From a5bb440f6be23a71ce2acd942b0f6459c6293d1c Mon Sep 17 00:00:00 2001 From: stephanbreimann Date: Mon, 2 Oct 2023 21:57:22 +0200 Subject: [PATCH] Finish tests for AAclust, add coverage and correlation --- aaanalysis/__pycache__/utils.cpython-39.pyc | Bin 15621 -> 15610 bytes aaanalysis/_archive/_archive_aaclust.py | 62 +++++ .../__pycache__/_check_data.cpython-39.pyc | Bin 4118 -> 4582 bytes .../__pycache__/_check_type.cpython-39.pyc | Bin 2886 -> 3235 bytes aaanalysis/_utils/_check_data.py | 12 +- aaanalysis/_utils/_check_type.py | 18 +- .../_aaclust_statics.cpython-39.pyc | Bin 5178 -> 4352 bytes .../__pycache__/aaclust.cpython-39.pyc | Bin 18255 -> 20594 bytes aaanalysis/aaclust/_aaclust_statics.py | 75 +++--- aaanalysis/aaclust/aaclust.py | 138 ++++++++--- aaanalysis/aaclust_plot/aaclust_plot.py | 20 +- .../__pycache__/plot_settings_.cpython-39.pyc | Bin 6876 -> 6876 bytes aaanalysis/utils.py | 18 +- docs/build/doctrees/environment.pickle | Bin 25729630 -> 25729630 bytes 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[l for l in unique_labels if not np.issubdtype(type(l), np.integer)] if wrong_types: raise ValueError(f"Labels in 'labels' should be type int, but contain: {set(map(type, wrong_types))}") @@ -79,6 +78,13 @@ def check_match_X_labels(X=None, labels=None): if n_samples != len(labels): raise ValueError(f"n_samples does not match for 'X' ({len(X)}) and 'labels' ({len(labels)}).") +# Check sets +def check_superset_subset(subset=None, superset=None, name_subset=None, name_superset=None): + """""" + wrong_elements = [x for x in subset if x not in superset] + if len(wrong_elements) != 0: + raise ValueError(f"'{name_superset}' does not contain the following elements of '{name_subset}': {wrong_elements}") + # df checking functions def check_col_in_df(df=None, name_df=None, col=None, col_type=None, accept_nan=False, error_if_exists=False): diff --git a/aaanalysis/_utils/_check_type.py b/aaanalysis/_utils/_check_type.py index 0ca2b452..5cd73ff0 100644 --- a/aaanalysis/_utils/_check_type.py +++ b/aaanalysis/_utils/_check_type.py @@ -1,6 +1,9 @@ """ Basic utility check functions for type checking """ +import pandas as pd +import numpy as np + # Type checking functions def check_number_val(name=None, val=None, accept_none=False, just_int=False): @@ -67,12 +70,21 @@ def check_tuple(name=None, val=None, n=None, check_n=True, accept_none=False): raise ValueError(f"'{name}' ({val}) should be a tuple with {n} elements.") -def check_list(name=None, val=None, accept_none=False): +def check_list(name=None, val=None, accept_none=False, convert=True): """""" if accept_none and val is None: return None - if not isinstance(val, list): - raise ValueError(f"'{name}' (type: {type(val)}) should be a list.") + if not convert: + if not isinstance(val, list): + raise ValueError(f"'{name}' (type: {type(val)}) should be a list.") + else: + allowed_types = (list, tuple, np.ndarray, pd.Series) + if not isinstance(val, allowed_types): + raise ValueError(f"'{name}' (type: {type(val)}) should be one of 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zLIfop$g~tP(!@;rrW01oj-AmnWh#>Eg+1AU*N-1wk=#W{t~@7EX@OjjAg?ZwOQ*=I zV~RkI2}m&GQic}UhrF)ZL132M;ij%6R0&iD)(KVy)I!pkbS2$M4^)Wvf;Z`fn(%2i zpvVHU7`$v}e(LnTBZQl~W8CcN;2-U7oUZv8R*ZsZT(fj4CrsclrpGlq2DD{^>Zm=rqBa{j)E*Y5A@onk zBWy-*d#cWovkO=2v)bhYaSN82dK3;gqpuP27aBr6Sv4y*#4K24OwZV0-r1NvDKB3f zSufhoW@0^ z0L!hy-atygBk{lcDu*Lz zes&-Y`!d~y_b4S$?$Y_vo=loC-_V*`RLAIt#n%jOH@w(XKjzTFKIW|hwK>1w`9inE z=|Fu*g*9P?gcT_)et>d^!|W2UM|uUbhTj+%Broyb2l@%)-Ghz0-T z42MS)0cdnPy8@ciZc!V|=I6s5@UP@nc%1C#wTB+$^kofTvbagx4+lvb?