CPP.run

CPP.run(labels, label_test=1, label_ref=0, n_filter=100, n_pre_filter=None, pct_pre_filter=5, max_std_test=0.2, max_overlap=0.5, max_cor=0.5, check_cat=True, parametric=False, start=1, tmd_len=20, jmd_n_len=10, jmd_c_len=10, n_jobs=None, vectorized=True, n_batches=None, n_sample_batches=None, return_stats=False, redundancy='legacy')[source]

Perform Comparative Physicochemical Profiling (CPP) algorithm: creation and two-step filtering of interpretable sequence-based features.

The aim of the CPP algorithm is to identify a set of unique, non-redundant features that are most discriminant between the test and reference group of sequences. See [Breimann25] for details on the algorithm.

Added in version 0.1.0.

Changed in version 1.1.0: Added the return_stats parameter, returning the filter-funnel statistics alongside df_feat.

Changed in version 1.1.0: Added the n_sample_batches parameter for sample-axis batching (memory bounded by batch size, not n).

Changed in version 1.1.0: When the constructor enables bootstrap stability annotation (CPP(bootstrap=True)), df_feat gains a selection_frequency column (the selected features are unchanged; see the bootstrap / bootstrap_kws constructor parameters and the Notes below).

Parameters:

  • labels (array-like, shape (n_samples,)) – Class labels for samples in sequence DataFrame (typically, test=1, reference=0).

  • label_test (int, default=1,) – Class label of test group in labels.

  • label_ref (int, default=0,) – Class label of reference group in labels.

  • n_filter (int, default=100) – Number of features to be filtered/selected by CPP algorithm. With bootstrap stability selection (CPP(bootstrap=True)) this still caps the final redundancy-filtered output computed on the full dataset.

  • n_pre_filter (int, optional) – Number of feature to be pre-filtered by CPP algorithm. If None, a percentage of all features is used.

  • pct_pre_filter (int, default=5) – Percentage of all features that should remain after the pre-filtering step.

  • max_std_test (float, default=0.2) – Maximum standard deviation [>0-<1] within the test group used as threshold for pre-filtering.

  • max_overlap (float, default=0.5) – Maximum positional overlap [0-1] of features used as threshold for filtering.

  • max_cor (float, default=0.5) – Maximum Pearson correlation [0-1] of feature scales used as threshold for filtering.

  • check_cat (bool, default=True) – Whether to check for redundancy within scale categories during filtering.

  • redundancy ({'legacy', 'exact'}, default='legacy') – Position-overlap criterion of the redundancy-reduction step. 'legacy' (default) keeps results reproducible across versions; 'exact' compares the actual residue positions (see Notes).

  • parametric (bool, default=False) – Whether to use parametric (T-test) or non-parametric (Mann-Whitney U test) test for p-value computation. This also sets the p-value column name in df_feat (‘p_val_ttest_indep’ vs ‘p_val_mann_whitney’).

  • start (int, default=1) – Position label of first residue position (starting at N-terminus).

  • tmd_len (int, default=20) – Length of target middle domain (TMD) (>0).

  • jmd_n_len (int, default=10) – Length of JMD-N (>=0).

  • jmd_c_len (int, default=10) – Length of JMD-C (>=0).

  • n_jobs (int, None, or -1, default=None) –

    Number of CPU cores (>=1) used for multiprocessing. If None, the number is optimized automatically. If -1, the number is set to all available cores. Overridden by options['n_jobs'] when set.

    Warning

    On Python 3.14 + macOS, calling this with n_jobs > 1 (or -1 / None) from a script that lacks an if __name__ == "__main__": guard (or from a bare REPL / heredoc) can trigger a recursive process spawn (FileNotFoundError / EOFError / cannot pickle '_thread.RLock'). Guard your entry point, or run serially with n_jobs=1. See also CPPGrid (default backend="threads"), which sidesteps this.

  • vectorized (bool, default=True) – Whether to apply sequence splitting and the Mann-Whitney U test in ‘vectorized’ mode (True), improving speed but increasing memory consumption. The vectorized Mann-Whitney U test uses a fast normal approximation of the p-value (roughly an order of magnitude faster on the test step); vectorized=False instead computes the exact scipy.stats.mannwhitneyu() p-value, which is slower but reproducible bit-for-bit. This choice changes only the reported ‘p_val_mann_whitney’ and ‘p_val_fdr_bh’ columns: feature ranking and selection are driven by ‘abs_auc’ and ‘abs_mean_dif’, so the selected features are identical in either mode.

  • n_batches (int, None, default=None) – Number of batches (>=2) used for batch processing. If None, single-processing is used, which is faster but more memory-intensive. Increasing n_batches (up to the maximum number of scales in df_scales) reduces memory consumption but slows down processing.

  • n_sample_batches (int, None, default=None) – Number of sample-axis batches (>=2, up to the number of samples) for sample-batched processing. If None, sample-batching is disabled. Bounds peak memory by the batch size rather than the full sample count n, so it is the option for very large n. Mutually exclusive with n_batches (which batches over scales).

  • return_stats (bool, default=False) – If True, also return the filter-funnel statistics (last_filter_stats_) as a second element (df_feat, stats); if False, return only df_feat.

