diff --git a/aaanalysis/__pycache__/utils.cpython-39.pyc b/aaanalysis/__pycache__/utils.cpython-39.pyc index f622f0b3..57e44848 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__/check_data.cpython-39.pyc b/aaanalysis/_utils/__pycache__/check_data.cpython-39.pyc index dbf0115e..3b20cc0e 100644 Binary files a/aaanalysis/_utils/__pycache__/check_data.cpython-39.pyc and b/aaanalysis/_utils/__pycache__/check_data.cpython-39.pyc differ diff --git a/aaanalysis/_utils/__pycache__/check_models.cpython-39.pyc b/aaanalysis/_utils/__pycache__/check_models.cpython-39.pyc index 272e82b5..f9758aea 100644 Binary files a/aaanalysis/_utils/__pycache__/check_models.cpython-39.pyc and b/aaanalysis/_utils/__pycache__/check_models.cpython-39.pyc differ diff --git a/aaanalysis/_utils/check_data.py b/aaanalysis/_utils/check_data.py index fbbb719e..a173db38 100644 --- a/aaanalysis/_utils/check_data.py +++ b/aaanalysis/_utils/check_data.py @@ -1,9 +1,8 @@ """ -This is a script for ... +This is a script for data checking utility functions. """ import pandas as pd import numpy as np -# Write wrapper around scikit checkers from sklearn.utils import check_array # Helper functions diff --git a/aaanalysis/_utils/utils_output.py b/aaanalysis/_utils/utils_output.py index 8b5e5860..0371d94b 100644 --- a/aaanalysis/_utils/utils_output.py +++ b/aaanalysis/_utils/utils_output.py @@ -1,5 +1,5 @@ """ -This is a script for adjust output (mainly for python console) +This is a script for adjusting terminal output. """ import numpy as np diff --git a/aaanalysis/_utils/utils_ploting.py b/aaanalysis/_utils/utils_ploting.py new file mode 100644 index 00000000..0e2ffadb --- /dev/null +++ b/aaanalysis/_utils/utils_ploting.py @@ -0,0 +1,3 @@ +""" +This is a script for internal plotting utility functions used in the backend. +""" \ No newline at end of file diff --git a/aaanalysis/feature_engineering/__pycache__/_aaclust.cpython-39.pyc b/aaanalysis/feature_engineering/__pycache__/_aaclust.cpython-39.pyc index d8a58022..e33af240 100644 Binary files a/aaanalysis/feature_engineering/__pycache__/_aaclust.cpython-39.pyc and b/aaanalysis/feature_engineering/__pycache__/_aaclust.cpython-39.pyc differ diff --git a/aaanalysis/feature_engineering/__pycache__/_aaclust_plot.cpython-39.pyc b/aaanalysis/feature_engineering/__pycache__/_aaclust_plot.cpython-39.pyc index bdd98b0d..3f65ed93 100644 Binary files a/aaanalysis/feature_engineering/__pycache__/_aaclust_plot.cpython-39.pyc and b/aaanalysis/feature_engineering/__pycache__/_aaclust_plot.cpython-39.pyc differ diff --git a/aaanalysis/feature_engineering/_aaclust.py b/aaanalysis/feature_engineering/_aaclust.py index af799a36..0dbc8160 100644 --- a/aaanalysis/feature_engineering/_aaclust.py +++ b/aaanalysis/feature_engineering/_aaclust.py @@ -1,5 +1,6 @@ """ -This is a script for the interface of the AAclust class, used for clustering wrapper method. +This is a script for the interface of the AAclust class, a clustering wrapper object to obtain redundancy-reduced +scale subsets. """ import numpy as np from typing import Optional, Dict, Union, List, Tuple, Type diff --git a/aaanalysis/feature_engineering/_aaclust_plot.py b/aaanalysis/feature_engineering/_aaclust_plot.py index 21faa983..0246087b 100644 --- a/aaanalysis/feature_engineering/_aaclust_plot.py +++ b/aaanalysis/feature_engineering/_aaclust_plot.py @@ -1,9 +1,11 @@ """ -This is a script for the interface of the AAclustPlot class, used for plotting the results of AAclust. +This is a script for the frontend of the AAclustPlot class, used for plotting of the AAclust results. """ +import pandas as pd from sklearn.decomposition import PCA from typing import Optional, Dict, Union, List, Tuple, Type from sklearn.base import TransformerMixin +import matplotlib.pyplot as plt import aaanalysis as aa import aaanalysis.utils as ut @@ -12,11 +14,51 @@ # I Helper Functions -def _get_components(data=None, model_class=None): +def check_match_data_names(data=None, names=None): """""" + n_samples = len(data) + if names is not None: + if len(names) != n_samples: + raise ValueError(f"n_samples does not match for 'data' ({n_samples}) and 'names' ({len(names)}).") + else: + names = [f"Set {i}" for i in range(1, n_samples + 1)] + if not isinstance(data, pd.DataFrame): + data = ut.check_array_like(name=data, val=data) + n_samples, n_features = data.shape + # Check matching number of features + if n_features != 4: + raise ValueError(f"'data' should contain the following four columns: {ut.COLS_EVAL_AACLUST}") + df_eval = pd.DataFrame(data, columns=ut.COLS_EVAL_AACLUST, index=names) + else: + df_eval = data + # Check data for missing columns + missing_cols = [x for x in ut.COLS_EVAL_AACLUST if x not in list(df_eval)] + if len(missing_cols) > 0: + raise ValueError(f"'data' must contain the following columns: {missing_cols}") + df_eval.index = names + return df_eval + + +def check_dict_xlims(dict_xlims=None): + """""" + if dict_xlims is None: + return + ut.check_dict(name="dict_xlims", val=dict_xlims) + wrong_keys = [x for x in list(dict_xlims) if x not in ut.COLS_EVAL_AACLUST] + if len(wrong_keys) > 0: + raise ValueError(f"'dict_xlims' should not contain the following keys: {wrong_keys}") + for key in dict_xlims: + if len(dict_xlims[key]) != 2: + raise ValueError("'dict_xlims' values should be tuple with two numbers.") + xmin, xmax = dict_xlims[key] + ut.check_number_val(name="dict_xlims:min", val=xmin, just_int=False, accept_none=False) + ut.check_number_val(name="dict_xlims:max", val=xmax, just_int=False, accept_none=False) + if xmin >= xmax: + raise ValueError(f"'dict_xlims:min' ({xmin}) should be < 'dict_xlims:max' ({xmax}) for '{key}'.") + -# TODO add check functions finish other methods, testing, compression +# TODO add check functions finish other methods, testing, compression # II Main Functions class AAclustPlot: """Plot results of AAclust analysis. @@ -34,36 +76,76 @@ class AAclustPlot: See Also -------- * Scikit-learn `decomposition model classes `_. - """ def __init__(self, model_class: Type[TransformerMixin] = PCA, model_kwargs: Optional[Dict] = None): # Model parameters model_class = ut.check_mode_class(model_class=model_class) - model_kwargs = ut.check_model_kwargs(model_class=model_class, model_kwargs=model_kwargs, + model_kwargs = ut.check_model_kwargs(model_class=model_class, + model_kwargs=model_kwargs, param_to_check="n_components") self.model_class = model_class self.model_kwargs = model_kwargs @staticmethod - def eval(data : ut.ArrayLike2D, - names : Optional[List[str]] = None, - dict_xlims : Optional[Union[None, dict]] = None, - figsize : Optional[Tuple[int, int]] = (7, 6)): - """Plot eval output of n_clusters, BIC, CH, SC""" - columns = ["n_clusters", "BIC", "CH", "SC"] - colors = aa.plot_get_clist(n_colors=4) - + def eval(data: ut.ArrayLike2D, + names: Optional[List[str]] = None, + dict_xlims: Optional[Union[None, dict]] = None, + figsize: Optional[Tuple[int, int]] = (7, 6) + ) -> Tuple[plt.Figure, plt.Axes]: + """ + Evaluates and plots ``n_clusters`` and clustering metrics ``BIC``, ``CH``, and ``SC`` for the provided data. + + The clustering evaluation metrics (BIC, CH, and SC) are ranked by the average of their independent rankings. + + Parameters + ---------- + data : `array-like, shape (n_samples, n_features)` + Evaluation matrix or DataFrame. `Rows` correspond to scale sets and `columns` to the following + four evaluation measures: + + - ``n_clusters``: Number of clusters. + - ``BIC``: Bayesian Information Criterion. + - ``CH``: Calinski-Harabasz Index. + - ``SC``: Silhouette Coefficient. + + names + Names of scale sets from ``data``. If None, names are internally generated as 'Set 1', 'Set 2' etc. + dict_xlims + A dictionary containing x-axis limits (``xmin``, ``xmax``) for selected evaluation measure metric subplots. + Keys should be names of the ``evaluation measures`` (e.g., 'BIC'). If None, x-axis are auto-scaled. + figsize + Width and height of the figure in inches. + + Returns + ------- + fig + Figure object containing the plots. + axes + Axes object(s) containing four subplots. + + Notes + ----- + - The data is ranked in ascending order of the average ranking of the scale sets. + + See Also + -------- + * :meth:`AAclust.eval` for details on evaluation measures. + """ # Check input - data = ut.check_array_like(name="data", val=data) - n_samples, n_features = data.shape - if n_features != 4: - raise ValueError(f"'data' should contain the following four columns: {columns}") - if names is None: - names = [f"Model {i}" for i in range(1, n_samples+1)] + ut.check_array_like(name="data", val=data) + ut.check_list_like(name="names", val=names, accept_none=True) + df_eval = check_match_data_names(data=data, names=names) + check_dict_xlims(dict_xlims=dict_xlims) + ut.check_tuple(name="figsize", val=figsize, n=2, accept_none=True) # Plotting - fig, axes = plot_eval() + colors = aa.plot_get_clist(n_colors=4) + fig, axes = plot_eval(df_eval=df_eval, + dict_xlims=dict_xlims, + figsize=figsize, + colors=colors) + return fig, axes def center(self, data): diff --git