>=0 1: - list_names.extend(list_short_names) + if shorten_names: + list_names.extend(list_short_names) # Obtain information to check criteria for sorting scale names df_counts = pd.Series(list_names).value_counts().to_frame().reset_index() # Compute frequencies of names df_counts.columns = ["name", "count"] @@ -75,7 +87,7 @@ def remove_2nd_info(name_): names_cluster = [name_unclassified] return names_cluster -def name_clusters(X, labels=None, names=None): +def name_clusters(X, labels=None, names=None, shorten_names=True): """""" medoids, medoid_labels, medoid_ind = compute_medoids(X, labels=labels) dict_medoids = dict(zip(medoid_labels, medoid_ind)) @@ -87,7 +99,8 @@ def name_clusters(X, labels=None, names=None): name_medoid = names[dict_medoids[clust]] list_names = [names[i] for i in range(0, len(names)) if labels[i] == clust] names_cluster = _get_cluster_names(list_names=list_names, name_medoid=name_medoid, - name_unclassified=ut.STR_UNCLASSIFIED) + name_unclassified=ut.STR_UNCLASSIFIED, + shorten_names=shorten_names) assigned = False for name in names_cluster: if name not in dict_cluster_names.values() or name == ut.STR_UNCLASSIFIED: diff --git a/aaanalysis/aaclust/aaclust.py b/aaanalysis/aaclust/aaclust.py index 44f2d1d7..d40b1933 100644 --- a/aaanalysis/aaclust/aaclust.py +++ b/aaanalysis/aaclust/aaclust.py @@ -11,6 +11,7 @@ from sklearn.base import ClusterMixin from sklearn.exceptions import ConvergenceWarning import warnings +import pandas as pd from aaanalysis.aaclust._aaclust_bic import bic_score import aaanalysis.utils as ut @@ -79,6 +80,26 @@ def check_match_X_n_clusters(X=None, n_clusters=None, accept_none=True): if n_unique_samples < n_clusters: raise ValueError(f"'n_clusters' ({n_clusters}) should be >= n_unique_samples={n_unique_samples} (in 'X').") +def check_X_X_ref(X=None, X_ref=None): + """""" + n_samples, n_features = X.shape + n_samples_ref, n_features_ref = X_ref.shape + if n_features != n_features_ref: + raise ValueError(f"n_features does not match for 'X' ({n_features}) and 'X_ref' ({n_features_ref}).") + + +def check_labels_cor(labels=None, labels_name="labels"): + """""" + if labels is None: + raise ValueError(f"'{labels_name}' should not be None.") + # Convert labels to a numpy array if it's not already + labels = np.asarray(labels) + unique_labels = set(labels) + wrong_types = [l for l in unique_labels if not np.issubdtype(type(l), np.integer)] + if wrong_types: + raise ValueError(f"Labels in '{labels_name}' should be type int, but contain: {set(map(type, wrong_types))}") + return labels + # Post check functions def post_check_n_clusters(n_clusters_actual=None, n_clusters=None): @@ -198,7 +219,7 @@ def fit(self, - ``cosine``: Cosine distance names - Sample names. If provided, sets :attr:`AAclust.medoid_names_` attribute. + List of sample names. If provided, sets :attr:`AAclust.medoid_names_` attribute. Returns ------- @@ -227,7 +248,7 @@ def fit(self, # Check input X = ut.check_X(X=X) ut.check_X_unique_samples(X=X) - ut.check_list(name="names", val=names, accept_none=True) + names = ut.check_list(name="names", val=names, accept_none=True) ut.check_number_range(name="mint_th", val=min_th, min_val=0, max_val=1, just_int=False, accept_none=False) ut.check_number_range(name="n_clusters", val=n_clusters, min_val=1, just_int=True, accept_none=True) check_merge_metric(merge_metric=merge_metric) @@ -341,7 +362,8 @@ def eval(X: ut.ArrayLike2D, @staticmethod def name_clusters(X: ut.ArrayLike2D, labels: ut.ArrayLike1D = None, - names: List[str] = None + names: List[str] = None, + shorten_names : bool = True, ) -> List[str]: """ Assigns names to clusters based on the frequency of names. @@ -356,7 +378,9 @@ def name_clusters(X: ut.ArrayLike2D, labels : `array-like, shape (n_samples, )` Cluster labels for each sample in ``X``. names - List of scale names corresponding to each sample. + List of sample names. + shorten_names + If ``True`` shorten version of the names will be used. Returns ------- @@ -367,11 +391,12 @@ def name_clusters(X: ut.ArrayLike2D, X = ut.check_X(X=X) ut.check_X_unique_samples(X=X) labels = ut.check_labels(labels=labels) - ut.check_list(name="names", val=names, accept_none=False) + names = ut.check_list(name="names", val=names, accept_none=False) + ut.check_bool(name="shorten_names", val=shorten_names) ut.check_match_X_labels(X=X, labels=labels) check_match_X_names(X=X, names=names, accept_none=False) # Get cluster names - cluster_names = name_clusters(X, labels=labels, names=names) + cluster_names = name_clusters(X, labels=labels, names=names, shorten_names=shorten_names) return cluster_names @staticmethod @@ -436,13 +461,12 @@ def comp_medoids(X: ut.ArrayLike2D, @staticmethod def