diff --git a/aaanalysis/__pycache__/utils.cpython-39.pyc b/aaanalysis/__pycache__/utils.cpython-39.pyc index 93844be0..8f096ef8 100644 Binary files a/aaanalysis/__pycache__/utils.cpython-39.pyc and b/aaanalysis/__pycache__/utils.cpython-39.pyc differ diff --git a/aaanalysis/_utils/__pycache__/_utils_check.cpython-39.pyc b/aaanalysis/_utils/__pycache__/_utils_check.cpython-39.pyc index 1c0a77f9..8420f63e 100644 Binary files a/aaanalysis/_utils/__pycache__/_utils_check.cpython-39.pyc and b/aaanalysis/_utils/__pycache__/_utils_check.cpython-39.pyc differ diff --git a/aaanalysis/_utils/__pycache__/utils_aaclust.cpython-39.pyc b/aaanalysis/_utils/__pycache__/utils_aaclust.cpython-39.pyc index e38ecd09..3882a91b 100644 Binary files a/aaanalysis/_utils/__pycache__/utils_aaclust.cpython-39.pyc and b/aaanalysis/_utils/__pycache__/utils_aaclust.cpython-39.pyc differ diff --git a/aaanalysis/_utils/_utils_check.py b/aaanalysis/_utils/_utils_check.py index 6e136c27..56bd1f31 100644 --- a/aaanalysis/_utils/_utils_check.py +++ b/aaanalysis/_utils/_utils_check.py @@ -80,14 +80,13 @@ def check_tuple(name=None, val=None, n=None): # Array checking functions def check_feat_matrix(X=None, names=None, labels=None): - """Check if X and y match (y can be labels or names). Otherwise, transpose X or give error.""" - # TODO type check - X = check_array(X) + """Transpose matrix and check if X and y match (y can be labels or names). Transpose back otherwise """ + X = check_array(X).transpose() if labels is not None: check_consistent_length(X, labels) n_samples, n_features = X.shape if n_samples == 0 or n_features == 0: - raise ValueError(f"Shape of X ({n_samples}, {n_features}) indicates empty feature matrix.") + raise ValueError(f"Shape of 'X' ({n_samples}, {n_features}) indicates empty feature matrix.") if names is None: return X, names else: diff --git a/aaanalysis/_utils/utils_aaclust.py b/aaanalysis/_utils/utils_aaclust.py index 26c45173..6974766e 100644 --- a/aaanalysis/_utils/utils_aaclust.py +++ b/aaanalysis/_utils/utils_aaclust.py @@ -37,3 +37,9 @@ def check_merge_metric(merge_metric=None): error = f"'merge_metric' should be None or one of following: {LIST_METRICS}" raise ValueError(error) return merge_metric + +def check_feat_matrix_n_clust_match(X=None, n_clusters=None): + """""" + n_samples, n_features = X.shape + if n_samples <= n_clusters: + raise ValueError(f"'X' must contain more samples ({n_samples}) then 'n_clusters' ({n_clusters})") diff --git a/aaanalysis/aaclust/__pycache__/aaclust.cpython-39.pyc b/aaanalysis/aaclust/__pycache__/aaclust.cpython-39.pyc index 0c20615d..b68040b4 100644 Binary files a/aaanalysis/aaclust/__pycache__/aaclust.cpython-39.pyc and b/aaanalysis/aaclust/__pycache__/aaclust.cpython-39.pyc differ diff --git a/aaanalysis/aaclust/aaclust.py b/aaanalysis/aaclust/aaclust.py index 6b5628b0..a0833b84 100644 --- a/aaanalysis/aaclust/aaclust.py +++ b/aaanalysis/aaclust/aaclust.py @@ -11,6 +11,8 @@ # I Helper Functions + + # Obtain centroids and medoids def cluster_center(X): """Compute cluster center (i.e., arithmetical mean over all data points/observations of a cluster)""" @@ -29,7 +31,7 @@ def _cluster_medoid(X): """Obtain cluster medoids (i.e., scale closest to cluster center used as representative scale for a cluster)""" # Create new array with cluster center and given center_X = np.concatenate([cluster_center(X), X], axis=0) - # Get index for scale with highest correlation with cluster center + # Get index for scale with the highest correlation with cluster center ind_max = np.corrcoef(center_X)[0, 1:].argmax() return ind_max @@ -410,6 +412,7 @@ def fit(self, X, names=None, on_center=True, min_th=0, merge_metric="euclidean" ut.check_min_th(min_th=min_th) merge_metric = ut.check_merge_metric(merge_metric=merge_metric) X, names = ut.check_feat_matrix(X=X, names=names) + ut.check_feat_matrix_n_clust_match(X=X, n_clusters=n_clusters) args = dict(model=self.model, model_kwargs=self._model_kwargs, min_th=min_th, on_center=on_center) # Clustering using given clustering models if n_clusters is not None: diff --git a/aaanalysis/cpp/__pycache__/cpp.cpython-39.pyc b/aaanalysis/cpp/__pycache__/cpp.cpython-39.pyc index 9d669888..88affe85 100644 Binary files a/aaanalysis/cpp/__pycache__/cpp.cpython-39.pyc and b/aaanalysis/cpp/__pycache__/cpp.cpython-39.pyc differ diff --git a/aaanalysis/utils.py b/aaanalysis/utils.py index 2bc53737..f512ce89 100644 --- a/aaanalysis/utils.py +++ b/aaanalysis/utils.py @@ -13,6 +13,7 @@ check_feat_matrix, check_col_in_df) from aaanalysis._utils._utils_output import (print_red, print_start_progress, print_progress, print_finished_progress) from aaanalysis._utils.utils_aaclust import (check_model, check_min_th, check_merge_metric, + check_feat_matrix_n_clust_match, METRIC_CORRELATION, LIST_METRICS) from aaanalysis._utils.utils_cpp import (check_color, check_y_categorical, check_labels, check_ylim, check_args_len, check_args_len, check_list_parts, diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 797868a7..bdff3fef 100644 Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ diff --git a/docs/build/doctrees/generated/aaanalysis.AAclust.doctree b/docs/build/doctrees/generated/aaanalysis.AAclust.doctree index 706d8507..c5989ddb 100644 Binary files a/docs/build/doctrees/generated/aaanalysis.AAclust.doctree and b/docs/build/doctrees/generated/aaanalysis.AAclust.doctree differ diff --git a/docs/build/doctrees/generated/tutorial1_quick_start.doctree b/docs/build/doctrees/generated/tutorial1_quick_start.doctree index 81728585..47be535c 100644 Binary files a/docs/build/doctrees/generated/tutorial1_quick_start.doctree and b/docs/build/doctrees/generated/tutorial1_quick_start.doctree differ diff --git a/docs/build/html/_images/output_13_1.png b/docs/build/html/_images/output_13_1.png index d7e83427..4a4b3449 100644 Binary files a/docs/build/html/_images/output_13_1.png and b/docs/build/html/_images/output_13_1.png differ diff --git a/docs/build/html/_sources/generated/tutorial1_quick_start.rst.txt b/docs/build/html/_sources/generated/tutorial1_quick_start.rst.txt index 23e6ef58..419f2e19 100644 --- a/docs/build/html/_sources/generated/tutorial1_quick_start.rst.txt +++ b/docs/build/html/_sources/generated/tutorial1_quick_start.rst.txt @@ -34,9 +34,8 @@ available at your fingertips with the ``aa.load_scales()`` function. .. code:: ipython3 import aaanalysis as aa - # Load scales and scale categories (AAontology) + df_scales = aa.load_scales() - # Load training data df_seq = aa.load_dataset(name="DOM_GSEC", n=50) df_seq.head(5) @@ -156,11 +155,12 @@ set of 100 scales, as defined by the ``n_clusters`` parameters: from sklearn.cluster import AgglomerativeClustering import numpy as np - aac = aa.AAclust(model=AgglomerativeClustering, model_kwargs=dict(linkage="ward")) + + aac = aa.AAclust(model=AgglomerativeClustering) X = np.array(df_scales) - scales = aac.fit(X, n_clusters=100, names=list(df_scales)) + scales = aac.fit(X, names=list(df_scales), n_clusters=100) df_scales = df_scales[scales] - df_scales + df_scales[scales[0:4]].head(5) @@ -189,23 +189,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters: SIMZ760101 NAKH900106 AURR980112 - CORJ870107 - ROBB760113 - MIYS990104 - BIGC670101 - ROSG850102 - ZIMJ680105 - ... - YUTK870102 - SUEM840102 - VASM830102 - VELV850101 - VENT840101 - MONM990101 - GEOR030102 - GEOR030106 - KARS160120 - LINS030117 AA @@ -213,23 +196,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters: - - - - - - - - - - - - - - - - - @@ -239,23 +205,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters: 0.268 0.237 0.787 - 0.446 - 0.101 - 0.479 - 0.164 - 0.564 - 0.444 - ... - 0.557 - 0.103 - 0.617 - 0.295 - 0 - 0.077 - 0.250 - 0.516 - 0.952 - 0.186 C @@ -263,23 +212,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters: 0.258 0.303 0.104 - 0.725 - 0.849 - 0.000 - 0.323 - 1.000 - 0.000 - ... - 0.680 - 0.337 - 0.734 - 0.657 - 0 - 0.154 - 0.246 - 0.000 - 0.952 - 0.000 D @@ -287,23 +219,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters: 0.206 0.000 0.451 - 0.000 - 0.790 - 0.803 - 0.324 - 0.256 - 0.000 - ... - 0.574 - 0.909 - 0.225 - 1.000 - 0 - 0.923 - 0.091 - 0.404 - 0.952 - 0.186 E @@ -311,23 +226,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters: 0.210 0.090 0.823 - 0.233 - 0.092 - 0.859 - 0.488 - 0.256 - 0.025 - ... - 0.402 - 0.077 - 0.531 - 0.046 - 0 - 0.923 - 0.404 - 0.610 - 0.952 - 0.349 F @@ -335,387 +233,9 @@ set of 100 scales, as defined by the ``n_clusters`` parameters: 0.887 0.724 0.402 - 0.950 - 0.328 - 0.000 - 0.783 - 0.923 - 1.000 - ... - 0.680 - 0.233 - 0.023 - 0.749 - 1 - 0.000 - 0.536 - 0.712 - 0.952 - 0.326 - - - G - 0.025 - 0.032 - 0.259 - 0.055 - 0.352 - 1.000 - 0.662 - 0.000 - 0.513 - 0.175 - ... - 0.525 - 0.000 - 0.455 - 0.040 - 0 - 0.692 - 0.000 - 0.210 - 0.952 - 0.023 - - - H - 0.840 - 0.387 - 0.401 - 0.463 - 0.610 - 0.454 - 0.479 - 0.561 - 0.667 - 0.338 - ... - 0.754 - 0.000 - 0.345 - 0.191 - 0 - 0.923 - 0.201 - 0.612 - 0.562 - 0.419 - - - I - 0.000 - 0.990 - 0.697 - 0.512 - 0.969 - 0.151 - 0.056 - 0.663 - 0.923 - 0.894 - ... - 0.820 - 0.714 - 0.070 - 0.000 - 1 - 0.154 - 0.161 - 0.457 - 0.583 - 0.140 - - - K - 0.506 - 0.516 - 0.127 - 0.591 - 0.027 - 0.613 - 1.000 - 0.694 - 0.000 - 0.044 - ... - 0.615 - 0.012 - 0.688 - 0.294 - 0 - 0.923 - 0.195 - 0.536 - 0.912 - 1.000 - - - L - 0.272 - 0.835 - 0.905 - 0.732 - 1.000 - 0.076 - 0.014 - 0.663 - 0.846 - 0.925 - ... - 1.000 - 0.428 - 0.771 - 0.000 - 1 - 0.000 - 0.513 - 0.690 - 0.952 - 0.186 - - - M - 0.704 - 0.452 - 1.000 - 1.000 - 0.883 - 0.084 - 0.113 - 0.620 - 0.846 - 0.756 - ... - 0.689 - 0.701 - 0.512 - 0.651 - 0 - 0.077 - 0.151 - 0.670 - 0.952 - 0.372 - - - N - 0.988 - 0.029 - 0.381 - 0.287 - 0.171 - 0.924 - 0.718 - 0.398 - 0.282 - 0.162 - ... - 0.508 - 0.000 - 0.313 - 0.028 - 0 - 1.000 - 0.277 - 0.342 - 0.952 - 0.093 - - - P - 0.605 - 0.871 - 0.403 - 0.000 - 0.130 - 0.824 - 0.803 - 0.376 - 0.308 - 0.750 - ... - 0.566 - 0.545 - 0.937 - 0.157 - 0 - 1.000 - 1.000 - 1.000 - 0.952 - 0.698 - - - Q - 0.519 - 0.000 - 0.203 - 0.805 - 0.238 - 0.546 - 0.732 - 0.539 - 0.256 - 0.388 - ... - 0.697 - 0.428 - 0.446 - 0.602 - 0 - 0.923 - 0.478 - 0.530 - 0.952 - 0.256 - - - R - 0.531 - 0.268 - 0.061 - 0.738 - 0.482 - 0.748 - 0.634 - 0.735 - 0.308 - 0.112 - ... - 0.000 - 0.000 - 0.550 - 0.760 - 0 - 1.000 - 0.549 - 0.728 - 0.952 - 0.372 - - - S - 0.679 - 0.045 - 0.450 - 0.293 - 0.293 - 0.798 - 0.704 - 0.188 - 0.359 - 0.256 - ... - 0.656 - 0.000 - 0.868 - 0.657 - 0 - 0.231 - 0.168 - 0.399 - 0.952 - 0.186 - - - T - 0.494 - 0.174 - 0.619 - 0.360 - 0.279 - 0.529 - 0.577 - 0.352 - 0.462 - 0.419 - ... - 0.574 - 0.000 - 1.000 - 0.745 - 0 - 0.000 - 0.344 - 0.513 - 0.000 - 0.419 - - - V - 0.000 - 0.577 - 0.183 - 0.451 - 0.907 - 0.000 - 0.127 - 0.492 - 0.872 - 0.719 - ... - 0.770 - 0.000 - 0.408 - 0.045 - 1 - 0.077 - 0.151 - 0.467 - 0.952 - 0.163 - - - W - 0.926 - 1.000 - 0.707 - 0.805 - 0.500 - 0.773 - 0.070 - 1.000 - 0.846 - 0.894 - ... - 0.467 - 1.000 - 0.138 - 0.434 - 1 - 0.231 - 0.066 - 0.440 - 1.000 - 0.349 - - - Y - 0.802 - 0.990 - 0.425 - 0.524 - 0.771 - 0.798 - 0.127 - 0.806 - 0.615 - 0.762 - ... - 0.557 - 0.857 - 0.000 - 0.408 - 1 - 0.154 - 0.110 - 0.666 - 0.736 - 0.349 -

