Explore tests

View and learn more about the tests available in the ValidMind Developer Framework, including code examples and usage of key functions.

In this notebook, we’ll dive deep into the utilities available for viewing and understanding the various tests that ValidMind provides through the tests module. Whether you’re just getting started or looking for advanced tips, you’ll find clear examples and explanations to assist you every step of the way.

Before we go into the details, let’s import the describe_test and list_tests functions from the validmind.tests module. These are the two functions that can be used to easily filter through tests and view details for individual tests.

from validmind.tests import (
    describe_test,
    list_tests,
    list_task_types,
    list_tags,
    list_tasks_and_tags,
)

Contents

• Listing All Tests
• Understanding Tags and Task Types
• Searching for Specific Tests using tags and task_types
• Delving into Test Details with describe_test
• Next steps
  • Discover more learning resources

Listing All Tests

The list_tests function provides a convenient way to retrieve all available tests in the validmind.tests module. When invoked without any parameters, it returns a pandas DataFrame containing detailed information about each test.

list_tests()

ID	Name	Description	Required Inputs	Params
validmind.prompt_validation.Bias	Bias	Evaluates bias in a Large Language Model based on the order and distribution of exemplars in a prompt....	['model.prompt']	{'min_threshold': 7}
validmind.prompt_validation.Clarity	Clarity	Evaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines....	['model.prompt']	{'min_threshold': 7}
validmind.prompt_validation.Specificity	Specificity	Evaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity,...	['model.prompt']	{'min_threshold': 7}
validmind.prompt_validation.Robustness	Robustness	Assesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts....	['model']	{'num_tests': 10}
validmind.prompt_validation.NegativeInstruction	Negative Instruction	Evaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts....	['model.prompt']	{'min_threshold': 7}
validmind.prompt_validation.Conciseness	Conciseness	Analyzes and grades the conciseness of prompts provided to a Large Language Model....	['model.prompt']	{'min_threshold': 7}
validmind.prompt_validation.Delimitation	Delimitation	Evaluates the proper use of delimiters in prompts provided to Large Language Models....	['model.prompt']	{'min_threshold': 7}
validmind.model_validation.BertScore	Bert Score	Evaluates the quality of machine-generated text using BERTScore metrics and visualizes the results through histograms...	['dataset', 'model']	{}
validmind.model_validation.RegardScore	Regard Score	Computes and visualizes the regard score for each text instance, assessing sentiment and potential biases....	['dataset', 'model']	{}
validmind.model_validation.BleuScore	Bleu Score	Evaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms...	['dataset', 'model']	{}
validmind.model_validation.RegressionResidualsPlot	Regression Residuals Plot	Evaluates regression model performance using residual distribution and actual vs. predicted plots....	['model', 'dataset']	{'bin_size': 0.1}
validmind.model_validation.FeaturesAUC	Features AUC	Evaluates the discriminatory power of each individual feature within a binary classification model by calculating the Area Under the Curve (AUC) for each feature separately....	['model', 'dataset']	{'fontsize': 12, 'figure_height': 500}
validmind.model_validation.ContextualRecall	Contextual Recall	Evaluates a Natural Language Generation model's ability to generate contextually relevant and factually correct text, visualizing the results through histograms and bar charts, alongside compiling a comprehensive table of descriptive statistics for contextual recall scores....	['dataset', 'model']	{}
validmind.model_validation.MeteorScore	Meteor Score	Computes and visualizes the METEOR score for each text generation instance, assessing translation quality....	['dataset', 'model']	{}
validmind.model_validation.RougeScore	Rouge Score	Evaluates the quality of machine-generated text using ROUGE metrics and visualizes the results through histograms...	['dataset', 'model']	{'metric': 'rouge-1'}
validmind.model_validation.ModelMetadata	Model Metadata	Extracts and summarizes critical metadata from a machine learning model instance for comprehensive analysis....	['model']	{}
validmind.model_validation.ClusterSizeDistribution	Cluster Size Distribution	Compares and visualizes the distribution of cluster sizes in model predictions and actual data for assessing...	['model', 'dataset']	{}
validmind.model_validation.TokenDisparity	Token Disparity	Evaluates the token disparity between reference and generated texts, visualizing the results through histograms...	['dataset', 'model']	{}
validmind.model_validation.ToxicityScore	Toxicity Score	Computes and visualizes the toxicity score for input text, true text, and predicted text, assessing content quality and potential risk....	['dataset', 'model']	{}
validmind.model_validation.embeddings.CosineSimilarityComparison	Cosine Similarity Comparison	Computes pairwise cosine similarities between model embeddings and visualizes the results through bar charts,...	['dataset', 'models']	{}
validmind.model_validation.embeddings.EmbeddingsVisualization2D	Embeddings Visualization2 D	Visualizes 2D representation of text embeddings generated by a model using t-SNE technique....	['model', 'dataset']	{'cluster_column': None, 'perplexity': 30}
validmind.model_validation.embeddings.StabilityAnalysisRandomNoise	Stability Analysis Random Noise	Evaluate robustness of embeddings models to random noise introduced by using...	['model', 'dataset']	{'mean_similarity_threshold': 0.7, 'probability': 0.02}
validmind.model_validation.embeddings.TSNEComponentsPairwisePlots	TSNE Components Pairwise Plots	Plots individual scatter plots for pairwise combinations of t-SNE components of embeddings....	['dataset', 'model']	{'n_components': 2, 'perplexity': 30, 'title': 't-SNE'}
validmind.model_validation.embeddings.CosineSimilarityDistribution	Cosine Similarity Distribution	Assesses the similarity between predicted text embeddings from a model using a Cosine Similarity distribution...	['model', 'dataset']	{}
validmind.model_validation.embeddings.PCAComponentsPairwisePlots	PCA Components Pairwise Plots	Generates scatter plots for pairwise combinations of principal component analysis (PCA) components of model embeddings....	['dataset', 'model']	{'n_components': 3}
validmind.model_validation.embeddings.CosineSimilarityHeatmap	Cosine Similarity Heatmap	Generates an interactive heatmap to visualize the cosine similarities among embeddings derived from a given model....	['dataset', 'model']	{'title': 'Cosine Similarity Matrix', 'color': 'Cosine Similarity', 'xaxis_title': 'Index', 'yaxis_title': 'Index', 'color_scale': 'Blues'}
validmind.model_validation.embeddings.StabilityAnalysisTranslation	Stability Analysis Translation	Evaluate robustness of embeddings models to noise introduced by translating...	['model', 'dataset']	{'source_lang': 'en', 'target_lang': 'fr', 'mean_similarity_threshold': 0.7}
validmind.model_validation.embeddings.EuclideanDistanceComparison	Euclidean Distance Comparison	Computes pairwise Euclidean distances between model embeddings and visualizes the results through bar charts,...	['dataset', 'models']	{}
validmind.model_validation.embeddings.ClusterDistribution	Cluster Distribution	Assesses the distribution of text embeddings across clusters produced by a model using KMeans clustering....	['model', 'dataset']	{'num_clusters': 5}
validmind.model_validation.embeddings.EuclideanDistanceHeatmap	Euclidean Distance Heatmap	Generates an interactive heatmap to visualize the Euclidean distances among embeddings derived from a given model....	['dataset', 'model']	{'title': 'Euclidean Distance Matrix', 'color': 'Euclidean Distance', 'xaxis_title': 'Index', 'yaxis_title': 'Index', 'color_scale': 'Blues'}
validmind.model_validation.embeddings.StabilityAnalysis	Stability Analysis	Base class for embeddings stability analysis tests	['model', 'dataset']	{'mean_similarity_threshold': 0.7}