Returns:

df_feat – Feature DataFrame with a unique identifier, scale information, statistics, and positions for each feature.

Return type:

pd.DataFrame, shape (n_features, n_feature_info)

Notes

  • Pre-filtering can be adjusted by the following parameters: {‘n_pre_filter’, ‘pct_pre_filter’, ‘max_std_test’}.

  • Filtering can be adjusted by the following parameters: {‘n_filter’, ‘max_overlap’, ‘max_cor’, ‘check_cat’, ‘redundancy’}.

  • redundancy='exact' is an optional enhancement of the redundancy step, not a correctness fix: it compares the true residue positions and tends to yield a more concentrated signature (fewer redundant subcategories) rather than higher predictive performance, which stays essentially unchanged. Default 'legacy' keeps prior results reproducible. For a stronger, more efficient redundancy reduction, see CPP.simplify().

  • Bootstrap stability annotation (CPP(bootstrap=True)) wraps this run: the data is resampled bootstrap_kws['rounds'] times (per bootstrap_kws) and re-selected each round to score how often each feature is selected, then this ordinary full-data run is returned with a ``selection_frequency`` column (0 to 1) appended after positions. The selected features are exactly those of the non-bootstrap run (n_filter is the selection criterion); selection_frequency flags which are reproducible under resampling. The run is otherwise unchanged (bootstrap=False, the default, is byte-identical). Not combinable with n_batches / n_sample_batches.

  • Binary by design. run compares one test group against one reference group. For multi-class or regression tasks, build binary label contrasts with the SequenceFeature.get_labels_* helpers and loop run over them (see the CPP class notes and the P8: Prediction protocol).

  • Cost scales as O(n_scales x n_parts x n_splits) (the candidate feature count), so larger scale sets / wider split_kws are proportionally slower — budget a sweep accordingly, or use CPPGrid (which runs CPP once per n_filter group and slices the rest).

  • Classifier head tracks the metric when training a downstream model on df_feat: in practice SVM tends to be best for AP (ranking), logistic regression for balanced accuracy, and random forest for MCC at a fixed threshold (detection). Pick the head to match the objective you report.

  • For large datasets (due to long sequences or a high number of samples) or memory-limited systems, memory consumption can be reduced by:

    • Disabling vectorized mode (vectorized=False)

    • Reducing n_jobs (down to n_jobs=1)

    • Using batch processing (n_batches>=2, with higher values reducing memory usage)

    While this helps to prevent crashes, it may slow down processing.

  • df_feat follows a standardized, deterministic column order (the canonical schema), with the unique feature id (1), scale information (2-5), statistical results for filtering and ranking (6-12), and feature positions (13):

    1. ‘feature’: Feature ID (PART-SPLIT-SCALE)

    2. ‘category’: Scale category

    3. ‘subcategory’: Sub category of scales

    4. ‘scale_name’: Name of scales

    5. ‘scale_description’: Description of the scale

    6. ‘abs_auc’: Absolute adjusted AUC (area under the curve) [-0.5 to 0.5]

    7. ‘abs_mean_dif’: Absolute mean differences between test and reference group [0 to 1]

    8. ‘mean_dif’: Mean differences between test and reference group [-1 to 1]

    9. ‘std_test’: Standard deviation in test group

    10. ‘std_ref’: Standard deviation in reference group

    11. ‘p_val_mann_whitney’ or ‘p_val_ttest_indep’: p-value of the non-parametric Mann-Whitney test (default) or, when parametric=True, the independent t-test. The column name reflects which test was run.

    12. ‘p_val_fdr_bh’: Benjamini-Hochberg False Discovery Rate (FDR) corrected p-values

    13. ‘positions’: Feature positions for default settings

    The feature id (column 1) is an opaque PART-SPLIT-SCALE string; split it with aaanalysis.utils.split_feat_id() rather than parsing it by hand. Columns added downstream — the explainable-AI columns (‘feat_importance’, ‘feat_impact’) and the per-substrate SHAP columns (‘feat_impact_<name>’, ‘mean_dif_<name>’, …) added by TreeModel / ShapModel — are appended after ‘positions’ in a stable order, so the canonical order is a lower bound, never a restriction.

  • Compositional vs positional features are not a separate setting — the distinction emerges from split_kws. A single whole-part average (n_split_max=1 with no Pattern / PeriodicPattern) yields compositional features (an amino-acid-composition-like mean over the entire part, position-agnostic); using n_split_max>1 and/or patterns yields positional features resolved to specific sub-regions.