a/aaanalysis/feature_engineering/_backend/aaclust/__pycache__/aaclust_plot.cpython-39.pyc b/aaanalysis/feature_engineering/_backend/aaclust/__pycache__/aaclust_plot.cpython-39.pyc index 7e713775..72e82acf 100644 Binary files a/aaanalysis/feature_engineering/_backend/aaclust/__pycache__/aaclust_plot.cpython-39.pyc and b/aaanalysis/feature_engineering/_backend/aaclust/__pycache__/aaclust_plot.cpython-39.pyc differ diff --git a/aaanalysis/feature_engineering/_backend/aaclust/aaclust_plot.py b/aaanalysis/feature_engineering/_backend/aaclust/aaclust_plot.py index a77ceb6c..5ed64901 100644 --- a/aaanalysis/feature_engineering/_backend/aaclust/aaclust_plot.py +++ b/aaanalysis/feature_engineering/_backend/aaclust/aaclust_plot.py @@ -1,51 +1,83 @@ """ -This is a script for the AAclust plot_eval method. +This is a script for the backend of the AAclustPlot object for all plotting functions. """ import time import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np - import aaanalysis.utils as ut +import matplotlib.ticker as mticker # I Helper Functions -def _get_rank(data): +# Computation helper functions +def _get_mean_rank(data): """""" _df = data.copy() - _df['BIC_rank'] = _df['BIC'].rank(ascending=False) - _df['CH_rank'] = _df['CH'].rank(ascending=False) - _df['SC_rank'] = _df['SC'].rank(ascending=False) - return _df[['BIC_rank', 'CH_rank', 'SC_rank']].mean(axis=1).round(2) + _df['BIC_rank'] = _df[ut.COL_BIC].rank(ascending=False) + _df['CH_rank'] = _df[ut.COL_CH].rank(ascending=False) + _df['SC_rank'] = _df[ut.COL_SC].rank(ascending=False) + rank = _df[['BIC_rank', 'CH_rank', 'SC_rank']].mean(axis=1).round(2) + return rank -# II Main Functions -def plot_eval(data=None, names=None, dict_xlims=None, figsize=None, columns=None, colors=None): +def _get_components(data=None, model_class=None): """""" - data = pd.DataFrame(data, columns=columns, index=names) - data["rank"] = _get_rank(data) - data = data.sort_values(by="rank", ascending=True) + +# Plotting helper functions +def _adjust_spines(ax=None): + """Adjust spines to be in middle if data range from <0 to >0""" + min_val, max_val = ax.get_xlim() + if max_val > 0 and min_val >= 0: + sns.despine(ax=ax) + else: + sns.despine(ax=ax, left=True) + current_lw = ax.spines['bottom'].get_linewidth() + ax.axvline(0, color='black', linewidth=current_lw) + val = max([abs(min_val), abs(max_val)]) + ax.set_xlim(-val, val) + return ax + + +def _x_ticks_0(ax): + """Apply custom formatting for x-axis ticks.""" + def custom_x_ticks(x, pos): + """Format x-axis ticks.""" + return f'{x:.2f}' if x else f'{x:.0f}' + ax.xaxis.set_major_formatter(mticker.FuncFormatter(custom_x_ticks)) + + + + +# II Main Functions +def plot_eval(df_eval=None, dict_xlims=None, figsize=None, colors=None): + """Plot evaluation of AAclust clustering results""" + df_eval[ut.COL_RANK] = _get_mean_rank(df_eval) + df_eval = df_eval.sort_values(by=ut.COL_RANK, ascending=True) # Plotting fig, axes = plt.subplots(1, 4, sharey=True, figsize=figsize) - for i, col in enumerate(columns): + for i, col in enumerate(ut.COLS_EVAL_AACLUST): ax = axes[i] - sns.barplot(ax=ax, data=data, y=data.index, x=col, color=colors[i]) + sns.barplot(ax=ax, data=df_eval, y=df_eval.index, x=col, color=colors[i]) # Customize subplots ax.set_ylabel("") ax.set_xlabel(col) - ax.axvline(0, color='black') # , linewidth=aa.plot_gcfs("axes.linewidth")) + # Adjust spines + ax = _adjust_spines(ax=ax) + # Manual xlims, if needed if dict_xlims and col in dict_xlims: ax.set_xlim(dict_xlims[col]) if i == 0: - ax.set_title("Number of clusters", weight="bold") + ax.set_title("Clustering", weight="bold") elif i == 2: ax.set_title("Quality measures", weight="bold") - sns.despine(ax=ax, left=True) ax.tick_params(axis='y', which='both', left=False) + _x_ticks_0(ax=ax) plt.tight_layout() plt.subplots_adjust(wspace=0.25, hspace=0) return fig, axes + def _plot_pca(df_pred=None, filter_classes=None, x=None, y=None, others=True, highlight_rel=True, figsize=(6, 6), highlight_mean=True, list_classes=None): """""" diff --git a/aaanalysis/feature_engineering/_sequence_feature.py b/aaanalysis/feature_engineering/_sequence_feature.py new file mode 100644 index 00000000..e69de29b diff --git a/aaanalysis/utils.py b/aaanalysis/utils.py index dbde32e7..771af94b 100644 --- a/aaanalysis/utils.py +++ b/aaanalysis/utils.py @@ -59,10 +59,16 @@ def _folder_path(super_folder, folder_name): NAMES_SCALE_SETS = [STR_SCALES, STR_SCALES_RAW, STR_SCALE_CAT, STR_SCALES_PC, STR_TOP60, STR_TOP60_EVAL] -# Options +# AAclust METRIC_CORRELATION = "correlation" LIST_METRICS = [METRIC_CORRELATION, "manhattan", "euclidean", "cosine"] STR_UNCLASSIFIED = "Unclassified" +COL_N_CLUST = "n_clusters" +COL_BIC = "BIC" +COL_CH = "CH" +COL_SC = "SC" +COL_RANK = "rank" +COLS_EVAL_AACLUST = [COL_N_CLUST, COL_BIC, COL_CH, COL_SC] # Column names for primary df # df_seq @@ -247,7 +253,7 @@ def check_df_parts(df_parts=None, verbose=True): print(warning) #raise ValueError("'df_part' should not be None") else: - if not (isinstance(df_parts, pd.DataFrame)): + if not isinstance(df_parts, pd.DataFrame): raise ValueError(f"'df_parts' ({type(df_parts)}) must be type pd.DataFrame") if len(list(df_parts)) == 0 or len(df_parts) == 0: raise ValueError("'df_parts' should not be empty pd.DataFrame") diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index a2ecedd4..524677fc 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 cfe1df45..4f995a71 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/aaanalysis.AAclustPlot.doctree b/docs/build/doctrees/generated/aaanalysis.AAclustPlot.doctree index e32255f7..46f63d74 100644 Binary files a/docs/build/doctrees/generated/aaanalysis.AAclustPlot.doctree and b/docs/build/doctrees/generated/aaanalysis.AAclustPlot.doctree differ diff --git a/docs/build/html/_downloads/004048c0cbb6684bdb9047282ab71735/aaanalysis-plot_settings-2.pdf b/docs/build/html/_downloads/004048c0cbb6684bdb9047282ab71735/aaanalysis-plot_settings-2.pdf index 26e18d3b..59378616 100644 Binary files a/docs/build/html/_downloads/004048c0cbb6684bdb9047282ab71735/aaanalysis-plot_settings-2.pdf and b/docs/build/html/_downloads/004048c0cbb6684bdb9047282ab71735/aaanalysis-plot_settings-2.pdf differ diff --git a/docs/build/html/_downloads/163aacac4bd235c9af7a62d7b4d0c89f/aaanalysis-plot_get_cdict-1.pdf b/docs/build/html/_downloads/163aacac4bd235c9af7a62d7b4d0c89f/aaanalysis-plot_get_cdict-1.pdf index 709d9fee..b6a54c5b 100644 Binary files a/docs/build/html/_downloads/163aacac4bd235c9af7a62d7b4d0c89f/aaanalysis-plot_get_cdict-1.pdf and b/docs/build/html/_downloads/163aacac4bd235c9af7a62d7b4d0c89f/aaanalysis-plot_get_cdict-1.pdf differ diff --git a/docs/build/html/_downloads/1f3abea1675a65bb341756c52c9927f4/aaanalysis-plot_gcfs-1.pdf b/docs/build/html/_downloads/1f3abea1675a65bb341756c52c9927f4/aaanalysis-plot_gcfs-1.pdf index c25bd49a..8e0549fd 100644 Binary files a/docs/build/html/_downloads/1f3abea1675a65bb341756c52c9927f4/aaanalysis-plot_gcfs-1.pdf and b/docs/build/html/_downloads/1f3abea1675a65bb341756c52c9927f4/aaanalysis-plot_gcfs-1.pdf differ diff --git a/docs/build/html/_downloads/72c2e4be500ecf10c85a4e6f81c365fc/aaanalysis-plot_legend-1.pdf b/docs/build/html/_downloads/72c2e4be500ecf10c85a4e6f81c365fc/aaanalysis-plot_legend-1.pdf index 305b1aac..94493971 100644 Binary files a/docs/build/html/_downloads/72c2e4be500ecf10c85a4e6f81c365fc/aaanalysis-plot_legend-1.pdf and b/docs/build/html/_downloads/72c2e4be500ecf10c85a4e6f81c365fc/aaanalysis-plot_legend-1.pdf differ diff --git a/docs/build/html/_downloads/795a736e5af756908120c8bda412fd28/aaanalysis-plot_get_cmap-1.pdf b/docs/build/html/_downloads/795a736e5af756908120c8bda412fd28/aaanalysis-plot_get_cmap-1.pdf index 7eea1cd4..a18bb6db 100644 Binary files a/docs/build/html/_downloads/795a736e5af756908120c8bda412fd28/aaanalysis-plot_get_cmap-1.pdf and b/docs/build/html/_downloads/795a736e5af756908120c8bda412fd28/aaanalysis-plot_get_cmap-1.pdf differ diff --git a/docs/build/html/_downloads/88d7f3f7cb5a284c0bfaa377fb4ce1d8/aaanalysis-plot_get_clist-1.pdf b/docs/build/html/_downloads/88d7f3f7cb5a284c0bfaa377fb4ce1d8/aaanalysis-plot_get_clist-1.pdf index 532df34f..7e5dc82d 100644 Binary files a/docs/build/html/_downloads/88d7f3f7cb5a284c0bfaa377fb4ce1d8/aaanalysis-plot_get_clist-1.pdf and b/docs/build/html/_downloads/88d7f3f7cb5a284c0bfaa377fb4ce1d8/aaanalysis-plot_get_clist-1.pdf differ diff --git a/docs/build/html/_downloads/fb29bffb69140db4f68c4eb913c6f7d3/aaanalysis-plot_settings-1.pdf b/docs/build/html/_downloads/fb29bffb69140db4f68c4eb913c6f7d3/aaanalysis-plot_settings-1.pdf index c661fe64..185c72fd 100644 Binary files a/docs/build/html/_downloads/fb29bffb69140db4f68c4eb913c6f7d3/aaanalysis-plot_settings-1.pdf and b/docs/build/html/_downloads/fb29bffb69140db4f68c4eb913c6f7d3/aaanalysis-plot_settings-1.pdf differ diff --git a/docs/build/html/generated/aaanalysis.AAclust.html b/docs/build/html/generated/aaanalysis.AAclust.html index 7c7cbddb..cf57155e 100644 --- a/docs/build/html/generated/aaanalysis.AAclust.html +++ b/docs/build/html/generated/aaanalysis.AAclust.html @@ -129,7 +129,7 @@