validmind.model_validation.embeddings.StabilityAnalysisKeyword	Stability Analysis Keyword	Evaluate robustness of embeddings models to keyword swaps on the test dataset...	['model', 'dataset']	{'keyword_dict': None, 'mean_similarity_threshold': 0.7}
validmind.model_validation.embeddings.StabilityAnalysisSynonyms	Stability Analysis Synonyms	Evaluates the stability of text embeddings models when words in test data are replaced by their synonyms randomly....	['model', 'dataset']	{'probability': 0.02, 'mean_similarity_threshold': 0.7}
validmind.model_validation.embeddings.DescriptiveAnalytics	Descriptive Analytics	Evaluates statistical properties of text embeddings in an ML model via mean, median, and standard deviation...	['model', 'dataset']	{}
validmind.model_validation.ragas.ContextEntityRecall	Context Entity Recall	Evaluates the context entity recall for dataset entries and visualizes the results....	['dataset']	{'contexts_column': 'contexts', 'ground_truth_column': 'ground_truth'}
validmind.model_validation.ragas.Faithfulness	Faithfulness	Evaluates the faithfulness of the generated answers with respect to retrieved contexts....	['dataset']	{'answer_column': 'answer', 'contexts_column': 'contexts'}
validmind.model_validation.ragas.AspectCritique	Aspect Critique	Evaluates generations against the following aspects: harmfulness, maliciousness,...	['dataset']	{'question_column': 'question', 'answer_column': 'answer', 'contexts_column': 'contexts', 'aspects': ['coherence', 'conciseness', 'correctness', 'harmfulness', 'maliciousness'], 'additional_aspects': None}
validmind.model_validation.ragas.AnswerSimilarity	Answer Similarity	Calculates the semantic similarity between generated answers and ground truths...	['dataset']	{'answer_column': 'answer', 'ground_truth_column': 'ground_truth'}
validmind.model_validation.ragas.AnswerCorrectness	Answer Correctness	Evaluates the correctness of answers in a dataset with respect to the provided ground...	['dataset']	{'question_column': 'question', 'answer_column': 'answer', 'ground_truth_column': 'ground_truth'}
validmind.model_validation.ragas.ContextRecall	Context Recall	Context recall measures the extent to which the retrieved context aligns with the...	['dataset']	{'question_column': 'question', 'contexts_column': 'contexts', 'ground_truth_column': 'ground_truth'}
validmind.model_validation.ragas.ContextRelevancy	Context Relevancy	Evaluates the context relevancy metric for entries in a dataset and visualizes the...	['dataset']	{'question_column': 'question', 'contexts_column': 'contexts'}
validmind.model_validation.ragas.ContextPrecision	Context Precision	Context Precision is a metric that evaluates whether all of the ground-truth...	['dataset']	{'question_column': 'question', 'contexts_column': 'contexts', 'ground_truth_column': 'ground_truth'}
validmind.model_validation.ragas.AnswerRelevance	Answer Relevance	Assesses how pertinent the generated answer is to the given prompt....	['dataset']	{'question_column': 'question', 'contexts_column': 'contexts', 'answer_column': 'answer'}
validmind.model_validation.sklearn.RegressionModelsPerformanceComparison	Regression Models Performance Comparison	Compares and evaluates the performance of multiple regression models using five different metrics: MAE, MSE, RMSE,...	['dataset', 'models']	{}
validmind.model_validation.sklearn.AdjustedMutualInformation	Adjusted Mutual Information	Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...	['model', 'datasets']	{}
validmind.model_validation.sklearn.SilhouettePlot	Silhouette Plot	Calculates and visualizes Silhouette Score, assessing degree of data point suitability to its cluster in ML models....	['model', 'dataset']	{}
validmind.model_validation.sklearn.RobustnessDiagnosis	Robustness Diagnosis	Evaluates the robustness of a machine learning model by injecting Gaussian noise to input data and measuring...	['model', 'datasets']	{'features_columns': None, 'scaling_factor_std_dev_list': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5], 'accuracy_decay_threshold': 4}
validmind.model_validation.sklearn.AdjustedRandIndex	Adjusted Rand Index	Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...	['model', 'datasets']	{}
validmind.model_validation.sklearn.SHAPGlobalImportance	SHAP Global Importance	Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....	['model', 'dataset']	{'kernel_explainer_samples': 10, 'tree_or_linear_explainer_samples': 200}
validmind.model_validation.sklearn.ConfusionMatrix	Confusion Matrix	Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...	['model', 'dataset']	{}
validmind.model_validation.sklearn.HomogeneityScore	Homogeneity Score	Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...	['model', 'datasets']	{}
validmind.model_validation.sklearn.CompletenessScore	Completeness Score	Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....	['model', 'datasets']	{}
validmind.model_validation.sklearn.OverfitDiagnosis	Overfit Diagnosis	Detects and visualizes overfit regions in an ML model by comparing performance on training and test datasets....	['model', 'datasets']	{'features_columns': None, 'cut_off_percentage': 4}
validmind.model_validation.sklearn.ClusterPerformanceMetrics	Cluster Performance Metrics	Evaluates the performance of clustering machine learning models using multiple established metrics....	['model', 'datasets']	{}
validmind.model_validation.sklearn.PermutationFeatureImportance	Permutation Feature Importance	Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...	['model', 'dataset']	{'fontsize': None, 'figure_height': 1000}
validmind.model_validation.sklearn.FowlkesMallowsScore	Fowlkes Mallows Score	Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...	['model', 'datasets']	{}
validmind.model_validation.sklearn.MinimumROCAUCScore	Minimum ROCAUC Score	Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....	['model', 'dataset']	{'min_threshold': 0.5}
validmind.model_validation.sklearn.ClusterCosineSimilarity	Cluster Cosine Similarity	Measures the intra-cluster similarity of a clustering model using cosine similarity....	['model', 'dataset']	{}
validmind.model_validation.sklearn.PrecisionRecallCurve	Precision Recall Curve	Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....	['model', 'dataset']	{}
validmind.model_validation.sklearn.ClassifierPerformance	Classifier Performance	Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...	['model', 'dataset']	{}
validmind.model_validation.sklearn.VMeasure	V Measure	Evaluates homogeneity and completeness of a clustering model using the V Measure Score....	['model', 'datasets']	{}
validmind.model_validation.sklearn.MinimumF1Score	Minimum F1 Score	Evaluates if the model's F1 score on the validation set meets a predefined minimum threshold....	['model', 'dataset']	{'min_threshold': 0.5}
validmind.model_validation.sklearn.ROCCurve	ROC Curve	Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...	['model', 'dataset']	{}
validmind.model_validation.sklearn.RegressionR2Square	Regression R2 Square	**Purpose**: The purpose of the RegressionR2Square Metric test is to measure the overall goodness-of-fit of a...	['model', 'datasets']	{}
validmind.model_validation.sklearn.RegressionErrors	Regression Errors	**Purpose**: This metric is used to measure the performance of a regression model. It gauges the model's accuracy...	['model', 'datasets']	{}
validmind.model_validation.sklearn.ClusterPerformance	Cluster Performance	Evaluates and compares a clustering model's performance on training and testing datasets using multiple defined...	['model', 'datasets']	{}
validmind.model_validation.sklearn.TrainingTestDegradation	Training Test Degradation	Tests if model performance degradation between training and test datasets exceeds a predefined threshold....	['model', 'datasets']	{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}
validmind.model_validation.sklearn.HyperParametersTuning	Hyper Parameters Tuning	Exerts exhaustive grid search to identify optimal hyperparameters for the model, improving performance....	['model', 'dataset']	{'param_grid': None, 'scoring': None}
validmind.model_validation.sklearn.KMeansClustersOptimization	K Means Clusters Optimization	Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....	['model', 'dataset']	{'n_clusters': None}