See also

Examples

To demonstrate the CPP().run() method, we load the DOM_GSEC example dataset (see [Breimann25]):

import aaanalysis as aa
aa.options["verbose"] = False
df_seq = aa.load_dataset(name="DOM_GSEC")
labels = df_seq["label"].to_list()
sf = aa.SequenceFeature()
df_parts = sf.get_df_parts(df_seq=df_seq)

You just need to provide df_parts to the CPP object and run the algorithm with its respective labels using the CPP().run() method:

cpp = aa.CPP(df_parts=df_parts)
# Create >500,000 feature and filter them down to 100 features
df_feat = cpp.run(labels=labels)
aa.display_df(df_feat, n_rows=10, show_shape=True)

DataFrame shape: (100, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_C_JMD_C-Seg...2,3)-QIAN880106 Conformation α-helix α-helix (middle) Weights for alp...ejnowski, 1988) 0.387000 0.118000 0.118000 0.068000 0.080000 0.000000 0.000000 27,28,29,30,31,32,33
2 TMD_C_JMD_C-Pat...,14)-CRAJ730103 Conformation β-turn β-turn Normalized freq...d et al., 1973) 0.377000 0.285000 -0.285000 0.164000 0.177000 0.000000 0.000000 27,31
3 TMD_C_JMD_C-Seg...6,9)-FAUJ880104 Shape Side chain length Steric parameter STERIMOL length...e et al., 1988) 0.367000 0.263000 0.263000 0.161000 0.168000 0.000000 0.000000 32,33
4 TMD_C_JMD_C-Seg...6,9)-ONEK900101 Others Unclassified (Others) ΔG values in peptides Delta G values ...-DeGrado, 1990) 0.366000 0.111000 0.111000 0.070000 0.114000 0.000000 0.000000 32,33
5 TMD_C_JMD_C-Pat...,15)-QIAN880107 Conformation α-helix α-helix (middle) Weights for alp...ejnowski, 1988) 0.363000 0.162000 0.162000 0.091000 0.118000 0.000000 0.000000 24,28,32,35
6 TMD_C_JMD_C-Seg...3,4)-HUTJ700103 Energy Entropy Entropy Entropy of form...Hutchens, 1970) 0.360000 0.187000 0.187000 0.115000 0.128000 0.000000 0.000000 31,32,33,34,35
7 TMD_C_JMD_C-Seg...2,3)-WOLS870103 Others PC 4 Principal Component 3 (Wold) Principal prope...d et al., 1987) 0.359000 0.159000 -0.159000 0.090000 0.130000 0.000000 0.000000 27,28,29,30,31,32,33
8 TMD_C_JMD_C-Pat...,12)-CRAJ730103 Conformation β-turn β-turn Normalized freq...d et al., 1973) 0.352000 0.227000 -0.227000 0.150000 0.170000 0.000000 0.000000 24,28,32
9 TMD_C_JMD_C-Seg...6,9)-MUNV940102 Energy Free energy (folding) Free energy (α-helix) Free energy in ...-Serrano, 1994) 0.350000 0.129000 -0.129000 0.079000 0.124000 0.000000 0.000000 32,33
10 TMD_C_JMD_C-Seg...3,4)-WOLS870103 Others PC 4 Principal Component 3 (Wold) Principal prope...d et al., 1987) 0.341000 0.214000 -0.214000 0.128000 0.177000 0.000000 0.000000 31,32,33,34,35

Adjust Parts, Splits, and Scales as follows:

df_parts = sf.get_df_parts(df_seq=df_seq, list_parts=["tmd_jmd"])
split_kws = sf.get_split_kws(split_types=["Segment"], n_split_min=1, n_split_max=5)
# Load one of the provided top scale datasets
df_scales = aa.load_scales(top60_n=38)
# Create ~700 feature and filter them down to 19 features
cpp = aa.CPP(df_parts=df_parts, split_kws=split_kws, df_scales=df_scales)
df_feat = cpp.run(labels=labels)
aa.display_df(df_feat, n_rows=10, show_shape=True)

DataFrame shape: (19, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 25,26,27,28,29,30,31,32
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
3 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 25,26,27,28,29,30,31,32
4 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000000 33,34,35,36,37,38,39,40
5 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000000 33,34,35,36,37,38,39,40
6 TMD_JMD-Segment...5,5)-ZIMJ680103 Polarity Hydrophilicity Polarity (hydrophilicity) Polarity (Zimme...n et al., 1968) 0.289000 0.178000 0.178000 0.159000 0.163000 0.000000 0.000000 33,34,35,36,37,38,39,40
7 TMD_JMD-Segment...4,5)-FUKS010106 Composition Membrane proteins (MPs) Proteins of mesophiles (INT) Interior compos...ishikawa, 2001) 0.277000 0.123000 0.123000 0.104000 0.127000 0.000000 0.000000 25,26,27,28,29,30,31,32
8 TMD_JMD-Segment...4,4)-WOLR790101 Polarity Hydrophobicity (surrounding) Hydration potential Hydrophobicity ...n et al., 1979) 0.267000 0.105000 -0.105000 0.100000 0.113000 0.000000 0.000001 31,32,33,34,35,36,37,38,39,40
9 TMD_JMD-Segment...2,2)-CEDJ970105 Composition AA composition Nuclear proteins Composition of ...o et al., 1997) 0.263000 0.062000 0.062000 0.062000 0.069000 0.000000 0.000001 21,22,23,24,25,...,36,37,38,39,40
10 TMD_JMD-Segment...5,5)-MITS020101 Polarity Amphiphilicity Amphiphilicity Amphiphilicity ...u et al., 2002) 0.262000 0.073000 0.073000 0.071000 0.086000 0.000000 0.000001 33,34,35,36,37,38,39,40

The maximum number of final features can be adjusted using the n_filter (default=100) parameter. The actual number of features may be less, depending on: (a) the initial number of features generated (defined by the part-split-scale combinations), and (b) the strictness of both pre-filtering and filtering criteria.

