AAclust.select_scales
- AAclust.select_scales(df_scales=None, n_clusters=None, on_center=True, min_th=0.3, metric='euclidean')[source]
Select a redundancy-reduced subset of scales directly from a scales DataFrame.
Convenience wrapper around
AAclust.fit()for the common scale-selection workflow: it clusters the scales indf_scalesand returns the columns of the representative scales (medoids), one per cluster [Breimann24a]. This avoids the manual transpose and name bookkeeping otherwise needed to mapfitresults back ontodf_scales. The fitted attributes (e.g.AAclust.labels_,AAclust.medoid_names_) remain accessible on the instance afterwards.Added in version 1.1.0.
- Parameters:
df_scales (pd.DataFrame, shape (n_letters, n_scales)) – DataFrame with amino acid scales. Rows correspond to amino acids (one-letter codes) and columns to scale IDs.
n_clusters (int, optional) – Pre-defined number of clusters (selected scales). If provided, k is not optimized. Must be 0 < n_clusters < n_scales.
min_th (float, default=0.3) – Pearson correlation threshold for clustering optimization (between 0 and 1).
on_center (bool, default=True) – If
True,min_this applied to the cluster center. Otherwise, to all cluster members.metric ({'correlation', 'euclidean', 'manhattan', 'cosine'}, default='euclidean') –
Similarity measure used for optional cluster merging and obtaining medoids:
correlation: Pearson correlation (maximum)euclidean: Euclidean distance (minimum)manhattan: Manhattan distance (minimum)cosine: Cosine distance (minimum)
- Returns:
df_scales_selected – Subset of
df_scalescontaining only the columns of the selected (medoid) scales.- Return type:
pd.DataFrame, shape (n_letters, n_clusters)
See also
AAclust.fit(): The underlying clustering used for scale selection.AAclust.filter_coverage(): Scale selection that additionally guarantees subcategory coverage.
Examples
The
AAclust().select_scales()method returns a redundancy-reduced subset of an amino acid scales DataFrame in a single call. It clusters the scales and keeps one representative scale (medoid) per cluster, returning the matching columns of the inputdf_scalesdirectly — ready to hand toCPP.import aaanalysis as aa aa.options["verbose"] = False # Load amino acid scales (rows = amino acids, columns = scale IDs) df_scales = aa.load_scales() aa.display_df(df_scales, n_rows=10, show_shape=True)
DataFrame shape: (20, 586)
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0.044000 0.065000 0.021000 0.122000 0.212000 0.000000 0.125000 0.125000 0.022000 0.125000 0.186000 0.045000 0.081000 0.119000 0.184000 0.147000 0.012000 0.082000 0.078000 0.233000 0.148000 0.167000 0.000000 0.118000 0.000000 0.333000 0.259000 Provide
df_scalesand a pre-defined number of clusters vian_clustersto obtain exactly that many representative scales. The returned DataFrame keeps the amino acid index and only the selected scale columns:aac = aa.AAclust() df_scales_selected = aac.select_scales(df_scales, n_clusters=100) aa.display_df(df_scales_selected, n_rows=10, show_shape=True)
DataFrame shape: (20, 100)
