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Z d Z d Z d Z d Z Gd d ä d â Z d S)zMIt consists of a set of custom plots, using Matplotlib and Scikit libraries.
Ú )┌Dict┌List┌TypeVar┌Any)┌metrics)┌validation_curve)┌learning_curve)┌confusion_matrix)┌
KernelDensityN┌ DataFrame┌FigurezMohsen Mesgarpourz-Copyright 2016, https://github.com/mesgarpour┌GPLz1.1zmohsen.mesgarpour@gmail.com┌Releasec @ sŠ e Z d Z e e d d g â d d d e e e e e e e e g d d d É ä â Z
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k rÎd n d âqůWt j â | | f S)aą Plot the confusion matrix.
:param predicted_scores: the predicted Scores.
:param feature_target: the target feature, which is being estimated.
:param model_labals: the target labels (default [0, 1]).
:param normalize: to normalise the labels.
:param title: the figure title.
:param cmap: the plot color.
:return: the plot object, and the data used to plot.
Z
cnf_matrix┌ precisionÚ ┌nrowsr ┌ncolsz Average Precision={0:0.2f}┌
avg_precisionz
True labelzPredicted label┌rotationÚ- ┌
interpolation┌nearestr ┌float┌axisNg @r ┌horizontalalignment┌center┌color┌white┌black) ┌dict┌np┌arange┌lenr ┌set_printoptions┌plt┌subplots┌clfr ┌format┌ylabel┌xlabel┌xticks┌yticks┌grid┌colorbar┌imshow┌astype┌sum┌newaxis┌max┌ itertools┌product┌range┌shape┌text┌ tight_layout)
r r r r r r ┌ summariesZ
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zPlots.confusion_matrixzStep-Wise Train & Testr )rB r ┌lwr c C s| t j d d d d â \ } } t j â t j | â t j d d g â t j d â t j d â t j â t j | d | d d
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d ât j d d â | S) a│ Plot a performance summary plot for the step-wise training and testing.
:param summaries: the summary statistics which will be used for plotting.
It must contain 'Train_Precision', 'Train_Recall', 'Train_ROC', 'Test_Precision', 'Test_Recall', and 'Test_ROC'
for each training and testing step.
:param title: the figure title.
:param lw: the line-width.
:return: the plot object.
r r r g g═╠╠╠╠╠?zNumber of FeatureszSummary StatisticsZStepZTrain_PrecisionrJ r% ┌r┌labelzTrain - PrecisionZ Train_Recall┌gzTrain - RecallZ Train_ROC┌bz Train - ROCZTest_Precision┌brownzTest - PrecisionZ Test_Recall┌orangez
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******zPlots.stepwise_modelzPrecision-Recall Curve)r r r rJ r c C s t â } t j | | â \ | d <| d <} t j | | â | d <t j d d d d â \ } } t j â t j | d j | d â â t j d d g â t j
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| d d d d ât j d d â | | f S)a└ Plot the precision-recall curve.
"The precision-recall plot is a model-wide measure for evaluating binary classifiers
and closely related to the ROC plot."
:param predicted_scores: the predicted Scores.
:param feature_target: the target feature, which is being estimated.
:param title: the figure title.
:param lw: the line-width.
:return: the plot object, and the data used to plot.
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zPlots.precision_recallÚ i' )
┌predicted_scores_list┌feature_target_list┌
label_list┌ marker_list┌linestyle_list┌
color_listr rJ ┌
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i d | â | | f S) aÁ Plot the precision-recall curve.
"The precision-recall plot is a model-wide measure for evaluating binary classifiers
and closely related to the ROC plot."
:param predicted_scores_list: the predicted Scores (one or multiple).
:param feature_target_list: the target feature, which is being estimated (one or multiple).
:param label_list: the line label (one or multiple).
:param marker_list: the line marker (one or multiple).
:param linestyle_list: the line style (one or multiple).
:param color_list: the line color (one or multiple).
:param title: the figure title.
:param lw: the line-width.
:param markersize: the marker size.
:param markevery: to mark every x point.
:param legend_prop: the legend proportion
:param legend_markerscale: The legend's marker scale.
:return: the plot object, and the data used to plot.
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d d | d d ât j d d â | | f S)aE Plot the Receiver Operating Characteristic (ROC)
:param predicted_scores: the predicted Scores.
:param feature_target: the target feature, which is being estimated.
:param title: the figure title.
:param lw: the line-width.
:return: the plot object, and the data used to plot.
┌fpr┌tpr┌roc_aucr r r z
AUC={0:0.2f}g g ?g═╠╠╠╠╠?zFalse Positive RatezTrue Positive Rater% rK rJ rL zROC curve (area = %0.2f)r rZ rn z--rR z lower right)r( r ┌ roc_curve┌aucr- r. r/ r r0 r] rS r2 r1 r5 rT rU )r r r rJ rB r^ rC rD rH rH rI ┌rocŃ s #
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i d | â | | f S)"a: Plot the Receiver Operating Characteristic (ROC)
:param predicted_scores_list: the predicted Scores (one or multiple).
:param feature_target_list: the target feature, which is being estimated (one or multiple).
:param label_list: the line label (one or multiple).
:param marker_list: the line marker (one or multiple).
:param linestyle_list: the line style (one or multiple).
:param color_list: the line color (one or multiple).
:param title: the figure title.
:param lw: the line-width.
:param markersize: the marker size.
:param markevery: to mark every x point.
:param legend_prop: the legend proportion
:param legend_markerscale: the legend's marker scale.
:return: the plot object, and the data used to plot.