# Create ~700 feature and filter them down to 10 features
df_feat = cpp.run(labels=labels, n_filter=10)
aa.display_df(df_feat, n_rows=10, show_shape=True)

DataFrame shape: (10, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 25,26,27,28,29,30,31,32
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
3 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 25,26,27,28,29,30,31,32
4 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000000 33,34,35,36,37,38,39,40
5 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000000 33,34,35,36,37,38,39,40
6 TMD_JMD-Segment...5,5)-ZIMJ680103 Polarity Hydrophilicity Polarity (hydrophilicity) Polarity (Zimme...n et al., 1968) 0.289000 0.178000 0.178000 0.159000 0.163000 0.000000 0.000000 33,34,35,36,37,38,39,40
7 TMD_JMD-Segment...4,5)-FUKS010106 Composition Membrane proteins (MPs) Proteins of mesophiles (INT) Interior compos...ishikawa, 2001) 0.277000 0.123000 0.123000 0.104000 0.127000 0.000000 0.000000 25,26,27,28,29,30,31,32
8 TMD_JMD-Segment...4,4)-WOLR790101 Polarity Hydrophobicity (surrounding) Hydration potential Hydrophobicity ...n et al., 1979) 0.267000 0.105000 -0.105000 0.100000 0.113000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
9 TMD_JMD-Segment...5,5)-MIYS990104 Composition MPs (anchor) Partition energy Optimized relat...Jernigan, 1999) 0.243000 0.103000 0.103000 0.095000 0.126000 0.000002 0.000004 33,34,35,36,37,38,39,40
10 TMD_JMD-Segment...4,5)-ANDN920101 Structure-Activity Backbone-dynamics (-CH) α-CH chemical s...kbone-dynamics) alpha-CH chemic...n et al., 1992) 0.229000 0.102000 -0.102000 0.097000 0.125000 0.000009 0.000012 25,26,27,28,29,30,31,32

In the initial CPP pre-filtering step, you can either set the number of retained features using n_pre_filter or define a percentage of initial features with pct_pre_filter (default with 5%). Additionally, adjust the maximum standard deviation allowed in the test dataset for each feature via max_std_test:

# Pre-filtering by allowing 50% with 0.5 maximum std in the test set
# Create ~700 feature and filter them down to 26 features
df_feat = cpp.run(labels=labels, pct_pre_filter=50, max_std_test=0.5)
aa.display_df(df_feat, n_rows=10, show_shape=True)

DataFrame shape: (26, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 25,26,27,28,29,30,31,32
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
3 TMD_JMD-Segment...2,2)-ONEK900101 Others Unclassified (Others) ΔG values in peptides Delta G values ...-DeGrado, 1990) 0.310000 0.041000 0.041000 0.028000 0.044000 0.000000 0.000000 21,22,23,24,25,...,36,37,38,39,40
4 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 25,26,27,28,29,30,31,32
5 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000001 33,34,35,36,37,38,39,40
6 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000001 33,34,35,36,37,38,39,40
7 TMD_JMD-Segment...5,5)-ZIMJ680103 Polarity Hydrophilicity Polarity (hydrophilicity) Polarity (Zimme...n et al., 1968) 0.289000 0.178000 0.178000 0.159000 0.163000 0.000000 0.000001 33,34,35,36,37,38,39,40
8 TMD_JMD-Segment...4,5)-FUKS010106 Composition Membrane proteins (MPs) Proteins of mesophiles (INT) Interior compos...ishikawa, 2001) 0.277000 0.123000 0.123000 0.104000 0.127000 0.000000 0.000001 25,26,27,28,29,30,31,32
9 TMD_JMD-Segment...3,4)-WOLR790101 Polarity Hydrophobicity (surrounding) Hydration potential Hydrophobicity ...n et al., 1979) 0.274000 0.052000 0.052000 0.034000 0.060000 0.000000 0.000001 21,22,23,24,25,26,27,28,29,30
10 TMD_JMD-Segment...1,2)-WEBA780101 Others Mutability RF value RF value in hig...er-Lacey, 1978) 0.268000 0.042000 0.042000 0.039000 0.046000 0.000000 0.000002 1,2,3,4,5,6,7,8...,16,17,18,19,20

For the final CPP filtering step, you can use the following parameters: max_overlap setting the allowed maximum positional overlap of similar features (the higher, the less strict), max_cor defining the allowed maximum Pearson correlation for scales of similar features (the higher, the less strict), check_cat setting whether redundancy of scale categories should be considered or not (setting it to False will result in stricter filtering since features across all categories are compared), and redundancy selecting the position-overlap criterion ('legacy' by default, kept so results stay reproducible; 'exact' compares the actual residue positions):

# Disable filtering by setting max_overlap and max_cor to 1
# Create ~700 feature and filter them down to 100 features
df_feat = cpp.run(labels=labels, max_overlap=1, max_cor=1)
aa.display_df(df_feat, n_rows=10, show_shape=True)