20 rows × 100 columns

@@ -731,15 +251,15 @@ sequences: the test set and the reference set. Supported by the C-terminal adjacent regions (JMD-N and JMD-C, respectively), obtained ``sf.get_df_parts``. - ``Splits``: These ``Parts`` can be split into various continuous segments or discontinuous patterns, specified -``sf.get_split_kws()``. - ``Scales``: Sets of amino acid scales. We -first use SequenceFeature to obtain Parts and Splits: +``sf.get_split_kws()``. - ``Scales``: Sets of amino acid scales. + +We use SequenceFeature to obtain Parts and Splits: .. code:: ipython3 - # Feature Engineering y = list(df_seq["label"]) sf = aa.SequenceFeature() - df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10, list_parts=["tmd_jmd"]) + df_parts = sf.get_df_parts(df_seq=df_seq, list_parts=["tmd_jmd"]) split_kws = sf.get_split_kws(n_split_max=1, split_types=["Segment"]) df_parts.head(5) @@ -803,9 +323,9 @@ As a baseline approach, we use CPP to compute the average values for the .. code:: ipython3 - # Small set of features (100 features created) - cpp = aa.CPP(df_parts=df_parts, df_scales=df_scales, split_kws=split_kws, verbose=False) - df_feat = cpp.run(labels=y, tmd_len=20, jmd_n_len=10, jmd_c_len=10, n_filter=100) # Default values for lengths are used + # Small set of CPP features (100 features are created) + cpp = aa.CPP(df_scales=df_scales, df_parts=df_parts, split_kws=split_kws, verbose=False) + df_feat = cpp.run(labels=y) df_feat @@ -927,16 +447,16 @@ A feature matrix from a given set of CPP features can be created using from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import cross_val_score - X = sf.feat_matrix(df_parts=df_parts, df_scales=df_scales, features=df_feat["feature"]) - # ML evaluation + + X = sf.feat_matrix(df_parts=df_parts, features=df_feat["feature"]) rf = RandomForestClassifier() - cv_base = cross_val_score(rf, X, y, scoring="accuracy", cv=5, n_jobs=8) # Set n_jobs=1 to disable multi-processing + cv_base = cross_val_score(rf, X, y, scoring="accuracy") print(f"Mean accuracy of {round(np.mean(cv_base), 2)}") .. parsed-literal:: - Mean accuracy of 0.57 + Mean accuracy of 0.58 Creating more features with CPP will take some more time. but improve @@ -944,12 +464,11 @@ prediction performance: .. code:: ipython3 - # Default CPP features (around 100.000 features) - split_kws = sf.get_split_kws() - df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10) - cpp = aa.CPP(df_parts=df_parts, df_scales=df_scales, split_kws=split_kws, verbose=False) - df_feat = cpp.run(labels=y, n_processes=8, n_filter=100) - df_feat + # CPP features with default splits (around 100.000 features) + df_parts = sf.get_df_parts(df_seq=df_seq) + cpp = aa.CPP(df_scales=df_scales, df_parts=df_parts, verbose=False) + df_feat = cpp.run(labels=y) + df_feat.head(10) @@ -1071,104 +590,87 @@ prediction performance: 32,33 - ... - ... - ... - ... - ... - ... - ... - ... - ... - ... - ... - ... - ... - ... + 5 + TMD_C_JMD_C-Segment(4,9)-ROBB760113 + Conformation + β-turn + β-turn + Information measure for loop (Robson-Suzuki, 1... + 0.337 + 0.319440 + -0.319440 + 0.175203 + 0.255754 + 6.100000e-09 + 1.185395e-06 + 27,28 - 95 - JMD_N_TMD_N-Pattern(C,6,9)-NAKH900106 - Composition - Mitochondrial proteins - Mitochondrial proteins - Normalized composition from animal (Nakashima ... - 0.228 - 0.172120 - -0.172120 - 0.180254 - 0.199987 - 8.754340e-05 - 2.693037e-04 - 12,15 + 6 + TMD_C_JMD_C-Segment(2,2)-EISD860102 + Energy + Isoelectric point + Atom-based hydrophobic moment + Atom-based hydrophobic moment (Eisenberg-McLac... + 0.337 + 0.139567 + 0.139567 + 0.098917 + 0.101842 + 6.300000e-09 + 1.185395e-06 + 31,32,33,34,35,36,37,38,39,40 - 96 - JMD_N_TMD_N-Pattern(C,6,9,12)-ZIMJ680105 - Others - PC 2 - Principal Component 1 (Zimmerman) - RF rank (Zimmerman et al., 1968) - 0.227 - 0.133867 - -0.133867 - 0.160532 - 0.161415 - 9.118090e-05 - 2.778863e-04 - 9,12,15 + 7 + TMD_C_JMD_C-Segment(4,5)-RICJ880113 + Conformation + α-helix (C-cap) + α-helix (C-terminal, inside) + Relative preference value at C2 (Richardson-Ri... + 0.336 + 0.223765 + 0.223765 + 0.133513 + 0.178217 + 7.100000e-09 + 1.185395e-06 + 33,34,35,36 - 97 - JMD_N_TMD_N-Segment(7,8)-KARS160107 + 8 + TMD_C_JMD_C-Segment(5,7)-KARS160107 Shape Side chain length Eccentricity (maximum) Diameter (maximum eccentricity) (Karkbara-Knis... - 0.227 - 0.098674 - -0.098674 - 0.104428 - 0.124875 - 8.945330e-05 - 2.740061e-04 - 16,17 + 0.331 + 0.217594 + 0.217594 + 0.136011 + 0.172395 + 1.130000e-08 + 1.331786e-06 + 32,33,34 - 98 - JMD_N_TMD_N-Pattern(C,6,9,12)-SIMZ760101 + 9 + TMD_C_JMD_C-Pattern(C,4,8)-JURD980101 Polarity Hydrophobicity - Transfer free energy (TFE) to outside - Transfer free energy (Simon, 1976), Cited by C... - 0.225 - 0.161307 - -0.161307 - 0.192235 - 0.212741 - 1.036749e-04 - 3.042894e-04 - 9,12,15 - - - 99 - JMD_N_TMD_N-Pattern(C,3,6)-TANS770102 - Conformation - α-helix (C-term, out) - α-helix (C-terminal, outside) - Normalized frequency of isolated helix (Tanaka... - 0.224 - 0.108020 - -0.108020 - 0.133731 - 0.139419 - 1.143783e-04 - 3.272494e-04 - 15,18 + Hydrophobicity + Modified Kyte-Doolittle hydrophobicity scale (... + 0.329 + 0.264720 + -0.264720 + 0.141666 + 0.233134 + 1.480000e-08 + 1.425259e-06 + 33,37 -

100 rows × 13 columns

@@ -1182,21 +684,23 @@ Which can be again used for machine learning: warnings.simplefilter(action='ignore', category=FutureWarning) import matplotlib.pyplot as plt import pandas as pd - X = sf.feat_matrix(df_parts=df_parts, df_scales=df_scales, features=df_feat["feature"]) - # ML evaluation + + X = sf.feat_matrix(df_parts=df_parts, features=df_feat["feature"]) rf = RandomForestClassifier() cv = cross_val_score(rf, X, y, scoring="accuracy", cv=5, n_jobs=1) print(f"Mean accuracy of {round(np.mean(cv), 2)}") + aa.plot_settings(font_scale=1.1) sns.barplot(pd.DataFrame({"Baseline": cv_base, "CPP": cv}), palette=["tab:blue", "tab:red"]) plt.ylabel("Mean accuracy", size=aa.plot_gcfs()+1) + plt.ylim(0, 1) sns.despine() plt.show() .. parsed-literal:: - Mean accuracy of 0.95 + Mean accuracy of 0.9 diff --git a/docs/build/html/generated/aaanalysis.AAclust.html b/docs/build/html/generated/aaanalysis.AAclust.html index 51764f6c..2ecc5c34 100644 --- a/docs/build/html/generated/aaanalysis.AAclust.html +++ b/docs/build/html/generated/aaanalysis.AAclust.html @@ -128,7 +128,7 @@

aaanalysis.AAclust

-class aaanalysis.AAclust(model=None, model_kwargs=None, verbose=False)[source]
+class aaanalysis.AAclust(model=None, model_kwargs=None, verbose=False)[source]

Bases: object

AAclust: A k-optimized clustering framework for selecting redundancy-reduced set of numerical scales.

AAclust is designed primarily for amino acid scales but is versatile enough for any set of numerical indices. @@ -224,7 +224,7 @@

aaanalysis.AAclust
-__init__(model=None, model_kwargs=None, verbose=False)[source]
+__init__(model=None, model_kwargs=None, verbose=False)[source]

Methods

@@ -255,7 +255,7 @@

aaanalysis.AAclust
-fit(X, names=None, on_center=True, min_th=0, merge_metric='euclidean', n_clusters=None)[source]
+fit(X, names=None, on_center=True, min_th=0, merge_metric='euclidean', n_clusters=None)[source]

Fit the AAclust model on the data, optimizing cluster formation using Pearson correlation.