aaanalysis.AAclust

-class aaanalysis.AAclust(model_class=<class 'sklearn.cluster._kmeans.KMeans'>, model_kwargs=None, verbose=None)[source]
+class aaanalysis.AAclust(model_class=<class 'sklearn.cluster._kmeans.KMeans'>, model_kwargs=None, verbose=None)[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), @@ -259,7 +259,7 @@

aaanalysis.AAclust
-__init__(model_class=<class 'sklearn.cluster._kmeans.KMeans'>, model_kwargs=None, verbose=None)[source]
+__init__(model_class=<class 'sklearn.cluster._kmeans.KMeans'>, model_kwargs=None, verbose=None)[source]
Parameters:
    @@ -306,7 +306,7 @@

    aaanalysis.AAclust
    -fit(X, n_clusters=None, on_center=True, min_th=0.3, merge_metric='euclidean', names=None)[source]
    +fit(X, n_clusters=None, on_center=True, min_th=0.3, 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 @@ -373,7 +373,7 @@

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

    Evaluates the quality of clustering using three established measures.

    Clustering quality is quantified using:

    @@ -421,7 +421,7 @@

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

    @@ -445,7 +445,7 @@

    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:
    @@ -466,7 +466,7 @@

    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:
    @@ -487,7 +487,7 @@

    aaanalysis.AAclust
    -static comp_correlation(X, labels=None, X_ref=None, labels_ref=None, names=None, names_ref=None)[source]
    +static comp_correlation(X, labels=None, X_ref=None, labels_ref=None, names=None, names_ref=None)[source]

    Computes the Pearson correlation of given data with reference data.