DataFrame shape: (100, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 25,26,27,28,29,30,31,32
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
3 TMD_JMD-Segment...3,3)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.304000 0.069000 0.069000 0.051000 0.073000 0.000000 0.000000 27,28,29,30,31,...,36,37,38,39,40
4 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 25,26,27,28,29,30,31,32
5 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000000 33,34,35,36,37,38,39,40
6 TMD_JMD-Segment...2,2)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.292000 0.058000 0.058000 0.045000 0.054000 0.000000 0.000000 21,22,23,24,25,...,36,37,38,39,40
7 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000000 33,34,35,36,37,38,39,40
8 TMD_JMD-Segment...4,4)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.291000 0.127000 0.127000 0.097000 0.121000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
9 TMD_JMD-Segment...5,5)-ZIMJ680103 Polarity Hydrophilicity Polarity (hydrophilicity) Polarity (Zimme...n et al., 1968) 0.289000 0.178000 0.178000 0.159000 0.163000 0.000000 0.000000 33,34,35,36,37,38,39,40
10 TMD_JMD-Segment...4,4)-ZIMJ680103 Polarity Hydrophilicity Polarity (hydrophilicity) Polarity (Zimme...n et al., 1968) 0.288000 0.164000 0.164000 0.135000 0.145000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40 
# Perform stricter filtering by setting check_cat=False
# Create ~700 feature and filter them down to 11 features
df_feat = cpp.run(labels=labels, check_cat=False)
aa.display_df(df_feat, n_rows=10, show_shape=True)

DataFrame shape: (11, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 25,26,27,28,29,30,31,32
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
3 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 25,26,27,28,29,30,31,32
4 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000000 33,34,35,36,37,38,39,40
5 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000000 33,34,35,36,37,38,39,40
6 TMD_JMD-Segment...2,2)-CEDJ970105 Composition AA composition Nuclear proteins Composition of ...o et al., 1997) 0.263000 0.062000 0.062000 0.062000 0.069000 0.000000 0.000001 21,22,23,24,25,...,36,37,38,39,40
7 TMD_JMD-Segment...5,5)-MITS020101 Polarity Amphiphilicity Amphiphilicity Amphiphilicity ...u et al., 2002) 0.262000 0.073000 0.073000 0.071000 0.086000 0.000000 0.000001 33,34,35,36,37,38,39,40
8 TMD_JMD-Segment...1,2)-SIMZ760101 Polarity Hydrophobicity Transfer free e...TFE) to outside Transfer free e...-Charton (1982) 0.259000 0.064000 -0.064000 0.069000 0.072000 0.000001 0.000002 1,2,3,4,5,6,7,8...,16,17,18,19,20
9 TMD_JMD-Segment...4,5)-ANDN920101 Structure-Activity Backbone-dynamics (-CH) α-CH chemical s...kbone-dynamics) alpha-CH chemic...n et al., 1992) 0.229000 0.102000 -0.102000 0.097000 0.125000 0.000009 0.000017 25,26,27,28,29,30,31,32
10 TMD_JMD-Segment...4,4)-YUTK870103 Energy Free energy (unfolding) Free energy (unfolding) Activation Gibb...i et al., 1987) 0.201000 0.084000 -0.084000 0.115000 0.118000 0.000103 0.000143 31,32,33,34,35,36,37,38,39,40

Switch the position-overlap criterion to 'exact'. This is an enhancement, not a bug fix: it compares the true residue positions and tends to yield a more concentrated signature (fewer redundant subcategories), while predictive performance is essentially unchanged. For a stronger, more efficient redundancy reduction, see CPP().simplify():

# Switch the position-overlap criterion to the exact residue positions
# (default is "legacy", kept for reproducibility)
df_feat = cpp.run(labels=labels, redundancy="exact")
aa.display_df(df_feat, n_rows=10, show_shape=True)

DataFrame shape: (29, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 25,26,27,28,29,30,31,32
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
3 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 25,26,27,28,29,30,31,32
4 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000000 33,34,35,36,37,38,39,40
5 TMD_JMD-Segment...2,2)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.292000 0.058000 0.058000 0.045000 0.054000 0.000000 0.000000 21,22,23,24,25,...,36,37,38,39,40
6 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000000 33,34,35,36,37,38,39,40
7 TMD_JMD-Segment...5,5)-ZIMJ680103 Polarity Hydrophilicity Polarity (hydrophilicity) Polarity (Zimme...n et al., 1968) 0.289000 0.178000 0.178000 0.159000 0.163000 0.000000 0.000000 33,34,35,36,37,38,39,40
8 TMD_JMD-Segment...2,2)-CHOP780212 Conformation β-sheet (C-term) β-turn (1st residue) Frequency of th...-Fasman, 1978b) 0.280000 0.066000 -0.066000 0.048000 0.067000 0.000000 0.000000 21,22,23,24,25,...,36,37,38,39,40
9 TMD_JMD-Segment...4,5)-FUKS010106 Composition Membrane proteins (MPs) Proteins of mesophiles (INT) Interior compos...ishikawa, 2001) 0.277000 0.123000 0.123000 0.104000 0.127000 0.000000 0.000000 25,26,27,28,29,30,31,32
10 TMD_JMD-Segment...4,4)-WOLR790101 Polarity Hydrophobicity (surrounding) Hydration potential Hydrophobicity ...n et al., 1979) 0.267000 0.105000 -0.105000 0.100000 0.113000 0.000000 0.000001 31,32,33,34,35,36,37,38,39,40