comp_correlation(X: ut.ArrayLike2D, - X_ref: ut.ArrayLike2D, + X_ref: Optional[ut.ArrayLike2D] = None, labels: ut.ArrayLike1D = None, - labels_ref: ut.ArrayLike1D = None, - n: int = 3, - positive: bool = True, - on_center: bool = False - ) -> List[str]: + labels_ref: Optional[ut.ArrayLike1D] = None, + names : Optional[List[str]] = None, + names_ref : Optional[List[str]] = None + ) -> pd.DataFrame: """ Computes the Pearson correlation of given data with reference data. @@ -451,43 +475,83 @@ def comp_correlation(X: ut.ArrayLike2D, X : `array-like, shape (n_samples, n_features)` Feature matrix. Rows correspond to scales and columns to amino acids. X_ref : `array-like, shape (n_samples, n_features)` - Feature matrix of reference data. + Feature matrix of reference data. If given, samples of ``X`` are compared with samples of ``X_ref``. labels : `array-like, shape (n_samples, )` Cluster labels for each sample in ``X``. - labels_ref : `array-like, shape (n_samples, )` - Cluster labels for the reference data. - n - Number of top centers to consider based on correlation strength. - positive - If True, considers positive correlations. Else, negative correlations. - on_center - If True, correlation is computed with cluster centers. Otherwise, with all cluster members. + labels_ref : `array-like, shape (n_samples_ref, )` + Cluster labels for each sample in ``X_ref``. + names + List of sample names corresponding to ``X``. + names_ref + List of sample names corresponding to ``X_ref``. Returns ------- - list_top_center_name_corr : list - Names and correlations of centers having the strongest (positive/negative) correlation with test data samples. + df_corr + DataFrame with correlation either for each pair in ``X`` of shape (n_samples, n_samples) or + for each pair between ``X`` and ``X_ref`` of shape (n_samples, n_samples_ref). + + Notes + ----- + * Rows will be sorted in ascending order of ``labels``. + * Columns will be sorted in ascending order of ``labels`` or ``labels_ref`` if given. + * Labels are replaced by respective names if given. See Also -------- - * :func:`numpy.corrcoe` was used to compute the correlation. + * :func:`pandas.DataFrame.corr` used to compute the correlation. """ # Check input - X = ut.check_X(X=X) - ut.check_X_unique_samples(X=X) - labels = ut.check_labels(labels=labels) + X = ut.check_X(X=X, min_n_samples=2) + ut.check_X_unique_samples(X=X, min_n_unique_samples=2) + labels = check_labels_cor(labels=labels, labels_name="labels") ut.check_match_X_labels(X=X, labels=labels) - X = ut.check_X(X=X_ref) - ut.check_X_unique_samples(X=X_ref) - labels = ut.check_labels(labels=labels_ref) - ut.check_match_X_labels(X=X_ref, labels=labels_ref) - ut.check_number_range(name="n", val=n, min_val=2) + check_match_X_names(X=X, names=names, accept_none=True) + if X_ref is not None: + X_ref = ut.check_X(X=X_ref, min_n_samples=1) + labels_ref = check_labels_cor(labels=labels_ref, labels_name="labels_ref") + ut.check_match_X_labels(X=X_ref, labels=labels_ref) + check_match_X_names(X=X_ref, names=names_ref, accept_none=True) + check_X_X_ref(X=X, X_ref=X_ref) # Get correlations - list_top_center_name_corr = compute_correlation(X, X_ref, labels=labels, labels_ref=labels_ref, - n=n, positive=positive, on_center=on_center) - return list_top_center_name_corr + df_corr = compute_correlation(X, X_ref=X_ref, + labels=labels, labels_ref=labels_ref, + names=names, names_ref=names_ref) + return df_corr @staticmethod - def comp_coverage(names=None, names_ref=None): - """Computes coverage of """ + def comp_coverage(names : [List[str]] =None, + names_ref : [List[str]] =None + ) -> float : + """ + Computes the percentage of unique names from ``names`` that are present in ``names_ref``. + + This method helps in understanding the coverage of a particular set of names (subset) + within a reference set of names (universal set). Each name from both ``names`` and ``names_ref`` + are considered only once, regardless of repetition. + + Parameters + ---------- + names + List of sample names. Should be subset of ``names_ref``. + names_ref + List of reference sample names. Should superset of ``names``. + + Returns + ------- + coverage + Percentage of unique names from ``names`` that are found in ``names_ref``. + """ + names = ut.check_list(name="names", val=names, accept_none=False) + names_ref = ut.check_list(name="names_ref", val=names_ref, accept_none=False) + ut.check_superset_subset(subset=names, name_subset="names", + superset=names_ref, name_superset="names_ref") + # Compute coverage + n_unique_names = len(set(names)) + n_unique_ref = len(set(names_ref)) + coverage = round(n_unique_names/n_unique_ref*100, 2) + return coverage + + + diff --git a/aaanalysis/aaclust_plot/aaclust_plot.py b/aaanalysis/aaclust_plot/aaclust_plot.py index e46b3a48..a7d9e47c 100644 --- a/aaanalysis/aaclust_plot/aaclust_plot.py +++ b/aaanalysis/aaclust_plot/aaclust_plot.py @@ -15,15 +15,21 @@ # II Main Functions class AAclustPlot: - """""" - def __int__(self): - pass + """Plot results of AAclust analysis""" + def __int__(self, model): + self.model = model + @staticmethod def eval(): - """Plot eval output of dPULearn (n per component)""" + """Plot eval output of BIC, CH, SC""" - @staticmethod - def eval_cat(df_seq=None): - """Plot donut plot for proportion of categories""" + def center(self): + """PCA plot of clustering with centers highlighted""" + def medoids(self): + """PCA plot of clustering with medoids highlighted""" + + @staticmethod + def correlation(): + """Heatmap for correlation""" diff --git a/aaanalysis/plotting/__pycache__/plot_settings_.cpython-39.pyc b/aaanalysis/plotting/__pycache__/plot_settings_.cpython-39.pyc index f0519b01848609c187f93302fc8dacbf1965e52c..f118c2475f05831381b19a97b885bfc5b4c44abe 100644 GIT binary patch delta 19 Zcmca(ddHM2k(ZZ?0SNa0*~oQC3IIE*1{44Q delta 19 Zcmca(ddHM2k(ZZ?0SLHPZRENn1pqbJ1r7iJ diff --git a/aaanalysis/utils.py b/aaanalysis/utils.py index e6cf8781..f458a729 100644 --- a/aaanalysis/utils.py +++ b/aaanalysis/utils.py @@ -14,22 +14,24 @@ check_dict, check_tuple, check_list, check_ax) from aaanalysis._utils._check_data import (check_X, check_X_unique_samples, check_labels, check_match_X_labels, + check_superset_subset, check_col_in_df) +from aaanalysis._utils.utils_cpp import (check_color, check_y_categorical, check_labels_, check_ylim, + check_args_len, check_args_len, check_list_parts, + check_split_kws, check_split, + get_dict_all_scales, get_vf_scale, + STR_SEGMENT, STR_PATTERN, STR_PERIODIC_PATTERN, STR_AA_GAP, + LIST_PARTS, LIST_ALL_PARTS, SPLIT_DESCRIPTION) from aaanalysis._utils._new_types import ArrayLike1D, ArrayLike2D + from aaanalysis._utils._decorators import (catch_runtime_warnings, CatchRuntimeWarnings, catch_convergence_warning, ClusteringConvergenceException, catch_invalid_divide_warning) -from aaanalysis._utils._utils_output import (_print_red, print_out, _print_blue, - print_start_progress, print_progress, print_finished_progress) +from aaanalysis._utils._utils_output import (print_out, print_start_progress, print_progress, print_finished_progress) + -from aaanalysis._utils.utils_cpp import (check_color, check_y_categorical, check_labels_, check_ylim, - check_args_len, check_args_len, check_list_parts, - check_split_kws, check_split, - get_dict_all_scales, get_vf_scale, - STR_SEGMENT, STR_PATTERN, STR_PERIODIC_PATTERN, STR_AA_GAP, - LIST_PARTS, LIST_ALL_PARTS, SPLIT_DESCRIPTION) # Folder structure diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 9e1da05638ccf2754f55fb0213fc0d5eea138c25..e8c9925fca6f82834cceab3cadee1a202f19732a 100644 GIT binary patch delta 1429966 zcmaG}30zgh_wOS6zJnle?|rO_2(GyS?hB~6?+Pj|PX$cWv=nU7NL2J8rz{n<%q_*R zC{4vu)7-GE%q1}`%_XU>nRXE<2AyGRzftDhyQOJmFQ?!kU8`LptKpPQ03ZDwh4gRHzM zrNx1iR3`)qrIwvJQI>zZg;=_DNfboOuAC4{;Z-NQ;xLkq2!WQ%`OcQic@oupDL7cZ z&Iz)Vck>V$v3guPbs9fy*3A6U;t)nE$db|BL68|Mezg62!OK!JFO2d~jPf=J_Lgk#Kbz%ejhmJ|6Sy&6o(d#)cJ_0#r)1Bue9|>SsmZxZMn=Z8 z-275npCd%t6-UtevqFR=w`+`tmCCrR36nsjdPZ#J*WF_q6a?5m^GuIU-*-RSXluzS 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-class aaanalysis.AAclust(model_class=<class 'sklearn.cluster._kmeans.KMeans'>, model_kwargs=None, verbose=False)[source]
+class aaanalysis.AAclust(model_class=<class 'sklearn.cluster._kmeans.KMeans'>, model_kwargs=None, verbose=False)[source]