Nrv rw rx rk rl r r r g g ?g═╠╠╠╠╠?zFalse Positive RatezTrue Positive Raterg rm rh rn ro r% rL zAUC(z
)={0:0.2f}r rZ rJ z--rR z lower rightrp rq rr )rl rl )r+ r> r( r ry rz r- r/ rs rt r r] rS r2 r1 r5 rT r0 rU )ra rb rc rd re rf r rJ rg rh ri rj rB rF r^ rC rD rH rH rI ┌ roc_multiple s8
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(."zPlots.roc_multiplezLearning CurveNgÜÖÖÖÖÖ╣?g ?Ú ) ┌ estimator┌features_indep_dfr r rS ┌cv┌n_jobs┌ train_sizesr c
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| | d | d | d | d d d d d ât j | | d d d d d d ât j | | d d d d d d ât j d d â | | f S)u┘ Plot the learning curve.
"A learning curve shows the validation and training score of an estimator for varying numbers of training
samples. It is a tool to find out how much we benefit from adding more training data and whether the estimator
suffers more from a variance error or a bias error. If both the validation score and the training score
converge to a value that is too low with increasing size of the training set, we will not benefit much
from more training data."
:param estimator: the object type that implements the ÔÇťfitÔÇŁ and ÔÇťpredictÔÇŁ methods.
An object of that type which is cloned for each validation.
:param features_indep_df: the independent features, which are inputted into the model.
:param feature_target: the target feature, which is being estimated.
:param title: the figure title.
:param ylim: the y-limit for the axis.
:param cv: the cross-validation splitting strategy (optional).
:param n_jobs: the number of jobs to run in parallel (default -1).
:param train_sizes: the size of the training samples for the learning curve.
:return: the plot object, and the data used to plot.
rÇ rü ré r" r ┌train_scores_mean┌train_scores_std┌test_scores_mean┌test_scores_stdr r NzTraining examples┌Score┌alphagÜÖÖÖÖÖ╣?r% rK rM zo-rL zTraining scorezCross-validation scorerR ┌best)r( r r) ┌mean┌stdr- r. r/ r rS r2 r1 r5 ┌ fill_betweenrT rU )
r~ r r r rS rÇ rü ré rB ┌ train_scores┌ test_scoresrC rD rH rH rI r F s0
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f S) uM Plot the validation curve
"it is sometimes helpful to plot the influence of a single hyperparameter on the training score and the
validation score to find out whether the estimator is overfitting or underfitting for some hyperparameter
values."
:param estimator: the object type that implements the ÔÇťfitÔÇŁ and ÔÇťpredictÔÇŁ methods.
An object of that type which is cloned for each validation.
:param features_indep_df: the independent features, which are inputted into the model.
:param feature_target: the target feature, which is being estimated.
:param param_name: the N=name of the parameter that will be varied.
:param param_range: the values of the parameter that will be evaluated.
:param title: the figure title.
:param ylim: the y-limit for the axis.
:param cv: the cross-validation splitting strategy (optional).
:param lw: the line-width.
:param n_jobs: the number of jobs to run in parallel (default -1).
:return: the plot object, and the data used to plot.
rĆ rÉ rÇ ┌scoring┌accuracyrü r" r râ rä rů rć r r Nz$\gamma$rç rL zTraining scorer% ┌
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C s┤ t j | â } | j â t j d d d d â \ } } t j â t j | â | d k rg t j | î t j | â t j d â t j
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d â| S) a Plot distribution, using bar plot.
:param feature: the value of the feature.
:param feature_name: the name of the feature.
:param title: the figure title.
:param ylim: the y-limit for the axis.
:return: the plot object.
r r r N┌ Probability┌bins┌normed┌ facecolor┌greenrł g Ó?) r) ┌unique┌sortr- r. r/ r rS r2 r1 r5 ┌hist)rö rĽ r rS ┌uniquesrC rD rH rH rI ┌distribution_bar└ s
%zPlots.distribution_barÚ2 )rö rĽ r ┌num_binsrS r c C sś t j d d d d â \ } } t j â t j | â | d k rN t j | î t j | â t j d â t j â t j | | d d d d d d
â| S) aG Plot distribution, using histogram.
:param feature: the value of the feature.
:param feature_name: the name of the feature.
:param title: the figure title.
:param num_bins: number of bins in the histogram.
:param ylim: the y-limit for the axis.
:return: the plot object.
r r r Nrľ rś rÖ rÜ rł g Ó?) r- r. r/ r rS r2 r1 r5 rŁ )rö rĽ r rí rS rC rD rH rH rI ┌distribution_histŢ s
"zPlots.distribution_hist┌gaussiang Ó?)rö rĽ r ┌x_values┌kernel┌ bandwidthrS r c C s\ | d k rI t j t | â t | â t | â â d d ů t j f } n | d d ů t j f } t j d d d d â \ } } t j â t j | â | d k r░ t j
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â t d | d | â j t j | â d d ů t j f â } | j | â }
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d j | â â| S) aű Plot distribution, using Kernel Density Estimation (KDE).
:param feature: the value of the feature.
:param feature_name: the name of the feature.
:param title: the figure title.
:param x_values: the grid to use for plotting (default: based on the feature range and size)
:param kernel: the kernel to use. Valid kernels are
:param bandwidth: the bandwidth of the kernel.
:param ylim: the y-limit for the axis.
:return: the plot object.
Nr r r rľ rą rŽ r ˙-rL zkernel = '{0}')r) ┌linspace┌minr; r+ r: r- r. r/ r rS r2 r1 r5 r
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score_samplesrT ┌expr0 ) rö rĽ r rĄ rą rŽ rS rC rD ┌kdeZlog_densrH rH rI ┌distribution_kde¨ s =
:;zPlots.distribution_kdeÚ r░ ) ┌__name__┌
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