    Parameters:
    @@ -526,7 +526,7 @@

    aaanalysis.AAclust
    -static comp_coverage(names=None, names_ref=None)[source]
    +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 diff --git a/docs/build/html/generated/aaanalysis.AAclustPlot.html b/docs/build/html/generated/aaanalysis.AAclustPlot.html index 24ea54c2..7972e413 100644 --- a/docs/build/html/generated/aaanalysis.AAclustPlot.html +++ b/docs/build/html/generated/aaanalysis.AAclustPlot.html @@ -129,7 +129,7 @@

    aaanalysis.AAclustPlot

    -class aaanalysis.AAclustPlot(model_class=<class 'sklearn.decomposition._pca.PCA'>, model_kwargs=None)[source]
    +class aaanalysis.AAclustPlot(model_class=<class 'sklearn.decomposition._pca.PCA'>, model_kwargs=None)[source]

    Bases: object

    Plot results of AAclust analysis.

    Dimensionality reduction is performed for visualization using decomposition models such as @@ -150,7 +150,7 @@

    aaanalysis.AAclustPlot
    -__init__(model_class=<class 'sklearn.decomposition._pca.PCA'>, model_kwargs=None)[source]
    +__init__(model_class=<class 'sklearn.decomposition._pca.PCA'>, model_kwargs=None)[source]
    Parameters:
      @@ -178,7 +178,7 @@

      aaanalysis.AAclustPlot

      Heatmap for correlation

      eval(data[, names, dict_xlims, figsize])

      -

      Plot eval output of n_clusters, BIC, CH, SC

      +

      Evaluates and plots n_clusters and clustering metrics BIC, CH, and SC for the provided data.

      medoids(data)

      PCA plot of clustering with medoids highlighted

      @@ -187,35 +187,64 @@

      aaanalysis.AAclustPlot
      -static eval(data, names=None, dict_xlims=None, figsize=(7, 6))[source]
      -

      Plot eval output of n_clusters, BIC, CH, SC

      +static eval(data, names=None, dict_xlims=None, figsize=(7, 6))[source] +

      Evaluates and plots n_clusters and clustering metrics BIC, CH, and SC for the provided data.

      +

      The clustering evaluation metrics (BIC, CH, and SC) are ranked by the average of their independent rankings.

      Parameters:
        -

      • data (NewType()(ArrayLike2D, Union[Sequence[Sequence[Union[int, float]]], ndarray, DataFrame])) –

        • -

      • names (Optional[List[str]]) –

        • -

      • dict_xlims (Optional[dict]) –

        • -

      • figsize (Optional[Tuple[int, int]]) –

        • +

      • data (array-like, shape (n_samples, n_features)) –

        Evaluation matrix or DataFrame. Rows correspond to scale sets and columns to the following +four evaluation measures:

        +
          +

        • n_clusters: Number of clusters.

          • +

        • BIC: Bayesian Information Criterion.

          • +

        • CH: Calinski-Harabasz Index.

          • +

        • SC: Silhouette Coefficient.

          • +
        +

        • +

      • names (Optional[List[str]]) – Names of scale sets from data. If None, names are internally generated as ‘Set 1’, ‘Set 2’ etc.

        • +

      • dict_xlims (Optional[dict]) – A dictionary containing x-axis limits (xmin, xmax) for selected evaluation measure metric subplots. +Keys should be names of the evaluation measures (e.g., ‘BIC’). If None, x-axis are auto-scaled.

        • +

      • figsize (Optional[Tuple[int, int]]) – Width and height of the figure in inches.

        • +
      +
      +
      Returns:
      +

        +

      • fig – Figure object containing the plots.

        • +

      • axes – Axes object(s) containing four subplots.

      +

      +
      +

      Notes

      +
        +

      • The data is ranked in ascending order of the average ranking of the scale sets.