The residue positions can be adjusted using the start, tmd_len, jmd_n_len, and jmd_c_len parameters:

# Shift positions by 10 residues
df_feat = cpp.run(labels=labels, start=11)
aa.display_df(df_feat, n_rows=10, show_shape=True)

DataFrame shape: (19, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 35,36,37,38,39,40,41,42
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 41,42,43,44,45,46,47,48,49,50
3 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 35,36,37,38,39,40,41,42
4 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000000 43,44,45,46,47,48,49,50
5 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000000 43,44,45,46,47,48,49,50
6 TMD_JMD-Segment...5,5)-ZIMJ680103 Polarity Hydrophilicity Polarity (hydrophilicity) Polarity (Zimme...n et al., 1968) 0.289000 0.178000 0.178000 0.159000 0.163000 0.000000 0.000000 43,44,45,46,47,48,49,50
7 TMD_JMD-Segment...4,5)-FUKS010106 Composition Membrane proteins (MPs) Proteins of mesophiles (INT) Interior compos...ishikawa, 2001) 0.277000 0.123000 0.123000 0.104000 0.127000 0.000000 0.000000 35,36,37,38,39,40,41,42
8 TMD_JMD-Segment...4,4)-WOLR790101 Polarity Hydrophobicity (surrounding) Hydration potential Hydrophobicity ...n et al., 1979) 0.267000 0.105000 -0.105000 0.100000 0.113000 0.000000 0.000001 41,42,43,44,45,46,47,48,49,50
9 TMD_JMD-Segment...2,2)-CEDJ970105 Composition AA composition Nuclear proteins Composition of ...o et al., 1997) 0.263000 0.062000 0.062000 0.062000 0.069000 0.000000 0.000001 31,32,33,34,35,...,46,47,48,49,50
10 TMD_JMD-Segment...5,5)-MITS020101 Polarity Amphiphilicity Amphiphilicity Amphiphilicity ...u et al., 2002) 0.262000 0.073000 0.073000 0.071000 0.086000 0.000000 0.000001 43,44,45,46,47,48,49,50 
# Increase TMD length from 20 to 50
df_feat = cpp.run(labels=labels, tmd_len=50)
aa.display_df(df_feat, n_rows=10, show_shape=True)

DataFrame shape: (19, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 43,44,45,46,47,...,52,53,54,55,56
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 53,54,55,56,57,...,66,67,68,69,70
3 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 43,44,45,46,47,...,52,53,54,55,56
4 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000000 57,58,59,60,61,...,66,67,68,69,70
5 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000000 57,58,59,60,61,...,66,67,68,69,70
6 TMD_JMD-Segment...5,5)-ZIMJ680103 Polarity Hydrophilicity Polarity (hydrophilicity) Polarity (Zimme...n et al., 1968) 0.289000 0.178000 0.178000 0.159000 0.163000 0.000000 0.000000 57,58,59,60,61,...,66,67,68,69,70
7 TMD_JMD-Segment...4,5)-FUKS010106 Composition Membrane proteins (MPs) Proteins of mesophiles (INT) Interior compos...ishikawa, 2001) 0.277000 0.123000 0.123000 0.104000 0.127000 0.000000 0.000000 43,44,45,46,47,...,52,53,54,55,56
8 TMD_JMD-Segment...4,4)-WOLR790101 Polarity Hydrophobicity (surrounding) Hydration potential Hydrophobicity ...n et al., 1979) 0.267000 0.105000 -0.105000 0.100000 0.113000 0.000000 0.000001 53,54,55,56,57,...,66,67,68,69,70
9 TMD_JMD-Segment...2,2)-CEDJ970105 Composition AA composition Nuclear proteins Composition of ...o et al., 1997) 0.263000 0.062000 0.062000 0.062000 0.069000 0.000000 0.000001 36,37,38,39,40,...,66,67,68,69,70
10 TMD_JMD-Segment...5,5)-MITS020101 Polarity Amphiphilicity Amphiphilicity Amphiphilicity ...u et al., 2002) 0.262000 0.073000 0.073000 0.071000 0.086000 0.000000 0.000001 57,58,59,60,61,...,66,67,68,69,70

Multiprocessing can be enabled by using the n_jobs parameter, which is set to the maximum if n_jobs=None. However, this is only recommend for more than ~1000 features per core due to potential process management overhead.

import time

# Run without multiprocessing
time_start = time.time()
df_feat = cpp.run(labels=labels, n_jobs=1)
time_no_mp = round(time.time() - time_start, 2)
print(f"Time without multiprocessing: {time_no_mp} seconds")

# Run with multiprocessing
time_start = time.time()
df_feat = cpp.run(labels=labels, n_jobs=None)
time_mp = round(time.time() - time_start, 2)
print(f"Time with multiprocessing. {time_mp} seconds")