AAclust determines the optimal number of clusters, k, without pre-specification. It partitions data(X) into clusters by maximizing the within-cluster Pearson correlation beyond the ‘min_th’ threshold. The quality of @@ -296,7 +296,7 @@

aaanalysis.AAclust
-cluster_naming(names=None, labels=None, name_unclassified='Unclassified')[source]
+cluster_naming(names=None, labels=None, name_unclassified='Unclassified')[source]

Assigns names to clusters based on scale names and their frequency.

This method renames clusters based on the names of the scales in each cluster, with priority given to the most frequent scales. If the name is already used or does not exist, it defaults to ‘name_unclassified’.

@@ -319,7 +319,7 @@

aaanalysis.AAclust
-static get_cluster_centers(X, labels=None)[source]
+static get_cluster_centers(X, labels=None)[source]

Computes the center of each cluster based on the given labels.

Parameters:
@@ -340,7 +340,7 @@

aaanalysis.AAclust
-static get_cluster_medoids(X, labels=None)[source]
+static get_cluster_medoids(X, labels=None)[source]

Computes the medoid of each cluster based on the given labels.

Parameters:
@@ -365,7 +365,7 @@

aaanalysis.AAclust
-static correlation(X_test, X_ref, labels_test=None, labels_ref=None, n=3, positive=True, on_center=False, except_unclassified=True)[source]
+static correlation(X_test, X_ref, labels_test=None, labels_ref=None, n=3, positive=True, on_center=False, except_unclassified=True)[source]

Computes the correlation of test data with reference cluster centers.

Parameters:
@@ -391,7 +391,7 @@

aaanalysis.AAclust
-eval()[source]
+eval()[source]

diff --git a/docs/build/html/generated/tutorial1_quick_start.html b/docs/build/html/generated/tutorial1_quick_start.html index a03664d5..7ef47c67 100644 --- a/docs/build/html/generated/tutorial1_quick_start.html +++ b/docs/build/html/generated/tutorial1_quick_start.html @@ -151,9 +151,8 @@

1. Loading Sequences and ScalesAAontology), are available at your fingertips with the aa.load_scales() function.

import aaanalysis as aa
-# Load scales and scale categories (AAontology)
+
 df_scales = aa.load_scales()
-# Load training data
 df_seq = aa.load_dataset(name="DOM_GSEC", n=50)
 df_seq.head(5)
@@ -260,11 +259,12 @@

AAclustn_clusters parameters:

from sklearn.cluster import AgglomerativeClustering
 import numpy as np
-aac = aa.AAclust(model=AgglomerativeClustering, model_kwargs=dict(linkage="ward"))
+
+aac = aa.AAclust(model=AgglomerativeClustering)
 X = np.array(df_scales)
-scales = aac.fit(X, n_clusters=100, names=list(df_scales))
+scales = aac.fit(X, names=list(df_scales), n_clusters=100)
 df_scales = df_scales[scales]
-df_scales
+df_scales[scales[0:4]].head(5)
@@ -289,23 +289,6 @@

AAclust

Comparative Physicochemical Profiling (CPP)

@@ -827,12 +347,11 @@

Comparative Physicochemical Profiling (CPP)sf.get_df_parts. - Splits: These Parts can be split into various continuous segments or discontinuous patterns, specified -sf.get_split_kws(). - Scales: Sets of amino acid scales. We -first use SequenceFeature to obtain Parts and Splits:

-
# Feature Engineering
-y = list(df_seq["label"])
+sf.get_split_kws(). - Scales: Sets of amino acid scales.

+
We use SequenceFeature to obtain Parts and Splits:

+
y = list(df_seq["label"])
 sf = aa.SequenceFeature()
-df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10, list_parts=["tmd_jmd"])
+df_parts = sf.get_df_parts(df_seq=df_seq, list_parts=["tmd_jmd"])
 split_kws = sf.get_split_kws(n_split_max=1, split_types=["Segment"])
 df_parts.head(5)
 

@@ -885,9 +404,9 @@ 
Comparative Physicochemical Profiling (CPP)
# Small set of features (100 features created)
-cpp = aa.CPP(df_parts=df_parts, df_scales=df_scales, split_kws=split_kws, verbose=False)
-df_feat = cpp.run(labels=y, tmd_len=20, jmd_n_len=10, jmd_c_len=10, n_filter=100)  # Default values for lengths are used
+
# Small set of CPP features (100 features are created)
+cpp = aa.CPP(df_scales=df_scales, df_parts=df_parts, split_kws=split_kws, verbose=False)
+df_feat = cpp.run(labels=y)
 df_feat
 

 

@@ -999,24 +518,23 @@ 
3. Protein Predictionsf.feat_matrix and used for machine learning:

 
from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import cross_val_score
-X = sf.feat_matrix(df_parts=df_parts, df_scales=df_scales, features=df_feat["feature"])
-# ML evaluation
+
+X = sf.feat_matrix(df_parts=df_parts, features=df_feat["feature"])
 rf = RandomForestClassifier()
-cv_base = cross_val_score(rf, X, y, scoring="accuracy", cv=5, n_jobs=8) # Set n_jobs=1 to disable multi-processing
+cv_base = cross_val_score(rf, X, y, scoring="accuracy")
 print(f"Mean accuracy of {round(np.mean(cv_base), 2)}")
 

 

-
Mean accuracy of 0.57
+
Mean accuracy of 0.58
 

 

 
Creating more features with CPP will take some more time. but improve
 prediction performance:

-
# Default CPP features  (around 100.000 features)
-split_kws = sf.get_split_kws()
-df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10)
-cpp = aa.CPP(df_parts=df_parts, df_scales=df_scales, split_kws=split_kws, verbose=False)
-df_feat = cpp.run(labels=y, n_processes=8, n_filter=100)
-df_feat
+
# CPP features with default splits (around 100.000 features)
+df_parts = sf.get_df_parts(df_seq=df_seq)
+cpp = aa.CPP(df_scales=df_scales, df_parts=df_parts, verbose=False)
+df_feat = cpp.run(labels=y)
+df_feat.head(10)
 

 

 

@@ -1134,123 +652,108 @@ 
3. Protein Prediction32,33
     
     
-      ...
-      ...
-      ...
-      ...
-      ...
-      ...
-      ...
-      ...
-      ...
-      ...
-      ...
-      ...
-      ...
-      ...
+      5
+      TMD_C_JMD_C-Segment(4,9)-ROBB760113
+      Conformation
+      β-turn
+      β-turn
+      Information measure for loop (Robson-Suzuki, 1...
+      0.337
+      0.319440
+      -0.319440
+      0.175203
+      0.255754
+      6.100000e-09
+      1.185395e-06
+      27,28
     
     
-      95
-      JMD_N_TMD_N-Pattern(C,6,9)-NAKH900106
-      Composition
-      Mitochondrial proteins
-      Mitochondrial proteins
-      Normalized composition from animal (Nakashima ...
-      0.228
-      0.172120
-      -0.172120
-      0.180254
-      0.199987
-      8.754340e-05
-      2.693037e-04
-      12,15
+      6
+      TMD_C_JMD_C-Segment(2,2)-EISD860102
+      Energy
+      Isoelectric point
+      Atom-based hydrophobic moment
+      Atom-based hydrophobic moment (Eisenberg-McLac...
+      0.337
+      0.139567
+      0.139567
+      0.098917
+      0.101842
+      6.300000e-09
+      1.185395e-06
+      31,32,33,34,35,36,37,38,39,40
     
     
-      96
-      JMD_N_TMD_N-Pattern(C,6,9,12)-ZIMJ680105
-      Others
-      PC 2
-      Principal Component 1 (Zimmerman)
-      RF rank (Zimmerman et al., 1968)
-      0.227
-      0.133867
-      -0.133867
-      0.160532
-      0.161415
-      9.118090e-05
-      2.778863e-04
-      9,12,15
+      7
+      TMD_C_JMD_C-Segment(4,5)-RICJ880113
+      Conformation
+      α-helix (C-cap)
+      α-helix (C-terminal, inside)
+      Relative preference value at C2 (Richardson-Ri...
+      0.336
+      0.223765
+      0.223765
+      0.133513
+      0.178217
+      7.100000e-09
+      1.185395e-06
+      33,34,35,36
     
     
-      97
-      JMD_N_TMD_N-Segment(7,8)-KARS160107
+      8
+      TMD_C_JMD_C-Segment(5,7)-KARS160107
       Shape
       Side chain length
       Eccentricity (maximum)
       Diameter (maximum eccentricity) (Karkbara-Knis...
-      0.227
-      0.098674
-      -0.098674
-      0.104428
-      0.124875
-      8.945330e-05
-      2.740061e-04
-      16,17
+      0.331
+      0.217594
+      0.217594
+      0.136011
+      0.172395
+      1.130000e-08
+      1.331786e-06
+      32,33,34
     
     
-      98
-      JMD_N_TMD_N-Pattern(C,6,9,12)-SIMZ760101
+      9
+      TMD_C_JMD_C-Pattern(C,4,8)-JURD980101
       Polarity
       Hydrophobicity
-      Transfer free energy (TFE) to outside
-      Transfer free energy (Simon, 1976), Cited by C...
-      0.225
-      0.161307
-      -0.161307
-      0.192235
-      0.212741
-      1.036749e-04
-      3.042894e-04
-      9,12,15
-    
-    
-      99
-      JMD_N_TMD_N-Pattern(C,3,6)-TANS770102
-      Conformation
-      α-helix (C-term, out)
-      α-helix (C-terminal, outside)
-      Normalized frequency of isolated helix (Tanaka...
-      0.224
-      0.108020
-      -0.108020
-      0.133731
-      0.139419
-      1.143783e-04
-      3.272494e-04
-      15,18
+      Hydrophobicity
+      Modified Kyte-Doolittle hydrophobicity scale (...
+      0.329
+      0.264720
+      -0.264720
+      0.141666
+      0.233134
+      1.480000e-08
+      1.425259e-06
+      33,37
     
   
 
-
100 rows × 13 columns

 
Which can be again used for machine learning:

 
import seaborn as sns
 import warnings
 warnings.simplefilter(action='ignore', category=FutureWarning)
 import matplotlib.pyplot as plt
 import pandas as pd
-X = sf.feat_matrix(df_parts=df_parts, df_scales=df_scales, features=df_feat["feature"])
-# ML evaluation
+
+X = sf.feat_matrix(df_parts=df_parts, features=df_feat["feature"])
 rf = RandomForestClassifier()
 cv = cross_val_score(rf, X, y, scoring="accuracy", cv=5, n_jobs=1)
 print(f"Mean accuracy of {round(np.mean(cv), 2)}")
+
 aa.plot_settings(font_scale=1.1)
 sns.barplot(pd.DataFrame({"Baseline": cv_base, "CPP": cv}), palette=["tab:blue", "tab:red"])
 plt.ylabel("Mean accuracy", size=aa.plot_gcfs()+1)
+plt.ylim(0, 1)
 sns.despine()
 plt.show()
 

 

-
Mean accuracy of 0.95
+
Mean accuracy of 0.9
 

 

