178) -> pd.DataFrame:
+179    """Compute higher order features via xxhash-based trick."""
+180
+181    all_columns = [
+182        x for x in input_dataframe.columns if x != args.label_column
+183    ]
+184    join_string = ' AND_REL ' if is_3mr else ' AND '
+185    interaction_order = 2 if is_3mr else args.interaction_order
 186
-187    if args.combination_number_upper_bound:
-188        random.shuffle(full_combination_space)
-189        full_combination_space = full_combination_space[
-190            : args.combination_number_upper_bound
-191        ]
-192
-193    com_counter = 0
-194    new_feature_hash = {}
-195    for new_combination in full_combination_space:
-196        pbar.set_description(
-197            f'Created {com_counter}/{len(full_combination_space)}',
-198        )
-199        combined_feature: list[str] = [str(0)] * input_dataframe.shape[0]
-200        for feature in new_combination:
-201            tmp_feature = input_dataframe[feature].tolist()
-202            for enx, el in enumerate(tmp_feature):
-203                combined_feature[enx] = str(
-204                    internal_hash(
-205                        str(combined_feature[enx]) + str(el),
-206                    ),
-207                )
-208        ftr_name = join_string.join(str(x) for x in new_combination)
-209        new_feature_hash[ftr_name] = combined_feature
-210        com_counter += 1
-211    tmp_df = pd.DataFrame(new_feature_hash)
-212    pbar.set_description('Concatenating into final frame ..')
-213    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
-214    del tmp_df
-215
-216    return input_dataframe
-217
-218
-219def compute_expanded_multivalue_features(
-220    input_dataframe: pd.DataFrame, logger: Any, args: Any, pbar: Any,
-221) -> pd.DataFrame:
-222    """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."""
-223
-224    considered_multivalue_features = args.explode_multivalue_features.split(
-225        ';',
-226    )
-227    new_feature_hash = {}
-228    missing_symbols = set(args.missing_value_symbols.split(','))
-229
-230    for multivalue_feature in considered_multivalue_features:
-231        multivalue_feature_vector = input_dataframe[multivalue_feature].values.tolist(
-232        )
-233        multivalue_feature_vector = [
-234            x.replace(',', '-') for x in multivalue_feature_vector
-235        ]
-236        multivalue_sets = [
-237            set(x.split('-'))
-238            for x in multivalue_feature_vector
+187    full_combination_space = list(
+188        itertools.combinations(all_columns, interaction_order),
+189    )
+190
+191    if args.combination_number_upper_bound:
+192        random.shuffle(full_combination_space)
+193        full_combination_space = full_combination_space[
+194            : args.combination_number_upper_bound
+195        ]
+196
+197    com_counter = 0
+198    new_feature_hash = {}
+199    for new_combination in full_combination_space:
+200        pbar.set_description(
+201            f'Created {com_counter}/{len(full_combination_space)}',
+202        )
+203        combined_feature: list[str] = [str(0)] * input_dataframe.shape[0]
+204        for feature in new_combination:
+205            tmp_feature = input_dataframe[feature].tolist()
+206            for enx, el in enumerate(tmp_feature):
+207                combined_feature[enx] = str(
+208                    internal_hash(
+209                        str(combined_feature[enx]) + str(el),
+210                    ),
+211                )
+212        ftr_name = join_string.join(str(x) for x in new_combination)
+213        new_feature_hash[ftr_name] = combined_feature
+214        com_counter += 1
+215    tmp_df = pd.DataFrame(new_feature_hash)
+216    pbar.set_description('Concatenating into final frame ..')
+217    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
+218    del tmp_df
+219
+220    return input_dataframe
+221
+222
+223def compute_expanded_multivalue_features(
+224    input_dataframe: pd.DataFrame, logger: Any, args: Any, pbar: Any,
+225) -> pd.DataFrame:
+226    """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."""
+227
+228    considered_multivalue_features = args.explode_multivalue_features.split(
+229        ';',
+230    )
+231    new_feature_hash = {}
+232    missing_symbols = set(args.missing_value_symbols.split(','))
+233
+234    for multivalue_feature in considered_multivalue_features:
+235        multivalue_feature_vector = input_dataframe[multivalue_feature].values.tolist(
+236        )
+237        multivalue_feature_vector = [
+238            x.replace(',', '-') for x in multivalue_feature_vector
 239        ]
-240        unique_values = set.union(*multivalue_sets)
-241
-242        for missing_symbol in missing_symbols:
-243            if missing_symbol in unique_values:
-244                unique_values.remove(missing_symbol)
+240        multivalue_sets = [
+241            set(x.split('-'))
+242            for x in multivalue_feature_vector
+243        ]
+244        unique_values = set.union(*multivalue_sets)
 245
-246        for unique_value in unique_values:
-247            tmp_vec = []
-248            for enx, multivalue in enumerate(multivalue_sets):
-249                if unique_value in multivalue:
-250                    tmp_vec.append('1')
-251                else:
-252                    tmp_vec.append('')
-253
-254            new_feature_hash[f'MULTIEX-{multivalue_feature}-{unique_value}'] = tmp_vec
-255
-256    tmp_df = pd.DataFrame(new_feature_hash)
-257    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
-258    del tmp_df
+246        for missing_symbol in missing_symbols:
+247            if missing_symbol in unique_values:
+248                unique_values.remove(missing_symbol)
+249
+250        for unique_value in unique_values:
+251            tmp_vec = []
+252            for enx, multivalue in enumerate(multivalue_sets):
+253                if unique_value in multivalue:
+254                    tmp_vec.append('1')
+255                else:
+256                    tmp_vec.append('')
+257
+258            new_feature_hash[f'MULTIEX-{multivalue_feature}-{unique_value}'] = tmp_vec
 259
-260    return input_dataframe
-261
-262
-263def compute_subfeatures(
-264    input_dataframe: pd.DataFrame, logger: Any, args: Any, pbar: Any,
-265) -> pd.DataFrame:
-266    """Compute derived features that are more fine-grained. Implements logic around two operators that govern feature construction.
-267    ->: One sided construction - every value from left side is fine, separate ones from the right side feature will be considered.
-268    <->: 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)
-269    """
-270
-271    all_subfeature_pair_seeds = args.subfeature_mapping.split(';')
-272    new_feature_hash = dict()
-273
-274    for seed_pair in all_subfeature_pair_seeds:
-275        if '<->' in seed_pair:
-276            feature_first, feature_second = seed_pair.split('<->')
+260    tmp_df = pd.DataFrame(new_feature_hash)
+261    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
+262    del tmp_df
+263
+264    return input_dataframe
+265
+266
+267def compute_subfeatures(
+268    input_dataframe: pd.DataFrame, logger: Any, args: Any, pbar: Any,
+269) -> pd.DataFrame:
+270    """Compute derived features that are more fine-grained. Implements logic around two operators that govern feature construction.
+271    ->: One sided construction - every value from left side is fine, separate ones from the right side feature will be considered.
+272    <->: 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)
+273    """
+274
+275    all_subfeature_pair_seeds = args.subfeature_mapping.split(';')
+276    new_feature_hash = dict()
 277
-278        elif '->' in seed_pair:
-279            feature_first, feature_second = seed_pair.split('->')
-280
-281        else:
-282            raise NotImplementedError(
-283                'Please specify valid subfeature operator (<-> or ->)',
-284            )
-285
-286        subframe = input_dataframe[[feature_first, feature_second]]
-287        unique_feature_second = subframe[feature_second].unique()
-288        feature_first_vec = subframe[feature_first].tolist()
-289        feature_second_vec = subframe[feature_second].tolist()
-290        out_template_feature = [
-291            (a, b) for a, b in zip(feature_first_vec, feature_second_vec)
-292        ]
-293
-294        if '<->' in seed_pair:
-295            unique_feature_first = subframe[feature_first].unique()
-296
-297            mask_types = []
-298            for unique_target_feature_value in unique_feature_second:
-299                for unique_seed_feature_value in unique_feature_first:
-300                    mask_types.append(
-301                        (unique_seed_feature_value, unique_target_feature_value),
-302                    )
-303
-304            for mask_type in mask_types:
-305                new_feature = []
-306                for value_tuple in out_template_feature:
-307                    if (
-308                        value_tuple[0] == mask_type[0]
-309                        and value_tuple[1] == mask_type[1]
-310                    ):
-311                        new_feature.append(str(1))
-312                    else:
-313                        new_feature.append(str(0))
-314                feature_name = (
-315                    f'SUBFEATURE|{feature_first}|{feature_second}-'
-316                    + mask_type[0]
-317                    + '&'
-318                    + mask_type[1]
-319                )
-320                new_feature_hash[feature_name] = new_feature
-321
-322            del new_feature
-323
-324        elif '->' in seed_pair:
-325            for unique_target_feature_value in unique_feature_second:
-326                tmp_new_feature = [
-327                    'AND'.join(
-328                        x,
-329                    ) if x[1] == unique_target_feature_value else ''
-330                    for x in out_template_feature
-331                ]
-332                feature_name_final = (
-333                    'SUBFEATURE-' + feature_first + '&' + unique_target_feature_value
-334                )
-335                new_feature_hash[feature_name_final] = tmp_new_feature
-336
-337    tmp_df = pd.DataFrame(new_feature_hash)
-338    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
-339
-340    del tmp_df
-341    return input_dataframe
-342
+278    for seed_pair in all_subfeature_pair_seeds:
+279        if '<->' in seed_pair:
+280            feature_first, feature_second = seed_pair.split('<->')
+281
+282        elif '->' in seed_pair:
+283            feature_first, feature_second = seed_pair.split('->')
+284
+285        else:
+286            raise NotImplementedError(
+287                'Please specify valid subfeature operator (<-> or ->)',
+288            )
+289
+290        subframe = input_dataframe[[feature_first, feature_second]]
+291        unique_feature_second = subframe[feature_second].unique()
+292        feature_first_vec = subframe[feature_first].tolist()
+293        feature_second_vec = subframe[feature_second].tolist()
+294        out_template_feature = [
+295            (a, b) for a, b in zip(feature_first_vec, feature_second_vec)
+296        ]
+297
+298        if '<->' in seed_pair:
+299            unique_feature_first = subframe[feature_first].unique()
+300
+301            mask_types = []
+302            for unique_target_feature_value in unique_feature_second:
+303                for unique_seed_feature_value in unique_feature_first:
+304                    mask_types.append(
+305                        (unique_seed_feature_value, unique_target_feature_value),
+306                    )
+307
+308            for mask_type in mask_types:
+309                new_feature = []
+310                for value_tuple in out_template_feature:
+311                    if (
+312                        value_tuple[0] == mask_type[0]
+313                        and value_tuple[1] == mask_type[1]
+314                    ):
+315                        new_feature.append(str(1))
+316                    else:
+317                        new_feature.append(str(0))
+318                feature_name = (
+319                    f'SUBFEATURE|{feature_first}|{feature_second}-'
+320                    + mask_type[0]
+321                    + '&'
+322                    + mask_type[1]
+323                )
+324                new_feature_hash[feature_name] = new_feature
+325
+326            del new_feature
+327
+328        elif '->' in seed_pair:
+329            for unique_target_feature_value in unique_feature_second:
+330                tmp_new_feature = [
+331                    'AND'.join(
+332                        x,
+333                    ) if x[1] == unique_target_feature_value else ''
+334                    for x in out_template_feature
+335                ]
+336                feature_name_final = (
+337                    'SUBFEATURE-' + feature_first + '&' + unique_target_feature_value
+338                )
+339                new_feature_hash[feature_name_final] = tmp_new_feature
+340
+341    tmp_df = pd.DataFrame(new_feature_hash)
+342    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
 343
-344def include_noisy_features(
-345    input_dataframe: pd.DataFrame, logger: Any, args: Any,
-346) -> pd.DataFrame:
-347    """Add randomized features that serve as a sanity check"""
-348
-349    transformer = FeatureTransformerNoise()
-350    transformed_df = transformer.construct_new_features(
-351        input_dataframe, args.label_column,
-352    )
-353
-354    return transformed_df
-355
-356
-357def compute_coverage(input_dataframe: pd.DataFrame, args: Any) -> dict[str, set[str]]:
-358    """Compute coverage of features, incrementally"""
-359    output_storage_cov = defaultdict(set)
-360    all_missing_symbols = set(args.missing_value_symbols.split(','))
-361    for column in input_dataframe:
-362        all_missing = sum(
-363            [
-364                input_dataframe[column].values.tolist().count(x)
-365                for x in all_missing_symbols
-366            ],
-367        )
-368
-369        output_storage_cov[column] = (
-370            1 - (all_missing / input_dataframe.shape[0])
-371        ) * 100
+344    del tmp_df
+345    return input_dataframe
+346
+347
+348def include_noisy_features(
+349    input_dataframe: pd.DataFrame, logger: Any, args: Any,
+350) -> pd.DataFrame:
+351    """Add randomized features that serve as a sanity check"""
+352
+353    transformer = FeatureTransformerNoise()
+354    transformed_df = transformer.construct_new_features(
+355        input_dataframe, args.label_column,
+356    )
+357
+358    return transformed_df
+359
+360
+361def compute_coverage(input_dataframe: pd.DataFrame, args: Any) -> dict[str, set[str]]:
+362    """Compute coverage of features, incrementally"""
+363    output_storage_cov = defaultdict(set)
+364    all_missing_symbols = set(args.missing_value_symbols.split(','))
+365    for column in input_dataframe:
+366        all_missing = sum(
+367            [
+368                input_dataframe[column].values.tolist().count(x)
+369                for x in all_missing_symbols
+370            ],
+371        )
 372
-373    return output_storage_cov
-374
-375
-376def compute_feature_memory_consumption(input_dataframe: pd.DataFrame, args: Any) -> dict[str, set[str]]:
-377    """An approximation of how much feature take up"""
-378    output_storage_features = defaultdict(set)
-379    for col in input_dataframe.columns:
-380        specific_column = [
-381            str(x).strip() for x in input_dataframe[col].astype(str).values.tolist()
-382        ]
-383        col_size = sum(
-384            len(x.encode())
-385            for x in specific_column
-386        ) / input_dataframe.shape[0]
-387        output_storage_features[col] = col_size
-388    return output_storage_features
-389
-390
-391def compute_value_counts(input_dataframe: pd.DataFrame, args: Any):
-392    """Update the count structure"""
+373        output_storage_cov[column] = (
+374            1 - (all_missing / input_dataframe.shape[0])
+375        ) * 100
+376
+377    return output_storage_cov
+378
+379
+380def compute_feature_memory_consumption(input_dataframe: pd.DataFrame, args: Any) -> dict[str, set[str]]:
+381    """An approximation of how much feature take up"""
+382    output_storage_features = defaultdict(set)
+383    for col in input_dataframe.columns:
+384        specific_column = [
+385            str(x).strip() for x in input_dataframe[col].astype(str).values.tolist()
+386        ]
+387        col_size = sum(
+388            len(x.encode())
+389            for x in specific_column
+390        ) / input_dataframe.shape[0]
+391        output_storage_features[col] = col_size
+392    return output_storage_features
 393
-394    global GLOBAL_RARE_VALUE_STORAGE
-395    global IGNORED_VALUES
-396
-397    for column in input_dataframe.columns:
-398        main_values = input_dataframe[column].values
-399        for value in main_values:
-400            if value not in IGNORED_VALUES:
-401                GLOBAL_RARE_VALUE_STORAGE.update({(column, value): 1})
-402
-403    for key, val in GLOBAL_RARE_VALUE_STORAGE.items():
-404        if val > args.rare_value_count_upper_bound:
-405            IGNORED_VALUES.add(key)
+394
+395def compute_value_counts(input_dataframe: pd.DataFrame, args: Any):
+396    """Update the count structure"""
+397
+398    global GLOBAL_RARE_VALUE_STORAGE
+399    global IGNORED_VALUES
+400
+401    for column in input_dataframe.columns:
+402        main_values = input_dataframe[column].values
+403        for value in main_values:
+404            if value not in IGNORED_VALUES:
+405                GLOBAL_RARE_VALUE_STORAGE.update({(column, value): 1})
 406
-407    for to_remove_val in IGNORED_VALUES:
-408        del GLOBAL_RARE_VALUE_STORAGE[to_remove_val]
-409
+407    for key, val in GLOBAL_RARE_VALUE_STORAGE.items():
+408        if val > args.rare_value_count_upper_bound:
+409            IGNORED_VALUES.add(key)
 410
-411def compute_cardinalities(input_dataframe: pd.DataFrame, pbar: Any) -> None:
-412    """Compute cardinalities of features, incrementally"""
+411    for to_remove_val in IGNORED_VALUES:
+412        del GLOBAL_RARE_VALUE_STORAGE[to_remove_val]
 413
-414    global GLOBAL_CARDINALITY_STORAGE
-415    output_storage_card = defaultdict(set)
-416    for enx, column in enumerate(input_dataframe):
-417        output_storage_card[column] = set(input_dataframe[column].unique())
-418        if column not in GLOBAL_CARDINALITY_STORAGE:
-419            GLOBAL_CARDINALITY_STORAGE[column] = HyperLogLog(
-420                HYPERLL_ERROR_BOUND,
-421            )
-422
-423        for unique_value in set(input_dataframe[column].unique()):
-424            if unique_value:
-425                GLOBAL_CARDINALITY_STORAGE[column].add(
-426                    internal_hash(unique_value),
-427                )
-428        pbar.set_description(
-429            f'Computing cardinality (Hyperloglog update) {enx}/{input_dataframe.shape[1]}',
-430        )
-431
-432
-433def compute_bounds_increment(
-434    input_dataframe: pd.DataFrame, numeric_column_types: set[str],
-435) -> dict[str, Any]:
-436    all_features = input_dataframe.columns
-437    numeric_column_types = set(numeric_column_types)
-438    summary_object = {}
-439    summary_storage: Any = {}
-440    for feature in all_features:
-441        if feature in numeric_column_types:
-442            feature_vector = pd.to_numeric(
-443                input_dataframe[feature], errors='coerce',
-444            )
-445            minimum = np.min(feature_vector)
-446            maximum = np.max(feature_vector)
-447            mean = np.mean(feature_vector)
-448            summary_storage = NumericFeatureSummary(
-449                feature, minimum, maximum, mean, len(
-450                    np.unique(feature_vector),
-451                ),
-452            )
-453            summary_object[feature] = summary_storage
-454
-455        else:
-456            feature_vector = input_dataframe[feature].values
-457            summary_storage = NominalFeatureSummary(
-458                feature, len(np.unique(feature_vector)),
-459            )
-460            summary_object[feature] = summary_storage
-461
-462    return summary_object
-463
-464
-465def compute_batch_ranking(
-466    line_tmp_storage: list[list[Any]],
-467    numeric_column_types: set[str],
-468    args: Any,
-469    cpu_pool: Any,
-470    column_descriptions: list[str],
-471    logger: Any,
-472    pbar: Any,
-473) -> tuple[BatchRankingSummary, dict[str, Any], dict[str, set[str]], dict[str, set[str]]]:
-474    """Enrich the feature space and compute the batch importances"""
-475
-476    input_dataframe = pd.DataFrame(line_tmp_storage)
-477    input_dataframe.columns = column_descriptions
-478    pbar.set_description('Control features')
+414
+415def compute_cardinalities(input_dataframe: pd.DataFrame, pbar: Any) -> None:
+416    """Compute cardinalities of features, incrementally"""
+417
+418    global GLOBAL_CARDINALITY_STORAGE
+419    output_storage_card = defaultdict(set)
+420    for enx, column in enumerate(input_dataframe):
+421        output_storage_card[column] = set(input_dataframe[column].unique())
+422        if column not in GLOBAL_CARDINALITY_STORAGE:
+423            GLOBAL_CARDINALITY_STORAGE[column] = HyperLogLog(
+424                HYPERLL_ERROR_BOUND,
+425            )
+426
+427        for unique_value in set(input_dataframe[column].unique()):
+428            if unique_value:
+429                GLOBAL_CARDINALITY_STORAGE[column].add(
+430                    internal_hash(unique_value),
+431                )
+432        pbar.set_description(
+433            f'Computing cardinality (Hyperloglog update) {enx}/{input_dataframe.shape[1]}',
+434        )
+435
+436
+437def compute_bounds_increment(
+438    input_dataframe: pd.DataFrame, numeric_column_types: set[str],
+439) -> dict[str, Any]:
+440    all_features = input_dataframe.columns
+441    numeric_column_types = set(numeric_column_types)
+442    summary_object = {}
+443    summary_storage: Any = {}
+444    for feature in all_features:
+445        if feature in numeric_column_types:
+446            feature_vector = pd.to_numeric(
+447                input_dataframe[feature], errors='coerce',
+448            )
+449            minimum = np.min(feature_vector)
+450            maximum = np.max(feature_vector)
+451            mean = np.mean(feature_vector)
+452            summary_storage = NumericFeatureSummary(
+453                feature, minimum, maximum, mean, len(
+454                    np.unique(feature_vector),
+455                ),
+456            )
+457            summary_object[feature] = summary_storage
+458
+459        else:
+460            feature_vector = input_dataframe[feature].values
+461            summary_storage = NominalFeatureSummary(
+462                feature, len(np.unique(feature_vector)),
+463            )
+464            summary_object[feature] = summary_storage
+465
+466    return summary_object
+467
+468
+469def compute_batch_ranking(
+470    line_tmp_storage: list[list[Any]],
+471    numeric_column_types: set[str],
+472    args: Any,
+473    cpu_pool: Any,
+474    column_descriptions: list[str],
+475    logger: Any,
+476    pbar: Any,
+477) -> tuple[BatchRankingSummary, dict[str, Any], dict[str, set[str]], dict[str, set[str]]]:
+478    """Enrich the feature space and compute the batch importances"""
 479
-480    if args.feature_set_focus:
-481        if args.feature_set_focus == '_all_from_reference_JSON':
-482            focus_set = extract_features_from_reference_JSON(
-483                args.reference_model_JSON,
-484            )
-485
-486        else:
-487            focus_set = set(args.feature_set_focus.split(','))
-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[list(focus_set)]
+480    input_dataframe = pd.DataFrame(line_tmp_storage)
+481    input_dataframe.columns = column_descriptions
+482    pbar.set_description('Control features')
+483
+484    if args.feature_set_focus:
+485        if args.feature_set_focus == '_all_from_reference_JSON':
+486            focus_set = extract_features_from_reference_JSON(
+487                args.reference_model_JSON,
+488            )
+489
+490        else:
+491            focus_set = set(args.feature_set_focus.split(','))
 492
-493    if args.transformers != 'none':
-494        pbar.set_description('Adding transformations')
-495        input_dataframe = enrich_with_transformations(
-496            input_dataframe, numeric_column_types, logger, args,
-497        )
-498
-499    if args.explode_multivalue_features != 'False':
-500        pbar.set_description('Constructing new features from multivalue ones')
-501        input_dataframe = compute_expanded_multivalue_features(
-502            input_dataframe, logger, args, pbar,
-503        )
-504
-505    if args.subfeature_mapping != 'False':
-506        pbar.set_description('Constructing new (sub)features')
-507        input_dataframe = compute_subfeatures(
-508            input_dataframe, logger, args, pbar,
-509        )
-510
-511    if args.interaction_order > 1:
-512        pbar.set_description('Constructing new features')
-513        input_dataframe = compute_combined_features(
-514            input_dataframe, logger, args, pbar,
-515        )
-516
-517    # in case of 3mr we compute the score of combinations against the target
-518    if '3mr' in args.heuristic:
-519        pbar.set_description(
-520            'Constructing features for computing relations in 3mr',
-521        )
-522        input_dataframe = compute_combined_features(
-523            input_dataframe, logger, args, pbar, True,
-524        )
-525
-526    if args.include_noise_baseline_features == 'True' and args.heuristic != 'Constant':
-527        pbar.set_description('Computing baseline features')
-528        input_dataframe = include_noisy_features(input_dataframe, logger, args)
+493        focus_set.add(args.label_column)
+494        focus_set = {x for x in focus_set if x in input_dataframe.columns}
+495        input_dataframe = input_dataframe[list(focus_set)]
+496
+497    if args.transformers != 'none':
+498        pbar.set_description('Adding transformations')
+499        input_dataframe = enrich_with_transformations(
+500            input_dataframe, numeric_column_types, logger, args,
+501        )
+502
+503    if args.explode_multivalue_features != 'False':
+504        pbar.set_description('Constructing new features from multivalue ones')
+505        input_dataframe = compute_expanded_multivalue_features(
+506            input_dataframe, logger, args, pbar,
+507        )
+508
+509    if args.subfeature_mapping != 'False':
+510        pbar.set_description('Constructing new (sub)features')
+511        input_dataframe = compute_subfeatures(
+512            input_dataframe, logger, args, pbar,
+513        )
+514
+515    if args.interaction_order > 1:
+516        pbar.set_description('Constructing new features')
+517        input_dataframe = compute_combined_features(
+518            input_dataframe, logger, args, pbar,
+519        )
+520
+521    # in case of 3mr we compute the score of combinations against the target
+522    if '3mr' in args.heuristic:
+523        pbar.set_description(
+524            'Constructing features for computing relations in 3mr',
+525        )
+526        input_dataframe = compute_combined_features(
+527            input_dataframe, logger, args, pbar, True,
+528        )
 529
-530    # Compute incremental statistic useful for data inspection/transformer generation
-531    pbar.set_description('Computing coverage')
-532    coverage_storage = compute_coverage(input_dataframe, args)
-533    feature_memory_consumption = compute_feature_memory_consumption(
-534        input_dataframe, args,
-535    )
-536    compute_cardinalities(input_dataframe, pbar)
-537
-538    if args.task == 'identify_rare_values':
-539        compute_value_counts(input_dataframe, args)
-540
-541    bounds_storage = compute_bounds_increment(
-542        input_dataframe, numeric_column_types,
-543    )
+530    if args.include_noise_baseline_features == 'True' and args.heuristic != 'Constant':
+531        pbar.set_description('Computing baseline features')
+532        input_dataframe = include_noisy_features(input_dataframe, logger, args)
+533
+534    # Compute incremental statistic useful for data inspection/transformer generation
+535    pbar.set_description('Computing coverage')
+536    coverage_storage = compute_coverage(input_dataframe, args)
+537    feature_memory_consumption = compute_feature_memory_consumption(
+538        input_dataframe, args,
+539    )
+540    compute_cardinalities(input_dataframe, pbar)
+541
+542    if args.task == 'identify_rare_values':
+543        compute_value_counts(input_dataframe, args)
 544
-545    pbar.set_description(
-546        f'Computing ranks for {input_dataframe.shape[1]} features',
+545    bounds_storage = compute_bounds_increment(
+546        input_dataframe, numeric_column_types,
 547    )
 548
-549    return (
-550        mixed_rank_graph(input_dataframe, args, cpu_pool, pbar),
-551        bounds_storage,
-552        coverage_storage,
-553        feature_memory_consumption,
-554    )
-555
-556
-557def get_num_of_instances(fname: str) -> int:
-558    """Count the number of lines in a file, fast - useful for progress logging"""
+549    pbar.set_description(
+550        f'Computing ranks for {input_dataframe.shape[1]} features',
+551    )
+552
+553    return (
+554        mixed_rank_graph(input_dataframe, args, cpu_pool, pbar),
+555        bounds_storage,
+556        coverage_storage,
+557        feature_memory_consumption,
+558    )
 559
-560    def _make_gen(reader):
-561        while True:
-562            b = reader(2**16)
-563            if not b:
-564                break
-565            yield b
-566
-567    with open(fname, 'rb') as f:
-568        count = sum(buf.count(b'\n') for buf in _make_gen(f.raw.read))
-569    return count
+560
+561def get_num_of_instances(fname: str) -> int:
+562    """Count the number of lines in a file, fast - useful for progress logging"""
+563
+564    def _make_gen(reader):
+565        while True:
+566            b = reader(2**16)
+567            if not b:
+568                break
+569            yield b
 570
-571
-572def get_grouped_df(importances_df_list: list[tuple[str, str, float]]) -> pd.DataFrame:
-573    """A helper method that enables median-based aggregation after processing"""
+571    with open(fname, 'rb') as f:
+572        count = sum(buf.count(b'\n') for buf in _make_gen(f.raw.read))
+573    return count
 574
-575    importances_df = pd.DataFrame(importances_df_list)
-576    if len(importances_df) == 0:
-577        return None
-578    importances_df.columns = ['FeatureA', 'FeatureB', 'Score']
-579    grouped = importances_df.groupby(
-580        ['FeatureA', 'FeatureB'],
-581    ).median().reset_index()
-582    return grouped
-583
-584
-585def checkpoint_importances_df(importances_batch: list[tuple[str, str, float]]) -> None:
-586    """A helper which stores intermediary state - useful for longer runs"""
+575
+576def get_grouped_df(importances_df_list: list[tuple[str, str, float]]) -> pd.DataFrame:
+577    """A helper method that enables median-based aggregation after processing"""
+578
+579    importances_df = pd.DataFrame(importances_df_list)
+580    if len(importances_df) == 0:
+581        return None
+582    importances_df.columns = ['FeatureA', 'FeatureB', 'Score']
+583    grouped = importances_df.groupby(
+584        ['FeatureA', 'FeatureB'],
+585    ).median().reset_index()
+586    return grouped
 587
-588    gdf = get_grouped_df(importances_batch)
-589    if gdf is not None:
-590        gdf.to_csv('ranking_checkpoint_tmp.tsv', sep='\t')
+588
+589def checkpoint_importances_df(importances_batch: list[tuple[str, str, float]]) -> None:
+590    """A helper which stores intermediary state - useful for longer runs"""
 591
-592
-593def estimate_importances_minibatches(
-594    input_file: str,
-595    column_descriptions: list,
-596    fw_col_mapping: dict[str, str],
-597    numeric_column_types: set,
-598    batch_size: int = 100000,
-599    args: Any = None,
-600    data_encoding: str = 'utf-8',
-601    cpu_pool: Any = None,
-602    delimiter: str = '\t',
-603    feature_construction_mode: bool = False,
-604    logger: Any = None,
-605) -> tuple[list[dict[str, Any]], Any, dict[Any, Any], list[dict[str, Any]], list[dict[str, set[str]]], defaultdict[str, list[set[str]]], dict[str, Any]]:
-606    """Interaction score estimator - suitable for example for csv-like input data types.
-607    This type of data is normally a single large csv, meaning that minibatch processing needs to
-608    happen during incremental handling of the file (that"s not the case for pre-separated ob data)
-609    """
-610
-611    invalid_line_queue: Any = deque([], maxlen=2**5)
-612
-613    invalid_lines = 0
-614    line_counter = 0
-615
-616    importances_df: list[Any] = []
-617    line_tmp_storage = []
-618    bounds_storage_batch = []
-619    memory_storage_batch = []
-620    step_timing_checkpoints = []
-621
-622    local_coverage_object = defaultdict(list)
-623    local_pbar = tqdm.tqdm(
-624        total=get_num_of_instances(input_file) - 1, position=0,
-625    )
-626
-627    file_name, file_extension = os.path.splitext(input_file)
-628
-629    if file_extension == '.gz':
-630        file_stream = gzip.open(input_file, 'rt', encoding=data_encoding)
-631
-632    else:
-633        file_stream = open(input_file, encoding=data_encoding)
-634
-635    file_stream.readline()
-636
-637    local_pbar.set_description('Starting ranking computation')
-638    for line in file_stream:
-639        line_counter += 1
-640        local_pbar.update(1)
-641
-642        if line_counter % args.subsampling != 0:
-643            continue
-644
-645        parsed_line = generic_line_parser(
-646            line, delimiter, args, fw_col_mapping, column_descriptions,
-647        )
+592    gdf = get_grouped_df(importances_batch)
+593    if gdf is not None:
+594        gdf.to_csv('ranking_checkpoint_tmp.tsv', sep='\t')
+595
+596
+597def estimate_importances_minibatches(
+598    input_file: str,
+599    column_descriptions: list,
+600    fw_col_mapping: dict[str, str],
+601    numeric_column_types: set,
+602    batch_size: int = 100000,
+603    args: Any = None,
+604    data_encoding: str = 'utf-8',
+605    cpu_pool: Any = None,
+606    delimiter: str = '\t',
+607    feature_construction_mode: bool = False,
+608    logger: Any = None,
+609) -> tuple[list[dict[str, Any]], Any, dict[Any, Any], list[dict[str, Any]], list[dict[str, set[str]]], defaultdict[str, list[set[str]]], dict[str, Any]]:
+610    """Interaction score estimator - suitable for example for csv-like input data types.
+611    This type of data is normally a single large csv, meaning that minibatch processing needs to
+612    happen during incremental handling of the file (that"s not the case for pre-separated ob data)
+613    """
+614
+615    invalid_line_queue: Any = deque([], maxlen=2**5)
+616
+617    invalid_lines = 0
+618    line_counter = 0
+619
+620    importances_df: list[Any] = []
+621    line_tmp_storage = []
+622    bounds_storage_batch = []
+623    memory_storage_batch = []
+624    step_timing_checkpoints = []
+625
+626    local_coverage_object = defaultdict(list)
+627    local_pbar = tqdm.tqdm(
+628        total=get_num_of_instances(input_file) - 1, position=0,
+629    )
+630
+631    file_name, file_extension = os.path.splitext(input_file)
+632
+633    if file_extension == '.gz':
+634        file_stream = gzip.open(input_file, 'rt', encoding=data_encoding)
+635
+636    else:
+637        file_stream = open(input_file, encoding=data_encoding)
+638
+639    file_stream.readline()
+640
+641    local_pbar.set_description('Starting ranking computation')
+642    for line in file_stream:
+643        line_counter += 1
+644        local_pbar.update(1)
+645
+646        if line_counter % args.subsampling != 0:
+647            continue
 648
-649        if len(parsed_line) == len(column_descriptions):
-650            line_tmp_storage.append(parsed_line)
-651
-652        else:
-653            invalid_line_queue.appendleft(str(parsed_line))
-654            invalid_lines += 1
+649        parsed_line = generic_line_parser(
+650            line, delimiter, args, fw_col_mapping, column_descriptions,
+651        )
+652
+653        if len(parsed_line) == len(column_descriptions):
+654            line_tmp_storage.append(parsed_line)
 655
-656        # Batches need to be processed on-the-fly
-657        if len(line_tmp_storage) >= args.minibatch_size:
-658
-659            importances_batch, bounds_storage, coverage_storage, memory_storage = compute_batch_ranking(
-660                line_tmp_storage,
-661                numeric_column_types,
-662                args,
-663                cpu_pool,
-664                column_descriptions,
-665                logger,
-666                local_pbar,
-667            )
-668
-669            bounds_storage_batch.append(bounds_storage)
-670            memory_storage_batch.append(memory_storage)
-671            for k, v in coverage_storage.items():
-672                local_coverage_object[k].append(v)
-673
-674            del coverage_storage
-675
-676            line_tmp_storage = []
-677            step_timing_checkpoints.append(importances_batch.step_times)
-678            importances_df += importances_batch.triplet_scores
+656        else:
+657            invalid_line_queue.appendleft(str(parsed_line))
+658            invalid_lines += 1
+659
+660        # Batches need to be processed on-the-fly
+661        if len(line_tmp_storage) >= args.minibatch_size:
+662
+663            importances_batch, bounds_storage, coverage_storage, memory_storage = compute_batch_ranking(
+664                line_tmp_storage,
+665                numeric_column_types,
+666                args,
+667                cpu_pool,
+668                column_descriptions,
+669                logger,
+670                local_pbar,
+671            )
+672
+673            bounds_storage_batch.append(bounds_storage)
+674            memory_storage_batch.append(memory_storage)
+675            for k, v in coverage_storage.items():
+676                local_coverage_object[k].append(v)
+677
+678            del coverage_storage
 679
-680            if args.heuristic != 'Constant':
-681                local_pbar.set_description('Creating checkpoint')
-682                checkpoint_importances_df(importances_df)
+680            line_tmp_storage = []
+681            step_timing_checkpoints.append(importances_batch.step_times)
+682            importances_df += importances_batch.triplet_scores
 683
-684    file_stream.close()
-685
-686    local_pbar.set_description('Parsing the remainder')
-687    if invalid_lines > 0:
-688        logger.info(
-689            f"Detected {invalid_lines} invalid lines. If this number is very high, it's possible your header is off - re-check your data/attribute-feature mappings please!",
-690        )
-691
-692        invalid_lines_log = '\n INVALID_LINE ====> '.join(
-693            list(invalid_line_queue)[0:5],
+684            if args.heuristic != 'Constant':
+685                local_pbar.set_description('Creating checkpoint')
+686                checkpoint_importances_df(importances_df)
+687
+688    file_stream.close()
+689
+690    local_pbar.set_description('Parsing the remainder')
+691    if invalid_lines > 0:
+692        logger.info(
+693            f"Detected {invalid_lines} invalid lines. If this number is very high, it's possible your header is off - re-check your data/attribute-feature mappings please!",
 694        )
-695        logger.info(
-696            f'5 samples of invalid lines are printed below\n {invalid_lines_log}',
-697        )
-698
-699    remaining_batch_size = len(line_tmp_storage)
-700
-701    if remaining_batch_size > 2**10:
-702        line_tmp_storage = line_tmp_storage[: args.minibatch_size]
-703        importances_batch, bounds_storage, coverage_storage, _ = compute_batch_ranking(
-704            line_tmp_storage,
-705            numeric_column_types,
-706            args,
-707            cpu_pool,
-708            column_descriptions,
-709            logger,
-710            local_pbar,
-711        )
-712
-713        for k, v in coverage_storage.items():
-714            local_coverage_object[k].append(v)
-715
-716        step_timing_checkpoints.append(importances_batch.step_times)
-717        importances_df += importances_batch.triplet_scores
-718        bounds_storage = dict()
-719        bounds_storage_batch.append(bounds_storage)
-720        checkpoint_importances_df(importances_df)
-721
-722    local_pbar.set_description('Wrapping up')
-723    local_pbar.close()
-724
-725    return (
-726        step_timing_checkpoints,
-727        get_grouped_df(importances_df),
-728        GLOBAL_CARDINALITY_STORAGE,
-729        bounds_storage_batch,
-730        memory_storage_batch,
-731        local_coverage_object,
-732        GLOBAL_RARE_VALUE_STORAGE,
-733    )
+695
+696        invalid_lines_log = '\n INVALID_LINE ====> '.join(
+697            list(invalid_line_queue)[0:5],
+698        )
+699        logger.info(
+700            f'5 samples of invalid lines are printed below\n {invalid_lines_log}',
+701        )
+702
+703    remaining_batch_size = len(line_tmp_storage)
+704
+705    if remaining_batch_size > 2**10:
+706        line_tmp_storage = line_tmp_storage[: args.minibatch_size]
+707        importances_batch, bounds_storage, coverage_storage, _ = compute_batch_ranking(
+708            line_tmp_storage,
+709            numeric_column_types,
+710            args,
+711            cpu_pool,
+712            column_descriptions,
+713            logger,
+714            local_pbar,
+715        )
+716
+717        for k, v in coverage_storage.items():
+718            local_coverage_object[k].append(v)
+719
+720        step_timing_checkpoints.append(importances_batch.step_times)
+721        importances_df += importances_batch.triplet_scores
+722        bounds_storage = dict()
+723        bounds_storage_batch.append(bounds_storage)
+724        checkpoint_importances_df(importances_df)
+725
+726    local_pbar.set_description('Wrapping up')
+727    local_pbar.close()
+728
+729    return (
+730        step_timing_checkpoints,
+731        get_grouped_df(importances_df),
+732        GLOBAL_CARDINALITY_STORAGE,
+733        bounds_storage_batch,
+734        memory_storage_batch,
+735        local_coverage_object,
+736        GLOBAL_RARE_VALUE_STORAGE,
+737    )
    @@ -940,20 +947,20 @@