validmind.model_validation.sklearn.ModelsPerformanceComparison	Models Performance Comparison	Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...	['dataset', 'models']	{}
validmind.model_validation.sklearn.WeakspotsDiagnosis	Weakspots Diagnosis	Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...	['model', 'datasets']	{'features_columns': None, 'thresholds': {'accuracy': 0.75, 'precision': 0.5, 'recall': 0.5, 'f1': 0.7}}
validmind.model_validation.sklearn.PopulationStabilityIndex	Population Stability Index	Evaluates the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...	['model', 'datasets']	{'num_bins': 10, 'mode': 'fixed'}
validmind.model_validation.sklearn.MinimumAccuracy	Minimum Accuracy	Checks if the model's prediction accuracy meets or surpasses a specified threshold....	['model', 'dataset']	{'min_threshold': 0.7}
validmind.model_validation.statsmodels.RegressionModelsCoeffs	Regression Models Coeffs	Compares feature importance by evaluating and contrasting coefficients of different regression models....	['models']	{}
validmind.model_validation.statsmodels.BoxPierce	Box Pierce	Detects autocorrelation in time-series data through the Box-Pierce test to validate model performance....	['dataset']	{}
validmind.model_validation.statsmodels.RegressionCoeffsPlot	Regression Coeffs Plot	Visualizes regression coefficients with 95% confidence intervals to assess predictor variables' impact on response...	['models']	{}
validmind.model_validation.statsmodels.RegressionModelSensitivityPlot	Regression Model Sensitivity Plot	Tests the sensitivity of a regression model to variations in independent variables by applying shocks and...	['models', 'datasets']	{'transformation': None, 'shocks': [0.1]}
validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels	Regression Model Forecast Plot Levels	Compares and visualizes forecasted and actual values of regression models on both raw and transformed datasets....	['models', 'datasets']	{'transformation': None}
validmind.model_validation.statsmodels.ScorecardHistogram	Scorecard Histogram	Creates histograms of credit scores, from both default and non-default instances, generated by a credit-risk model....	['datasets']	{'title': 'Histogram of Scores', 'score_column': 'score'}
validmind.model_validation.statsmodels.LJungBox	L Jung Box	Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature....	['dataset']	{}
validmind.model_validation.statsmodels.JarqueBera	Jarque Bera	Assesses normality of dataset features in an ML model using the Jarque-Bera test....	['dataset']	{}
validmind.model_validation.statsmodels.KolmogorovSmirnov	Kolmogorov Smirnov	Executes a feature-wise Kolmogorov-Smirnov test to evaluate alignment with normal distribution in datasets....	['dataset']	{'dist': 'norm'}
validmind.model_validation.statsmodels.ShapiroWilk	Shapiro Wilk	Evaluates feature-wise normality of training data using the Shapiro-Wilk test....	['dataset']	{}
validmind.model_validation.statsmodels.CumulativePredictionProbabilities	Cumulative Prediction Probabilities	Visualizes cumulative probabilities of positive and negative classes for both training and testing in logistic...	['model', 'datasets']	{'title': 'Cumulative Probabilities'}
validmind.model_validation.statsmodels.RegressionFeatureSignificance	Regression Feature Significance	Assesses and visualizes the statistical significance of features in a set of regression models....	['models']	{'fontsize': 10, 'p_threshold': 0.05}
validmind.model_validation.statsmodels.RegressionModelSummary	Regression Model Summary	Evaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE....	['model', 'dataset']	{}
validmind.model_validation.statsmodels.Lilliefors	Lilliefors	Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....	['dataset']	{}
validmind.model_validation.statsmodels.RunsTest	Runs Test	Executes Runs Test on ML model to detect non-random patterns in output data sequence....	['dataset']	{}
validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance	Regression Permutation Feature Importance	Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...	['model', 'dataset']	{'fontsize': 12, 'figure_height': 500}
validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram	Prediction Probabilities Histogram	Generates and visualizes histograms of the Probability of Default predictions for both positive and negative...	['model', 'datasets']	{'title': 'Histogram of Predictive Probabilities'}
validmind.model_validation.statsmodels.AutoARIMA	Auto ARIMA	Evaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria....	['dataset']	{}
validmind.model_validation.statsmodels.GINITable	GINI Table	Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....	['model', 'datasets']	{}
validmind.model_validation.statsmodels.RegressionModelForecastPlot	Regression Model Forecast Plot	Generates plots to visually compare the forecasted outcomes of one or more regression models against actual...	['models', 'datasets']	{'start_date': None, 'end_date': None}
validmind.model_validation.statsmodels.DurbinWatsonTest	Durbin Watson Test	Assesses autocorrelation in time series data features using the Durbin-Watson statistic....	['dataset']	{}
validmind.data_validation.MissingValuesRisk	Missing Values Risk	Assesses and quantifies the risk related to missing values in a dataset used for training an ML model....	['dataset']	{}
validmind.data_validation.IQROutliersTable	IQR Outliers Table	Determines and summarizes outliers in numerical features using Interquartile Range method....	['dataset']	{'features': None, 'threshold': 1.5}
validmind.data_validation.BivariateFeaturesBarPlots	Bivariate Features Bar Plots	Generates visual bar plots to analyze the relationship between paired features within categorical data in the model....	['dataset']	{'features_pairs': None}
validmind.data_validation.Skewness	Skewness	Evaluates the skewness of numerical data in a machine learning model and checks if it falls below a set maximum...	['dataset']	{'max_threshold': 1}
validmind.data_validation.Duplicates	Duplicates	Tests dataset for duplicate entries, ensuring model reliability via data quality verification....	['dataset']	{'min_threshold': 1}
validmind.data_validation.MissingValuesBarPlot	Missing Values Bar Plot	Creates a bar plot showcasing the percentage of missing values in each column of the dataset with risk...	['dataset']	{'threshold': 80, 'fig_height': 600}
validmind.data_validation.DatasetDescription	Dataset Description	Provides comprehensive analysis and statistical summaries of each field in a machine learning model's dataset....	['dataset']	{}
validmind.data_validation.ZivotAndrewsArch	Zivot Andrews Arch	Evaluates the order of integration and stationarity of time series data using Zivot-Andrews unit root test....	['dataset']	{}
validmind.data_validation.ScatterPlot	Scatter Plot	Creates a scatter plot matrix to visually analyze feature relationships, patterns, and outliers in a dataset....	['dataset']	{}
validmind.data_validation.TimeSeriesOutliers	Time Series Outliers	Identifies and visualizes outliers in time-series data using z-score method....	['dataset']	{'zscore_threshold': 3}
validmind.data_validation.TabularCategoricalBarPlots	Tabular Categorical Bar Plots	Generates and visualizes bar plots for each category in categorical features to evaluate dataset's composition....	['dataset']	{}
validmind.data_validation.AutoStationarity	Auto Stationarity	Automates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame....	['dataset']	{'max_order': 5, 'threshold': 0.05}
validmind.data_validation.DescriptiveStatistics	Descriptive Statistics	Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...	['dataset']	{}
validmind.data_validation.ANOVAOneWayTable	ANOVA One Way Table	Applies one-way ANOVA (Analysis of Variance) to identify statistically significant numerical features in the...	['dataset']	{'features': None, 'p_threshold': 0.05}
validmind.data_validation.TargetRateBarPlots	Target Rate Bar Plots	Generates bar plots visualizing the default rates of categorical features for a classification machine learning...	['dataset']	{'default_column': None, 'columns': None}
validmind.data_validation.PearsonCorrelationMatrix	Pearson Correlation Matrix	Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....	['dataset']	{}