Bases: Wrapper

A k-optimized clustering wrapper for selecting redundancy-reduced sets of numerical scales.

AAclust uses clustering models that require a pre-defined number of clusters (k, set by n_clusters), @@ -258,7 +258,7 @@

aaanalysis.AAclust
-__init__(model_class=<class 'sklearn.cluster._kmeans.KMeans'>, model_kwargs=None, verbose=False)[source]
+__init__(model_class=<class 'sklearn.cluster._kmeans.KMeans'>, model_kwargs=None, verbose=False)[source]
Parameters:
    @@ -283,11 +283,11 @@

    aaanalysis.AAclust

    comp_centers(X[, labels])

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

    -

    comp_correlation(X, X_ref[, labels, ...])

    +

    comp_correlation(X[, X_ref, labels, ...])

    Computes the Pearson correlation of given data with reference data.

    comp_coverage([names, names_ref])

    -

    Computes coverage of

    +

    Computes the percentage of unique names from names that are present in names_ref.

    comp_medoids(X[, labels])

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

    @@ -298,14 +298,14 @@

    aaanalysis.AAclust

    fit(X[, n_clusters, on_center, min_th, ...])

    Applies AAclust algorithm to feature matrix (X).

    -

    name_clusters(X[, labels, names])

    +

    name_clusters(X[, labels, names, shorten_names])

    Assigns names to clusters based on the frequency of names.

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

    Applies AAclust algorithm to feature matrix (X).

    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 @@ -330,7 +330,7 @@

    aaanalysis.AAclustOptional[List[str]]) – Sample names. If provided, sets AAclust.medoid_names_ attribute.

    +

  • names (Optional[List[str]]) – List of sample names. If provided, sets AAclust.medoid_names_ attribute.

Returns:
@@ -372,7 +372,7 @@

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

Evaluates the quality of clustering using three established measures.

Clustering quality is quantified using:

@@ -418,7 +418,7 @@

aaanalysis.AAclust
-static name_clusters(X, labels=None, names=None)[source]
+static name_clusters(X, labels=None, names=None, shorten_names=True)[source]

Assigns names to clusters based on the frequency of names.

Names with higher frequency are prioritized. If a name is already assigned to a cluster, or the cluster contains one sample, its name is set to ‘Unclassified’.

@@ -427,7 +427,8 @@

aaanalysis.AAclust

Returns:
@@ -441,7 +442,7 @@

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

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

Parameters:
@@ -462,7 +463,7 @@

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

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

Parameters:
@@ -483,39 +484,64 @@

aaanalysis.AAclust
-static comp_correlation(X, X_ref, labels=None, labels_ref=None, n=3, positive=True, on_center=False)[source]
+static comp_correlation(X, X_ref=None, labels=None, labels_ref=None, names=None, names_ref=None)[source]

Computes the Pearson correlation of given data with reference data.

Parameters:

  • X (array-like, shape (n_samples, n_features)) – Feature matrix. Rows correspond to scales and columns to amino acids.

    • -

  • X_ref (array-like, shape (n_samples, n_features)) – Feature matrix of reference data.

    • +

  • X_ref (array-like, shape (n_samples, n_features)) – Feature matrix of reference data. If given, samples of X are compared with samples of X_ref.

  • labels (array-like, shape (n_samples, )) – Cluster labels for each sample in X.

    • -

  • labels_ref (array-like, shape (n_samples, )) – Cluster labels for the reference data.

    • -

  • n (int) – Number of top centers to consider based on correlation strength.

    • -

  • positive (bool) – If True, considers positive correlations. Else, negative correlations.

    • -

  • on_center (bool) – If True, correlation is computed with cluster centers. Otherwise, with all cluster members.

    • +

  • labels_ref (array-like, shape (n_samples_ref, )) – Cluster labels for each sample in X_ref.

    • +

  • names (Optional[List[str]]) – List of sample names corresponding to X.

    • +

  • names_ref (Optional[List[str]]) – List of sample names corresponding to X_ref.

Returns:
-

list_top_center_name_corr – Names and correlations of centers having the strongest (positive/negative) correlation with test data samples.

+

DataFrame with correlation either for each pair in X of shape (n_samples, n_samples) or +for each pair between X and X_ref of shape (n_samples, n_samples_ref).

Return type:
-

list

+

df_corr

+
+

Notes

+
    +

  • Rows will be sorted in ascending order of labels.

    • +

  • Columns will be sorted in ascending order of labels or labels_ref if given.

    • +

  • Labels are replaced by respective names if given.

    • +
+

See also

    -

  • numpy.corrcoe() was used to compute the correlation.

    • +

  • pandas.DataFrame.corr() used to compute the correlation.

-static comp_coverage(names=None, names_ref=None)[source]
-

Computes coverage of

+static comp_coverage(names=None, names_ref=None)[source] +

Computes the percentage of unique names from names that are present in names_ref.