ANDN920101 ARGP820101 ARGP820103 KANM800103 CORJ870105 ROBB760113 MIYS990103 KRIW790103 ROSG850102 MEEJ810102 RADA880102 MIYS990101 BULH740102 BLAM930101 OOBM850102 TANS770101 KANM800102 FAUJ880101 KARS160101 CHAM820102 GEIM800111 QIAN880123 CHAM830105 CHAM830107 CHAM830108 LEVM760102 GUYH850105 CHOC760103 CHOP780204 QIAN880107 QIAN880114 PALJ810105 CHOP780214 FINA910102 KOEP990101 CIDH920105 LEVM780101 JUKT750101 DAYM780201 PONP800102 WOLR790101 NADH010102 QIAN880109 HUTJ700102 FAUJ880108 LINS030112 LINS030117 FINA910101 ZIMJ680104 FINA910104 MITS020101 GEIM800102 QIAN880121 GRAR740101 KOEH090102 HOPA770101 LEVM760101 AURR980112 ISOY800102 ISOY800106 RICJ880105 MAXF760104 LINS030104 YUTK870104 JOND920101 LINS030114 FUKS010104 KRIW710101 KARS160108 MAXF760105 PONP800107 RICJ880103 CEDJ970105 FUKS010108 FUKS010106 OOBM770105 WILM950104 QIAN880125 WARP780101 KUMS000103 QIAN880126 QIAN880117 QIAN880127 QIAN880129 QIAN880135 QIAN880138 RACS820103 MUNV940102 RICJ880101 AURR980105 ROBB760107 VASM830101 VASM830102 VELV850101 VENT840101 AURR980118 GEOR030106 MONM990201 KOEP990102 KARS160120 AA A 0.494000 0.230000 0.504000 0.765000 0.477000 0.101000 0.500000 0.189000 0.564000 0.221000 0.480000 0.688000 0.468000 1.000000 0.752000 0.814000 0.248000 0.305000 0.100000 0.262000 0.321000 0.133000 0.000000 0.000000 0.000000 0.207000 0.189000 0.627000 0.425000 1.000000 0.289000 0.276000 0.124000 0.024000 0.210000 0.393000 0.811000 1.000000 0.707000 0.365000 0.979000 0.749000 0.754000 0.141000 0.000000 0.120000 0.186000 0.154000 0.404000 0.562000 0.000000 0.620000 0.459000 0.000000 0.174000 0.141000 0.453000 0.787000 0.270000 0.543000 0.565000 0.034000 0.093000 0.966000 0.818000 0.625000 0.384000 0.629000 0.458000 0.294000 0.324000 0.118000 0.938000 0.437000 0.843000 0.826000 0.064000 0.517000 1.000000 1.000000 0.451000 0.455000 0.493000 0.000000 0.276000 0.114000 0.287000 0.000000 0.250000 0.248000 0.333000 0.437000 0.617000 0.295000 0.000000 0.356000 0.516000 0.077000 0.266000 0.952000 C 0.864000 0.404000 0.387000 0.302000 0.598000 0.849000 0.029000 0.307000 1.000000 0.420000 0.345000 0.403000 0.232000 0.844000 0.718000 0.204000 0.566000 0.422000 0.200000 0.947000 0.312000 0.676000 0.000000 0.000000 1.000000 0.371000 0.204000 0.746000 0.110000 0.574000 0.193000 0.157000 0.584000 0.024000 0.586000 0.647000 0.621000 0.111000 0.017000 1.000000 0.837000 1.000000 0.645000 0.666000 0.812000 0.000000 0.000000 0.154000 0.285000 0.562000 0.000000 1.000000 0.788000 1.000000 0.115000 0.000000 0.375000 0.104000 0.986000 0.510000 0.391000 0.189000 0.000000 0.929000 0.078000 0.000000 0.037000 0.000000 0.611000 0.099000 0.210000 0.147000 0.111000 0.054000 0.017000 0.695000 0.783000 0.517000 0.796000 0.000000 0.324000 0.000000 0.960000 0.816000 0.297000 0.482000 0.000000 0.420000 0.188000 0.000000 0.820000 0.521000 0.734000 0.657000 0.000000 0.000000 0.000000 0.308000 0.057000 0.952000 D 1.000000 0.174000 0.000000 0.432000 0.000000 0.790000 0.786000 0.275000 0.256000 0.138000 0.345000 0.912000 0.000000 0.844000 1.000000 0.451000 0.159000 0.382000 0.400000 0.601000 0.587000 0.362000 0.000000 1.000000 0.000000 0.535000 0.464000 0.237000 0.790000 0.391000 0.566000 0.622000 0.933000 0.081000 0.401000 0.034000 0.547000 0.622000 0.759000 0.088000 0.459000 0.371000 0.340000 0.364000 1.000000 0.800000 0.186000 1.000000 0.000000 0.375000 0.000000 0.240000 0.329000 0.502000 1.000000 1.000000 0.922000 0.451000 0.041000 