validmind.data_validation.FeatureTargetCorrelationPlot	Feature Target Correlation Plot	Visualizes the correlation between input features and model's target output in a color-coded horizontal bar plot....	['dataset']	{'features': None, 'fig_height': 600}
validmind.data_validation.TabularNumericalHistograms	Tabular Numerical Histograms	Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...	['dataset']	{}
validmind.data_validation.IsolationForestOutliers	Isolation Forest Outliers	Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....	['dataset']	{'random_state': 0, 'contamination': 0.1, 'features_columns': None}
validmind.data_validation.ChiSquaredFeaturesTable	Chi Squared Features Table	Executes Chi-Squared test for each categorical feature against a target column to assess significant association....	['dataset']	{'cat_features': None, 'p_threshold': 0.05}
validmind.data_validation.HighCardinality	High Cardinality	Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....	['dataset']	{'num_threshold': 100, 'percent_threshold': 0.1, 'threshold_type': 'percent'}
validmind.data_validation.MissingValues	Missing Values	Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....	['dataset']	{'min_threshold': 1}
validmind.data_validation.PhillipsPerronArch	Phillips Perron Arch	Executes Phillips-Perron test to assess the stationarity of time series data in each ML model feature....	['dataset']	{}
validmind.data_validation.RollingStatsPlot	Rolling Stats Plot	This test evaluates the stationarity of time series data by plotting its rolling mean and standard deviation....	['dataset']	{'window_size': 12}
validmind.data_validation.TabularDescriptionTables	Tabular Description Tables	Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....	['dataset']	{}
validmind.data_validation.AutoMA	Auto MA	Automatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on...	['dataset']	{'max_ma_order': 3}
validmind.data_validation.UniqueRows	Unique Rows	Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....	['dataset']	{'min_percent_threshold': 1}
validmind.data_validation.TooManyZeroValues	Too Many Zero Values	Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...	['dataset']	{'max_percent_threshold': 0.03}
validmind.data_validation.HighPearsonCorrelation	High Pearson Correlation	Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....	['dataset']	{'max_threshold': 0.3}
validmind.data_validation.ACFandPACFPlot	AC Fand PACF Plot	Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to...	['dataset']	{}
validmind.data_validation.BivariateHistograms	Bivariate Histograms	Generates bivariate histograms for paired features, aiding in visual inspection of categorical variables'...	['dataset']	{'features_pairs': None, 'target_filter': None}
validmind.data_validation.WOEBinTable	WOE Bin Table	Calculates and assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature in a ML model....	['dataset']	{'breaks_adj': None}
validmind.data_validation.HeatmapFeatureCorrelations	Heatmap Feature Correlations	Creates a heatmap to visually represent correlation patterns between pairs of numerical features in a dataset....	['dataset']	{'declutter': None, 'fontsize': None, 'num_features': None}
validmind.data_validation.TimeSeriesFrequency	Time Series Frequency	Evaluates consistency of time series data frequency and generates a frequency plot....	['dataset']	{}
validmind.data_validation.DatasetSplit	Dataset Split	Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...	['datasets']	{}
validmind.data_validation.SpreadPlot	Spread Plot	Visualizes the spread relationship between pairs of time-series variables in a dataset, thereby aiding in...	['dataset']	{}
validmind.data_validation.TimeSeriesLinePlot	Time Series Line Plot	Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time....	['dataset']	{}
validmind.data_validation.KPSS	KPSS	Executes KPSS unit root test to validate stationarity of time-series data in machine learning model....	['dataset']	{}
validmind.data_validation.AutoSeasonality	Auto Seasonality	Automatically identifies and quantifies optimal seasonality in time series data to improve forecasting model...	['dataset']	{'min_period': 1, 'max_period': 4}
validmind.data_validation.BivariateScatterPlots	Bivariate Scatter Plots	Generates bivariate scatterplots to visually inspect relationships between pairs of predictor variables in machine...	['dataset']	{'selected_columns': None}
validmind.data_validation.EngleGrangerCoint	Engle Granger Coint	Validates co-integration in pairs of time series data using the Engle-Granger test and classifies them as...	['dataset']	{'threshold': 0.05}
validmind.data_validation.TimeSeriesMissingValues	Time Series Missing Values	Validates time-series data quality by confirming the count of missing values is below a certain threshold....	['dataset']	{'min_threshold': 1}
validmind.data_validation.TimeSeriesHistogram	Time Series Histogram	Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines....	['dataset']	{}
validmind.data_validation.LaggedCorrelationHeatmap	Lagged Correlation Heatmap	Assesses and visualizes correlation between target variable and lagged independent variables in a time-series...	['dataset']	{}
validmind.data_validation.SeasonalDecompose	Seasonal Decompose	Decomposes dataset features into observed, trend, seasonal, and residual components to identify patterns and...	['dataset']	{'seasonal_model': 'additive'}
validmind.data_validation.WOEBinPlots	WOE Bin Plots	Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...	['dataset']	{'breaks_adj': None, 'fig_height': 600, 'fig_width': 500}
validmind.data_validation.ClassImbalance	Class Imbalance	Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....	['dataset']	{'min_percent_threshold': 10}
validmind.data_validation.IQROutliersBarPlot	IQR Outliers Bar Plot	Visualizes outlier distribution across percentiles in numerical data using Interquartile Range (IQR) method....	['dataset']	{'threshold': 1.5, 'num_features': None, 'fig_width': 800}
validmind.data_validation.DFGLSArch	DFGLS Arch	Executes Dickey-Fuller GLS metric to determine order of integration and check stationarity in time series data....	['dataset']	{}
validmind.data_validation.AutoAR	Auto AR	Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria....	['dataset']	{'max_ar_order': 3}
validmind.data_validation.TabularDateTimeHistograms	Tabular Date Time Histograms	Generates histograms to provide graphical insight into the distribution of time intervals in model's datetime data....	['dataset']	{}
validmind.data_validation.ADF	ADF	Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test....	['dataset']	{}
validmind.data_validation.nlp.Toxicity	Toxicity	Analyzes the toxicity of text data within a dataset using a pre-trained toxicity model....	['dataset']	{}
validmind.data_validation.nlp.PolarityAndSubjectivity	Polarity And Subjectivity	Analyzes the polarity and subjectivity of text data within a dataset....	['dataset']	{}
validmind.data_validation.nlp.Punctuations	Punctuations	Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset....	['dataset']	{}
validmind.data_validation.nlp.Sentiment	Sentiment	Analyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool....	['dataset']	{}
validmind.data_validation.nlp.CommonWords	Common Words	Identifies and visualizes the 40 most frequent non-stopwords in a specified text column within a dataset....	['dataset']	{}
validmind.data_validation.nlp.Hashtags	Hashtags	Assesses hashtag frequency in a text column, highlighting usage trends and potential dataset bias or spam....	['dataset']	{'top_hashtags': 25}
validmind.data_validation.nlp.LanguageDetection	Language Detection	Detects the language of each text entry in a dataset and visualizes the distribution of languages...	['dataset']	{}
validmind.data_validation.nlp.Mentions	Mentions	Calculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis....	['dataset']	{'top_mentions': 25}
validmind.data_validation.nlp.TextDescription	Text Description	Performs comprehensive textual analysis on a dataset using NLTK, evaluating various parameters and generating...	['dataset']	{'unwanted_tokens': {'``', "''", 's', ' ', 'ms', 'mrs', 'dr', "s'", 'dollar', "'s", 'us', 'mr'}, 'num_top_words': 3, 'lang': 'english'}
validmind.data_validation.nlp.StopWords	Stop Words	Evaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold....	['dataset']	{'min_percent_threshold': 0.5, 'num_words': 25}