    - 
    47def prior_combinations_sample(combinations: list[tuple[Any, ...]], args: Any) -> list[tuple[Any, ...]]:
-48    """Make sure only relevant subspace of combinations is selected based on prior counts"""
-49
-50    if len(GLOBAL_PRIOR_COMB_COUNTS) == 0:
-51        for combination in combinations:
-52            GLOBAL_PRIOR_COMB_COUNTS[combination] += 1
-53        tmp = combinations[:args.combination_number_upper_bound]
-54    else:
-55        tmp = list(x[0] for x in sorted(GLOBAL_PRIOR_COMB_COUNTS.items(), key=lambda x:x[1], reverse=False))[:args.combination_number_upper_bound]
-56
-57    for combination in tmp:
-58        GLOBAL_PRIOR_COMB_COUNTS[combination] += 1
-59
-60    return tmp
+            
    48def prior_combinations_sample(combinations: list[tuple[Any, ...]], args: Any) -> list[tuple[Any, ...]]:
+49    """Make sure only relevant subspace of combinations is selected based on prior counts"""
+50
+51    if len(GLOBAL_PRIOR_COMB_COUNTS) == 0:
+52        for combination in combinations:
+53            GLOBAL_PRIOR_COMB_COUNTS[combination] += 1
+54        tmp = combinations[:args.combination_number_upper_bound]
+55    else:
+56        tmp = list(x[0] for x in sorted(GLOBAL_PRIOR_COMB_COUNTS.items(), key=lambda x:x[1], reverse=False))[:args.combination_number_upper_bound]
+57
+58    for combination in tmp:
+59        GLOBAL_PRIOR_COMB_COUNTS[combination] += 1
+60
+61    return tmp
 