0.642000 1.000000 0.215000 0.588000 0.934000 0.494000 0.893000 0.606000 0.753000 0.733000 0.307000 0.137000 0.588000 0.494000 0.062000 0.182000 0.572000 0.246000 0.663000 0.241000 0.400000 0.745000 0.491000 0.760000 0.158000 0.611000 0.614000 0.923000 0.270000 0.688000 1.000000 0.099000 0.976000 0.225000 1.000000 0.000000 0.658000 0.404000 0.923000 0.775000 0.952000 E 0.420000 0.177000 0.032000 0.667000 0.285000 0.092000 0.871000 0.426000 0.256000 0.227000 0.138000 0.918000 0.261000 0.876000 0.859000 1.000000 0.000000 0.372000 0.500000 0.561000 0.431000 0.286000 0.667000 1.000000 0.000000 0.707000 0.404000 0.288000 1.000000 0.817000 0.434000 0.228000 0.360000 0.081000 0.031000 0.000000 0.968000 0.556000 0.724000 0.127000 0.435000 0.263000 0.754000 0.463000 0.500000 0.880000 0.349000 0.423000 0.056000 0.375000 0.183000 0.760000 0.239000 0.335000 0.802000 0.953000 0.922000 0.823000 0.000000 0.576000 0.739000 0.056000 0.804000 0.935000 0.623000 1.000000 0.833000 0.742000 0.764000 0.266000 0.256000 0.088000 0.716000 0.054000 0.194000 0.441000 0.419000 0.652000 0.172000 0.621000 0.471000 0.227000 0.533000 0.026000 0.360000 0.561000 0.916000 0.048000 0.438000 0.471000 0.613000 0.608000 0.531000 0.046000 0.000000 0.548000 0.610000 0.769000 0.689000 0.952000 F 0.877000 0.762000 0.670000 0.611000 1.000000 0.328000 0.057000 0.794000 0.923000 0.862000 0.890000 0.021000 0.697000 0.893000 0.684000 0.584000 0.593000 0.702000 0.700000 0.462000 0.119000 0.429000 0.667000 0.000000 1.000000 0.798000 0.089000 0.831000 0.085000 0.680000 0.289000 0.307000 0.292000 0.024000 0.228000 0.844000 0.579000 0.333000 0.198000 0.628000 0.859000 0.915000 0.764000 0.602000 0.250000 0.000000 0.326000 0.154000 0.339000 0.562000 0.000000 0.380000 0.667000 0.000000 0.068000 0.203000 0.141000 0.402000 0.676000 0.351000 0.261000 0.056000 0.082000 0.934000 0.338000 0.125000 0.092000 0.472000 0.802000 0.117000 0.815000 0.029000 0.247000 0.373000 0.321000 0.216000 0.543000 0.506000 0.635000 0.350000 0.186000 0.655000 0.680000 0.895000 0.230000 0.298000 0.000000 0.301000 0.562000 0.488000 0.396000 0.269000 0.023000 0.749000 1.000000 0.521000 0.712000 0.000000 0.041000 0.952000 G 0.025000 0.026000 0.170000 0.198000 0.305000 1.000000 0.671000 0.000000 0.513000 0.177000 0.345000 0.809000 0.120000 0.723000 0.843000 0.000000 0.310000 0.000000 0.000000 0.240000 0.798000 0.629000 0.000000 0.000000 0.000000 0.000000 0.207000 0.593000 0.160000 0.243000 0.783000 1.000000 0.994000 0.049000 1.000000 0.115000 0.042000 0.889000 0.267000 0.305000 1.000000 0.561000 0.498000 0.000000 0.062000 0.147000 0.023000 0.154000 0.400000 0.875000 0.000000 0.020000 0.324000 0.269000 0.328000 0.156000 0.531000 0.055000 0.054000 0.000000 0.261000 1.000000 0.144000 0.967000 0.779000 0.714000 0.433000 0.966000 0.000000 1.000000 0.227000 0.500000 0.691000 0.256000 0.539000 1.000000 0.189000 0.966000 0.637000 0.286000 0.676000 1.000000 0.373000 0.289000 0.485000 0.439000 0.570000 0.639000 0.812000 0.504000 0.027000 0.143000 0.455000 0.040000 0.000000 0.562000 0.210000 0.615000 0.627000 0.952000 H 0.840000 0.230000 0.053000 0.568000 0.808000 0.454000 0.486000 0.543000 0.667000 0.044000 0.593000 0.694000 0.310000 0.887000 0.743000 0.619000 0.345000 0.714000 0.600000 