crunchers.sklearn_helpers package

Submodules

crunchers.sklearn_helpers.assessment module

Provide helper functions for working with scikit-learn based objects.

crunchers.sklearn_helpers.assessment.confusion_matrix_to_pandas(cm, labels)

Return the confusion matrix as a pandas dataframe.

It is created from the confusion matrix stored in cm with rows and columns labeled with labels.

crunchers.sklearn_helpers.assessment.normalize_confusion_matrix(cm)

Return confusion matrix with values as fractions of outcomes instead of specific cases.

crunchers.sklearn_helpers.assessment.plot_confusion_matrix(cm, labels=None, cmap='Blues', title=None, norm=False, context=None, annot=True)

Plot and return the confusion matrix heatmap figure.

crunchers.sklearn_helpers.exploration module

Provide functions that help quickly explore datasets with sklearn.

class crunchers.sklearn_helpers.exploration.KMeansReport(data, n_clusters, seed=None, n_jobs=-1, palette='deep')

Bases: object

Manage KMeans Clustering and exploration of results.

cluster()

Fit each estimator.

eval_silhouette(verbose=True)

Evaluate each estimator via silhouette score.

init_estimators()

Set up and return dictionary of estimators with key = n_clusters.

plot_silhouette_results(feature_names=None, feature_space=None)

Perform plotting similar to that from sklearn link below.

http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html

class crunchers.sklearn_helpers.exploration.PCAReport(data, pca=None, n_components=None, data_labels=None, color_palette=None, label_colors=None, name=None)

Bases: object

Manage PCA and exploration of results.

filter_by_loadings(kind, column, hi_thresh, lo_thresh)

Return index of row names.

kind (str): either [‘pearsonr’,’spearmanr’] column (str): which PC column to filter hi_thresh (float): retain rows with >= hi_thresh lo_thresh (float): retain rows with <= lo_thresh

get_loading_corr(kind='pearsonr')

Return dataframe of correlation based “loadings” repective of kind.

get_pcs(rerun=True)

Fit and Transform via our local PCA object; store results in self.pcs.

n_components

Provide access to the number of PCs.

plot_pcs(components=None, label_colors=None, diag='kde', diag_kws=None, **kwargs)

Plot scatter-plots below the diagonal and density plots on the diagonal.

components (list): list of components to plot

label_colors = {‘label1’:’g’,

‘label2’:’r’, ‘label3’:’b’ }

plot_variance_accumulation(thresh=6, verbose=False)

Plot variance accumulation over PCs.

plot_variance_decay(thresh=6, verbose=False)

Plot variance decay over PCs.

crunchers.sklearn_helpers.misc module

Collect misc sklearn helpers here.

crunchers.sklearn_helpers.misc.repandasify(array, y_names, X_names=None)

Convert numpy array into pandas dataframe using provided index and column names.

Module contents