    
 
 
@@ -961,6 +968,53 @@
    +

    +
    + +
    + + def + get_combinations_from_columns( all_columns: pandas.core.indexes.base.Index, args: Any) -> list[tuple[typing.Any, ...]]: + + + +
    + + 
    64def get_combinations_from_columns(all_columns: pd.Index, args: Any) -> list[tuple[Any, ...]]:
+65    """Return feature-feature & feature-label combinations, depending on the heuristic and ranking scope"""
+66
+67    if '3mr' in args.heuristic:
+68        rel_columns = [column for column in all_columns if ' AND_REL ' in column]
+69        non_rel_columns = sorted(set(all_columns) - set(rel_columns))
+70
+71        combinations = list(
+72            itertools.combinations_with_replacement(non_rel_columns, 2),
+73        )
+74        combinations += [(column, args.label_column) for column in rel_columns]
+75    else:
+76        _combinations = itertools.combinations_with_replacement(all_columns, 2)
+77
+78        # Some applications do not require the full feature-feature triangular matrix
+79        if args.target_ranking_only == 'True':
+80            combinations = [x for x in _combinations if args.label_column in x]
+81        else:
+82            combinations = list(_combinations)
+83
+84    if args.target_ranking_only != 'True':
+85        # Diagonal elements (non-label)
+86        combinations += [
+87            (individual_column, individual_column)
+88            for individual_column in all_columns
+89            if individual_column != args.label_column
+90        ]
+91    return combinations
+
    + + +

    Return feature-feature & feature-label combinations, depending on the heuristic and ranking scope

    +
    + +
    @@ -973,94 +1027,67 @@

    - 
     63def mixed_rank_graph(
- 64    input_dataframe: pd.DataFrame, args: Any, cpu_pool: Any, pbar: Any,
- 65) -> BatchRankingSummary:
- 66    """Compute the full mixed rank graph corresponding to all pairwise feature interactions based on the selected heuristic"""
- 67
- 68    all_columns = input_dataframe.columns
- 69
- 70    triplets = []
- 71    tmp_df = input_dataframe.copy().astype('category')
- 72    out_time_struct = {}
- 73
- 74    # Handle cont. types prior to interaction evaluation
- 75    pbar.set_description('Encoding 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
- 82    # Helper method for parallel estimation
- 83    combinations = list(
- 84        itertools.combinations_with_replacement(all_columns, 2),
- 85    )
- 86
- 87    if '3mr' in args.heuristic:
- 88        rel_columns = [
- 89            column for column in all_columns if ' AND_REL ' in column
- 90        ]
- 91        non_rel_columns = list(set(all_columns) - set(rel_columns))
- 92        combinations = list(
- 93            itertools.combinations_with_replacement(non_rel_columns, 2),
- 94        )
- 95        combinations += [(column, args.label_column) for column in rel_columns]
- 96    else:
- 97        combinations = list(
- 98            itertools.combinations_with_replacement(all_columns, 2),
- 99        )
+            
     94def mixed_rank_graph(
+ 95    input_dataframe: pd.DataFrame, args: Any, cpu_pool: Any, pbar: Any,
+ 96) -> BatchRankingSummary:
+ 97    """Compute the full mixed rank graph corresponding to all pairwise feature interactions based on the selected heuristic"""
+ 98
+ 99    all_columns = input_dataframe.columns
 100
-101    # Diagonal elements
-102    for individual_column in all_columns:
-103        if individual_column != args.label_column:
-104            combinations += [(individual_column, individual_column)]
-105
-106    # Some applications do not require the full feature-feature triangular matrix
-107    if (args.target_ranking_only == 'True') and ('3mr' not in args.heuristic):
-108        combinations = [x for x in combinations if args.label_column in x]
+101    triplets = []
+102    tmp_df = input_dataframe.copy().astype('category')
+103    out_time_struct = {}
+104
+105    # Handle cont. types prior to interaction evaluation
+106    pbar.set_description('Encoding columns')
+107    start_enc_timer = timer()
+108    tmp_df = pd.DataFrame({k : tmp_df[k].cat.codes for k in all_columns})
 109
-110    combinations = prior_combinations_sample(combinations, args)
-111    random.shuffle(combinations)
+110    end_enc_timer = timer()
+111    out_time_struct['encoding_columns'] = end_enc_timer - start_enc_timer
 112
-113    if args.heuristic == 'Constant':
-114        final_constant_imp = []
-115        for c1, c2 in combinations:
-116            final_constant_imp.append((c1, c2, 0.0))
-117
-118        out_time_struct['feature_score_computation'] = end_enc_timer - \
-119            start_enc_timer
-120        return BatchRankingSummary(final_constant_imp, out_time_struct)
+113    combinations = get_combinations_from_columns(all_columns, args)
+114    combinations = prior_combinations_sample(combinations, args)
+115    random.shuffle(combinations)
+116
+117    if args.heuristic == 'Constant':
+118        final_constant_imp = []
+119        for c1, c2 in combinations:
+120            final_constant_imp.append((c1, c2, 0.0))
 121
-122    # Map the scoring calls to the worker pool
-123    pbar.set_description('Allocating thread pool')
-124
-125    # starmap is an alternative that is slower unfortunately (but nicer)
-126    def get_grounded_importances_estimate(combination: tuple[str]) -> Any:
-127        return get_importances_estimate_pairwise(combination, args, tmp_df=tmp_df)
+122        out_time_struct['feature_score_computation'] = end_enc_timer - \
+123            start_enc_timer
+124        return BatchRankingSummary(final_constant_imp, out_time_struct)
+125
+126    # Map the scoring calls to the worker pool
+127    pbar.set_description('Allocating thread pool')
 128
-129    start_enc_timer = timer()
-130    with cpu_pool as p:
-131        pbar.set_description(f'Computing (#ftr={len(combinations)})')
-132        results = p.amap(get_grounded_importances_estimate, combinations)
-133        while not results.ready():
-134            time.sleep(4)
-135        triplets = results.get()
-136    end_enc_timer = timer()
-137    out_time_struct['feature_score_computation'] = end_enc_timer - \
-138        start_enc_timer
-139
-140    # Gather the final triplets
-141    pbar.set_description('Aggregation of ranking results')
-142    final_triplets = []
-143    for triplet in triplets:
-144        inv = (triplet[1], triplet[0], triplet[2])
-145        final_triplets.append(inv)
-146        final_triplets.append(triplet)
-147        triplets = final_triplets
-148
-149    pbar.set_description('Proceeding to the next batch of data')
-150    return BatchRankingSummary(triplets, out_time_struct)
+129    # starmap is an alternative that is slower unfortunately (but nicer)
+130    def get_grounded_importances_estimate(combination: tuple[str]) -> Any:
+131        return get_importances_estimate_pairwise(combination, args, tmp_df=tmp_df)
+132
+133    start_enc_timer = timer()
+134    with cpu_pool as p:
+135        pbar.set_description(f'Computing (#ftr={len(combinations)})')
+136        results = p.amap(get_grounded_importances_estimate, combinations)
+137        while not results.ready():
+138            time.sleep(4)
+139        triplets = results.get()
+140    end_enc_timer = timer()
+141    out_time_struct['feature_score_computation'] = end_enc_timer - \
+142        start_enc_timer
+143
+144    # Gather the final triplets
+145    pbar.set_description('Aggregation of ranking results')
+146    final_triplets = []
+147    for triplet in triplets:
+148        inv = (triplet[1], triplet[0], triplet[2])
+149        final_triplets.append(inv)
+150        final_triplets.append(triplet)
+151        triplets = final_triplets
+152
+153    pbar.set_description('Proceeding to the next batch of data')
+154    return BatchRankingSummary(triplets, out_time_struct)
 
    
 