Understanding Tags and Task Types

Effectively using ValidMind’s tests involves a deep understanding of its ‘tags’ and ‘task types’. Here’s a breakdown:

• Task Types: Represent the kind of modeling task associated with a test. For instance:

  • classification: Works with Classification Models and Datasets
  • regression: Works with Regression Models and Datasets
  • text classification: Works with Text Classification Models and Datasets
  • text summarization: Works with Text Summarization Models and Datasets

• Tags: Free-form descriptors providing more details about the test, what data and models the test is compatible with and what category the test falls into etc. Some examples include:

  • llm: Tests that work with Large Language Models
  • nlp: Tests relevant for natural language processing.
  • binary_classification: Tests for binary classification tasks.
  • forecasting: Tests for forecasting and time-series analysis.
  • tabular_data: Tests for tabular data like CSVs and Excel spreadsheets.

You can use the functions list_task_types() and list_tags() to view all the task_types and tags used for classifying all the tests available in the developer framework:

list_task_types()

['text_summarization',
 'regression',
 'text_generation',
 'text_qa',
 'clustering',
 'feature_extraction',
 'classification',
 'nlp',
 'text_classification']

list_tags()

['embeddings',
 'dimensionality_reduction',
 'frequency_analysis',
 'forecasting',
 'correlation',
 'ragas',
 'statsmodels',
 'model_diagnosis',
 'multiclass_classification',
 'tabular_data',
 'zero_shot',
 'senstivity_analysis',
 'rag_performance',
 'model_metadata',
 'binary_classification',
 'AUC',
 'model_performance',
 'model_comparison',
 'logistic_regression',
 'anomaly_detection',
 'time_series_data',
 'data_distribution',
 'text_embeddings',
 'categorical_data',
 'numerical_data',
 'visualization',
 'credit_risk',
 'statistical_test',
 'risk_analysis',
 'retrieval_performance',
 'nlp',
 'few_shot',
 'kmeans',
 'unit_root_test',
 'data_quality',
 'feature_importance',
 'model_selection',
 'text_data',
 'model_interpretation',
 'llm',
 'seasonality',
 'qualitative',
 'stationarity',
 'sklearn',
 'data_validation']

If you want to see which tags correspond to which task type, you can use the function list_tasks_and_tags():

list_tasks_and_tags()

Task Type	Tags
text_classification	frequency_analysis, ragas, model_diagnosis, multiclass_classification, tabular_data, zero_shot, model_metadata, binary_classification, model_performance, model_comparison, time_series_data, visualization, retrieval_performance, nlp, few_shot, feature_importance, text_data, llm, sklearn
text_summarization	embeddings, rag_performance, nlp, time_series_data, dimensionality_reduction, frequency_analysis, text_data, visualization, llm, qualitative, model_metadata, ragas, retrieval_performance, few_shot, tabular_data, zero_shot
regression	forecasting, correlation, statsmodels, tabular_data, senstivity_analysis, model_metadata, model_performance, model_comparison, data_distribution, time_series_data, categorical_data, numerical_data, visualization, statistical_test, risk_analysis, unit_root_test, data_quality, feature_importance, model_selection, text_data, model_interpretation, seasonality, stationarity, sklearn
classification	correlation, statsmodels, model_diagnosis, multiclass_classification, tabular_data, model_metadata, logistic_regression, model_performance, AUC, model_comparison, binary_classification, anomaly_detection, data_distribution, time_series_data, categorical_data, numerical_data, visualization, credit_risk, statistical_test, risk_analysis, data_quality, feature_importance, text_data, sklearn
clustering	sklearn, model_performance, kmeans
text_qa	embeddings, rag_performance, dimensionality_reduction, visualization, llm, qualitative, ragas, retrieval_performance
text_generation	embeddings, rag_performance, dimensionality_reduction, visualization, llm, qualitative, ragas, retrieval_performance
feature_extraction	llm, text_data, text_embeddings, visualization
nlp	nlp, text_data, data_validation

Searching for Specific Tests using tags and task_types

While listing all tests is valuable, there are times when you need to narrow down your search. The list_tests function offers filter, task, and tags parameters to assist in this.