 ../_images/output_13_1.png
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 780cbe6e..b18eb997 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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18, "doc": 18, "workflow": 21, "algorithm": 23, "us": [23, 28], "case": 23, "further": [23, 31], "inform": 23, "categori": 24, "subcategori": 24, "usag": 25, "classif": 26, "flow": 27, "enri": 27, "point": 27, "compon": 27, "entri": 27, "bridg": 27, "extern": 27, "librari": 27, "identifi": 28, "signatur": 28, "from": 29, "unbalanc": 29, "small": 29, "i": [29, 30]}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "nbsphinx": 4, "sphinx": 57}, "alltitles": {"API": [[0, "api"]], "Data": [[0, "data"]], "Feature Engineering": [[0, "feature-engineering"]], "PU Learning": [[0, "pu-learning"]], "Explainable AI": [[0, "explainable-ai"]], "Perturbation": [[0, "perturbation"]], "Plot Utilities": [[0, "plot-utilities"]], "aaanalysis.AAclust": [[1, "aaanalysis-aaclust"]], "Notes": [[1, null], [2, null], [2, null], [4, null], [4, null], [4, null], [4, null], [4, null], [5, null], [5, null], [6, null], [7, null], [12, null]], "aaanalysis.CPP": [[2, "aaanalysis-cpp"]], "aaanalysis.CPPPlot": [[3, "aaanalysis-cppplot"]], "Examples": [[3, null], [4, null], [4, null], [5, null], [6, null], [7, null], [11, null], [12, null]], "aaanalysis.SequenceFeature": [[4, "aaanalysis-sequencefeature"]], "aaanalysis.dPULearn": [[5, "aaanalysis-dpulearn"]], "aaanalysis.load_dataset": [[6, "aaanalysis-load-dataset"]], "aaanalysis.load_scales": [[7, "aaanalysis-load-scales"]], "aaanalysis.plot_gcfs": [[8, "aaanalysis-plot-gcfs"]], "aaanalysis.plot_get_cdict": [[9, "aaanalysis-plot-get-cdict"]], "aaanalysis.plot_get_cmap": [[10, "aaanalysis-plot-get-cmap"]], "aaanalysis.plot_set_legend": [[11, "aaanalysis-plot-set-legend"]], "aaanalysis.plot_settings": [[12, "aaanalysis-plot-settings"]], "Plotting prelude": [[13, "plotting-prelude"]], "Quick Start with AAanalysis": [[14, "quick-start-with-aaanalysis"]], "What You Will Learn:": [[14, "what-you-will-learn"]], "1. Loading Sequences and Scales": [[14, "loading-sequences-and-scales"]], "2. Feature Engineering": [[14, "feature-engineering"]], "AAclust": [[14, "aaclust"]], "Comparative Physicochemical Profiling (CPP)": [[14, "comparative-physicochemical-profiling-cpp"]], "3. Protein Prediction": [[14, "protein-prediction"]], "4. Explainable AI": [[14, "explainable-ai"]], "Explainable AI on group level": [[14, "explainable-ai-on-group-level"]], "Explainable AI on individual level": [[14, "explainable-ai-on-individual-level"]], "Data Loading Tutorial": [[15, "data-loading-tutorial"]], "Loading of protein benchmarks": [[15, "loading-of-protein-benchmarks"]], "Loading of protein benchmarks: Amino acid window size": [[15, "loading-of-protein-benchmarks-amino-acid-window-size"]], "Loading of protein benchmarks: Positive-Unlabeled (PU) datasets": [[15, "loading-of-protein-benchmarks-positive-unlabeled-pu-datasets"]], "Scale Loading Tutorial": [[16, "scale-loading-tutorial"]], "Three sets of numerical amino acid scales": [[16, "three-sets-of-numerical-amino-acid-scales"]], "AAontology": [[16, "aaontology"], [24, "aaontology"]], "Redundancy-reduce scale subsets": [[16, "redundancy-reduce-scale-subsets"]], "Filtering of scales": [[16, "filtering-of-scales"]], "Welcome to the AAanalysis documentation!": [[17, 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"Class Templates": [[18, "class-templates"]], "Function and Method Naming": [[18, "function-and-method-naming"]], "Code Philosophy": [[18, "code-philosophy"]], "Documentation Style": [[18, "documentation-style"]], "Documentation Layers": [[18, "documentation-layers"]], "Building the Docs": [[18, "building-the-docs"]], "Workflow": [[21, "workflow"]], "Overview of documentation": [[21, "overview-of-documentation"]], "References": [[23, "references"]], "Algorithms": [[23, "algorithms"]], "Datasets and Benchmarks": [[23, "datasets-and-benchmarks"]], "Use Cases": [[23, "use-cases"]], "Further Information": [[23, "further-information"]], "Tables": [[24, "tables"]], "Overview Table": [[24, "overview-table"]], "Protein Benchmark Datasets": [[24, "protein-benchmark-datasets"]], "Amino Acid Scale Datasets": [[24, "amino-acid-scale-datasets"]], "Categories": [[24, "categories"]], "Subcategories": [[24, "subcategories"]], "Usage Principles": [[25, "usage-principles"]], "AAontology: Classification of amino acid scales": [[26, "aaontology-classification-of-amino-acid-scales"]], "Data Flow and Enry Points": [[27, "data-flow-and-enry-points"]], "Data Flow: Components of AAanalysis": [[27, "data-flow-components-of-aaanalysis"]], "Entry Points: Bridges to External Libraries": [[27, "entry-points-bridges-to-external-libraries"]], "Identifying Physicochemical Signatures using CPP": [[28, "identifying-physicochemical-signatures-using-cpp"]], "Learning from unbalanced and small data": [[29, "learning-from-unbalanced-and-small-data"]], "What is PU learning?": [[29, "what-is-pu-learning"]], "Explainable AI at Sequence Level": [[30, "explainable-ai-at-sequence-level"]], "What is explainable AI?": [[30, "what-is-explainable-ai"]], "Tutorials": [[31, "tutorials"]], "Quick start": [[31, "quick-start"]], "Further Tutorials": [[31, "further-tutorials"]]}, "indexentries": {"aaclust (class in aaanalysis)": [[1, "aaanalysis.AAclust"]], "__init__() (aaanalysis.aaclust method)": [[1, 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\ No newline at end of file
diff --git a/docs/source/generated/output_13_1.png b/docs/source/generated/output_13_1.png
index d7e83427..4a4b3449 100644
Binary files a/docs/source/generated/output_13_1.png and b/docs/source/generated/output_13_1.png differ
diff --git a/docs/source/generated/tutorial1_quick_start.rst b/docs/source/generated/tutorial1_quick_start.rst
index 23e6ef58..419f2e19 100644
--- a/docs/source/generated/tutorial1_quick_start.rst
+++ b/docs/source/generated/tutorial1_quick_start.rst
@@ -34,9 +34,8 @@ available at your fingertips with the ``aa.load_scales()`` function.
 .. code:: ipython3
 
     import aaanalysis as aa
-    # Load scales and scale categories (AAontology) 
+    
     df_scales = aa.load_scales()
-    # Load training data
     df_seq = aa.load_dataset(name="DOM_GSEC", n=50)
     df_seq.head(5)
 
@@ -156,11 +155,12 @@ set of 100 scales, as defined by the ``n_clusters`` parameters:
 
     from sklearn.cluster import AgglomerativeClustering
     import numpy as np
-    aac = aa.AAclust(model=AgglomerativeClustering, model_kwargs=dict(linkage="ward"))
+    
+    aac = aa.AAclust(model=AgglomerativeClustering)
     X = np.array(df_scales)
-    scales = aac.fit(X, n_clusters=100, names=list(df_scales)) 
+    scales = aac.fit(X, names=list(df_scales), n_clusters=100) 
     df_scales = df_scales[scales]
-    df_scales
+    df_scales[scales[0:4]].head(5)
 
 
 
@@ -189,23 +189,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters:
           SIMZ760101
           NAKH900106
           AURR980112
-          CORJ870107
-          ROBB760113
-          MIYS990104
-          BIGC670101
-          ROSG850102
-          ZIMJ680105
-          ...
-          YUTK870102
-          SUEM840102
-          VASM830102
-          VELV850101
-          VENT840101
-          MONM990101
-          GEOR030102
-          GEOR030106
-          KARS160120
-          LINS030117
         
         
           AA
@@ -213,23 +196,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters:
           
           
           
-          
-          
-          
-          
-          
-          
-          
-          
-          
-          
-          
-          
-          
-          
-          
-          
-          
         
       
       
@@ -239,23 +205,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters:
           0.268
           0.237
           0.787
-          0.446
-          0.101
-          0.479
-          0.164
-          0.564
-          0.444
-          ...
-          0.557
-          0.103
-          0.617
-          0.295
-          0
-          0.077
-          0.250
-          0.516
-          0.952
-          0.186
         
         
           C
@@ -263,23 +212,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters:
           0.258
           0.303
           0.104
-          0.725
-          0.849
-          0.000
-          0.323
-          1.000
-          0.000
-          ...
-          0.680
-          0.337
-          0.734
-          0.657
-          0
-          0.154
-          0.246
-          0.000
-          0.952
-          0.000
         
         
           D
@@ -287,23 +219,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters:
           0.206
           0.000
           0.451
-          0.000
-          0.790
-          0.803
-          0.324
-          0.256
-          0.000
-          ...
-          0.574
-          0.909
-          0.225
-          1.000
-          0
-          0.923
-          0.091
-          0.404
-          0.952
-          0.186
         
         
           E
@@ -311,23 +226,6 @@ set of 100 scales, as defined by the ``n_clusters`` parameters:
           0.210
           0.090
           0.823
-          0.233
-          0.092
-          0.859
-          0.488
-          0.256
-          0.025
-          ...
-          0.402
-          0.077
-          0.531
-          0.046
-          0
-          0.923
-          0.404
-          0.610
-          0.952
-          0.349
         