 
@@ -1080,20 +1107,20 @@
    - 
    153def enrich_with_transformations(
-154    input_dataframe: pd.DataFrame, num_col_types: set[str], logger: Any, args: Any,
-155) -> pd.DataFrame:
-156    """Construct a collection of new features based on pre-defined transformations/rules"""
-157
-158    transformer = FeatureTransformerGeneric(
-159        num_col_types, preset=args.transformers,
-160    )
-161    transformed_df = transformer.construct_new_features(input_dataframe)
-162    logger.info(
-163        f'Constructed {len(transformer.constructed_feature_names)} new features ..',
+            
    157def enrich_with_transformations(
+158    input_dataframe: pd.DataFrame, num_col_types: set[str], logger: Any, args: Any,
+159) -> pd.DataFrame:
+160    """Construct a collection of new features based on pre-defined transformations/rules"""
+161
+162    transformer = FeatureTransformerGeneric(
+163        num_col_types, preset=args.transformers,
 164    )
-165
-166    return transformed_df
+165    transformed_df = transformer.construct_new_features(input_dataframe)
+166    logger.info(
+167        f'Constructed {len(transformer.constructed_feature_names)} new features ..',
+168    )
+169
+170    return transformed_df
 
    
 
 
@@ -1113,55 +1140,55 @@
    - 
    169def compute_combined_features(
-170    input_dataframe: pd.DataFrame,
-171    logger: Any,
-172    args: Any,
-173    pbar: Any,
-174    is_3mr: bool = False,
-175) -> pd.DataFrame:
-176    """Compute higher order features via xxhash-based trick."""
-177
-178    all_columns = [
-179        x for x in input_dataframe.columns if x != args.label_column
-180    ]
-181    join_string = ' AND_REL ' if is_3mr else ' AND '
-182    interaction_order = 2 if is_3mr else args.interaction_order
-183
-184    full_combination_space = list(
-185        itertools.combinations(all_columns, interaction_order),
-186    )
+            
    173def compute_combined_features(
+174    input_dataframe: pd.DataFrame,
+175    logger: Any,
+176    args: Any,
+177    pbar: Any,
+178    is_3mr: bool = False,
+179) -> pd.DataFrame:
+180    """Compute higher order features via xxhash-based trick."""
+181
+182    all_columns = [
+183        x for x in input_dataframe.columns if x != args.label_column
+184    ]
+185    join_string = ' AND_REL ' if is_3mr else ' AND '
+186    interaction_order = 2 if is_3mr else args.interaction_order
 187
-188    if args.combination_number_upper_bound:
-189        random.shuffle(full_combination_space)
-190        full_combination_space = full_combination_space[
-191            : args.combination_number_upper_bound
-192        ]
-193
-194    com_counter = 0
-195    new_feature_hash = {}
-196    for new_combination in full_combination_space:
-197        pbar.set_description(
-198            f'Created {com_counter}/{len(full_combination_space)}',
-199        )
-200        combined_feature: list[str] = [str(0)] * input_dataframe.shape[0]
-201        for feature in new_combination:
-202            tmp_feature = input_dataframe[feature].tolist()
-203            for enx, el in enumerate(tmp_feature):
-204                combined_feature[enx] = str(
-205                    internal_hash(
-206                        str(combined_feature[enx]) + str(el),
-207                    ),
-208                )
-209        ftr_name = join_string.join(str(x) for x in new_combination)
-210        new_feature_hash[ftr_name] = combined_feature
-211        com_counter += 1
-212    tmp_df = pd.DataFrame(new_feature_hash)
-213    pbar.set_description('Concatenating into final frame ..')
-214    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
-215    del tmp_df
-216
-217    return input_dataframe
+188    full_combination_space = list(
+189        itertools.combinations(all_columns, interaction_order),
+190    )
+191
+192    if args.combination_number_upper_bound:
+193        random.shuffle(full_combination_space)
+194        full_combination_space = full_combination_space[
+195            : args.combination_number_upper_bound
+196        ]
+197
+198    com_counter = 0
+199    new_feature_hash = {}
+200    for new_combination in full_combination_space:
+201        pbar.set_description(
+202            f'Created {com_counter}/{len(full_combination_space)}',
+203        )
+204        combined_feature: list[str] = [str(0)] * input_dataframe.shape[0]
+205        for feature in new_combination:
+206            tmp_feature = input_dataframe[feature].tolist()
+207            for enx, el in enumerate(tmp_feature):
+208                combined_feature[enx] = str(
+209                    internal_hash(
+210                        str(combined_feature[enx]) + str(el),
+211                    ),
+212                )
+213        ftr_name = join_string.join(str(x) for x in new_combination)
+214        new_feature_hash[ftr_name] = combined_feature
+215        com_counter += 1
+216    tmp_df = pd.DataFrame(new_feature_hash)
+217    pbar.set_description('Concatenating into final frame ..')
+218    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
+219    del tmp_df
+220
+221    return input_dataframe
 
    
 
 
@@ -1181,48 +1208,48 @@
    - 
    220def compute_expanded_multivalue_features(
-221    input_dataframe: pd.DataFrame, logger: Any, args: Any, pbar: Any,
-222) -> pd.DataFrame:
-223    """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."""
-224
-225    considered_multivalue_features = args.explode_multivalue_features.split(
-226        ';',
-227    )
-228    new_feature_hash = {}
-229    missing_symbols = set(args.missing_value_symbols.split(','))
-230
-231    for multivalue_feature in considered_multivalue_features:
-232        multivalue_feature_vector = input_dataframe[multivalue_feature].values.tolist(
-233        )
-234        multivalue_feature_vector = [
-235            x.replace(',', '-') for x in multivalue_feature_vector
-236        ]
-237        multivalue_sets = [
-238            set(x.split('-'))
-239            for x in multivalue_feature_vector
+            
    224def compute_expanded_multivalue_features(
+225    input_dataframe: pd.DataFrame, logger: Any, args: Any, pbar: Any,
+226) -> pd.DataFrame:
+227    """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."""
+228
+229    considered_multivalue_features = args.explode_multivalue_features.split(
+230        ';',
+231    )
+232    new_feature_hash = {}
+233    missing_symbols = set(args.missing_value_symbols.split(','))
+234
+235    for multivalue_feature in considered_multivalue_features:
+236        multivalue_feature_vector = input_dataframe[multivalue_feature].values.tolist(
+237        )
+238        multivalue_feature_vector = [
+239            x.replace(',', '-') for x in multivalue_feature_vector
 240        ]
-241        unique_values = set.union(*multivalue_sets)
-242
-243        for missing_symbol in missing_symbols:
-244            if missing_symbol in unique_values:
-245                unique_values.remove(missing_symbol)
+241        multivalue_sets = [
+242            set(x.split('-'))
+243            for x in multivalue_feature_vector
+244        ]
+245        unique_values = set.union(*multivalue_sets)
 246
-247        for unique_value in unique_values:
-248            tmp_vec = []
-249            for enx, multivalue in enumerate(multivalue_sets):
-250                if unique_value in multivalue:
-251                    tmp_vec.append('1')
-252                else:
-253                    tmp_vec.append('')
-254
-255            new_feature_hash[f'MULTIEX-{multivalue_feature}-{unique_value}'] = tmp_vec
-256
-257    tmp_df = pd.DataFrame(new_feature_hash)
-258    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
-259    del tmp_df
+247        for missing_symbol in missing_symbols:
+248            if missing_symbol in unique_values:
+249                unique_values.remove(missing_symbol)
+250
+251        for unique_value in unique_values:
+252            tmp_vec = []
+253            for enx, multivalue in enumerate(multivalue_sets):
+254                if unique_value in multivalue:
+255                    tmp_vec.append('1')
+256                else:
+257                    tmp_vec.append('')
+258
+259            new_feature_hash[f'MULTIEX-{multivalue_feature}-{unique_value}'] = tmp_vec
 260
-261    return input_dataframe
+261    tmp_df = pd.DataFrame(new_feature_hash)
+262    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
+263    del tmp_df
+264
+265    return input_dataframe
 
    
 
 
@@ -1242,85 +1269,85 @@
    - 
    264def compute_subfeatures(
-265    input_dataframe: pd.DataFrame, logger: Any, args: Any, pbar: Any,
-266) -> pd.DataFrame:
-267    """Compute derived features that are more fine-grained. Implements logic around two operators that govern feature construction.
-268    ->: One sided construction - every value from left side is fine, separate ones from the right side feature will be considered.
-269    <->: 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)
-270    """
-271
-272    all_subfeature_pair_seeds = args.subfeature_mapping.split(';')
-273    new_feature_hash = dict()
-274
-275    for seed_pair in all_subfeature_pair_seeds:
-276        if '<->' in seed_pair:
-277            feature_first, feature_second = seed_pair.split('<->')
+            
    268def compute_subfeatures(
+269    input_dataframe: pd.DataFrame, logger: Any, args: Any, pbar: Any,
+270) -> pd.DataFrame:
+271    """Compute derived features that are more fine-grained. Implements logic around two operators that govern feature construction.
+272    ->: One sided construction - every value from left side is fine, separate ones from the right side feature will be considered.
+273    <->: 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)
+274    """
+275
+276    all_subfeature_pair_seeds = args.subfeature_mapping.split(';')
+277    new_feature_hash = dict()
 278
-279        elif '->' in seed_pair:
-280            feature_first, feature_second = seed_pair.split('->')
-281
-282        else:
-283            raise NotImplementedError(
-284                'Please specify valid subfeature operator (<-> or ->)',
-285            )
-286
-287        subframe = input_dataframe[[feature_first, feature_second]]
-288        unique_feature_second = subframe[feature_second].unique()
-289        feature_first_vec = subframe[feature_first].tolist()
-290        feature_second_vec = subframe[feature_second].tolist()
-291        out_template_feature = [
-292            (a, b) for a, b in zip(feature_first_vec, feature_second_vec)
-293        ]
-294
-295        if '<->' in seed_pair:
-296            unique_feature_first = subframe[feature_first].unique()
-297
-298            mask_types = []
-299            for unique_target_feature_value in unique_feature_second:
-300                for unique_seed_feature_value in unique_feature_first:
-301                    mask_types.append(
-302                        (unique_seed_feature_value, unique_target_feature_value),
-303                    )
-304
-305            for mask_type in mask_types:
-306                new_feature = []
-307                for value_tuple in out_template_feature:
-308                    if (
-309                        value_tuple[0] == mask_type[0]
-310                        and value_tuple[1] == mask_type[1]
-311                    ):
-312                        new_feature.append(str(1))
-313                    else:
-314                        new_feature.append(str(0))
-315                feature_name = (
-316                    f'SUBFEATURE|{feature_first}|{feature_second}-'
-317                    + mask_type[0]
-318                    + '&'
-319                    + mask_type[1]
-320                )
-321                new_feature_hash[feature_name] = new_feature
-322
-323            del new_feature
-324
-325        elif '->' in seed_pair:
-326            for unique_target_feature_value in unique_feature_second:
-327                tmp_new_feature = [
-328                    'AND'.join(
-329                        x,
-330                    ) if x[1] == unique_target_feature_value else ''
-331                    for x in out_template_feature
-332                ]
-333                feature_name_final = (
-334                    'SUBFEATURE-' + feature_first + '&' + unique_target_feature_value
-335                )
-336                new_feature_hash[feature_name_final] = tmp_new_feature
-337
-338    tmp_df = pd.DataFrame(new_feature_hash)
-339    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
-340
-341    del tmp_df
-342    return input_dataframe
+279    for seed_pair in all_subfeature_pair_seeds:
+280        if '<->' in seed_pair:
+281            feature_first, feature_second = seed_pair.split('<->')
+282
+283        elif '->' in seed_pair:
+284            feature_first, feature_second = seed_pair.split('->')
+285
+286        else:
+287            raise NotImplementedError(
+288                'Please specify valid subfeature operator (<-> or ->)',
+289            )
+290
+291        subframe = input_dataframe[[feature_first, feature_second]]
+292        unique_feature_second = subframe[feature_second].unique()
+293        feature_first_vec = subframe[feature_first].tolist()
+294        feature_second_vec = subframe[feature_second].tolist()
+295        out_template_feature = [
+296            (a, b) for a, b in zip(feature_first_vec, feature_second_vec)
+297        ]
+298
+299        if '<->' in seed_pair:
+300            unique_feature_first = subframe[feature_first].unique()
+301
+302            mask_types = []
+303            for unique_target_feature_value in unique_feature_second:
+304                for unique_seed_feature_value in unique_feature_first:
+305                    mask_types.append(
+306                        (unique_seed_feature_value, unique_target_feature_value),
+307                    )
+308
+309            for mask_type in mask_types:
+310                new_feature = []
+311                for value_tuple in out_template_feature:
+312                    if (
+313                        value_tuple[0] == mask_type[0]
+314                        and value_tuple[1] == mask_type[1]
+315                    ):
+316                        new_feature.append(str(1))
+317                    else:
+318                        new_feature.append(str(0))
+319                feature_name = (
+320                    f'SUBFEATURE|{feature_first}|{feature_second}-'
+321                    + mask_type[0]
+322                    + '&'
+323                    + mask_type[1]
+324                )
+325                new_feature_hash[feature_name] = new_feature
+326
+327            del new_feature
+328
+329        elif '->' in seed_pair:
+330            for unique_target_feature_value in unique_feature_second:
+331                tmp_new_feature = [
+332                    'AND'.join(
+333                        x,
+334                    ) if x[1] == unique_target_feature_value else ''
+335                    for x in out_template_feature
+336                ]
+337                feature_name_final = (
+338                    'SUBFEATURE-' + feature_first + '&' + unique_target_feature_value
+339                )
+340                new_feature_hash[feature_name_final] = tmp_new_feature
+341
+342    tmp_df = pd.DataFrame(new_feature_hash)
+343    input_dataframe = pd.concat([input_dataframe, tmp_df], axis=1)
+344
+345    del tmp_df
+346    return input_dataframe
 