Time without multiprocessing: 0.03 seconds
Time with multiprocessing. 3.93 seconds

run exposes several more options. label_test / label_ref name the two groups in labels (default 1 / 0); parametric switches the p-value test (independent t-test instead of the Mann-Whitney U test); return_stats also returns the filter-funnel statistics; and vectorized, n_batches and n_sample_batches trade memory for speed on large inputs:

df_feat, stats = cpp.run(labels=labels, label_test=1, label_ref=0,
                         parametric=True, vectorized=True, return_stats=True)
print("filter stats:", stats)
aa.display_df(df_feat, n_rows=5, show_shape=True)

filter stats: {'n_candidates': 690, 'n_after_prefilter': 100, 'n_after_redundancy': 19, 'n_final': 19}
DataFrame shape: (19, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_ttest_indep p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 25,26,27,28,29,30,31,32
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
3 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 25,26,27,28,29,30,31,32
4 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000000 33,34,35,36,37,38,39,40
5 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000000 33,34,35,36,37,38,39,40

Further parameters. run also exposes the JMD geometry (jmd_n_len / jmd_c_len), an explicit pre-filter count n_pre_filter (instead of the pct_pre_filter percentage), and two mutually exclusive memory-bounding batch axes: n_batches (over scales) and n_sample_batches (over samples):

# JMD geometry + an explicit pre-filter count + scale-axis batching (n_batches, >=2)
df_feat = cpp.run(labels=labels, jmd_n_len=10, jmd_c_len=10,
                  n_pre_filter=100, n_batches=2, n_jobs=1)
aa.display_df(df_feat, n_rows=10, show_shape=True)

# Sample-axis batching bounds peak memory by batch size (n_sample_batches, >=2);
# mutually exclusive with n_batches
df_feat = cpp.run(labels=labels, n_sample_batches=2, n_jobs=1)
aa.display_df(df_feat, n_rows=10, show_shape=True)

DataFrame shape: (19, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 25,26,27,28,29,30,31,32
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
3 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 25,26,27,28,29,30,31,32
4 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000000 33,34,35,36,37,38,39,40
5 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000000 33,34,35,36,37,38,39,40
6 TMD_JMD-Segment...5,5)-ZIMJ680103 Polarity Hydrophilicity Polarity (hydrophilicity) Polarity (Zimme...n et al., 1968) 0.289000 0.178000 0.178000 0.159000 0.163000 0.000000 0.000000 33,34,35,36,37,38,39,40
7 TMD_JMD-Segment...4,5)-FUKS010106 Composition Membrane proteins (MPs) Proteins of mesophiles (INT) Interior compos...ishikawa, 2001) 0.277000 0.123000 0.123000 0.104000 0.127000 0.000000 0.000000 25,26,27,28,29,30,31,32
8 TMD_JMD-Segment...4,4)-WOLR790101 Polarity Hydrophobicity (surrounding) Hydration potential Hydrophobicity ...n et al., 1979) 0.267000 0.105000 -0.105000 0.100000 0.113000 0.000000 0.000001 31,32,33,34,35,36,37,38,39,40
9 TMD_JMD-Segment...2,2)-CEDJ970105 Composition AA composition Nuclear proteins Composition of ...o et al., 1997) 0.263000 0.062000 0.062000 0.062000 0.069000 0.000000 0.000003 21,22,23,24,25,...,36,37,38,39,40
10 TMD_JMD-Segment...5,5)-MITS020101 Polarity Amphiphilicity Amphiphilicity Amphiphilicity ...u et al., 2002) 0.262000 0.073000 0.073000 0.071000 0.086000 0.000000 0.000001 33,34,35,36,37,38,39,40 
DataFrame shape: (19, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_JMD-Segment...4,5)-ROBB760113 Conformation β-turn β-turn Information mea...n-Suzuki, 1976) 0.316000 0.137000 -0.137000 0.102000 0.108000 0.000000 0.000000 25,26,27,28,29,30,31,32
2 TMD_JMD-Segment...4,4)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.312000 0.099000 0.099000 0.069000 0.095000 0.000000 0.000000 31,32,33,34,35,36,37,38,39,40
3 TMD_JMD-Segment...4,5)-KANM800103 Conformation α-helix α-helix Average relativ...sa-Tsong, 1980) 0.297000 0.086000 0.086000 0.077000 0.068000 0.000000 0.000000 25,26,27,28,29,30,31,32
4 TMD_JMD-Segment...5,5)-LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 0.295000 0.141000 0.141000 0.115000 0.130000 0.000000 0.000000 33,34,35,36,37,38,39,40
5 TMD_JMD-Segment...5,5)-JANJ780102 ASA/Volume Buried Buried Percentage of b...n et al., 1978) 0.291000 0.130000 -0.130000 0.099000 0.124000 0.000000 0.000000 33,34,35,36,37,38,39,40
6 TMD_JMD-Segment...5,5)-ZIMJ680103 Polarity Hydrophilicity Polarity (hydrophilicity) Polarity (Zimme...n et al., 1968) 0.289000 0.178000 0.178000 0.159000 0.163000 0.000000 0.000000 33,34,35,36,37,38,39,40
7 TMD_JMD-Segment...4,5)-FUKS010106 Composition Membrane proteins (MPs) Proteins of mesophiles (INT) Interior compos...ishikawa, 2001) 0.277000 0.123000 0.123000 0.104000 0.127000 0.000000 0.000000 25,26,27,28,29,30,31,32
8 TMD_JMD-Segment...4,4)-WOLR790101 Polarity Hydrophobicity (surrounding) Hydration potential Hydrophobicity ...n et al., 1979) 0.267000 0.105000 -0.105000 0.100000 0.113000 0.000000 0.000001 31,32,33,34,35,36,37,38,39,40
9 TMD_JMD-Segment...2,2)-CEDJ970105 Composition AA composition Nuclear proteins Composition of ...o et al., 1997) 0.263000 0.062000 0.062000 0.062000 0.069000 0.000000 0.000001 21,22,23,24,25,...,36,37,38,39,40
10 TMD_JMD-Segment...5,5)-MITS020101 Polarity Amphiphilicity Amphiphilicity Amphiphilicity ...u et al., 2002) 0.262000 0.073000 0.073000 0.071000 0.086000 0.000000 0.000001 33,34,35,36,37,38,39,40