         
           F
@@ -335,387 +233,9 @@ set of 100 scales, as defined by the ``n_clusters`` parameters:
           0.887
           0.724
           0.402
-          0.950
-          0.328
-          0.000
-          0.783
-          0.923
-          1.000
-          ...
-          0.680
-          0.233
-          0.023
-          0.749
-          1
-          0.000
-          0.536
-          0.712
-          0.952
-          0.326
-        
-        
-          G
-          0.025
-          0.032
-          0.259
-          0.055
-          0.352
-          1.000
-          0.662
-          0.000
-          0.513
-          0.175
-          ...
-          0.525
-          0.000
-          0.455
-          0.040
-          0
-          0.692
-          0.000
-          0.210
-          0.952
-          0.023
-        
-        
-          H
-          0.840
-          0.387
-          0.401
-          0.463
-          0.610
-          0.454
-          0.479
-          0.561
-          0.667
-          0.338
-          ...
-          0.754
-          0.000
-          0.345
-          0.191
-          0
-          0.923
-          0.201
-          0.612
-          0.562
-          0.419
-        
-        
-          I
-          0.000
-          0.990
-          0.697
-          0.512
-          0.969
-          0.151
-          0.056
-          0.663
-          0.923
-          0.894
-          ...
-          0.820
-          0.714
-          0.070
-          0.000
-          1
-          0.154
-          0.161
-          0.457
-          0.583
-          0.140
-        
-        
-          K
-          0.506
-          0.516
-          0.127
-          0.591
-          0.027
-          0.613
-          1.000
-          0.694
-          0.000
-          0.044
-          ...
-          0.615
-          0.012
-          0.688
-          0.294
-          0
-          0.923
-          0.195
-          0.536
-          0.912
-          1.000
-        
-        
-          L
-          0.272
-          0.835
-          0.905
-          0.732
-          1.000
-          0.076
-          0.014
-          0.663
-          0.846
-          0.925
-          ...
-          1.000
-          0.428
-          0.771
-          0.000
-          1
-          0.000
-          0.513
-          0.690
-          0.952
-          0.186
-        
-        
-          M
-          0.704
-          0.452
-          1.000
-          1.000
-          0.883
-          0.084
-          0.113
-          0.620
-          0.846
-          0.756
-          ...
-          0.689
-          0.701
-          0.512
-          0.651
-          0
-          0.077
-          0.151
-          0.670
-          0.952
-          0.372
-        
-        
-          N
-          0.988
-          0.029
-          0.381
-          0.287
-          0.171
-          0.924
-          0.718
-          0.398
-          0.282
-          0.162
-          ...
-          0.508
-          0.000
-          0.313
-          0.028
-          0
-          1.000
-          0.277
-          0.342
-          0.952
-          0.093
-        
-        
-          P
-          0.605
-          0.871
-          0.403
-          0.000
-          0.130
-          0.824
-          0.803
-          0.376
-          0.308
-          0.750
-          ...
-          0.566
-          0.545
-          0.937
-          0.157
-          0
-          1.000
-          1.000
-          1.000
-          0.952
-          0.698
-        
-        
-          Q
-          0.519
-          0.000
-          0.203
-          0.805
-          0.238
-          0.546
-          0.732
-          0.539
-          0.256
-          0.388
-          ...
-          0.697
-          0.428
-          0.446
-          0.602
-          0
-          0.923
-          0.478
-          0.530
-          0.952
-          0.256
-        
-        
-          R
-          0.531
-          0.268
-          0.061
-          0.738
-          0.482
-          0.748
-          0.634
-          0.735
-          0.308
-          0.112
-          ...
-          0.000
-          0.000
-          0.550
-          0.760
-          0
-          1.000
-          0.549
-          0.728
-          0.952
-          0.372
-        
-        
-          S
-          0.679
-          0.045
-          0.450
-          0.293
-          0.293
-          0.798
-          0.704
-          0.188
-          0.359
-          0.256
-          ...
-          0.656
-          0.000
-          0.868
-          0.657
-          0
-          0.231
-          0.168
-          0.399
-          0.952
-          0.186
-        
-        
-          T
-          0.494
-          0.174
-          0.619
-          0.360
-          0.279
-          0.529
-          0.577
-          0.352
-          0.462
-          0.419
-          ...
-          0.574
-          0.000
-          1.000
-          0.745
-          0
-          0.000
-          0.344
-          0.513
-          0.000
-          0.419
-        
-        
-          V
-          0.000
-          0.577
-          0.183
-          0.451
-          0.907
-          0.000
-          0.127
-          0.492
-          0.872
-          0.719
-          ...
-          0.770
-          0.000
-          0.408
-          0.045
-          1
-          0.077
-          0.151
-          0.467
-          0.952
-          0.163
-        
-        
-          W
-          0.926
-          1.000
-          0.707
-          0.805
-          0.500
-          0.773
-          0.070
-          1.000
-          0.846
-          0.894
-          ...
-          0.467
-          1.000
-          0.138
-          0.434
-          1
-          0.231
-          0.066
-          0.440
-          1.000
-          0.349
-        
-        
-          Y
-          0.802
-          0.990
-          0.425
-          0.524
-          0.771
-          0.798
-          0.127
-          0.806
-          0.615
-          0.762
-          ...
-          0.557
-          0.857
-          0.000
-          0.408
-          1
-          0.154
-          0.110
-          0.666
-          0.736
-          0.349
         
       
     
-    
20 rows × 100 columns

     

 
 
@@ -731,15 +251,15 @@ sequences: the test set and the reference set. Supported by the
 C-terminal adjacent regions (JMD-N and JMD-C, respectively), obtained
 ``sf.get_df_parts``. - ``Splits``: These ``Parts`` can be split into
 various continuous segments or discontinuous patterns, specified
-``sf.get_split_kws()``. - ``Scales``: Sets of amino acid scales. We
-first use SequenceFeature to obtain Parts and Splits:
+``sf.get_split_kws()``. - ``Scales``: Sets of amino acid scales.
+
+We use SequenceFeature to obtain Parts and Splits:
 
 .. code:: ipython3
 
-    # Feature Engineering
     y = list(df_seq["label"])
     sf = aa.SequenceFeature()
-    df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10, list_parts=["tmd_jmd"])
+    df_parts = sf.get_df_parts(df_seq=df_seq, list_parts=["tmd_jmd"])
     split_kws = sf.get_split_kws(n_split_max=1, split_types=["Segment"])
     df_parts.head(5)
 
@@ -803,9 +323,9 @@ As a baseline approach, we use CPP to compute the average values for the
 
 .. code:: ipython3
 
-    # Small set of features (100 features created)
-    cpp = aa.CPP(df_parts=df_parts, df_scales=df_scales, split_kws=split_kws, verbose=False)
-    df_feat = cpp.run(labels=y, tmd_len=20, jmd_n_len=10, jmd_c_len=10, n_filter=100)  # Default values for lengths are used
+    # Small set of CPP features (100 features are created)
+    cpp = aa.CPP(df_scales=df_scales, df_parts=df_parts, split_kws=split_kws, verbose=False)
+    df_feat = cpp.run(labels=y) 
     df_feat
 
 
@@ -927,16 +447,16 @@ A feature matrix from a given set of CPP features can be created using
 
     from sklearn.ensemble import RandomForestClassifier
     from sklearn.model_selection import cross_val_score
-    X = sf.feat_matrix(df_parts=df_parts, df_scales=df_scales, features=df_feat["feature"])
-    # ML evaluation
+    
+    X = sf.feat_matrix(df_parts=df_parts, features=df_feat["feature"])
     rf = RandomForestClassifier()
-    cv_base = cross_val_score(rf, X, y, scoring="accuracy", cv=5, n_jobs=8) # Set n_jobs=1 to disable multi-processing
+    cv_base = cross_val_score(rf, X, y, scoring="accuracy")
     print(f"Mean accuracy of {round(np.mean(cv_base), 2)}")
 
 
 .. parsed-literal::
 
-    Mean accuracy of 0.57
+    Mean accuracy of 0.58
 
 
 Creating more features with CPP will take some more time. but improve
@@ -944,12 +464,11 @@ prediction performance:
 
 .. code:: ipython3
 
-    # Default CPP features  (around 100.000 features)
-    split_kws = sf.get_split_kws()
-    df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10)
-    cpp = aa.CPP(df_parts=df_parts, df_scales=df_scales, split_kws=split_kws, verbose=False)
-    df_feat = cpp.run(labels=y, n_processes=8, n_filter=100)
-    df_feat
+    # CPP features with default splits (around 100.000 features)
+    df_parts = sf.get_df_parts(df_seq=df_seq)
+    cpp = aa.CPP(df_scales=df_scales, df_parts=df_parts, verbose=False)
+    df_feat = cpp.run(labels=y)
+    df_feat.head(10)
 
 
 
@@ -1071,104 +590,87 @@ prediction performance:
           32,33
         
         
-          ...
-          ...
-          ...
-          ...
-          ...
-          ...
-          ...
-          ...
-          ...
-          ...
-          ...
-          ...
-          ...
-          ...
+          5
+          TMD_C_JMD_C-Segment(4,9)-ROBB760113
+          Conformation
+          β-turn
+          β-turn
+          Information measure for loop (Robson-Suzuki, 1...
+          0.337
+          0.319440
+          -0.319440
+          0.175203
+          0.255754
+          6.100000e-09
+          1.185395e-06
+          27,28
         
         
-          95
-          JMD_N_TMD_N-Pattern(C,6,9)-NAKH900106
-          Composition
-          Mitochondrial proteins
-          Mitochondrial proteins
-          Normalized composition from animal (Nakashima ...
-          0.228
-          0.172120
-          -0.172120
-          0.180254
-          0.199987
-          8.754340e-05
-          2.693037e-04
-          12,15
+          6
+          TMD_C_JMD_C-Segment(2,2)-EISD860102
+          Energy
+          Isoelectric point
+          Atom-based hydrophobic moment
+          Atom-based hydrophobic moment (Eisenberg-McLac...
+          0.337
+          0.139567
+          0.139567
+          0.098917
+          0.101842
+          6.300000e-09
+          1.185395e-06
+          31,32,33,34,35,36,37,38,39,40
         
         
-          96
-          JMD_N_TMD_N-Pattern(C,6,9,12)-ZIMJ680105
-          Others
-          PC 2
-          Principal Component 1 (Zimmerman)
-          RF rank (Zimmerman et al., 1968)
-          0.227
-          0.133867
-          -0.133867
-          0.160532
-          0.161415
-          9.118090e-05
-          2.778863e-04
-          9,12,15
+          7
+          TMD_C_JMD_C-Segment(4,5)-RICJ880113
+          Conformation
+          α-helix (C-cap)
+          α-helix (C-terminal, inside)
+          Relative preference value at C2 (Richardson-Ri...
+          0.336
+          0.223765
+          0.223765
+          0.133513
+          0.178217
+          7.100000e-09
+          1.185395e-06
+          33,34,35,36
         
         
-          97
-          JMD_N_TMD_N-Segment(7,8)-KARS160107
+          8
+          TMD_C_JMD_C-Segment(5,7)-KARS160107
           Shape
           Side chain length
           Eccentricity (maximum)
           Diameter (maximum eccentricity) (Karkbara-Knis...
-          0.227
-          0.098674
-          -0.098674
-          0.104428
-          0.124875
-          8.945330e-05
-          2.740061e-04
-          16,17
+          0.331
+          0.217594
+          0.217594
+          0.136011
+          0.172395
+          1.130000e-08
+          1.331786e-06
+          32,33,34
         
         
-          98
-          JMD_N_TMD_N-Pattern(C,6,9,12)-SIMZ760101
+          9
+          TMD_C_JMD_C-Pattern(C,4,8)-JURD980101
           Polarity
           Hydrophobicity
-          Transfer free energy (TFE) to outside
-          Transfer free energy (Simon, 1976), Cited by C...
-          0.225
-          0.161307
-          -0.161307
-          0.192235
-          0.212741
-          1.036749e-04
-          3.042894e-04
-          9,12,15
-        
-        
-          99
-          JMD_N_TMD_N-Pattern(C,3,6)-TANS770102
-          Conformation
-          α-helix (C-term, out)
-          α-helix (C-terminal, outside)
-          Normalized frequency of isolated helix (Tanaka...
-          0.224
-          0.108020
-          -0.108020
-          0.133731
-          0.139419
-          1.143783e-04
-          3.272494e-04
-          15,18
+          Hydrophobicity
+          Modified Kyte-Doolittle hydrophobicity scale (...
+          0.329
+          0.264720
+          -0.264720
+          0.141666
+          0.233134
+          1.480000e-08
+          1.425259e-06
+          33,37
         
       
     
-    
100 rows × 13 columns

     

 
 
@@ -1182,21 +684,23 @@ Which can be again used for machine learning:
     warnings.simplefilter(action='ignore', category=FutureWarning)
     import matplotlib.pyplot as plt
     import pandas as pd
-    X = sf.feat_matrix(df_parts=df_parts, df_scales=df_scales, features=df_feat["feature"])
-    # ML evaluation
+    
+    X = sf.feat_matrix(df_parts=df_parts, features=df_feat["feature"])
     rf = RandomForestClassifier()
     cv = cross_val_score(rf, X, y, scoring="accuracy", cv=5, n_jobs=1) 
     print(f"Mean accuracy of {round(np.mean(cv), 2)}")
+    
     aa.plot_settings(font_scale=1.1)
     sns.barplot(pd.DataFrame({"Baseline": cv_base, "CPP": cv}), palette=["tab:blue", "tab:red"])
     plt.ylabel("Mean accuracy", size=aa.plot_gcfs()+1)
+    plt.ylim(0, 1)
     sns.despine()
     plt.show()
 
 
 .. parsed-literal::
 