        • +
      +
      +
      +

      See also

      + +
      -center(data)[source]
      +center(data)[source]

      PCA plot of clustering with centers highlighted

      -medoids(data)[source]
      +medoids(data)[source]

      PCA plot of clustering with medoids highlighted

      -static correlation(df_corr=None)[source]
      +static correlation(df_corr=None)[source]

      Heatmap for correlation

      diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index 54b43051..05d9c35f 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "generated/aaanalysis.AAclust", "generated/aaanalysis.AAclustPlot", "generated/aaanalysis.CPP", "generated/aaanalysis.CPPPlot", "generated/aaanalysis.SequenceFeature", "generated/aaanalysis.dPULearn", "generated/aaanalysis.load_dataset", "generated/aaanalysis.load_scales", "generated/aaanalysis.plot_gcfs", "generated/aaanalysis.plot_get_cdict", "generated/aaanalysis.plot_get_clist", "generated/aaanalysis.plot_get_cmap", "generated/aaanalysis.plot_legend", "generated/aaanalysis.plot_settings", "generated/plotting_prelude", "generated/tutorial1_quick_start", "generated/tutorial1_slow_start", "generated/tutorial2a_data_loader", "generated/tutorial2b_scales_loader", "index", "index/CONTRIBUTING_COPY", "index/badges", "index/citations", 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00000000..2635beb2 --- /dev/null +++ b/tests/unit/aaclust_plot_tests/test_aaclust_plot.py @@ -0,0 +1,40 @@ +""" +This is a script for testing the aa.AAclustPlot class. +""" +from hypothesis import given +import hypothesis.strategies as st +from sklearn.decomposition import PCA +import pytest + +import aaanalysis as aa + + +class TestAAclustPlot: + """Test AAclustPlot class""" + + # Positive Test Cases + @given(model_kwargs=st.dictionaries(keys=st.just("n_components"), values=st.integers(1, 5))) + def test_positive_model_kwargs(self, model_kwargs): + """Test positive cases for model_kwargs parameter.""" + aac_plot = aa.AAclustPlot(model_class=PCA, model_kwargs=model_kwargs) + assert aac_plot.model_kwargs == model_kwargs + assert aac_plot.model_class == PCA + + # Negative Test Cases + def test_invalid_key_model_kwargs(self, model_kwargs): + """Test invalid keys for model_kwargs parameter.""" + model_kwargs = {"Wrong key": 1} + with pytest.raises(ValueError): # Adjust depending on real check functions + aac_plot = aa.AAclustPlot(model_class=PCA, model_kwargs=model_kwargs) + + +class TestAAclustPlotComplex: + """Test AAclustPlot class for complex cases""" + + # Positive Test Cases + @given(model_kwargs=st.dictionaries(keys=st.just("n_components"), values=st.integers(1, 5))) + def test_complex_positive_model_kwargs(self, model_kwargs): + """Test complex positive cases involving multiple parameters.""" + aac_plot = aa.AAclustPlot(model_class=PCA, model_kwargs=model_kwargs) + + diff --git a/tests/unit/aaclust_plot_tests/test_aaclust_plot_eval.py b/tests/unit/aaclust_plot_tests/test_aaclust_plot_eval.py new file mode 100644 index 00000000..b770d468 --- /dev/null +++ b/tests/unit/aaclust_plot_tests/test_aaclust_plot_eval.py @@ -0,0 +1,154 @@ +""" +This is a script for testing the aa.AAclustPlot().eval function. +""" +import hypothesis.strategies as some +from hypothesis import given, settings +import pytest +from matplotlib import pyplot as plt +import numpy as np +import aaanalysis as aa + + +class TestAAclustPlotEval: + """Test aa.AAclustPlot().eval function""" + + # Positive tests + @settings(max_examples=10, deadline=1000) + @given(n_samples=some.integers(min_value=1, max_value=20)) + def test_data_input(self, n_samples): + """Test the 'data' parameter with valid data.""" + data = np.random.randn(n_samples, 4) + fig, axes = aa.AAclustPlot().eval(data=data) + assert isinstance(fig, plt.Figure) + assert isinstance(axes, np.ndarray) + assert len(axes) == 4 + assert isinstance(axes[0], plt.Axes) + plt.close() + + def test_names_input(self): + """Test the 'names' parameter with valid data.""" + data = np.random.randn(3, 4) + names = [f"Set {i}" for i in range(3)] + fig, axes = aa.AAclustPlot().eval(data=data, names=names) + assert isinstance(fig, plt.Figure) + assert isinstance(axes, np.ndarray) + assert len(axes) == 4 + assert isinstance(axes[0], plt.Axes) + plt.close() + + + def test_dict_xlims_input(self): + """Test the 'dict_xlims' parameter with valid data.""" + dict_xlims = {"BIC": (2, 5), "CH": (3, 5)} + data = np.random.randn(5, 4) + fig, axes = aa.AAclustPlot().eval(data=data, dict_xlims=dict_xlims) + assert isinstance(fig, plt.Figure) + assert isinstance(axes, np.ndarray) + assert len(axes) == 4 + assert isinstance(axes[0], plt.Axes) + plt.close() + + @settings(max_examples=5, deadline=1000) + @given(figsize=some.tuples(some.integers(min_value=4, max_value=20), some.integers(min_value=4, max_value=20))) + def test_figsize_input(self, figsize): + """Test the 'figsize' parameter with valid data.""" + data = np.random.randn(5, 4) + fig, axes = aa.AAclustPlot().eval(data=data, figsize=figsize) + assert fig.get_size_inches()[0] == figsize[0] + assert fig.get_size_inches()[1] == figsize[1] + plt.close() + + + # Negative test + @settings(max_examples=5) + @given(data=some.lists(some.lists(some.floats(allow_nan=True, allow_infinity=True), min_size=2, max_size=10), min_size=2, max_size=10)) + def test_data_with_nans_and_infs(self, data): + """Test the 'data' parameter with NaN and Inf.""" + with pytest.raises(ValueError): + fig, axes = aa.AAclustPlot().eval(data=data) + plt.close() + + + def test_invalid_data_shape(self): + """Test with invalid data shape.""" + data = [[0.5, 0.4], [0.3]] + with pytest.raises(ValueError): + aa.AAclustPlot().eval(data=data) + plt.close() + + def test_names_but_no_data(self): + """Test with names provided but no data.""" + data = [] + names = ["Set 1", "Set 2", "Set 3"] + with pytest.raises(ValueError): + aa.AAclustPlot().eval(data=data, names=names) + plt.close() + + @settings(max_examples=5) + @given(names=some.lists(some.text(), min_size=1, max_size=1)) + def test_insufficient_names_input(self, names): + """Test the 'names' parameter with insufficient data.""" + data = np.random.randn(5, 5) + with pytest.raises(ValueError): + fig, axes = aa.AAclustPlot().eval(data=data, names=names) + plt.close() + + def test_invalid_dict_xlims(self): + """Test the 'dict_xlims' parameter with valid data.""" + dict_xlims = {"BIC": (2, -5), "CH": (3, -5)} + data = np.random.randn(5, 4) + with pytest.raises(ValueError): + fig, axes = aa.AAclustPlot().eval(data=data, dict_xlims=dict_xlims) + plt.close() + + +class TestEvalComplex: + """Test eval function with complex scenarios""" + + def test_combination_of_params(self): + """Test with a valid use case.""" + data = [[0.5, 0.4, 5, 6], [0.3, 0.7, 1, 2], [0.9, 0.1, 5, 3]] + names = ["Set 1", "Set 2", "Set 3"] + dict_xlims = {"n_clusters": (2, 4), "BIC": (0.1, 1.0), "CH": (0.2, 0.8), "SC": (0.3, 0.9)} + figsize = (8, 7) + fig, axes = aa.AAclustPlot().eval(data=data, names=names, dict_xlims=dict_xlims, figsize=figsize) + assert isinstance(fig, plt.Figure) + assert isinstance(axes, np.ndarray) + assert len(axes) == 4 + assert isinstance(axes[0], plt.Axes) + plt.close() + fig, axes = aa.AAclustPlot().eval(data=data, dict_xlims=dict_xlims, figsize=figsize) + assert isinstance(fig, plt.Figure) + assert isinstance(axes, np.ndarray) + assert len(axes) == 4 + assert isinstance(axes[0], plt.Axes) + plt.close() + fig, axes = aa.AAclustPlot().eval(data=data, figsize=figsize) + assert isinstance(fig, plt.Figure) + assert isinstance(axes, np.ndarray) + assert len(axes) == 4 + assert isinstance(axes[0], plt.Axes) + plt.close() + fig, axes = aa.AAclustPlot().eval(data=data) + assert isinstance(fig, plt.Figure) + assert isinstance(axes, np.ndarray) + assert len(axes) == 4 + assert isinstance(axes[0], plt.Axes) + plt.close() + + # Negative tests + def test_combination_with_invalid_params(self): + """Test multiple parameters together with some invalid.""" + data = np.random.randn(5, 4) + names = ["Set 1", "Set 2", "Set 3"] # Insufficient names + with pytest.raises(ValueError): + fig, axes = aa.AAclustPlot().eval(data=data, names=names) + plt.close() + + def test_combination_with_nan_data(self): + """Test combination of parameters with NaN data.""" + data = np.array([[np.nan, 1.0], [1.0, np.nan]]) + names = ["Set 1", "Set 2"] + with pytest.raises(ValueError): + fig, axes = aa.AAclustPlot().eval(data=data, names=names) + plt.close() diff --git a/tests/unit/aaclust_tests/__pycache__/__init__.cpython-39.pyc b/tests/unit/aaclust_tests/__pycache__/__init__.cpython-39.pyc deleted file mode 100644 index d86a87d2..00000000 Binary files a/tests/unit/aaclust_tests/__pycache__/__init__.cpython-39.pyc and /dev/null differ