If you’re targeting a specific test or tests that match a particular task type, the filter parameter comes in handy. For example, to list tests that are compatible with ‘sklearn’ models:

list_tests(filter="sklearn")

ID	Name	Description	Required Inputs	Params
validmind.model_validation.sklearn.ClusterPerformanceMetrics	Cluster Performance Metrics	Evaluates the performance of clustering machine learning models using multiple established metrics....	['model', 'datasets']	{}
validmind.model_validation.sklearn.VMeasure	V Measure	Evaluates homogeneity and completeness of a clustering model using the V Measure Score....	['model', 'datasets']	{}
validmind.model_validation.sklearn.ClusterPerformance	Cluster Performance	Evaluates and compares a clustering model's performance on training and testing datasets using multiple defined...	['model', 'datasets']	{}
validmind.model_validation.sklearn.MinimumAccuracy	Minimum Accuracy	Checks if the model's prediction accuracy meets or surpasses a specified threshold....	['model', 'dataset']	{'min_threshold': 0.7}
validmind.model_validation.sklearn.WeakspotsDiagnosis	Weakspots Diagnosis	Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...	['model', 'datasets']	{'features_columns': None, 'thresholds': {'accuracy': 0.75, 'precision': 0.5, 'recall': 0.5, 'f1': 0.7}}
validmind.model_validation.sklearn.OverfitDiagnosis	Overfit Diagnosis	Detects and visualizes overfit regions in an ML model by comparing performance on training and test datasets....	['model', 'datasets']	{'features_columns': None, 'cut_off_percentage': 4}
validmind.model_validation.sklearn.SHAPGlobalImportance	SHAP Global Importance	Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....	['model', 'dataset']	{'kernel_explainer_samples': 10, 'tree_or_linear_explainer_samples': 200}
validmind.model_validation.sklearn.PopulationStabilityIndex	Population Stability Index	Evaluates the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...	['model', 'datasets']	{'num_bins': 10, 'mode': 'fixed'}
validmind.model_validation.sklearn.SilhouettePlot	Silhouette Plot	Calculates and visualizes Silhouette Score, assessing degree of data point suitability to its cluster in ML models....	['model', 'dataset']	{}
validmind.model_validation.sklearn.KMeansClustersOptimization	K Means Clusters Optimization	Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....	['model', 'dataset']	{'n_clusters': None}
validmind.model_validation.sklearn.RobustnessDiagnosis	Robustness Diagnosis	Evaluates the robustness of a machine learning model by injecting Gaussian noise to input data and measuring...	['model', 'datasets']	{'features_columns': None, 'scaling_factor_std_dev_list': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5], 'accuracy_decay_threshold': 4}
validmind.model_validation.sklearn.HyperParametersTuning	Hyper Parameters Tuning	Exerts exhaustive grid search to identify optimal hyperparameters for the model, improving performance....	['model', 'dataset']	{'param_grid': None, 'scoring': None}
validmind.model_validation.sklearn.PrecisionRecallCurve	Precision Recall Curve	Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....	['model', 'dataset']	{}
validmind.model_validation.sklearn.ClusterCosineSimilarity	Cluster Cosine Similarity	Measures the intra-cluster similarity of a clustering model using cosine similarity....	['model', 'dataset']	{}
validmind.model_validation.sklearn.CompletenessScore	Completeness Score	Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....	['model', 'datasets']	{}
validmind.model_validation.sklearn.MinimumROCAUCScore	Minimum ROCAUC Score	Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....	['model', 'dataset']	{'min_threshold': 0.5}
validmind.model_validation.sklearn.RegressionErrors	Regression Errors	**Purpose**: This metric is used to measure the performance of a regression model. It gauges the model's accuracy...	['model', 'datasets']	{}
validmind.model_validation.ClusterSizeDistribution	Cluster Size Distribution	Compares and visualizes the distribution of cluster sizes in model predictions and actual data for assessing...	['model', 'dataset']	{}
validmind.model_validation.sklearn.ClassifierPerformance	Classifier Performance	Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...	['model', 'dataset']	{}
validmind.model_validation.sklearn.PermutationFeatureImportance	Permutation Feature Importance	Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...	['model', 'dataset']	{'fontsize': None, 'figure_height': 1000}
validmind.model_validation.sklearn.ModelsPerformanceComparison	Models Performance Comparison	Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...	['dataset', 'models']	{}
validmind.model_validation.sklearn.AdjustedMutualInformation	Adjusted Mutual Information	Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...	['model', 'datasets']	{}
validmind.model_validation.sklearn.ConfusionMatrix	Confusion Matrix	Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...	['model', 'dataset']	{}
validmind.model_validation.sklearn.ROCCurve	ROC Curve	Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...	['model', 'dataset']	{}
validmind.model_validation.sklearn.HomogeneityScore	Homogeneity Score	Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...	['model', 'datasets']	{}
validmind.model_validation.sklearn.RegressionModelsPerformanceComparison	Regression Models Performance Comparison	Compares and evaluates the performance of multiple regression models using five different metrics: MAE, MSE, RMSE,...	['dataset', 'models']	{}
validmind.model_validation.sklearn.FowlkesMallowsScore	Fowlkes Mallows Score	Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...	['model', 'datasets']	{}
validmind.model_validation.sklearn.MinimumF1Score	Minimum F1 Score	Evaluates if the model's F1 score on the validation set meets a predefined minimum threshold....	['model', 'dataset']	{'min_threshold': 0.5}
validmind.model_validation.sklearn.TrainingTestDegradation	Training Test Degradation	Tests if model performance degradation between training and test datasets exceeds a predefined threshold....	['model', 'datasets']	{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}
validmind.model_validation.sklearn.AdjustedRandIndex	Adjusted Rand Index	Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...	['model', 'datasets']	{}
validmind.model_validation.sklearn.RegressionR2Square	Regression R2 Square	**Purpose**: The purpose of the RegressionR2Square Metric test is to measure the overall goodness-of-fit of a...	['model', 'datasets']	{}

The task parameter is designed for pinpointing tests that align with a specific task type. For instance, to find tests tailored for ‘classification’ tasks:

list_tests(task="classification")