-    Mean accuracy of 0.95
+    Mean accuracy of 0.9
 
 
 
diff --git a/tutorials/tutorial1_quick_start.ipynb b/tutorials/tutorial1_quick_start.ipynb
index 8c635148..38fb55e9 100644
--- a/tutorials/tutorial1_quick_start.ipynb
+++ b/tutorials/tutorial1_quick_start.ipynb
@@ -22,14 +22,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2023-09-24T06:49:44.600810538Z",
-     "start_time": "2023-09-24T06:49:44.523495924Z"
+     "end_time": "2023-09-24T11:18:19.227943399Z",
+     "start_time": "2023-09-24T11:18:19.176090140Z"
     }
    },
    "outputs": [
@@ -38,16 +38,15 @@
       "text/plain": "    entry                                           sequence  label  tmd_start  tmd_stop       jmd_n                      tmd       jmd_c\n0  Q14802  MQKVTLGLLVFLAGFPVLDANDLEDKNSPFYYDWHSLQVGGLICAG...      0         37        59  NSPFYYDWHS  LQVGGLICAGVLCAMGIIIVMSA  KCKCKFGQKS\n1  Q86UE4  MAARSWQDELAQQAEEGSARLREMLSVGLGFLRTELGLDLGLEPKR...      0         50        72  LGLEPKRYPG  WVILVGTGALGLLLLFLLGYGWA  AACAGARKKR\n2  Q969W9  MHRLMGVNSTAAAAAGQPNVSCTCNCKRSLFQSMEITELEFVQIII...      0         41        63  FQSMEITELE  FVQIIIIVVVMMVMVVVITCLLS  HYKLSARSFI\n3  P53801  MAPGVARGPTPYWRLRLGGAALLLLLIPVAAAQEPPGAACSQNTNK...      0         97       119  RWGVCWVNFE  ALIITMSVVGGTLLLGIAICCCC  CCRRKRSRKP\n4  Q8IUW5  MAPRALPGSAVLAAAVFVGGAVSSPLVAPDNGSSRTLHSRTETTPS...      0         59        81  NDTGNGHPEY  IAYALVPVFFIMGLFGVLICHLL  KKKGYRCTTE",
       "text/html": "
\n\n\n  \n    \n      \n      \n      \n      \n      \n      \n      \n      \n      \n    \n  \n  \n    \n      \n      \n      \n      \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n      \n      \n      \n      \n    \n  \n
entrysequencelabeltmd_starttmd_stopjmd_ntmdjmd_c
0Q14802MQKVTLGLLVFLAGFPVLDANDLEDKNSPFYYDWHSLQVGGLICAG...03759NSPFYYDWHSLQVGGLICAGVLCAMGIIIVMSAKCKCKFGQKS
1Q86UE4MAARSWQDELAQQAEEGSARLREMLSVGLGFLRTELGLDLGLEPKR...05072LGLEPKRYPGWVILVGTGALGLLLLFLLGYGWAAACAGARKKR
2Q969W9MHRLMGVNSTAAAAAGQPNVSCTCNCKRSLFQSMEITELEFVQIII...04163FQSMEITELEFVQIIIIVVVMMVMVVVITCLLSHYKLSARSFI
3P53801MAPGVARGPTPYWRLRLGGAALLLLLIPVAAAQEPPGAACSQNTNK...097119RWGVCWVNFEALIITMSVVGGTLLLGIAICCCCCCRRKRSRKP
4Q8IUW5MAPRALPGSAVLAAAVFVGGAVSSPLVAPDNGSSRTLHSRTETTPS...05981NDTGNGHPEYIAYALVPVFFIMGLFGVLICHLLKKKGYRCTTE
\n
"
      },
-     "execution_count": 14,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import aaanalysis as aa\n",
-    "# Load scales and scale categories (AAontology) \n",
+    "\n",
     "df_scales = aa.load_scales()\n",
-    "# Load training data\n",
     "df_seq = aa.load_dataset(name=\"DOM_GSEC\", n=50)\n",
     "df_seq.head(5)"
    ]
@@ -69,14 +68,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 12,
    "outputs": [
     {
      "data": {
-      "text/plain": "    ANDN920101  SIMZ760101  NAKH900106  AURR980112  CORJ870107  ROBB760113  MIYS990104  BIGC670101  ROSG850102  ZIMJ680105  ...  YUTK870102  SUEM840102  VASM830102  VELV850101  VENT840101  MONM990101  GEOR030102  GEOR030106  KARS160120  LINS030117\nAA                                                                                                                          ...                                                                                                                        \nA        0.494       0.268       0.237       0.787       0.446       0.101       0.479       0.164       0.564       0.444  ...       0.557       0.103       0.617       0.295           0       0.077       0.250       0.516       0.952       0.186\nC        0.864       0.258       0.303       0.104       0.725       0.849       0.000       0.323       1.000       0.000  ...       0.680       0.337       0.734       0.657           0       0.154       0.246       0.000       0.952       0.000\nD        1.000       0.206       0.000       0.451       0.000       0.790       0.803       0.324       0.256       0.000  ...       0.574       0.909       0.225       1.000           0       0.923       0.091       0.404       0.952       0.186\nE        0.420       0.210       0.090       0.823       0.233       0.092       0.859       0.488       0.256       0.025  ...       0.402       0.077       0.531       0.046           0       0.923       0.404       0.610       0.952       0.349\nF        0.877       0.887       0.724       0.402       0.950       0.328       0.000       0.783       0.923       1.000  ...       0.680       0.233       0.023       0.749           1       0.000       0.536       0.712       0.952       0.326\nG        0.025       0.032       0.259       0.055       0.352       1.000       0.662       0.000       0.513       0.175  ...       0.525       0.000       0.455       0.040           0       0.692       0.000       0.210       0.952       0.023\nH        0.840       0.387       0.401       0.463       0.610       0.454       0.479       0.561       0.667       0.338  ...       0.754       0.000       0.345       0.191           0       0.923       0.201       0.612       0.562       0.419\nI        0.000       0.990       0.697       0.512       0.969       0.151       0.056       0.663       0.923       0.894  ...       0.820       0.714       0.070       0.000           1       0.154       0.161       0.457       0.583       0.140\nK        0.506       0.516       0.127       0.591       0.027       0.613       1.000       0.694       0.000       0.044  ...       0.615       0.012       0.688       0.294           0       0.923       0.195       0.536       0.912       1.000\nL        0.272       0.835       0.905       0.732       1.000       0.076       0.014       0.663       0.846       0.925  ...       1.000       0.428       0.771       0.000           1       0.000       0.513       0.690       0.952       0.186\nM        0.704       0.452       1.000       1.000       0.883       0.084       0.113       0.620       0.846       0.756  ...       0.689       0.701       0.512       0.651           0       0.077       0.151       0.670       0.952       0.372\nN        0.988       0.029       0.381       0.287       0.171       0.924       0.718       0.398       0.282       0.162  ...       0.508       0.000       0.313       0.028           0       1.000       0.277       0.342       0.952       0.093\nP        0.605       0.871       0.403       0.000       0.130       0.824       0.803       0.376       0.308       0.750  ...       0.566       0.545       0.937       0.157           0       1.000       1.000       1.000       0.952       0.698\nQ        0.519       0.000       0.203       0.805       0.238       0.546       0.732       0.539       0.256       0.388  ...       0.697       0.428       0.446       0.602           0       0.923       0.478       0.530       0.952       0.256\nR        0.531       0.268       0.061       0.738       0.482       0.748       0.634       0.735       0.308       0.112  ...       0.000       0.000       0.550       0.760           0       1.000       0.549       0.728       0.952       0.372\nS        0.679       0.045       0.450       0.293       0.293       0.798       0.704       0.188       0.359       0.256  ...       0.656       0.000       0.868       0.657           0       0.231       0.168       0.399       0.952       0.186\nT        0.494       0.174       0.619       0.360       0.279       0.529       0.577       0.352       0.462       0.419  ...       0.574       0.000       1.000       0.745           0       0.000       0.344       0.513       0.000       0.419\nV        0.000       0.577       0.183       0.451       0.907       0.000       0.127       0.492       0.872       0.719  ...       0.770       0.000       0.408       0.045           1       0.077       0.151       0.467       0.952       0.163\nW        0.926       1.000       0.707       0.805       0.500       0.773       0.070       1.000       0.846       0.894  ...       0.467       1.000       0.138       0.434           1       0.231       0.066       0.440       1.000       0.349\nY        0.802       0.990       0.425       0.524       0.771       0.798       0.127       0.806       0.615       0.762  ...       0.557       0.857       0.000       0.408           1       0.154       0.110       0.666       0.736       0.349\n\n[20 rows x 100 columns]",
-      "text/html": "
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ANDN920101SIMZ760101NAKH900106AURR980112CORJ870107ROBB760113MIYS990104BIGC670101ROSG850102ZIMJ680105...YUTK870102SUEM840102VASM830102VELV850101VENT840101MONM990101GEOR030102GEOR030106KARS160120LINS030117
AA
A0.4940.2680.2370.7870.4460.1010.4790.1640.5640.444...0.5570.1030.6170.29500.0770.2500.5160.9520.186
C0.8640.2580.3030.1040.7250.8490.0000.3231.0000.000...0.6800.3370.7340.65700.1540.2460.0000.9520.000
D1.0000.2060.0000.4510.0000.7900.8030.3240.2560.000...0.5740.9090.2251.00000.9230.0910.4040.9520.186
E0.4200.2100.0900.8230.2330.0920.8590.4880.2560.025...0.4020.0770.5310.04600.9230.4040.6100.9520.349
F0.8770.8870.7240.4020.9500.3280.0000.7830.9231.000...0.6800.2330.0230.74910.0000.5360.7120.9520.326
G0.0250.0320.2590.0550.3521.0000.6620.0000.5130.175...0.5250.0000.4550.04000.6920.0000.2100.9520.023
H0.8400.3870.4010.4630.6100.4540.4790.5610.6670.338...0.7540.0000.3450.19100.9230.2010.6120.5620.419
I0.0000.9900.6970.5120.9690.1510.0560.6630.9230.894...0.8200.7140.0700.00010.1540.1610.4570.5830.140
K0.5060.5160.1270.5910.0270.6131.0000.6940.0000.044...0.6150.0120.6880.29400.9230.1950.5360.9121.000
L0.2720.8350.9050.7321.0000.0760.0140.6630.8460.925...1.0000.4280.7710.00010.0000.5130.6900.9520.186
M0.7040.4521.0001.0000.8830.0840.1130.6200.8460.756...0.6890.7010.5120.65100.0770.1510.6700.9520.372
N0.9880.0290.3810.2870.1710.9240.7180.3980.2820.162...0.5080.0000.3130.02801.0000.2770.3420.9520.093
P0.6050.8710.4030.0000.1300.8240.8030.3760.3080.750...0.5660.5450.9370.15701.0001.0001.0000.9520.698
Q0.5190.0000.2030.8050.2380.5460.7320.5390.2560.388...0.6970.4280.4460.60200.9230.4780.5300.9520.256
R0.5310.2680.0610.7380.4820.7480.6340.7350.3080.112...0.0000.0000.5500.76001.0000.5490.7280.9520.372
S0.6790.0450.4500.2930.2930.7980.7040.1880.3590.256...0.6560.0000.8680.65700.2310.1680.3990.9520.186
T0.4940.1740.6190.3600.2790.5290.5770.3520.4620.419...0.5740.0001.0000.74500.0000.3440.5130.0000.419
V0.0000.5770.1830.4510.9070.0000.1270.4920.8720.719...0.7700.0000.4080.04510.0770.1510.4670.9520.163
W0.9261.0000.7070.8050.5000.7730.0701.0000.8460.894...0.4671.0000.1380.43410.2310.0660.4401.0000.349
Y0.8020.9900.4250.5240.7710.7980.1270.8060.6150.762...0.5570.8570.0000.40810.1540.1100.6660.7360.349
\n
20 rows × 100 columns
\n
"