    
 
 
@@ -1342,17 +1369,17 @@
    - 
    345def include_noisy_features(
-346    input_dataframe: pd.DataFrame, logger: Any, args: Any,
-347) -> pd.DataFrame:
-348    """Add randomized features that serve as a sanity check"""
-349
-350    transformer = FeatureTransformerNoise()
-351    transformed_df = transformer.construct_new_features(
-352        input_dataframe, args.label_column,
-353    )
-354
-355    return transformed_df
+            
    349def include_noisy_features(
+350    input_dataframe: pd.DataFrame, logger: Any, args: Any,
+351) -> pd.DataFrame:
+352    """Add randomized features that serve as a sanity check"""
+353
+354    transformer = FeatureTransformerNoise()
+355    transformed_df = transformer.construct_new_features(
+356        input_dataframe, args.label_column,
+357    )
+358
+359    return transformed_df
 
    
 
 
@@ -1372,23 +1399,23 @@
    - 
    358def compute_coverage(input_dataframe: pd.DataFrame, args: Any) -> dict[str, set[str]]:
-359    """Compute coverage of features, incrementally"""
-360    output_storage_cov = defaultdict(set)
-361    all_missing_symbols = set(args.missing_value_symbols.split(','))
-362    for column in input_dataframe:
-363        all_missing = sum(
-364            [
-365                input_dataframe[column].values.tolist().count(x)
-366                for x in all_missing_symbols
-367            ],
-368        )
-369
-370        output_storage_cov[column] = (
-371            1 - (all_missing / input_dataframe.shape[0])
-372        ) * 100
+            
    362def compute_coverage(input_dataframe: pd.DataFrame, args: Any) -> dict[str, set[str]]:
+363    """Compute coverage of features, incrementally"""
+364    output_storage_cov = defaultdict(set)
+365    all_missing_symbols = set(args.missing_value_symbols.split(','))
+366    for column in input_dataframe:
+367        all_missing = sum(
+368            [
+369                input_dataframe[column].values.tolist().count(x)
+370                for x in all_missing_symbols
+371            ],
+372        )
 373
-374    return output_storage_cov
+374        output_storage_cov[column] = (
+375            1 - (all_missing / input_dataframe.shape[0])
+376        ) * 100
+377
+378    return output_storage_cov
 
    
 
 
@@ -1408,19 +1435,19 @@
    - 
    377def compute_feature_memory_consumption(input_dataframe: pd.DataFrame, args: Any) -> dict[str, set[str]]:
-378    """An approximation of how much feature take up"""
-379    output_storage_features = defaultdict(set)
-380    for col in input_dataframe.columns:
-381        specific_column = [
-382            str(x).strip() for x in input_dataframe[col].astype(str).values.tolist()
-383        ]
-384        col_size = sum(
-385            len(x.encode())
-386            for x in specific_column
-387        ) / input_dataframe.shape[0]
-388        output_storage_features[col] = col_size
-389    return output_storage_features
+            
    381def compute_feature_memory_consumption(input_dataframe: pd.DataFrame, args: Any) -> dict[str, set[str]]:
+382    """An approximation of how much feature take up"""
+383    output_storage_features = defaultdict(set)
+384    for col in input_dataframe.columns:
+385        specific_column = [
+386            str(x).strip() for x in input_dataframe[col].astype(str).values.tolist()
+387        ]
+388        col_size = sum(
+389            len(x.encode())
+390            for x in specific_column
+391        ) / input_dataframe.shape[0]
+392        output_storage_features[col] = col_size
+393    return output_storage_features
 
    
 
 
@@ -1440,24 +1467,24 @@
    - 
    392def compute_value_counts(input_dataframe: pd.DataFrame, args: Any):
-393    """Update the count structure"""
-394
-395    global GLOBAL_RARE_VALUE_STORAGE
-396    global IGNORED_VALUES
-397
-398    for column in input_dataframe.columns:
-399        main_values = input_dataframe[column].values
-400        for value in main_values:
-401            if value not in IGNORED_VALUES:
-402                GLOBAL_RARE_VALUE_STORAGE.update({(column, value): 1})
-403
-404    for key, val in GLOBAL_RARE_VALUE_STORAGE.items():
-405        if val > args.rare_value_count_upper_bound:
-406            IGNORED_VALUES.add(key)
+            
    396def compute_value_counts(input_dataframe: pd.DataFrame, args: Any):
+397    """Update the count structure"""
+398
+399    global GLOBAL_RARE_VALUE_STORAGE
+400    global IGNORED_VALUES
+401
+402    for column in input_dataframe.columns:
+403        main_values = input_dataframe[column].values
+404        for value in main_values:
+405            if value not in IGNORED_VALUES:
+406                GLOBAL_RARE_VALUE_STORAGE.update({(column, value): 1})
 407
-408    for to_remove_val in IGNORED_VALUES:
-409        del GLOBAL_RARE_VALUE_STORAGE[to_remove_val]
+408    for key, val in GLOBAL_RARE_VALUE_STORAGE.items():
+409        if val > args.rare_value_count_upper_bound:
+410            IGNORED_VALUES.add(key)
+411
+412    for to_remove_val in IGNORED_VALUES:
+413        del GLOBAL_RARE_VALUE_STORAGE[to_remove_val]
 
    
 
 
@@ -1477,26 +1504,26 @@
    - 
    412def compute_cardinalities(input_dataframe: pd.DataFrame, pbar: Any) -> None:
-413    """Compute cardinalities of features, incrementally"""
-414
-415    global GLOBAL_CARDINALITY_STORAGE
-416    output_storage_card = defaultdict(set)
-417    for enx, column in enumerate(input_dataframe):
-418        output_storage_card[column] = set(input_dataframe[column].unique())
-419        if column not in GLOBAL_CARDINALITY_STORAGE:
-420            GLOBAL_CARDINALITY_STORAGE[column] = HyperLogLog(
-421                HYPERLL_ERROR_BOUND,
-422            )
-423
-424        for unique_value in set(input_dataframe[column].unique()):
-425            if unique_value:
-426                GLOBAL_CARDINALITY_STORAGE[column].add(
-427                    internal_hash(unique_value),
-428                )
-429        pbar.set_description(
-430            f'Computing cardinality (Hyperloglog update) {enx}/{input_dataframe.shape[1]}',
-431        )
+            
    416def compute_cardinalities(input_dataframe: pd.DataFrame, pbar: Any) -> None:
+417    """Compute cardinalities of features, incrementally"""
+418
+419    global GLOBAL_CARDINALITY_STORAGE
+420    output_storage_card = defaultdict(set)
+421    for enx, column in enumerate(input_dataframe):
+422        output_storage_card[column] = set(input_dataframe[column].unique())
+423        if column not in GLOBAL_CARDINALITY_STORAGE:
+424            GLOBAL_CARDINALITY_STORAGE[column] = HyperLogLog(
+425                HYPERLL_ERROR_BOUND,
+426            )
+427
+428        for unique_value in set(input_dataframe[column].unique()):
+429            if unique_value:
+430                GLOBAL_CARDINALITY_STORAGE[column].add(
+431                    internal_hash(unique_value),
+432                )
+433        pbar.set_description(
+434            f'Computing cardinality (Hyperloglog update) {enx}/{input_dataframe.shape[1]}',
+435        )
 
    
 
 
@@ -1516,36 +1543,36 @@
    - 
    434def compute_bounds_increment(
-435    input_dataframe: pd.DataFrame, numeric_column_types: set[str],
-436) -> dict[str, Any]:
-437    all_features = input_dataframe.columns
-438    numeric_column_types = set(numeric_column_types)
-439    summary_object = {}
-440    summary_storage: Any = {}
-441    for feature in all_features:
-442        if feature in numeric_column_types:
-443            feature_vector = pd.to_numeric(
-444                input_dataframe[feature], errors='coerce',
-445            )
-446            minimum = np.min(feature_vector)
-447            maximum = np.max(feature_vector)
-448            mean = np.mean(feature_vector)
-449            summary_storage = NumericFeatureSummary(
-450                feature, minimum, maximum, mean, len(
-451                    np.unique(feature_vector),
-452                ),
-453            )
-454            summary_object[feature] = summary_storage
-455
-456        else:
-457            feature_vector = input_dataframe[feature].values
-458            summary_storage = NominalFeatureSummary(
-459                feature, len(np.unique(feature_vector)),
-460            )
-461            summary_object[feature] = summary_storage
-462
-463    return summary_object
+            
    438def compute_bounds_increment(
+439    input_dataframe: pd.DataFrame, numeric_column_types: set[str],
+440) -> dict[str, Any]:
+441    all_features = input_dataframe.columns
+442    numeric_column_types = set(numeric_column_types)
+443    summary_object = {}
+444    summary_storage: Any = {}
+445    for feature in all_features:
+446        if feature in numeric_column_types:
+447            feature_vector = pd.to_numeric(
+448                input_dataframe[feature], errors='coerce',
+449            )
+450            minimum = np.min(feature_vector)
+451            maximum = np.max(feature_vector)
+452            mean = np.mean(feature_vector)
+453            summary_storage = NumericFeatureSummary(
+454                feature, minimum, maximum, mean, len(
+455                    np.unique(feature_vector),
+456                ),
+457            )
+458            summary_object[feature] = summary_storage
+459
+460        else:
+461            feature_vector = input_dataframe[feature].values
+462            summary_storage = NominalFeatureSummary(
+463                feature, len(np.unique(feature_vector)),
+464            )
+465            summary_object[feature] = summary_storage
+466
+467    return summary_object
 
    
 
