Metadata-Version: 2.1
Name: metric
Version: 0.5.0
Summary: Metrics for Machine Learning evaluation  Data Science Measurement
Home-page: https://github.com/arita37/
Author: Kevin Noel
Author-email: brookm291@gmail.com
License: Apache 2.0
Description: 
        Metrics for evaluating machine learning models or Data Science
        
        Include :
           All metrics from SKLEARN.
           Category based metrics.
        ########################################################################
        
        
        
        from metric.metric import *
        
        
        ##### Classification metrics	
        	
        accuracy_score(y_true,Â y_pred[,Â â€¦])	Accuracy classification score.
        auc(x,Â y)	Compute Area Under the Curve (AUC) using the trapezoidal rule
        average_precision_score(y_true,Â y_score)	Compute average precision (AP) from prediction scores
        balanced_accuracy_score(y_true,Â y_pred)	Compute the balanced accuracy
        brier_score_loss(y_true,Â y_prob[,Â â€¦])	Compute the Brier score.
        classification_report(y_true,Â y_pred)	Build a text report showing the main classification metrics
        cohen_kappa_score(y1,Â y2[,Â labels,Â â€¦])	Cohenâ€™s kappa: a statistic that measures inter-annotator agreement.
        confusion_matrix(y_true,Â y_pred[,Â â€¦])	Compute confusion matrix to evaluate the accuracy of a classification.
        dcg_score(y_true,Â y_score[,Â k,Â â€¦])	Compute Discounted Cumulative Gain.
        f1_score(y_true,Â y_pred[,Â labels,Â â€¦])	Compute the F1 score, also known as balanced F-score or F-measure
        fbeta_score(y_true,Â y_pred,Â beta[,Â â€¦])	Compute the F-beta score
        hamming_loss(y_true,Â y_pred[,Â â€¦])	Compute the average Hamming loss.
        hinge_loss(y_true,Â pred_decision[,Â â€¦])	Average hinge loss (non-regularized)
        jaccard_score(y_true,Â y_pred[,Â â€¦])	Jaccard similarity coefficient score
        log_loss(y_true,Â y_pred[,Â eps,Â â€¦])	Log loss, aka logistic loss or cross-entropy loss.
        matthews_corrcoef(y_true,Â y_pred[,Â â€¦])	Compute the Matthews correlation coefficient (MCC)
        multilabel_confusion_matrix(y_true,Â â€¦)	Compute a confusion matrix for each class or sample
        ndcg_score(y_true,Â y_score[,Â k,Â â€¦])	Compute Normalized Discounted Cumulative Gain.
        precision_recall_curve(y_true,Â â€¦)	Compute precision-recall pairs for different probability thresholds
        precision_recall_fscore_support(â€¦)	Compute precision, recall, F-measure and support for each class
        precision_score(y_true,Â y_pred[,Â â€¦])	Compute the precision
        recall_score(y_true,Â y_pred[,Â â€¦])	Compute the recall
        roc_auc_score(y_true,Â y_score[,Â â€¦])	Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
        roc_curve(y_true,Â y_score[,Â â€¦])	Compute Receiver operating characteristic (ROC)
        zero_one_loss(y_true,Â y_pred[,Â â€¦])	Zero-one classification loss.
        
        
        	
        ##### Regression metrics	
        	
        explained_variance_score(y_true,Â y_pred)	Explained variance regression score function
        max_error(y_true,Â y_pred)	max_error metric calculates the maximum residual error.
        mean_absolute_error(y_true,Â y_pred)	Mean absolute error regression loss
        mean_squared_error(y_true,Â y_pred[,Â â€¦])	Mean squared error regression loss
        mean_squared_log_error(y_true,Â y_pred)	Mean squared logarithmic error regression loss
        median_absolute_error(y_true,Â y_pred)	Median absolute error regression loss
        r2_score(y_true,Â y_pred[,Â â€¦])	R^2 (coefficient of determination) regression score function.
        mean_poisson_deviance(y_true,Â y_pred)	Mean Poisson deviance regression loss.
        mean_gamma_deviance(y_true,Â y_pred)	Mean Gamma deviance regression loss.
        mean_tweedie_deviance(y_true,Â y_pred)	Mean Tweedie deviance regression loss.
        