+      "text/plain": "    ANDN920101  SIMZ760101  NAKH900106  AURR980112\nAA                                                \nA        0.494       0.268       0.237       0.787\nC        0.864       0.258       0.303       0.104\nD        1.000       0.206       0.000       0.451\nE        0.420       0.210       0.090       0.823\nF        0.877       0.887       0.724       0.402",
+      "text/html": "
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ANDN920101SIMZ760101NAKH900106AURR980112
AA
A0.4940.2680.2370.787
C0.8640.2580.3030.104
D1.0000.2060.0000.451
E0.4200.2100.0900.823
F0.8770.8870.7240.402
\n
"
      },
-     "execution_count": 31,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -84,17 +83,18 @@
    "source": [
     "from sklearn.cluster import AgglomerativeClustering\n",
     "import numpy as np\n",
-    "aac = aa.AAclust(model=AgglomerativeClustering, model_kwargs=dict(linkage=\"ward\"))\n",
+    "\n",
+    "aac = aa.AAclust(model=AgglomerativeClustering)\n",
     "X = np.array(df_scales)\n",
-    "scales = aac.fit(X, n_clusters=100, names=list(df_scales)) \n",
+    "scales = aac.fit(X, names=list(df_scales), n_clusters=100) \n",
     "df_scales = df_scales[scales]\n",
-    "df_scales"
+    "df_scales[scales[0:4]].head(5)"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-09-24T07:09:02.332982857Z",
-     "start_time": "2023-09-24T07:09:02.142147429Z"
+     "end_time": "2023-09-24T11:18:22.964537774Z",
+     "start_time": "2023-09-24T11:18:22.855795499Z"
     }
    }
   },
@@ -106,7 +106,8 @@
     "- ``Parts``: Are combination of a target middle domain (TMD) and N- and C-terminal adjacent regions (JMD-N and JMD-C, respectively), obtained ``sf.get_df_parts``.\n",
     "- ``Splits``: These `Parts` can be split into various continuous segments or discontinuous patterns, specified ``sf.get_split_kws()``. \n",
     "- ``Scales``: Sets of amino acid scales.\n",
-    "We first use SequenceFeature to obtain Parts and Splits:"
+    "\n",
+    "We use SequenceFeature to obtain Parts and Splits:"
    ],
    "metadata": {
     "collapsed": false
@@ -114,14 +115,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 13,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2023-09-24T07:09:02.340451349Z",
-     "start_time": "2023-09-24T07:09:02.251538089Z"
+     "end_time": "2023-09-24T11:18:25.253400531Z",
+     "start_time": "2023-09-24T11:18:25.143157741Z"
     }
    },
    "outputs": [
@@ -130,16 +131,15 @@
       "text/plain": "                                            tmd_jmd\nD3ZZK3  RIIGDGANSTVLLVSVSGSVVLVVILIAAFVISRRRSKYSQAK\nO14786  PGNVLKTLDPILITIIAMSALGVLLGAVCGVVLYCACWHNGMS\nO35516  SELESPRNAQLLYLLAVAVVIILFFILLGVIMAKRKRKHGFLW\nO43914  DCSCSTVSPGVLAGIVMGDLVLTVLIALAVYFLGRLVPRGRGA\nO75581  YPTEEPAPQATNTVGSVIGVIVTIFVSGTVYFICQRMLCPRMK",
       "text/html": "
\n\n\n  \n    \n      \n      \n    \n  \n  \n    \n      \n      \n    \n    \n      \n      \n    \n    \n      \n      \n    \n    \n      \n      \n    \n    \n      \n      \n    \n  \n
tmd_jmd
D3ZZK3RIIGDGANSTVLLVSVSGSVVLVVILIAAFVISRRRSKYSQAK
O14786PGNVLKTLDPILITIIAMSALGVLLGAVCGVVLYCACWHNGMS
O35516SELESPRNAQLLYLLAVAVVIILFFILLGVIMAKRKRKHGFLW
O43914DCSCSTVSPGVLAGIVMGDLVLTVLIALAVYFLGRLVPRGRGA
O75581YPTEEPAPQATNTVGSVIGVIVTIFVSGTVYFICQRMLCPRMK
\n
"
      },
-     "execution_count": 32,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Feature Engineering\n",
     "y = list(df_seq[\"label\"])\n",
     "sf = aa.SequenceFeature()\n",
-    "df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10, list_parts=[\"tmd_jmd\"])\n",
+    "df_parts = sf.get_df_parts(df_seq=df_seq, list_parts=[\"tmd_jmd\"])\n",
     "split_kws = sf.get_split_kws(n_split_max=1, split_types=[\"Segment\"])\n",
     "df_parts.head(5)"
    ]
@@ -156,29 +156,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 14,
    "outputs": [
     {
      "data": {
       "text/plain": "                           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\n0  TMD_JMD-Segment(1,1)-ANDN920101  Structure-Activity      Backbone-dynamics (-CH)  α-CH chemical shifts (backbone-dynamics)   alpha-CH chemical shifts (Andersen et al., 1992)    0.130      0.022966  0.022966  0.054433  0.053266            0.025737      0.099022  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,1...\n1  TMD_JMD-Segment(1,1)-VASM830101        Conformation  Unclassified (Conformation)                                   α-helix  Relative population of conformational state A ...    0.120      0.019298 -0.019298  0.046755  0.049127            0.039609      0.099022  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,1...\n2  TMD_JMD-Segment(1,1)-ROBB760113        Conformation                       β-turn                                    β-turn  Information measure for loop (Robson-Suzuki, 1...    0.108      0.021958  0.021958  0.060658  0.053190            0.062212      0.100670  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,1...\n3  TMD_JMD-Segment(1,1)-RACS820103        Conformation  Unclassified (Conformation)                     α-helix (left-handed)  Average relative fractional occurrence in AL(i...    0.080      0.019579 -0.019579  0.072260  0.047452            0.166907      0.166907  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,1...",
       "text/html": "
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featurecategorysubcategoryscale_namescale_descriptionabs_aucabs_mean_difmean_difstd_teststd_refp_val_mann_whitneyp_val_fdr_bhpositions
0TMD_JMD-Segment(1,1)-ANDN920101Structure-ActivityBackbone-dynamics (-CH)α-CH chemical shifts (backbone-dynamics)alpha-CH chemical shifts (Andersen et al., 1992)0.1300.0229660.0229660.0544330.0532660.0257370.0990221,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,1...
1TMD_JMD-Segment(1,1)-VASM830101ConformationUnclassified (Conformation)α-helixRelative population of conformational state A ...0.1200.019298-0.0192980.0467550.0491270.0396090.0990221,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,1...
2TMD_JMD-Segment(1,1)-ROBB760113Conformationβ-turnβ-turnInformation measure for loop (Robson-Suzuki, 1...0.1080.0219580.0219580.0606580.0531900.0622120.1006701,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,1...
3TMD_JMD-Segment(1,1)-RACS820103ConformationUnclassified (Conformation)α-helix (left-handed)Average relative fractional occurrence in AL(i...0.0800.019579-0.0195790.0722600.0474520.1669070.1669071,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,1...
\n
"
      },
-     "execution_count": 33,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Small set of features (100 features created)\n",
-    "cpp = aa.CPP(df_parts=df_parts, df_scales=df_scales, split_kws=split_kws, verbose=False)\n",
-    "df_feat = cpp.run(labels=y, tmd_len=20, jmd_n_len=10, jmd_c_len=10, n_filter=100)  # Default values for lengths are used\n",
+    "# Small set of CPP features (100 features are created)\n",
+    "cpp = aa.CPP(df_scales=df_scales, df_parts=df_parts, split_kws=split_kws, verbose=False)\n",
+    "df_feat = cpp.run(labels=y) \n",
     "df_feat"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-09-24T07:09:16.423080603Z",
-     "start_time": "2023-09-24T07:09:07.771862935Z"
+     "end_time": "2023-09-24T11:18:35.720886606Z",
+     "start_time": "2023-09-24T11:18:27.250917313Z"
     }
    }
   },
@@ -196,30 +196,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 15,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean accuracy of 0.57\n"
+      "Mean accuracy of 0.58\n"
      ]
     }
    ],
    "source": [
     "from sklearn.ensemble import RandomForestClassifier\n",
     "from sklearn.model_selection import cross_val_score\n",
-    "X = sf.feat_matrix(df_parts=df_parts, df_scales=df_scales, features=df_feat[\"feature\"])\n",
-    "# ML evaluation\n",
+    "\n",
+    "X = sf.feat_matrix(df_parts=df_parts, features=df_feat[\"feature\"])\n",
     "rf = RandomForestClassifier()\n",
-    "cv_base = cross_val_score(rf, X, y, scoring=\"accuracy\", cv=5, n_jobs=8) # Set n_jobs=1 to disable multi-processing\n",
+    "cv_base = cross_val_score(rf, X, y, scoring=\"accuracy\")\n",
     "print(f\"Mean accuracy of {round(np.mean(cv_base), 2)}\")"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-09-24T07:09:24.862316908Z",
-     "start_time": "2023-09-24T07:09:20.126515444Z"
+     "end_time": "2023-09-24T11:18:43.192973177Z",
+     "start_time": "2023-09-24T11:18:39.111479446Z"
     }
    }
   },
@@ -234,31 +234,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 16,
    "outputs": [
     {
      "data": {
-      "text/plain": "                                        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\n0           TMD_C_JMD_C-Segment(2,3)-QIAN880106  Conformation                 α-helix                       α-helix (middle)  Weights for alpha-helix at the window position...    0.387      0.121446  0.121446  0.069196  0.085013        0.000000e+00  0.000000e+00  27,28,29,30,31,32,33\n1           TMD_C_JMD_C-Segment(4,5)-ZIMJ680104        Energy       Isoelectric point                      Isoelectric point         Isoelectric point (Zimmerman et al., 1968)    0.373      0.220000  0.220000  0.123716  0.137350        1.000000e-10  2.475000e-07           33,34,35,36\n2   TMD_C_JMD_C-Pattern(N,5,8,12,15)-QIAN880106  Conformation                 α-helix                       α-helix (middle)  Weights for alpha-helix at the window position...    0.358      0.144860  0.144860  0.079321  0.117515        7.000000e-10  7.150000e-07           25,28,32,35\n3           TMD_C_JMD_C-Segment(5,7)-LINS030101    ASA/Volume                  Volume          Accessible surface area (ASA)  Total accessible surfaces of whole residues (b...    0.354      0.237161  0.237161  0.145884  0.164285        1.100000e-09  7.150000e-07              32,33,34\n4           TMD_C_JMD_C-Segment(6,9)-ZIMJ680104        Energy       Isoelectric point                      Isoelectric point         Isoelectric point (Zimmerman et al., 1968)    0.341      0.263651  0.263651  0.187136  0.171995        4.000000e-09  1.185395e-06                 32,33\n..                                          ...           ...                     ...                                    ...                                                ...      ...           ...       ...       ...       ...                 ...           ...                   ...\n95        JMD_N_TMD_N-Pattern(C,6,9)-NAKH900106   Composition  Mitochondrial proteins                 Mitochondrial proteins  Normalized composition from animal (Nakashima ...    0.228      0.172120 -0.172120  0.180254  0.199987        8.754340e-05  2.693037e-04                 12,15\n96     JMD_N_TMD_N-Pattern(C,6,9,12)-ZIMJ680105        Others                    PC 2      Principal Component 1 (Zimmerman)                   RF rank (Zimmerman et al., 1968)    0.227      0.133867 -0.133867  0.160532  0.161415        9.118090e-05  2.778863e-04               9,12,15\n97          JMD_N_TMD_N-Segment(7,8)-KARS160107         Shape       Side chain length                 Eccentricity (maximum)  Diameter (maximum eccentricity) (Karkbara-Knis...    0.227      0.098674 -0.098674  0.104428  0.124875        8.945330e-05  2.740061e-04                 16,17\n98     JMD_N_TMD_N-Pattern(C,6,9,12)-SIMZ760101      Polarity          Hydrophobicity  Transfer free energy (TFE) to outside  Transfer free energy (Simon, 1976), Cited by C...    0.225      0.161307 -0.161307  0.192235  0.212741        1.036749e-04  3.042894e-04               9,12,15\n99        JMD_N_TMD_N-Pattern(C,3,6)-TANS770102  Conformation   α-helix (C-term, out)          α-helix (C-terminal, outside)  Normalized frequency of isolated helix (Tanaka...    0.224      0.108020 -0.108020  0.133731  0.139419        1.143783e-04  3.272494e-04                 15,18\n\n[100 rows x 13 columns]",