+

This method helps in understanding the coverage of a particular set of names (subset) +within a reference set of names (universal set). Each name from both names and names_ref +are considered only once, regardless of repetition.

+
+
Parameters:
+
    +

  • names – List of sample names. Should be subset of names_ref.

    • +

  • names_ref – List of reference sample names. Should superset of names.

    • +
+
+
Returns:
+

Percentage of unique names from names that are found in names_ref.

+
+
Return type:
+

coverage

+
+

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check_invalid_conditions(X, labels, min_samples=2, check_unique=True): + n_samples, n_features = X.shape + n_unique_samples = len(set(map(tuple, X))) + conditions = [ + (np.any(np.isinf(X)) or np.any(np.isnan(X)), "X contains NaN or Inf"), + (n_samples < min_samples, f"n_samples={n_samples} should be >= {min_samples}"), + (n_features < 2, f"n_features={n_features} should be >= 2"), + (len(labels) != n_samples, "Length of labels should match n_samples."), + ] + if check_unique: + conditions.append((n_unique_samples == 1, "Feature matrix 'X' should not have all identical samples.")) + for condition, msg in conditions: + if condition: + return True + return False + + +# Normal Cases +class TestCompCorrelation: + """Test comp_correlation function of the AAclust class.""" + + @given(X=npst.arrays(dtype=np.float64, shape=npst.array_shapes(min_dims=2, max_dims=2, min_side=1, max_side=5), + elements=some.floats(allow_nan=True, allow_infinity=True))) + def test_valid_X(self, X): + """Test a valid 'X' parameter.""" + labels = np.random.randint(1, X.shape[0] + 1, size=X.shape[0]) + is_invalid = check_invalid_conditions(X, labels) + if is_invalid: + with pytest.raises(ValueError): + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + aa.AAclust.comp_correlation(X, labels=labels) + else: + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + result_df = aa.AAclust.comp_correlation(X, labels=labels) + assert isinstance(result_df, pd.DataFrame) + + @given(X_ref=npst.arrays(dtype=np.float64, shape=npst.array_shapes(min_dims=2, max_dims=2, min_side=1, max_side=5), + elements=some.floats(allow_nan=True, allow_infinity=True))) + def test_invalid_X_ref(self, X_ref): + """Test an invalid 'X_ref' parameter.""" + X = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) + labels = np.array([1, 2, 1]) + is_invalid = check_invalid_conditions(X, labels) + if is_invalid: + with pytest.raises(ValueError): + warnings.simplefilter("ignore", RuntimeWarning) + aa.AAclust.comp_correlation(X, X_ref, labels) + + @given(labels_ref=npst.arrays(dtype=np.int32, + shape=npst.array_shapes(min_dims=1, max_dims=1, min_side=10, max_side=30), + elements=some.integers(min_value=0, max_value=30))) + def test_valid_labels_ref(self, labels_ref): + """Test a valid 'labels_ref' parameter.""" + X = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) + X_ref = np.random.rand(labels_ref.size, 2) + labels = np.array([1, 2, 1]) + is_invalid = check_invalid_conditions(X, labels) or check_invalid_conditions(X_ref, labels_ref, min_samples=1, check_unique=False) + if is_invalid: + with pytest.raises(ValueError): + warnings.simplefilter("ignore", RuntimeWarning) + aa.AAclust.comp_correlation(X, X_ref=X_ref, labels=labels, labels_ref=labels_ref) + else: + assert isinstance(aa.AAclust.comp_correlation(X, X_ref, labels, labels_ref), pd.DataFrame) + + + + + @given(labels_ref=npst.arrays(dtype=np.float64, + shape=npst.array_shapes(min_dims=1, max_dims=1, min_side=1, max_side=5), + elements=some.floats(allow_nan=True, allow_infinity=True))) + def test_invalid_labels_ref_dtype(self, labels_ref): + """Test invalid 'labels_ref' datatype.""" + X = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) + X_ref = np.array([[1.0, 2.0], [3.0, 4.0]]) + with pytest.raises(ValueError): + aa.AAclust.comp_correlation(X, X_ref, labels=np.array([1, 2, 1]), labels_ref=labels_ref) + + @given(names=some.lists(some.text(), min_size=1, max_size=5)) + def test_valid_names(self, names): + """Test a valid 'names' parameter.""" + X = np.random.rand(len(names), 2) + labels = np.arange(len(names)) + is_invalid = check_invalid_conditions(X, labels) + if is_invalid: + with pytest.raises(ValueError): + warnings.simplefilter("ignore", RuntimeWarning) + aa.AAclust.comp_correlation(X, labels=labels, names=names) + else: + warnings.simplefilter("ignore", RuntimeWarning) + result_df = aa.AAclust.comp_correlation(X, labels=labels, names=names) + assert isinstance(result_df, pd.DataFrame) + + + @given(names=some.lists(some.integers(), min_size=1, max_size=5)) + def test_invalid_names_dtype(self, names): + """Test invalid 'names' datatype.""" + X = np.random.rand(len(names), 2) + labels = np.arange(len(names)) + is_invalid = check_invalid_conditions(X, labels) + if is_invalid: + with pytest.raises(ValueError): + aa.AAclust.comp_correlation(X, labels=labels, names=names) + else: + aa.AAclust.comp_correlation(X, labels=labels, names=names) + with pytest.raises(ValueError): + aa.AAclust.comp_correlation(X, labels=labels, names={}) + with pytest.raises(ValueError): + aa.AAclust.comp_correlation(X, labels=labels, names=pd.DataFrame()) + + @given(names_ref=some.lists(some.text(), min_size=1, max_size=5)) + def test_valid_names_ref(self, names_ref): + """Test a valid 'names_ref' parameter.""" + X = np.random.rand(3, 2) + X_ref = np.random.rand(len(names_ref), 2) + labels = np.array([1, 2, 1]) + labels_ref = np.arange(len(names_ref)) + is_invalid = check_invalid_conditions(X, labels) or check_invalid_conditions(X_ref, labels_ref, min_samples=1, check_unique=False) + if is_invalid: + with pytest.raises(ValueError): + aa.AAclust.comp_correlation(X, X_ref, labels, labels_ref, names_ref=names_ref) + else: + result_df = aa.AAclust.comp_correlation(X, X_ref, labels, labels_ref, names_ref=names_ref) + assert isinstance(result_df, pd.DataFrame) + + @given(names_ref=some.lists(some.integers(), min_size=1, max_size=5)) + def test_invalid_names_ref_dtype(self, names_ref): + """Test invalid 'names_ref' datatype.""" + X = np.random.rand(3, 2) + X_ref = np.random.rand(len(names_ref), 2) + labels = np.array([1, 2, 1]) + labels_ref = np.arange(len(names_ref)) + is_invalid = check_invalid_conditions(X, labels) or check_invalid_conditions(X_ref, labels_ref, min_samples=1, check_unique=False) + if is_invalid: + with pytest.raises(ValueError): + aa.AAclust.comp_correlation(X, X_ref, labels, labels_ref, names_ref=names_ref) + + +# Complex Cases +class TestCompCorrelationComplex: + """Test comp_correlation function of the AAclust class for Complex Cases.""" + + @settings(deadline=1000, max_examples=5) + @given(X=npst.arrays(dtype=np.float64, shape=npst.array_shapes(min_dims=2, max_dims=2), + elements=some.floats(allow_nan=False, allow_infinity=False))) + def test_combination_valid_parameters(self, X): + """Test combination of valid parameters.""" + labels = np.random.randint(1, X.shape[0] + 1, size=X.shape[0]) + X_ref = np.random.rand(X.shape[0], X.shape[1]) + labels_ref = np.random.randint(1, X_ref.shape[0] + 1, size=X_ref.shape[0]) + names = [f"Sample_{i}" for i in range(X.shape[0])] + names_ref = [f"Ref_{i}" for i in range(X_ref.shape[0])] + is_invalid = check_invalid_conditions(X, labels) or check_invalid_conditions(X_ref, labels_ref, min_samples=1, check_unique=False) + if is_invalid: + with pytest.raises(ValueError): + warnings.simplefilter("ignore", RuntimeWarning) + aa.AAclust.comp_correlation(X, X_ref=X_ref, labels=labels, labels_ref=labels_ref, names=names, names_ref=names_ref) + else: + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + result_df = aa.AAclust.comp_correlation(X, X_ref=X_ref, labels=labels, labels_ref=labels_ref, names=names, names_ref=names_ref) + assert isinstance(result_df, pd.DataFrame) + + @settings(deadline=1000, max_examples=5) + @given(X=npst.arrays(dtype=np.float64, shape=npst.array_shapes(min_dims=2, max_dims=2), + elements=some.floats(allow_nan=False, allow_infinity=False))) + def test_combination_with_missing_labels(self, X): + """Test combination with missing 'labels' parameter.""" + X_ref = np.random.rand(X.shape[0], X.shape[1]) + names = [f"Sample_{i}" for i in range(X.shape[0])] + with pytest.raises(ValueError): + aa.AAclust.comp_correlation(X, X_ref=X_ref, names=names) + + @settings(deadline=1000, max_examples=5) + @given(X=npst.arrays(dtype=np.float64, shape=npst.array_shapes(min_dims=2, max_dims=2), + elements=some.floats(allow_nan=False, allow_infinity=False))) + def test_combination_with_incompatible_X_and_X_ref(self, X): + """Test combination where 'X' and 'X_ref' have different number of features.""" + X_ref = np.random.rand(X.shape[0], X.shape[1] + 1) + labels = np.random.randint(1, X.shape[0] + 1, size=X.shape[0]) + labels_ref = np.random.randint(1, X_ref.shape[0] + 1, size=X_ref.shape[0]) + with