run_num is the numerical-mode twin of run: per-residue values come from a pre-sliced tensor dict_num_parts (built by :meth:NumericalFeature.get_parts) instead of an amino-acid-to-scale lookup. Use it for protein language model embeddings, structure features, or any per-residue numerical representation:

import numpy as np
nf = aa.NumericalFeature()
# One [0, 1]-normalized per-residue tensor (here 2 embedding-like dimensions) per entry
rng = np.random.default_rng(0)
dict_num = {row.entry: rng.random((len(row.sequence), 2)) for row in df_seq.itertuples()}
df_parts_num, dict_num_parts = nf.get_parts(df_seq=df_seq, dict_num=dict_num)
# df_scales columns name the D dimensions of dict_num_parts (2 here)
df_scales_num = aa.load_scales().iloc[:, :2]
cpp = aa.CPP(df_parts=df_parts_num, df_scales=df_scales_num)
df_feat_num = cpp.run_num(dict_num_parts=dict_num_parts, labels=labels,
                              n_filter=10, n_jobs=1)
aa.display_df(df_feat_num, n_rows=10, show_shape=True)

DataFrame shape: (10, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_C_JMD_C-Pat...,12)-ANDN920101 Structure-Activity Backbone-dynamics (-CH) α-CH chemical s...kbone-dynamics) alpha-CH chemic...n et al., 1992) 0.201000 0.108000 0.108000 0.129000 0.149000 0.000096 0.009487 29,32,36,40
2 TMD_C_JMD_C-Pat...,13)-ANDN920101 Structure-Activity Backbone-dynamics (-CH) α-CH chemical s...kbone-dynamics) alpha-CH chemic...n et al., 1992) 0.150000 0.069000 0.069000 0.139000 0.134000 0.003666 0.035587 28,32,35,38
3 TMD_C_JMD_C-Pat...,13)-ARGP820101 Polarity Hydrophobicity Hydrophobicity Hydrophobicity ...s et al., 1982) 0.137000 0.064000 0.064000 0.128000 0.129000 0.007890 0.038845 28,32,36,40
4 TMD-Pattern(C,1...,14)-ARGP820101 Polarity Hydrophobicity Hydrophobicity Hydrophobicity ...s et al., 1982) 0.124000 0.091000 -0.091000 0.196000 0.209000 0.016261 0.047435 17,21
5 TMD_C_JMD_C-Pat...,10)-ANDN920101 Structure-Activity Backbone-dynamics (-CH) α-CH chemical s...kbone-dynamics) alpha-CH chemic...n et al., 1992) 0.122000 0.064000 -0.064000 0.159000 0.153000 0.018320 0.047435 31,34,37
6 TMD_C_JMD_C-Pat...,14)-ARGP820101 Polarity Hydrophobicity Hydrophobicity Hydrophobicity ...s et al., 1982) 0.111000 0.084000 -0.084000 0.200000 0.205000 0.031612 0.059048 31,34
7 TMD-Segment(8,9)-ANDN920101 Structure-Activity Backbone-dynamics (-CH) α-CH chemical s...kbone-dynamics) alpha-CH chemic...n et al., 1992) 0.110000 0.057000 0.057000 0.147000 0.174000 0.033596 0.060472 26,27
8 JMD_N_TMD_N-Pat...,15)-ANDN920101 Structure-Activity Backbone-dynamics (-CH) α-CH chemical s...kbone-dynamics) alpha-CH chemic...n et al., 1992) 0.103000 0.068000 0.068000 0.189000 0.184000 0.045713 0.073752 6,10
9 JMD_N_TMD_N-Pat...,12)-ANDN920101 Structure-Activity Backbone-dynamics (-CH) α-CH chemical s...kbone-dynamics) alpha-CH chemic...n et al., 1992) 0.099000 0.057000 0.057000 0.168000 0.149000 0.054555 0.077156 4,8,12
10 TMD_C_JMD_C-Pat...,13)-ARGP820101 Polarity Hydrophobicity Hydrophobicity Hydrophobicity ...s et al., 1982) 0.096000 0.056000 0.056000 0.174000 0.206000 0.063378 0.082383 29,33