0.313000 0.349000 0.638000 0.667000 0.000000 1.000000 0.742000 0.418000 0.271000 0.145000 0.633000 0.795000 0.032000 0.449000 0.024000 0.167000 0.475000 0.737000 0.133000 0.414000 0.409000 0.432000 0.439000 0.847000 0.944000 0.562000 0.427000 0.419000 0.154000 0.603000 0.562000 0.209000 0.540000 0.559000 0.211000 0.650000 0.422000 0.453000 0.463000 0.541000 0.530000 0.174000 0.114000 0.423000 0.970000 0.117000 0.500000 0.141000 0.618000 0.917000 0.078000 0.302000 0.294000 0.173000 0.006000 0.080000 0.253000 0.000000 0.438000 0.548000 0.136000 0.696000 0.545000 0.467000 0.842000 0.205000 0.123000 0.000000 0.357000 0.562000 0.380000 0.405000 0.745000 0.345000 0.191000 0.000000 1.000000 0.612000 0.846000 0.865000 0.562000 I 0.000000 0.838000 0.543000 0.630000 0.986000 0.151000 0.071000 0.643000 0.923000 0.552000 0.877000 0.121000 0.884000 0.965000 0.666000 0.549000 0.841000 1.000000 0.400000 0.424000 0.000000 0.514000 0.000000 0.000000 0.000000 0.492000 0.000000 1.000000 0.115000 0.663000 0.000000 0.047000 0.000000 0.024000 0.074000 1.000000 0.474000 0.511000 0.672000 0.820000 0.989000 0.909000 0.690000 0.572000 0.000000 0.000000 0.140000 0.000000 0.407000 0.562000 0.000000 0.460000 1.000000 0.000000 0.000000 0.109000 0.250000 0.512000 0.878000 0.464000 0.174000 0.052000 0.010000 1.000000 0.506000 0.161000 0.149000 0.404000 0.733000 0.104000 1.000000 0.029000 0.370000 0.485000 0.735000 0.548000 1.000000 0.000000 0.766000 0.500000 0.314000 0.727000 0.907000 0.947000 0.121000 0.684000 0.811000 0.221000 0.375000 0.339000 0.324000 0.570000 0.070000 0.000000 1.000000 0.644000 0.457000 0.154000 0.000000 0.583000 K 0.506000 0.434000 0.004000 0.654000 0.012000 0.613000 1.000000 0.687000 0.000000 0.000000 0.366000 1.000000 0.736000 0.934000 0.660000 0.655000 0.212000 0.451000 0.500000 0.313000 0.303000 0.238000 0.667000 0.000000 1.000000 0.790000 0.704000 0.034000 0.110000 0.757000 0.542000 0.362000 0.331000 0.000000 0.123000 0.247000 0.747000 0.733000 0.328000 0.000000 0.467000 0.000000 0.872000 0.881000 0.062000 0.800000 1.000000 0.038000 0.872000 1.000000 0.530000 0.520000 0.329000 0.120000 0.809000 0.812000 1.000000 0.591000 0.149000 0.616000 0.304000 0.233000 1.000000 0.956000 0.584000 0.929000 1.000000 1.000000 0.764000 0.294000 0.000000 0.176000 0.889000 0.000000 0.109000 0.530000 0.444000 0.101000 0.000000 0.543000 0.088000 0.618000 0.000000 1.000000 0.276000 0.184000 1.000000 0.142000 0.438000 0.388000 0.054000 0.559000 0.688000 0.294000 0.000000 0.822000 0.536000 0.846000 0.434000 0.912000 L 0.272000 0.577000 0.989000 0.833000 0.974000 0.076000 0.000000 0.643000 0.846000 0.696000 0.804000 0.000000 1.000000 0.988000 0.651000 0.699000 0.628000 0.618000 0.400000 0.463000 0.009000 0.438000 0.000000 0.000000 0.000000 0.559000 0.129000 0.746000 0.070000 0.947000 0.229000 0.000000 0.129000 0.024000 0.000000 0.773000 0.821000 0.867000 0.190000 0.701000 0.995000 0.901000 0.936000 0.592000 0.000000 0.000000 0.186000 0.154000 0.402000 0.562000 0.000000 0.620000 0.689000 0.000000 0.012000 0.109000 0.250000 0.732000 0.473000 0.682000 0.000000 0.042000 0.041000 0.990000 1.000000 0.125000 0.327000 0.478000 0.733000 0.169000 0.693000 0.029000 0.827000 1.000000 1.000000 0.435000 0.399000 0.247000 0.778000 0.643000 0.059000 0.064000 0.613000 0.434000 0.000000 0.000000 0.000000 0.104000 0.375000 0.347000 0.000000 0.717000 0.771000 0.000000 1.000000 0.671000 0.690000 0.000000 0.377000 0.952000 The fitted attributes remain accessible on the instance, e.g. the names of the selected (medoid) scales, which equal the returned columns:
print("Selected scales:", df_scales_selected.shape[1]) print("Columns match medoid names:", list(df_scales_selected.columns) == aac.medoid_names_)
Selected scales: 100 Columns match medoid names: True
If
n_clustersis omitted (None), AAclust optimizes the number of clusters automatically. We use a smaller subset here for speed:df_scales_small = df_scales[list(df_scales.columns)[:50]] df_auto = aac.select_scales(df_scales_small) print("Optimized number of selected scales:", df_auto.shape[1])
Optimized number of selected scales: 5
The clustering optimization is controlled by
min_th(Pearson correlation threshold) andon_center(applymin_thto the cluster center vs. to all members):# Higher threshold on all members -> stricter, more clusters df_all = aac.select_scales(df_scales_small, on_center=False, min_th=0.5) print("min_cor_all, min_th=0.5:", df_all.shape[1]) # Same threshold on the center -> less strict, fewer clusters df_center = aac.select_scales(df_scales_small, on_center=True, min_th=0.5) print("min_cor_center, min_th=0.5:", df_center.shape[1])
min_cor_all, min_th=0.5: 25 min_cor_center, min_th=0.5: 8
The optional cluster-merging step uses the
metricparameter (distance/similarity measure for merging and medoid selection):for metric in ["euclidean", "correlation", "manhattan", "cosine"]: df_m = aac.select_scales(df_scales, n_clusters=100, metric=metric) print(f"{metric:>12}: {df_m.shape[1]} scales")
euclidean: 100 scales correlation: 100 scales manhattan: 100 scales cosine: 100 scales
The returned
df_scales_selectedis a valid scales DataFrame and can be passed straight toCPPto build features on a redundancy-reduced scale set:df_cat = aa.load_scales(name="scales_cat") df_cat_selected = df_cat[df_cat["scale_id"].isin(df_scales_selected.columns)] aa.display_df(df_cat_selected, n_rows=10, show_shape=True)
DataFrame shape: (100, 5)
scale_id category subcategory scale_name scale_description 5 LINS030104 ASA/Volume Accessible surface area (ASA) ASA (folded protein) Total median ac...s et al., 2003) 11 LINS030112 ASA/Volume Accessible surface area (ASA) Hydrophilic ASA...lded coil/turn) Hydrophilic med...s et al., 2003) 17 LINS030114 ASA/Volume Accessible surface area (ASA) Hydrophobic ASA...lded coil/turn) % Hydrophobic a...s et al., 2003) 21 GUYH850105 ASA/Volume Accessible surface area (ASA) Partition energy Apparent partit...dex (Guy, 1985) 28 ARGP820103 ASA/Volume Buried Buried Membrane-buried...s et al., 1982) 29 CHOC760103 ASA/Volume Buried Buried Proportion of r...(Chothia, 1976) 35 WARP780101 ASA/Volume Buried Interactivity Average interac...e-Morgan, 1978) 38 LINS030117 ASA/Volume Hydrophobic ASA Hydrophobic ASA...olded β-strand) Hydrophobic med...s et al., 2003) 39 PONP800107 ASA/Volume Partial specific volume Accessibility reduction ratio Accessibility r...y et al., 1980) 45 BULH740102 ASA/Volume Partial specific volume Partial specific volume Apparent partia...l-Breese, 1974)