pytest.raises(ValueError): + aa.AAclust.comp_correlation(X, X_ref=X_ref, labels=labels, labels_ref=labels_ref) + + @settings(deadline=1000, max_examples=5) + @given(X=npst.arrays(dtype=np.float64, shape=npst.array_shapes(min_dims=2, max_dims=2), + elements=some.floats(allow_nan=False, allow_infinity=False))) + def test_combination_with_duplicate_names(self, X): + """Test combination with duplicate 'names'.""" + labels = np.random.randint(1, X.shape[0] + 1, size=X.shape[0]) + names = [f"Sample_0" for _ in range(X.shape[0])] + is_invalid = check_invalid_conditions(X, labels) + if is_invalid: + with pytest.raises(ValueError): + aa.AAclust.comp_correlation(X, labels=labels, names=names) + else: + result_df = aa.AAclust.comp_correlation(X, labels=labels, names=names) + assert isinstance(result_df, pd.DataFrame) + + @settings(deadline=1000, max_examples=5) + @given(X=npst.arrays(dtype=np.float64, shape=npst.array_shapes(min_dims=2, max_dims=2), + elements=some.floats(allow_nan=False, allow_infinity=False))) + def test_combination_with_mismatched_labels_and_names(self, X): + """Test combination where number of 'labels' and 'names' do not match.""" + labels = np.random.randint(1, X.shape[0] + 1, size=X.shape[0] - 1) + names = [f"Sample_{i}" for i in range(X.shape[0])] + with pytest.raises(ValueError): + aa.AAclust.comp_correlation(X, labels=labels, names=names) + diff --git a/tests/unit/aaclust_tests/test_aaclust_comp_coverage.py b/tests/unit/aaclust_tests/test_aaclust_comp_coverage.py index e69de29b..1783df64 100644 --- a/tests/unit/aaclust_tests/test_aaclust_comp_coverage.py +++ b/tests/unit/aaclust_tests/test_aaclust_comp_coverage.py @@ -0,0 +1,67 @@ +"""This is a script to test the comp_coverage function.""" +from hypothesis import given, settings +import hypothesis.strategies as some +from hypothesis.extra import pandas as pdst +import pandas as pd +import numpy as np +import aaanalysis as aa +import pytest + + +class TestCompCoverage: + """Test comp_coverage function of the TARGET FUNCTION""" + + @given(names=some.lists(elements=some.text(), min_size=1)) + def test_valid_names(self, names): + """Test a valid 'names' parameter.""" + names_ref = names + ['additional_name'] + result = aa.AAclust().comp_coverage(names, names_ref) # Please replace your_class with actual class name + assert isinstance(result, float) + + @given(names=some.lists(elements=some.text(), min_size=2)) + def test_invalid_names(self, names): + """Test an invalid 'names' parameter.""" + names = list(set(names)) + names_ref = names[:-1] # names is a superset of names_ref + with pytest.raises(ValueError): + aa.AAclust().comp_coverage(names, names_ref) + + @given(names_ref=some.lists(elements=some.text(), unique=True, min_size=1)) + def test_valid_names_ref(self, names_ref): + """Test a valid 'names_ref' parameter.""" + names = names_ref[:-1] # names is a subset of names_ref + result = aa.AAclust().comp_coverage(names, names_ref) + assert isinstance(result, float) + + @given(names_ref=some.lists(elements=some.text(), unique=True, min_size=1)) + def test_invalid_names_ref(self, names_ref): + """Test an invalid 'names_ref' parameter.""" + names = names_ref + ['additional_name'] # names_ref is a subset of names + with pytest.raises(ValueError): + aa.AAclust().comp_coverage(names, names_ref) + + def test_names_none(self): + """Test 'names' parameter set to None.""" + names_ref = ['name'] + with pytest.raises(ValueError): + aa.AAclust().comp_coverage(None, names_ref) + + def test_names_ref_none(self): + """Test 'names_ref' parameter set to None.""" + names = ['name'] + with pytest.raises(ValueError): + aa.AAclust().comp_coverage(names, None) + + +class TestCompCoverageComplex: + """Test comp_coverage function of the TARGET FUNCTION for Complex Cases""" + + @settings(deadline=1000, max_examples=5) + @given(names=some.lists(elements=some.text(), min_size=1), + additional=some.lists(elements=some.text(), min_size=1)) + def test_combination_valid_parameters(self, names, additional): + """Test combination of valid parameters.""" + names_ref = names + additional + result = aa.AAclust().comp_coverage(names, names_ref) + assert isinstance(result, float) + diff --git a/tests/unit/cpp_tests/__pycache__/__init__.cpython-39.pyc b/tests/unit/cpp_tests/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e544b449e696f2b48ef0886c74b6589ed115bcf0 GIT binary patch literal 195 zcmYk0F$w}P5Jj_KA;K2+Uckn|Uc?i~f(Hm=cG$$-WJ4xf?2){Ztw*r4a)O0^_;2{H 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aaanalysis.AAclust