ID	Name	Description	Required Inputs	Params
validmind.model_validation.FeaturesAUC	Features AUC	Evaluates the discriminatory power of each individual feature within a binary classification model by calculating the Area Under the Curve (AUC) for each feature separately....	['model', 'dataset']	{'fontsize': 12, 'figure_height': 500}
validmind.model_validation.ModelMetadata	Model Metadata	Extracts and summarizes critical metadata from a machine learning model instance for comprehensive analysis....	['model']	{}
validmind.model_validation.sklearn.RobustnessDiagnosis	Robustness Diagnosis	Evaluates the robustness of a machine learning model by injecting Gaussian noise to input data and measuring...	['model', 'datasets']	{'features_columns': None, 'scaling_factor_std_dev_list': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5], 'accuracy_decay_threshold': 4}
validmind.model_validation.sklearn.SHAPGlobalImportance	SHAP Global Importance	Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....	['model', 'dataset']	{'kernel_explainer_samples': 10, 'tree_or_linear_explainer_samples': 200}
validmind.model_validation.sklearn.ConfusionMatrix	Confusion Matrix	Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...	['model', 'dataset']	{}
validmind.model_validation.sklearn.OverfitDiagnosis	Overfit Diagnosis	Detects and visualizes overfit regions in an ML model by comparing performance on training and test datasets....	['model', 'datasets']	{'features_columns': None, 'cut_off_percentage': 4}
validmind.model_validation.sklearn.PermutationFeatureImportance	Permutation Feature Importance	Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...	['model', 'dataset']	{'fontsize': None, 'figure_height': 1000}
validmind.model_validation.sklearn.MinimumROCAUCScore	Minimum ROCAUC Score	Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....	['model', 'dataset']	{'min_threshold': 0.5}
validmind.model_validation.sklearn.PrecisionRecallCurve	Precision Recall Curve	Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....	['model', 'dataset']	{}
validmind.model_validation.sklearn.ClassifierPerformance	Classifier Performance	Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...	['model', 'dataset']	{}
validmind.model_validation.sklearn.MinimumF1Score	Minimum F1 Score	Evaluates if the model's F1 score on the validation set meets a predefined minimum threshold....	['model', 'dataset']	{'min_threshold': 0.5}
validmind.model_validation.sklearn.ROCCurve	ROC Curve	Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...	['model', 'dataset']	{}
validmind.model_validation.sklearn.TrainingTestDegradation	Training Test Degradation	Tests if model performance degradation between training and test datasets exceeds a predefined threshold....	['model', 'datasets']	{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}
validmind.model_validation.sklearn.HyperParametersTuning	Hyper Parameters Tuning	Exerts exhaustive grid search to identify optimal hyperparameters for the model, improving performance....	['model', 'dataset']	{'param_grid': None, 'scoring': None}
validmind.model_validation.sklearn.ModelsPerformanceComparison	Models Performance Comparison	Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...	['dataset', 'models']	{}
validmind.model_validation.sklearn.WeakspotsDiagnosis	Weakspots Diagnosis	Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...	['model', 'datasets']	{'features_columns': None, 'thresholds': {'accuracy': 0.75, 'precision': 0.5, 'recall': 0.5, 'f1': 0.7}}
validmind.model_validation.sklearn.PopulationStabilityIndex	Population Stability Index	Evaluates the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...	['model', 'datasets']	{'num_bins': 10, 'mode': 'fixed'}
validmind.model_validation.sklearn.MinimumAccuracy	Minimum Accuracy	Checks if the model's prediction accuracy meets or surpasses a specified threshold....	['model', 'dataset']	{'min_threshold': 0.7}
validmind.model_validation.statsmodels.ScorecardHistogram	Scorecard Histogram	Creates histograms of credit scores, from both default and non-default instances, generated by a credit-risk model....	['datasets']	{'title': 'Histogram of Scores', 'score_column': 'score'}
validmind.model_validation.statsmodels.JarqueBera	Jarque Bera	Assesses normality of dataset features in an ML model using the Jarque-Bera test....	['dataset']	{}
validmind.model_validation.statsmodels.KolmogorovSmirnov	Kolmogorov Smirnov	Executes a feature-wise Kolmogorov-Smirnov test to evaluate alignment with normal distribution in datasets....	['dataset']	{'dist': 'norm'}
validmind.model_validation.statsmodels.ShapiroWilk	Shapiro Wilk	Evaluates feature-wise normality of training data using the Shapiro-Wilk test....	['dataset']	{}
validmind.model_validation.statsmodels.CumulativePredictionProbabilities	Cumulative Prediction Probabilities	Visualizes cumulative probabilities of positive and negative classes for both training and testing in logistic...	['model', 'datasets']	{'title': 'Cumulative Probabilities'}
validmind.model_validation.statsmodels.Lilliefors	Lilliefors	Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....	['dataset']	{}
validmind.model_validation.statsmodels.RunsTest	Runs Test	Executes Runs Test on ML model to detect non-random patterns in output data sequence....	['dataset']	{}
validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram	Prediction Probabilities Histogram	Generates and visualizes histograms of the Probability of Default predictions for both positive and negative...	['model', 'datasets']	{'title': 'Histogram of Predictive Probabilities'}
validmind.model_validation.statsmodels.GINITable	GINI Table	Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....	['model', 'datasets']	{}
validmind.data_validation.MissingValuesRisk	Missing Values Risk	Assesses and quantifies the risk related to missing values in a dataset used for training an ML model....	['dataset']	{}
validmind.data_validation.IQROutliersTable	IQR Outliers Table	Determines and summarizes outliers in numerical features using Interquartile Range method....	['dataset']	{'features': None, 'threshold': 1.5}
validmind.data_validation.BivariateFeaturesBarPlots	Bivariate Features Bar Plots	Generates visual bar plots to analyze the relationship between paired features within categorical data in the model....	['dataset']	{'features_pairs': None}
validmind.data_validation.Skewness	Skewness	Evaluates the skewness of numerical data in a machine learning model and checks if it falls below a set maximum...	['dataset']	{'max_threshold': 1}
validmind.data_validation.Duplicates	Duplicates	Tests dataset for duplicate entries, ensuring model reliability via data quality verification....	['dataset']	{'min_threshold': 1}
validmind.data_validation.MissingValuesBarPlot	Missing Values Bar Plot	Creates a bar plot showcasing the percentage of missing values in each column of the dataset with risk...	['dataset']	{'threshold': 80, 'fig_height': 600}
validmind.data_validation.DatasetDescription	Dataset Description	Provides comprehensive analysis and statistical summaries of each field in a machine learning model's dataset....	['dataset']	{}
validmind.data_validation.ScatterPlot	Scatter Plot	Creates a scatter plot matrix to visually analyze feature relationships, patterns, and outliers in a dataset....	['dataset']	{}
validmind.data_validation.TabularCategoricalBarPlots	Tabular Categorical Bar Plots	Generates and visualizes bar plots for each category in categorical features to evaluate dataset's composition....	['dataset']	{}
validmind.data_validation.DescriptiveStatistics	Descriptive Statistics	Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...	['dataset']	{}
validmind.data_validation.ANOVAOneWayTable	ANOVA One Way Table	Applies one-way ANOVA (Analysis of Variance) to identify statistically significant numerical features in the...	['dataset']	{'features': None, 'p_threshold': 0.05}
validmind.data_validation.TargetRateBarPlots	Target Rate Bar Plots	Generates bar plots visualizing the default rates of categorical features for a classification machine learning...	['dataset']	{'default_column': None, 'columns': None}
validmind.data_validation.PearsonCorrelationMatrix	Pearson Correlation Matrix	Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....	['dataset']	{}
validmind.data_validation.FeatureTargetCorrelationPlot	Feature Target Correlation Plot	Visualizes the correlation between input features and model's target output in a color-coded horizontal bar plot....	['dataset']	{'features': None, 'fig_height': 600}
validmind.data_validation.TabularNumericalHistograms	Tabular Numerical Histograms	Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...	['dataset']	{}
validmind.data_validation.IsolationForestOutliers	Isolation Forest Outliers	Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....	['dataset']	{'random_state': 0, 'contamination': 0.1, 'features_columns': None}
validmind.data_validation.ChiSquaredFeaturesTable	Chi Squared Features Table	Executes Chi-Squared test for each categorical feature against a target column to assess significant association....	['dataset']	{'cat_features': None, 'p_threshold': 0.05}
validmind.data_validation.HighCardinality	High Cardinality	Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....	['dataset']	{'num_threshold': 100, 'percent_threshold': 0.1, 'threshold_type': 'percent'}
validmind.data_validation.MissingValues	Missing Values	Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....	['dataset']	{'min_threshold': 1}
validmind.data_validation.TabularDescriptionTables	Tabular Description Tables	Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....	['dataset']	{}
validmind.data_validation.UniqueRows	Unique Rows	Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....	['dataset']	{'min_percent_threshold': 1}
validmind.data_validation.TooManyZeroValues	Too Many Zero Values	Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...	['dataset']	{'max_percent_threshold': 0.03}
validmind.data_validation.HighPearsonCorrelation	High Pearson Correlation	Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....	['dataset']	{'max_threshold': 0.3}
validmind.data_validation.BivariateHistograms	Bivariate Histograms	Generates bivariate histograms for paired features, aiding in visual inspection of categorical variables'...	['dataset']	{'features_pairs': None, 'target_filter': None}
validmind.data_validation.WOEBinTable	WOE Bin Table	Calculates and assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature in a ML model....	['dataset']	{'breaks_adj': None}
validmind.data_validation.HeatmapFeatureCorrelations	Heatmap Feature Correlations	Creates a heatmap to visually represent correlation patterns between pairs of numerical features in a dataset....	['dataset']	{'declutter': None, 'fontsize': None, 'num_features': None}
validmind.data_validation.DatasetSplit	Dataset Split	Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...	['datasets']	{}
validmind.data_validation.BivariateScatterPlots	Bivariate Scatter Plots	Generates bivariate scatterplots to visually inspect relationships between pairs of predictor variables in machine...	['dataset']	{'selected_columns': None}
validmind.data_validation.WOEBinPlots	WOE Bin Plots	Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...	['dataset']	{'breaks_adj': None, 'fig_height': 600, 'fig_width': 500}
validmind.data_validation.ClassImbalance	Class Imbalance	Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....	['dataset']	{'min_percent_threshold': 10}
validmind.data_validation.IQROutliersBarPlot	IQR Outliers Bar Plot	Visualizes outlier distribution across percentiles in numerical data using Interquartile Range (IQR) method....	['dataset']	{'threshold': 1.5, 'num_features': None, 'fig_width': 800}
validmind.data_validation.TabularDateTimeHistograms	Tabular Date Time Histograms	Generates histograms to provide graphical insight into the distribution of time intervals in model's datetime data....	['dataset']	{}