 
@@ -1563,96 +1590,96 @@
    - 
    466def compute_batch_ranking(
-467    line_tmp_storage: list[list[Any]],
-468    numeric_column_types: set[str],
-469    args: Any,
-470    cpu_pool: Any,
-471    column_descriptions: list[str],
-472    logger: Any,
-473    pbar: Any,
-474) -> tuple[BatchRankingSummary, dict[str, Any], dict[str, set[str]], dict[str, set[str]]]:
-475    """Enrich the feature space and compute the batch importances"""
-476
-477    input_dataframe = pd.DataFrame(line_tmp_storage)
-478    input_dataframe.columns = column_descriptions
-479    pbar.set_description('Control features')
+            
    470def compute_batch_ranking(
+471    line_tmp_storage: list[list[Any]],
+472    numeric_column_types: set[str],
+473    args: Any,
+474    cpu_pool: Any,
+475    column_descriptions: list[str],
+476    logger: Any,
+477    pbar: Any,
+478) -> tuple[BatchRankingSummary, dict[str, Any], dict[str, set[str]], dict[str, set[str]]]:
+479    """Enrich the feature space and compute the batch importances"""
 480
-481    if args.feature_set_focus:
-482        if args.feature_set_focus == '_all_from_reference_JSON':
-483            focus_set = extract_features_from_reference_JSON(
-484                args.reference_model_JSON,
-485            )
-486
-487        else:
-488            focus_set = set(args.feature_set_focus.split(','))
-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[list(focus_set)]
+481    input_dataframe = pd.DataFrame(line_tmp_storage)
+482    input_dataframe.columns = column_descriptions
+483    pbar.set_description('Control features')
+484
+485    if args.feature_set_focus:
+486        if args.feature_set_focus == '_all_from_reference_JSON':
+487            focus_set = extract_features_from_reference_JSON(
+488                args.reference_model_JSON,
+489            )
+490
+491        else:
+492            focus_set = set(args.feature_set_focus.split(','))
 493
-494    if args.transformers != 'none':
-495        pbar.set_description('Adding transformations')
-496        input_dataframe = enrich_with_transformations(
-497            input_dataframe, numeric_column_types, logger, args,
-498        )
-499
-500    if args.explode_multivalue_features != 'False':
-501        pbar.set_description('Constructing new features from multivalue ones')
-502        input_dataframe = compute_expanded_multivalue_features(
-503            input_dataframe, logger, args, pbar,
-504        )
-505
-506    if args.subfeature_mapping != 'False':
-507        pbar.set_description('Constructing new (sub)features')
-508        input_dataframe = compute_subfeatures(
-509            input_dataframe, logger, args, pbar,
-510        )
-511
-512    if args.interaction_order > 1:
-513        pbar.set_description('Constructing new features')
-514        input_dataframe = compute_combined_features(
-515            input_dataframe, logger, args, pbar,
-516        )
-517
-518    # in case of 3mr we compute the score of combinations against the target
-519    if '3mr' in args.heuristic:
-520        pbar.set_description(
-521            'Constructing features for computing relations in 3mr',
-522        )
-523        input_dataframe = compute_combined_features(
-524            input_dataframe, logger, args, pbar, True,
-525        )
-526
-527    if args.include_noise_baseline_features == 'True' and args.heuristic != 'Constant':
-528        pbar.set_description('Computing baseline features')
-529        input_dataframe = include_noisy_features(input_dataframe, logger, args)
+494        focus_set.add(args.label_column)
+495        focus_set = {x for x in focus_set if x in input_dataframe.columns}
+496        input_dataframe = input_dataframe[list(focus_set)]
+497
+498    if args.transformers != 'none':
+499        pbar.set_description('Adding transformations')
+500        input_dataframe = enrich_with_transformations(
+501            input_dataframe, numeric_column_types, logger, args,
+502        )
+503
+504    if args.explode_multivalue_features != 'False':
+505        pbar.set_description('Constructing new features from multivalue ones')
+506        input_dataframe = compute_expanded_multivalue_features(
+507            input_dataframe, logger, args, pbar,
+508        )
+509
+510    if args.subfeature_mapping != 'False':
+511        pbar.set_description('Constructing new (sub)features')
+512        input_dataframe = compute_subfeatures(
+513            input_dataframe, logger, args, pbar,
+514        )
+515
+516    if args.interaction_order > 1:
+517        pbar.set_description('Constructing new features')
+518        input_dataframe = compute_combined_features(
+519            input_dataframe, logger, args, pbar,
+520        )
+521
+522    # in case of 3mr we compute the score of combinations against the target
+523    if '3mr' in args.heuristic:
+524        pbar.set_description(
+525            'Constructing features for computing relations in 3mr',
+526        )
+527        input_dataframe = compute_combined_features(
+528            input_dataframe, logger, args, pbar, True,
+529        )
 530
-531    # Compute incremental statistic useful for data inspection/transformer generation
-532    pbar.set_description('Computing coverage')
-533    coverage_storage = compute_coverage(input_dataframe, args)
-534    feature_memory_consumption = compute_feature_memory_consumption(
-535        input_dataframe, args,
-536    )
-537    compute_cardinalities(input_dataframe, pbar)
-538
-539    if args.task == 'identify_rare_values':
-540        compute_value_counts(input_dataframe, args)
-541
-542    bounds_storage = compute_bounds_increment(
-543        input_dataframe, numeric_column_types,
-544    )
+531    if args.include_noise_baseline_features == 'True' and args.heuristic != 'Constant':
+532        pbar.set_description('Computing baseline features')
+533        input_dataframe = include_noisy_features(input_dataframe, logger, args)
+534
+535    # Compute incremental statistic useful for data inspection/transformer generation
+536    pbar.set_description('Computing coverage')
+537    coverage_storage = compute_coverage(input_dataframe, args)
+538    feature_memory_consumption = compute_feature_memory_consumption(
+539        input_dataframe, args,
+540    )
+541    compute_cardinalities(input_dataframe, pbar)
+542
+543    if args.task == 'identify_rare_values':
+544        compute_value_counts(input_dataframe, args)
 545
-546    pbar.set_description(
-547        f'Computing ranks for {input_dataframe.shape[1]} features',
+546    bounds_storage = compute_bounds_increment(
+547        input_dataframe, numeric_column_types,
 548    )
 549
-550    return (
-551        mixed_rank_graph(input_dataframe, args, cpu_pool, pbar),
-552        bounds_storage,
-553        coverage_storage,
-554        feature_memory_consumption,
-555    )
+550    pbar.set_description(
+551        f'Computing ranks for {input_dataframe.shape[1]} features',
+552    )
+553
+554    return (
+555        mixed_rank_graph(input_dataframe, args, cpu_pool, pbar),
+556        bounds_storage,
+557        coverage_storage,
+558        feature_memory_consumption,
+559    )
 
    
 
 
@@ -1672,19 +1699,19 @@
    - 
    558def get_num_of_instances(fname: str) -> int:
-559    """Count the number of lines in a file, fast - useful for progress logging"""
-560
-561    def _make_gen(reader):
-562        while True:
-563            b = reader(2**16)
-564            if not b:
-565                break
-566            yield b
-567
-568    with open(fname, 'rb') as f:
-569        count = sum(buf.count(b'\n') for buf in _make_gen(f.raw.read))
-570    return count
+            
    562def get_num_of_instances(fname: str) -> int:
+563    """Count the number of lines in a file, fast - useful for progress logging"""
+564
+565    def _make_gen(reader):
+566        while True:
+567            b = reader(2**16)
+568            if not b:
+569                break
+570            yield b
+571
+572    with open(fname, 'rb') as f:
+573        count = sum(buf.count(b'\n') for buf in _make_gen(f.raw.read))
+574    return count
 
    
 
 
@@ -1704,17 +1731,17 @@
    - 
    573def get_grouped_df(importances_df_list: list[tuple[str, str, float]]) -> pd.DataFrame:
-574    """A helper method that enables median-based aggregation after processing"""
-575
-576    importances_df = pd.DataFrame(importances_df_list)
-577    if len(importances_df) == 0:
-578        return None
-579    importances_df.columns = ['FeatureA', 'FeatureB', 'Score']
-580    grouped = importances_df.groupby(
-581        ['FeatureA', 'FeatureB'],
-582    ).median().reset_index()
-583    return grouped
+            
    577def get_grouped_df(importances_df_list: list[tuple[str, str, float]]) -> pd.DataFrame:
+578    """A helper method that enables median-based aggregation after processing"""
+579
+580    importances_df = pd.DataFrame(importances_df_list)
+581    if len(importances_df) == 0:
+582        return None
+583    importances_df.columns = ['FeatureA', 'FeatureB', 'Score']
+584    grouped = importances_df.groupby(
+585        ['FeatureA', 'FeatureB'],
+586    ).median().reset_index()
+587    return grouped
 
    
 
 
@@ -1734,12 +1761,12 @@
    - 
    586def checkpoint_importances_df(importances_batch: list[tuple[str, str, float]]) -> None:
-587    """A helper which stores intermediary state - useful for longer runs"""
-588
-589    gdf = get_grouped_df(importances_batch)
-590    if gdf is not None:
-591        gdf.to_csv('ranking_checkpoint_tmp.tsv', sep='\t')
+            
    590def checkpoint_importances_df(importances_batch: list[tuple[str, str, float]]) -> None:
+591    """A helper which stores intermediary state - useful for longer runs"""
+592
+593    gdf = get_grouped_df(importances_batch)
+594    if gdf is not None:
+595        gdf.to_csv('ranking_checkpoint_tmp.tsv', sep='\t')
 
    
 