        
        	
        ##### Multilabel ranking metrics	
        	
        coverage_error(y_true,Â y_score[,Â â€¦])	Coverage error measure
        label_ranking_average_precision_score(â€¦)	Compute ranking-based average precision
        label_ranking_loss(y_true,Â y_score)	Compute Ranking loss measure
        
        
        	
        ##### Clustering metrics	
        		
        supervised, which uses a ground truth class values for each sample.	
        unsupervised, which does not and measures the â€˜qualityâ€™ of the model itself.	
        	
        adjusted_mutual_info_score(â€¦[,Â â€¦])	Adjusted Mutual Information between two clusterings.
        adjusted_rand_score(labels_true,Â â€¦)	Rand index adjusted for chance.
        calinski_harabasz_score(X,Â labels)	Compute the Calinski and Harabasz score.
        davies_bouldin_score(X,Â labels)	Computes the Davies-Bouldin score.
        completeness_score(labels_true,Â â€¦)	Completeness metric of a cluster labeling given a ground truth.
        cluster.contingency_matrix(â€¦[,Â â€¦])	Build a contingency matrix describing the relationship between labels.
        fowlkes_mallows_score(labels_true,Â â€¦)	Measure the similarity of two clusterings of a set of points.
        homogeneity_completeness_v_measure(â€¦)	Compute the homogeneity and completeness and V-Measure scores at once.
        homogeneity_score(labels_true,Â â€¦)	Homogeneity metric of a cluster labeling given a ground truth.
        mutual_info_score(labels_true,Â â€¦)	Mutual Information between two clusterings.
        normalized_mutual_info_score(â€¦[,Â â€¦])	Normalized Mutual Information between two clusterings.
        silhouette_score(X,Â labels[,Â â€¦])	Compute the mean Silhouette Coefficient of all samples.
        silhouette_samples(X,Â labels[,Â metric])	Compute the Silhouette Coefficient for each sample.
        v_measure_score(labels_true,Â labels_pred)	V-measure cluster labeling given a ground truth.
        	
        
        Biclustering metrics	
        	
        consensus_score(a,Â b[,Â similarity])	The similarity of two sets of biclusters.
        
        
        	
        Pairwise metrics	
        	
        pairwise.additive_chi2_kernel(X[,Â Y])	Computes the additive chi-squared kernel between observations in X and Y
        pairwise.chi2_kernel(X[,Â Y,Â gamma])	Computes the exponential chi-squared kernel X and Y.
        pairwise.cosine_similarity(X[,Â Y,Â â€¦])	Compute cosine similarity between samples in X and Y.
        pairwise.cosine_distances(X[,Â Y])	Compute cosine distance between samples in X and Y.
        pairwise.distance_metrics()	Valid metrics for pairwise_distances.
        pairwise.euclidean_distances(X[,Â Y,Â â€¦])	Considering the rows of X (and Y=X) as vectors, compute the distance matrix between each pair of vectors.
        pairwise.haversine_distances(X[,Â Y])	Compute the Haversine distance between samples in X and Y
        pairwise.kernel_metrics()	Valid metrics for pairwise_kernels
        pairwise.laplacian_kernel(X[,Â Y,Â gamma])	Compute the laplacian kernel between X and Y.
        pairwise.linear_kernel(X[,Â Y,Â â€¦])	Compute the linear kernel between X and Y.
        pairwise.manhattan_distances(X[,Â Y,Â â€¦])	Compute the L1 distances between the vectors in X and Y.
        pairwise.nan_euclidean_distances(X)	Calculate the euclidean distances in the presence of missing values.
        pairwise.pairwise_kernels(X[,Â Y,Â â€¦])	Compute the kernel between arrays X and optional array Y.
        pairwise.polynomial_kernel(X[,Â Y,Â â€¦])	Compute the polynomial kernel between X and Y.
        pairwise.rbf_kernel(X[,Â Y,Â gamma])	Compute the rbf (gaussian) kernel between X and Y.
        pairwise.sigmoid_kernel(X[,Â Y,Â â€¦])	Compute the sigmoid kernel between X and Y.
        pairwise.paired_euclidean_distances(X,Â Y)	Computes the paired euclidean distances between X and Y
        pairwise.paired_manhattan_distances(X,Â Y)	Compute the L1 distances between the vectors in X and Y.
        pairwise.paired_cosine_distances(X,Â Y)	Computes the paired cosine distances between X and Y
        pairwise.paired_distances(X,Â Y[,Â metric])	Computes the paired distances between X and Y.
        pairwise_distances(X[,Â Y,Â metric,Â â€¦])	Compute the distance matrix from a vector array X and optional Y.
        pairwise_distances_argmin(X,Â Y[,Â â€¦])	Compute minimum distances between one point and a set of points.
        pairwise_distances_argmin_min(X,Â Y)	Compute minimum distances between one point and a set of points.
        pairwise_distances_chunked(X[,Â Y,Â â€¦])	Generate a distance matrix chunk by chunk with optional reduction
        	
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Environment :: Console
Classifier: Environment :: Web Environment
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Requires-Python: >=3.6.*
Description-Content-Type: text/markdown
