Package: metric
Metrics for evaluating machine learning models or Data ScienceInclude :
All metrics from SKLEARN.
Category based metrics.
########################################################################from metric.metric import *


##### Classification metrics	

accuracy_score(y_true, y_pred,...)	Accuracy classification score.
auc(x,y)	Compute Area Under the Curve (AUC) using the trapezoidal rule
average_precision_score(y_true,y_score)	Compute average precision (AP) from prediction scores
balanced_accuracy_score(y_true,y_pred)	Compute the balanced accuracy
brier_score_loss(y_true,y_prob,...)	Compute the Brier score.
classification_report(y_true,y_pred)	Build a text report showing the main classification metrics
cohen_kappa_score(y1,y2,...)	Cohenâ€™s kappa: a statistic that measures inter-annotator agreement.
confusion_matrix(y_true,y_pred,...)	Compute confusion matrix to evaluate the accuracy of a classification.
dcg_score(y_true,y_score ,Â k,Â ...  )	Compute Discounted Cumulative Gain.
f1_score(y_true,y_pred,...)	Compute the F1 score, also known as balanced F-score or F-measure
fbeta_score(y_true,y_pred,Â beta,...)	Compute the F-beta score
hamming_loss(y_true,y_pred,...)	Compute the average Hamming loss.
hinge_loss(y_true,Â pred_decision,...)	Average hinge loss (non-regularized)
jaccard_score(y_true,y_pred,...)	Jaccard similarity coefficient score
log_loss(y_true,y_pred ,Â eps,Â ...  )	Log loss, aka logistic loss or cross-entropy loss.
matthews_corrcoef(y_true,y_pred,...)	Compute the Matthews correlation coefficient (MCC)
multilabel_confusion_matrix(y_true,Â ...)	Compute a confusion matrix for each class or sample
ndcg_score(y_true,y_score ,Â k,Â ...  )	Compute Normalized Discounted Cumulative Gain.
precision_recall_curve(y_true,Â ...)	Compute precision-recall pairs for different probability thresholds
precision_recall_fscore_support(...)	Compute precision, recall, F-measure and support for each class
precision_score(y_true,y_pred,...)	Compute the precision
recall_score(y_true,y_pred,...)	Compute the recall
roc_auc_score(y_true,y_score,...)	Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
roc_curve(y_true,y_score,...)	Compute Receiver operating characteristic (ROC)
zero_one_loss(y_true,y_pred,...)	Zero-one classification loss.



##### Regression metrics	

explained_variance_score(y_true,y_pred)	Explained variance regression score function
max_error(y_true,y_pred)	max_error metric calculates the maximum residual error.
mean_absolute_error(y_true,y_pred)	Mean absolute error regression loss
mean_squared_error(y_true,y_pred,...)	Mean squared error regression loss
mean_squared_log_error(y_true,y_pred)	Mean squared logarithmic error regression loss
median_absolute_error(y_true,y_pred)	Median absolute error regression loss
r2_score(y_true,y_pred,...)	R^2 (coefficient of determination) regression score function.
mean_poisson_deviance(y_true,y_pred)	Mean Poisson deviance regression loss.
mean_gamma_deviance(y_true,y_pred)	Mean Gamma deviance regression loss.
mean_tweedie_deviance(y_true,y_pred)	Mean Tweedie deviance regression loss.



##### Multilabel ranking metrics	

coverage_error(y_true,y_score,...)	Coverage error measure
label_ranking_average_precision_score(...)	Compute ranking-based average precision
label_ranking_loss(y_true,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(...,...)	Adjusted Mutual Information between two clusterings.
adjusted_rand_score(labels_true,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 ...)	Completeness metric of a cluster labeling given a ground truth.
cluster.contingency_matrix(...,...)	Build a contingency matrix describing the relationship between labels.
fowlkes_mallows_score(labels_true,Â ...)	Measure the similarity of two clusterings of a set of points.
homogeneity_completeness_v_measure(...)	Compute the homogeneity and completeness and V-Measure scores at once.
homogeneity_score(labels_true,Â ...)	Homogeneity metric of a cluster labeling given a ground truth.
mutual_info_score(labels_true,Â ...)	Mutual Information between two clusterings.
normalized_mutual_info_score(...,...)	Normalized Mutual Information between two clusterings.
silhouette_score(X,Â labels,...)	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 ,y  )	Computes the additive chi-squared kernel between observations in X and Y
pairwise.chi2_kernel(X ,y,Â gamma  )	Computes the exponential chi-squared kernel X and Y.
pairwise.cosine_similarity(X ,y,Â ...  )	Compute cosine similarity between samples in X and Y.
pairwise.cosine_distances(X ,y  )	Compute cosine distance between samples in X and Y.
pairwise.distance_metrics()	Valid metrics for pairwise_distances.
pairwise.euclidean_distances(X ,y,Â ...  )	Considering the rows of X (and Y=X) as vectors, compute the distance matrix between each pair of vectors.
pairwise.haversine_distances(X ,y  )	Compute the Haversine distance between samples in X and Y
pairwise.kernel_metrics()	Valid metrics for pairwise_kernels
pairwise.laplacian_kernel(X ,y,Â gamma  )	Compute the laplacian kernel between X and Y.
pairwise.linear_kernel(X ,y,Â ...  )	Compute the linear kernel between X and Y.
pairwise.manhattan_distances(X ,y,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 ,y,Â ...  )	Compute the kernel between arrays X and optional array Y.
pairwise.polynomial_kernel(X ,y,Â ...  )	Compute the polynomial kernel between X and Y.
pairwise.rbf_kernel(X ,y,Â gamma  )	Compute the rbf (gaussian) kernel between X and Y.
pairwise.sigmoid_kernel(X ,y,Â ...  )	Compute the sigmoid kernel between X and Y.
pairwise.paired_euclidean_distances(X,y)	Computes the paired euclidean distances between X and Y
pairwise.paired_manhattan_distances(X,y)	Compute the L1 distances between the vectors in X and Y.
pairwise.paired_cosine_distances(X,y)	Computes the paired cosine distances between X and Y
pairwise.paired_distances(X,y ,Â metric  )	Computes the paired distances between X and Y.
pairwise_distances(X ,y,Â metric,Â ...  )	Compute the distance matrix from a vector array X and optional Y.
pairwise_distances_argmin(X,y,...)	Compute minimum distances between one point and a set of points.
pairwise_distances_argmin_min(X,y)	Compute minimum distances between one point and a set of points.
pairwise_distances_chunked(X ,y,Â ...  )	Generate a distance matrix chunk by chunk with optional reduction