 
@@ -1759,147 +1786,147 @@
    - 
    594def estimate_importances_minibatches(
-595    input_file: str,
-596    column_descriptions: list,
-597    fw_col_mapping: dict[str, str],
-598    numeric_column_types: set,
-599    batch_size: int = 100000,
-600    args: Any = None,
-601    data_encoding: str = 'utf-8',
-602    cpu_pool: Any = None,
-603    delimiter: str = '\t',
-604    feature_construction_mode: bool = False,
-605    logger: Any = None,
-606) -> tuple[list[dict[str, Any]], Any, dict[Any, Any], list[dict[str, Any]], list[dict[str, set[str]]], defaultdict[str, list[set[str]]], dict[str, Any]]:
-607    """Interaction score estimator - suitable for example for csv-like input data types.
-608    This type of data is normally a single large csv, meaning that minibatch processing needs to
-609    happen during incremental handling of the file (that"s not the case for pre-separated ob data)
-610    """
-611
-612    invalid_line_queue: Any = deque([], maxlen=2**5)
-613
-614    invalid_lines = 0
-615    line_counter = 0
-616
-617    importances_df: list[Any] = []
-618    line_tmp_storage = []
-619    bounds_storage_batch = []
-620    memory_storage_batch = []
-621    step_timing_checkpoints = []
-622
-623    local_coverage_object = defaultdict(list)
-624    local_pbar = tqdm.tqdm(
-625        total=get_num_of_instances(input_file) - 1, position=0,
-626    )
-627
-628    file_name, file_extension = os.path.splitext(input_file)
-629
-630    if file_extension == '.gz':
-631        file_stream = gzip.open(input_file, 'rt', encoding=data_encoding)
-632
-633    else:
-634        file_stream = open(input_file, encoding=data_encoding)
-635
-636    file_stream.readline()
-637
-638    local_pbar.set_description('Starting ranking computation')
-639    for line in file_stream:
-640        line_counter += 1
-641        local_pbar.update(1)
-642
-643        if line_counter % args.subsampling != 0:
-644            continue
-645
-646        parsed_line = generic_line_parser(
-647            line, delimiter, args, fw_col_mapping, column_descriptions,
-648        )
+            
    598def estimate_importances_minibatches(
+599    input_file: str,
+600    column_descriptions: list,
+601    fw_col_mapping: dict[str, str],
+602    numeric_column_types: set,
+603    batch_size: int = 100000,
+604    args: Any = None,
+605    data_encoding: str = 'utf-8',
+606    cpu_pool: Any = None,
+607    delimiter: str = '\t',
+608    feature_construction_mode: bool = False,
+609    logger: Any = None,
+610) -> tuple[list[dict[str, Any]], Any, dict[Any, Any], list[dict[str, Any]], list[dict[str, set[str]]], defaultdict[str, list[set[str]]], dict[str, Any]]:
+611    """Interaction score estimator - suitable for example for csv-like input data types.
+612    This type of data is normally a single large csv, meaning that minibatch processing needs to
+613    happen during incremental handling of the file (that"s not the case for pre-separated ob data)
+614    """
+615
+616    invalid_line_queue: Any = deque([], maxlen=2**5)
+617
+618    invalid_lines = 0
+619    line_counter = 0
+620
+621    importances_df: list[Any] = []
+622    line_tmp_storage = []
+623    bounds_storage_batch = []
+624    memory_storage_batch = []
+625    step_timing_checkpoints = []
+626
+627    local_coverage_object = defaultdict(list)
+628    local_pbar = tqdm.tqdm(
+629        total=get_num_of_instances(input_file) - 1, position=0,
+630    )
+631
+632    file_name, file_extension = os.path.splitext(input_file)
+633
+634    if file_extension == '.gz':
+635        file_stream = gzip.open(input_file, 'rt', encoding=data_encoding)
+636
+637    else:
+638        file_stream = open(input_file, encoding=data_encoding)
+639
+640    file_stream.readline()
+641
+642    local_pbar.set_description('Starting ranking computation')
+643    for line in file_stream:
+644        line_counter += 1
+645        local_pbar.update(1)
+646
+647        if line_counter % args.subsampling != 0:
+648            continue
 649
-650        if len(parsed_line) == len(column_descriptions):
-651            line_tmp_storage.append(parsed_line)
-652
-653        else:
-654            invalid_line_queue.appendleft(str(parsed_line))
-655            invalid_lines += 1
+650        parsed_line = generic_line_parser(
+651            line, delimiter, args, fw_col_mapping, column_descriptions,
+652        )
+653
+654        if len(parsed_line) == len(column_descriptions):
+655            line_tmp_storage.append(parsed_line)
 656
-657        # Batches need to be processed on-the-fly
-658        if len(line_tmp_storage) >= args.minibatch_size:
-659
-660            importances_batch, bounds_storage, coverage_storage, memory_storage = compute_batch_ranking(
-661                line_tmp_storage,
-662                numeric_column_types,
-663                args,
-664                cpu_pool,
-665                column_descriptions,
-666                logger,
-667                local_pbar,
-668            )
-669
-670            bounds_storage_batch.append(bounds_storage)
-671            memory_storage_batch.append(memory_storage)
-672            for k, v in coverage_storage.items():
-673                local_coverage_object[k].append(v)
-674
-675            del coverage_storage
-676
-677            line_tmp_storage = []
-678            step_timing_checkpoints.append(importances_batch.step_times)
-679            importances_df += importances_batch.triplet_scores
+657        else:
+658            invalid_line_queue.appendleft(str(parsed_line))
+659            invalid_lines += 1
+660
+661        # Batches need to be processed on-the-fly
+662        if len(line_tmp_storage) >= args.minibatch_size:
+663
+664            importances_batch, bounds_storage, coverage_storage, memory_storage = compute_batch_ranking(
+665                line_tmp_storage,
+666                numeric_column_types,
+667                args,
+668                cpu_pool,
+669                column_descriptions,
+670                logger,
+671                local_pbar,
+672            )
+673
+674            bounds_storage_batch.append(bounds_storage)
+675            memory_storage_batch.append(memory_storage)
+676            for k, v in coverage_storage.items():
+677                local_coverage_object[k].append(v)
+678
+679            del coverage_storage
 680
-681            if args.heuristic != 'Constant':
-682                local_pbar.set_description('Creating checkpoint')
-683                checkpoint_importances_df(importances_df)
+681            line_tmp_storage = []
+682            step_timing_checkpoints.append(importances_batch.step_times)
+683            importances_df += importances_batch.triplet_scores
 684
-685    file_stream.close()
-686
-687    local_pbar.set_description('Parsing the remainder')
-688    if invalid_lines > 0:
-689        logger.info(
-690            f"Detected {invalid_lines} invalid lines. If this number is very high, it's possible your header is off - re-check your data/attribute-feature mappings please!",
-691        )
-692
-693        invalid_lines_log = '\n INVALID_LINE ====> '.join(
-694            list(invalid_line_queue)[0:5],
+685            if args.heuristic != 'Constant':
+686                local_pbar.set_description('Creating checkpoint')
+687                checkpoint_importances_df(importances_df)
+688
+689    file_stream.close()
+690
+691    local_pbar.set_description('Parsing the remainder')
+692    if invalid_lines > 0:
+693        logger.info(
+694            f"Detected {invalid_lines} invalid lines. If this number is very high, it's possible your header is off - re-check your data/attribute-feature mappings please!",
 695        )
-696        logger.info(
-697            f'5 samples of invalid lines are printed below\n {invalid_lines_log}',
-698        )
-699
-700    remaining_batch_size = len(line_tmp_storage)
-701
-702    if remaining_batch_size > 2**10:
-703        line_tmp_storage = line_tmp_storage[: args.minibatch_size]
-704        importances_batch, bounds_storage, coverage_storage, _ = compute_batch_ranking(
-705            line_tmp_storage,
-706            numeric_column_types,
-707            args,
-708            cpu_pool,
-709            column_descriptions,
-710            logger,
-711            local_pbar,
-712        )
-713
-714        for k, v in coverage_storage.items():
-715            local_coverage_object[k].append(v)
-716
-717        step_timing_checkpoints.append(importances_batch.step_times)
-718        importances_df += importances_batch.triplet_scores
-719        bounds_storage = dict()
-720        bounds_storage_batch.append(bounds_storage)
-721        checkpoint_importances_df(importances_df)
-722
-723    local_pbar.set_description('Wrapping up')
-724    local_pbar.close()
-725
-726    return (
-727        step_timing_checkpoints,
-728        get_grouped_df(importances_df),
-729        GLOBAL_CARDINALITY_STORAGE,
-730        bounds_storage_batch,
-731        memory_storage_batch,
-732        local_coverage_object,
-733        GLOBAL_RARE_VALUE_STORAGE,
-734    )
+696
+697        invalid_lines_log = '\n INVALID_LINE ====> '.join(
+698            list(invalid_line_queue)[0:5],
+699        )
+700        logger.info(
+701            f'5 samples of invalid lines are printed below\n {invalid_lines_log}',
+702        )
+703
+704    remaining_batch_size = len(line_tmp_storage)
+705
+706    if remaining_batch_size > 2**10:
+707        line_tmp_storage = line_tmp_storage[: args.minibatch_size]
+708        importances_batch, bounds_storage, coverage_storage, _ = compute_batch_ranking(
+709            line_tmp_storage,
+710            numeric_column_types,
+711            args,
+712            cpu_pool,
+713            column_descriptions,
+714            logger,
+715            local_pbar,
+716        )
+717
+718        for k, v in coverage_storage.items():
+719            local_coverage_object[k].append(v)
+720
+721        step_timing_checkpoints.append(importances_batch.step_times)
+722        importances_df += importances_batch.triplet_scores
+723        bounds_storage = dict()
+724        bounds_storage_batch.append(bounds_storage)
+725        checkpoint_importances_df(importances_df)
+726
+727    local_pbar.set_description('Wrapping up')
+728    local_pbar.close()
+729
+730    return (
+731        step_timing_checkpoints,
+732        get_grouped_df(importances_df),
+733        GLOBAL_CARDINALITY_STORAGE,
+734        bounds_storage_batch,
+735        memory_storage_batch,
+736        local_coverage_object,
+737        GLOBAL_RARE_VALUE_STORAGE,
+738    )
 
    
 
 
diff --git a/docs/search.js b/docs/search.js
index e292afb..64b09e8 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. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" 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u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default 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

    • A minimal showcase of performing feature ranking on a generic CSV is demonstrated with this example.

      • \n

    • More examples demonstrating OutRank's capabilities are also available.

      • \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.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

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    \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!

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

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    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:

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    featureA    featureB    0.512\nfeatureA    featureC    0.125\n
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    Setup

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    \n
    pip install outrank\n
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    and test a minimal cycle with

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    outrank --task selftest\n
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    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

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    outrank --help\n
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    Example use cases

    \n\n
      \n

    • A minimal showcase of performing feature ranking on a generic CSV is demonstrated with this example.

      • \n

    • More examples demonstrating OutRank's capabilities are also available.

      • \n
    \n\n

    OutRank as a Python library

    \n\n

    Once installed, _OutRank_ can be used as any other Python library. For example, generic feature ranking algorithms can be accessed as

    \n\n
    \n
    from outrank.algorithms.feature_ranking.ranking_mi_numba import (\n    mutual_info_estimator_numba,\n)\n\n# Some synthetic minimal data (Numpy vectors)\na = np.array([1, 0, 0, 0, 1, 1, 1, 0], dtype=np.int32)\n\nlowest = np.array(np.random.permutation(a), dtype=np.int32)\nmedium = np.array([1, 1, 0, 0, 1, 1, 1, 1], dtype=np.int32)\nhigh = np.array([1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int32)\n\nlowest_score = mutual_info_estimator_numba(\n    a, lowest, np.float32(1.0), False,\n)\nmedium_score = mutual_info_estimator_numba(\n    a, medium, np.float32(1.0), False,\n)\nhigh_score = mutual_info_estimator_numba(\n    a, high, np.float32(1.0), False,\n)\n\nscores = [lowest_score, medium_score, high_score]\nsorted_score_indices = np.argsort(scores)\nassert np.sum(np.array([0, 1, 2]) - sorted_score_indices) ==  0\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.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.get_combinations_from_columns": {"fullname": "outrank.core_ranking.get_combinations_from_columns", "modulename": "outrank.core_ranking", "qualname": "get_combinations_from_columns", "kind": "function", "doc": "

    Return feature-feature & feature-label combinations, depending on the heuristic and ranking scope

    \n", "signature": "(\tall_columns: pandas.core.indexes.base.Index,\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": "

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

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    A method for visualization of the obtained feature interaction maps.

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{"tf": 1}, "outrank.core_utils.summarize_feature_bounds_for_transformers": {"tf": 1}}, "df": 4}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; // mirrored in build-search-index.js (part 1) // Also split on html tags. this is a cheap heuristic, but good enough. diff --git a/outrank/core_ranking.py b/outrank/core_ranking.py index c59293d..31cc17e 100644 --- a/outrank/core_ranking.py +++ b/outrank/core_ranking.py @@ -42,6 +42,7 @@ GLOBAL_PRIOR_COMB_COUNTS: dict[Any, int] = Counter() IGNORED_VALUES = set() HYPERLL_ERROR_BOUND = 0.02 +MAX_FEATURES_3MR = 10 ** 4 def prior_combinations_sample(combinations: list[tuple[Any, ...]], args: Any) -> list[tuple[Any, ...]]: @@ -64,6 +65,8 @@ def get_combinations_from_columns(all_columns: pd.Index, args: Any) -> list[tupl """Return feature-feature & feature-label combinations, depending on the heuristic and ranking scope""" if '3mr' in args.heuristic: + if args.combination_number_upper_bound > MAX_FEATURES_3MR: + args.combination_number_upper_bound = MAX_FEATURES_3MR rel_columns = [column for column in all_columns if ' AND_REL ' in column] non_rel_columns = sorted(set(all_columns) - set(rel_columns)) @@ -606,7 +609,7 @@ def estimate_importances_minibatches( delimiter: str = '\t', feature_construction_mode: bool = False, logger: Any = None, -) -> tuple[list[dict[str, Any]], Any, dict[Any, Any], list[dict[str, Any]], list[dict[str, set[str]]], defaultdict[str, list[set[str]]], dict[str, Any]]: +) -> tuple[list[dict[str, Any]], Any, dict[Any, Any], list[dict[str, Any]], list[dict[str, set[str]]], defaultdict[str, list[set[str]]], dict[str, Any], dict[str, Any]]: """Interaction score estimator - suitable for example for csv-like input data types. This type of data is normally a single large csv, meaning that minibatch processing needs to happen during incremental handling of the file (that"s not the case for pre-separated ob data) @@ -729,9 +732,10 @@ def estimate_importances_minibatches( return ( step_timing_checkpoints, get_grouped_df(importances_df), - GLOBAL_CARDINALITY_STORAGE, + GLOBAL_CARDINALITY_STORAGE.copy(), bounds_storage_batch, memory_storage_batch, local_coverage_object, - GLOBAL_RARE_VALUE_STORAGE, + GLOBAL_RARE_VALUE_STORAGE.copy(), + GLOBAL_PRIOR_COMB_COUNTS.copy(), ) diff --git a/outrank/task_ranking.py b/outrank/task_ranking.py index 0496ad8..1840bce 100644 --- a/outrank/task_ranking.py +++ b/outrank/task_ranking.py @@ -1,6 +1,7 @@ from __future__ import annotations import glob +import json import logging import os import signal @@ -100,6 +101,7 @@ def outrank_task_conduct_ranking(args: Any): memory_object_storage, coverage_object, RARE_VALUE_STORAGE, + GLOBAL_PRIOR_COMB_COUNTS, ) = estimate_importances_minibatches(**cmd_arguments) global_bounds_storage += bounds_object_storage @@ -276,6 +278,10 @@ def outrank_task_conduct_ranking(args: Any): os.path.join(args.output_folder, 'pairwise_ranks.tsv'), sep='\t', index=False, ) + with open(f'{args.output_folder}/combination_estimation_counts.json', 'w') as out_counts: + out_dict = {str(k): v for k, v in GLOBAL_PRIOR_COMB_COUNTS.items()} + out_counts.write(json.dumps(out_dict)) + # Write timings and config for replicability dfx = pd.DataFrame(all_timings) dfx.to_json(f'{args.output_folder}/timings.json') diff --git a/setup.py b/setup.py index fc6fb91..5d148e0 100644 --- a/setup.py +++ b/setup.py @@ -23,7 +23,7 @@ def _read_description(): packages = [x for x in setuptools.find_packages() if x != 'test'] setuptools.setup( name='outrank', - version='0.95.2', + version='0.95.3', description='OutRank: Feature ranking for massive sparse data sets.', long_description=_read_description(), long_description_content_type='text/markdown',