-      "text/html": "
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featurecategorysubcategoryscale_namescale_descriptionabs_aucabs_mean_difmean_difstd_teststd_refp_val_mann_whitneyp_val_fdr_bhpositions
0TMD_C_JMD_C-Segment(2,3)-QIAN880106Conformationα-helixα-helix (middle)Weights for alpha-helix at the window position...0.3870.1214460.1214460.0691960.0850130.000000e+000.000000e+0027,28,29,30,31,32,33
1TMD_C_JMD_C-Segment(4,5)-ZIMJ680104EnergyIsoelectric pointIsoelectric pointIsoelectric point (Zimmerman et al., 1968)0.3730.2200000.2200000.1237160.1373501.000000e-102.475000e-0733,34,35,36
2TMD_C_JMD_C-Pattern(N,5,8,12,15)-QIAN880106Conformationα-helixα-helix (middle)Weights for alpha-helix at the window position...0.3580.1448600.1448600.0793210.1175157.000000e-107.150000e-0725,28,32,35
3TMD_C_JMD_C-Segment(5,7)-LINS030101ASA/VolumeVolumeAccessible surface area (ASA)Total accessible surfaces of whole residues (b...0.3540.2371610.2371610.1458840.1642851.100000e-097.150000e-0732,33,34
4TMD_C_JMD_C-Segment(6,9)-ZIMJ680104EnergyIsoelectric pointIsoelectric pointIsoelectric point (Zimmerman et al., 1968)0.3410.2636510.2636510.1871360.1719954.000000e-091.185395e-0632,33
..........................................
95JMD_N_TMD_N-Pattern(C,6,9)-NAKH900106CompositionMitochondrial proteinsMitochondrial proteinsNormalized composition from animal (Nakashima ...0.2280.172120-0.1721200.1802540.1999878.754340e-052.693037e-0412,15
96JMD_N_TMD_N-Pattern(C,6,9,12)-ZIMJ680105OthersPC 2Principal Component 1 (Zimmerman)RF rank (Zimmerman et al., 1968)0.2270.133867-0.1338670.1605320.1614159.118090e-052.778863e-049,12,15
97JMD_N_TMD_N-Segment(7,8)-KARS160107ShapeSide chain lengthEccentricity (maximum)Diameter (maximum eccentricity) (Karkbara-Knis...0.2270.098674-0.0986740.1044280.1248758.945330e-052.740061e-0416,17
98JMD_N_TMD_N-Pattern(C,6,9,12)-SIMZ760101PolarityHydrophobicityTransfer free energy (TFE) to outsideTransfer free energy (Simon, 1976), Cited by C...0.2250.161307-0.1613070.1922350.2127411.036749e-043.042894e-049,12,15
99JMD_N_TMD_N-Pattern(C,3,6)-TANS770102Conformationα-helix (C-term, out)α-helix (C-terminal, outside)Normalized frequency of isolated helix (Tanaka...0.2240.108020-0.1080200.1337310.1394191.143783e-043.272494e-0415,18
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100 rows × 13 columns
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+      "text/plain": "                                       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\n0          TMD_C_JMD_C-Segment(2,3)-QIAN880106  Conformation            α-helix               α-helix (middle)  Weights for alpha-helix at the window position...    0.387      0.121446  0.121446  0.069196  0.085013        0.000000e+00  0.000000e+00           27,28,29,30,31,32,33\n1          TMD_C_JMD_C-Segment(4,5)-ZIMJ680104        Energy  Isoelectric point              Isoelectric point         Isoelectric point (Zimmerman et al., 1968)    0.373      0.220000  0.220000  0.123716  0.137350        1.000000e-10  2.475000e-07                    33,34,35,36\n2  TMD_C_JMD_C-Pattern(N,5,8,12,15)-QIAN880106  Conformation            α-helix               α-helix (middle)  Weights for alpha-helix at the window position...    0.358      0.144860  0.144860  0.079321  0.117515        7.000000e-10  7.150000e-07                    25,28,32,35\n3          TMD_C_JMD_C-Segment(5,7)-LINS030101    ASA/Volume             Volume  Accessible surface area (ASA)  Total accessible surfaces of whole residues (b...    0.354      0.237161  0.237161  0.145884  0.164285        1.100000e-09  7.150000e-07                       32,33,34\n4          TMD_C_JMD_C-Segment(6,9)-ZIMJ680104        Energy  Isoelectric point              Isoelectric point         Isoelectric point (Zimmerman et al., 1968)    0.341      0.263651  0.263651  0.187136  0.171995        4.000000e-09  1.185395e-06                          32,33\n5          TMD_C_JMD_C-Segment(4,9)-ROBB760113  Conformation             β-turn                         β-turn  Information measure for loop (Robson-Suzuki, 1...    0.337      0.319440 -0.319440  0.175203  0.255754        6.100000e-09  1.185395e-06                          27,28\n6          TMD_C_JMD_C-Segment(2,2)-EISD860102        Energy  Isoelectric point  Atom-based hydrophobic moment  Atom-based hydrophobic moment (Eisenberg-McLac...    0.337      0.139567  0.139567  0.098917  0.101842        6.300000e-09  1.185395e-06  31,32,33,34,35,36,37,38,39,40\n7          TMD_C_JMD_C-Segment(4,5)-RICJ880113  Conformation    α-helix (C-cap)   α-helix (C-terminal, inside)  Relative preference value at C2 (Richardson-Ri...    0.336      0.223765  0.223765  0.133513  0.178217        7.100000e-09  1.185395e-06                    33,34,35,36\n8          TMD_C_JMD_C-Segment(5,7)-KARS160107         Shape  Side chain length         Eccentricity (maximum)  Diameter (maximum eccentricity) (Karkbara-Knis...    0.331      0.217594  0.217594  0.136011  0.172395        1.130000e-08  1.331786e-06                       32,33,34\n9        TMD_C_JMD_C-Pattern(C,4,8)-JURD980101      Polarity     Hydrophobicity                 Hydrophobicity  Modified Kyte-Doolittle hydrophobicity scale (...    0.329      0.264720 -0.264720  0.141666  0.233134        1.480000e-08  1.425259e-06                          33,37",
+      "text/html": "
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featurecategorysubcategoryscale_namescale_descriptionabs_aucabs_mean_difmean_difstd_teststd_refp_val_mann_whitneyp_val_fdr_bhpositions
0TMD_C_JMD_C-Segment(2,3)-QIAN880106Conformationα-helixα-helix (middle)Weights for alpha-helix at the window position...0.3870.1214460.1214460.0691960.0850130.000000e+000.000000e+0027,28,29,30,31,32,33
1TMD_C_JMD_C-Segment(4,5)-ZIMJ680104EnergyIsoelectric pointIsoelectric pointIsoelectric point (Zimmerman et al., 1968)0.3730.2200000.2200000.1237160.1373501.000000e-102.475000e-0733,34,35,36
2TMD_C_JMD_C-Pattern(N,5,8,12,15)-QIAN880106Conformationα-helixα-helix (middle)Weights for alpha-helix at the window position...0.3580.1448600.1448600.0793210.1175157.000000e-107.150000e-0725,28,32,35
3TMD_C_JMD_C-Segment(5,7)-LINS030101ASA/VolumeVolumeAccessible surface area (ASA)Total accessible surfaces of whole residues (b...0.3540.2371610.2371610.1458840.1642851.100000e-097.150000e-0732,33,34
4TMD_C_JMD_C-Segment(6,9)-ZIMJ680104EnergyIsoelectric pointIsoelectric pointIsoelectric point (Zimmerman et al., 1968)0.3410.2636510.2636510.1871360.1719954.000000e-091.185395e-0632,33
5TMD_C_JMD_C-Segment(4,9)-ROBB760113Conformationβ-turnβ-turnInformation measure for loop (Robson-Suzuki, 1...0.3370.319440-0.3194400.1752030.2557546.100000e-091.185395e-0627,28
6TMD_C_JMD_C-Segment(2,2)-EISD860102EnergyIsoelectric pointAtom-based hydrophobic momentAtom-based hydrophobic moment (Eisenberg-McLac...0.3370.1395670.1395670.0989170.1018426.300000e-091.185395e-0631,32,33,34,35,36,37,38,39,40
7TMD_C_JMD_C-Segment(4,5)-RICJ880113Conformationα-helix (C-cap)α-helix (C-terminal, inside)Relative preference value at C2 (Richardson-Ri...0.3360.2237650.2237650.1335130.1782177.100000e-091.185395e-0633,34,35,36
8TMD_C_JMD_C-Segment(5,7)-KARS160107ShapeSide chain lengthEccentricity (maximum)Diameter (maximum eccentricity) (Karkbara-Knis...0.3310.2175940.2175940.1360110.1723951.130000e-081.331786e-0632,33,34
9TMD_C_JMD_C-Pattern(C,4,8)-JURD980101PolarityHydrophobicityHydrophobicityModified Kyte-Doolittle hydrophobicity scale (...0.3290.264720-0.2647200.1416660.2331341.480000e-081.425259e-0633,37
\n
"
      },
-     "execution_count": 35,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Default CPP features  (around 100.000 features)\n",
-    "split_kws = sf.get_split_kws()\n",
-    "df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10)\n",
-    "cpp = aa.CPP(df_parts=df_parts, df_scales=df_scales, split_kws=split_kws, verbose=False)\n",
-    "df_feat = cpp.run(labels=y, n_processes=8, n_filter=100)\n",
-    "df_feat"
+    "# CPP features with default splits (around 100.000 features)\n",
+    "df_parts = sf.get_df_parts(df_seq=df_seq)\n",
+    "cpp = aa.CPP(df_scales=df_scales, df_parts=df_parts, verbose=False)\n",
+    "df_feat = cpp.run(labels=y)\n",
+    "df_feat.head(10)"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-09-24T07:10:55.161445139Z",
-     "start_time": "2023-09-24T07:09:27.289343470Z"
+     "end_time": "2023-09-24T11:20:06.208160304Z",
+     "start_time": "2023-09-24T11:18:46.701291090Z"
     }
    }
   },
@@ -273,19 +272,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 18,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean accuracy of 0.95\n"
+      "Mean accuracy of 0.9\n"
      ]
     },
     {
      "data": {
       "text/plain": "
",
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -297,22 +296,24 @@
     "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
-    "X = sf.feat_matrix(df_parts=df_parts, df_scales=df_scales, features=df_feat[\"feature\"])\n",
-    "# ML evaluation\n",
+    "\n",
+    "X = sf.feat_matrix(df_parts=df_parts, features=df_feat[\"feature\"])\n",
     "rf = RandomForestClassifier()\n",
     "cv = cross_val_score(rf, X, y, scoring=\"accuracy\", cv=5, n_jobs=1) \n",
     "print(f\"Mean accuracy of {round(np.mean(cv), 2)}\")\n",
+    "\n",
     "aa.plot_settings(font_scale=1.1)\n",
     "sns.barplot(pd.DataFrame({\"Baseline\": cv_base, \"CPP\": cv}), palette=[\"tab:blue\", \"tab:red\"])\n",
     "plt.ylabel(\"Mean accuracy\", size=aa.plot_gcfs()+1)\n",
+    "plt.ylim(0, 1)\n",
     "sns.despine()\n",
     "plt.show()"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-09-24T07:11:52.473756689Z",
-     "start_time": "2023-09-24T07:11:45.847226209Z"
+     "end_time": "2023-09-24T11:20:45.657617985Z",
+     "start_time": "2023-09-24T11:20:38.934103291Z"
     }
    }
   },