The tags parameter facilitates searching tests by their tags. For instance, if you’re interested in only tests associated designed for model_performance that produce a plot (denoted by the visualization tag)

list_tests(tags=["model_performance", "visualization"])

ID	Name	Description	Required Inputs	Params
validmind.model_validation.sklearn.ConfusionMatrix	Confusion Matrix	Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...	['model', 'dataset']	{}
validmind.model_validation.sklearn.PrecisionRecallCurve	Precision Recall Curve	Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....	['model', 'dataset']	{}
validmind.model_validation.sklearn.ROCCurve	ROC Curve	Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...	['model', 'dataset']	{}
validmind.model_validation.sklearn.TrainingTestDegradation	Training Test Degradation	Tests if model performance degradation between training and test datasets exceeds a predefined threshold....	['model', 'datasets']	{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}
validmind.model_validation.statsmodels.GINITable	GINI Table	Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....	['model', 'datasets']	{}

The above parameters can be combined to create complex queries. For instance, to find tests that are compatible with ‘sklearn’ models, designed for ‘classification’ tasks, and produce a plot:

list_tests(
    tags=["model_performance", "visualization", "sklearn"], task="classification"
)

ID	Name	Description	Required Inputs	Params
validmind.model_validation.sklearn.ConfusionMatrix	Confusion Matrix	Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...	['model', 'dataset']	{}
validmind.model_validation.sklearn.PrecisionRecallCurve	Precision Recall Curve	Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....	['model', 'dataset']	{}
validmind.model_validation.sklearn.ROCCurve	ROC Curve	Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...	['model', 'dataset']	{}
validmind.model_validation.sklearn.TrainingTestDegradation	Training Test Degradation	Tests if model performance degradation between training and test datasets exceeds a predefined threshold....	['model', 'datasets']	{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}

Programmatic Use

To work with a specific set of tests programmatically, you can store the results in a variable. For instance, let’s list all tests that are designed for Text Summarization tests and store them in text_summarization_tests for further use.

text_summarization_tests = list_tests(task="text_summarization", pretty=False)
text_summarization_tests

['validmind.prompt_validation.Bias',
 'validmind.prompt_validation.Clarity',
 'validmind.prompt_validation.Specificity',
 'validmind.prompt_validation.Robustness',
 'validmind.prompt_validation.NegativeInstruction',
 'validmind.prompt_validation.Conciseness',
 'validmind.prompt_validation.Delimitation',
 'validmind.model_validation.BertScore',
 'validmind.model_validation.RegardScore',
 'validmind.model_validation.BleuScore',
 'validmind.model_validation.ContextualRecall',
 'validmind.model_validation.MeteorScore',
 'validmind.model_validation.RougeScore',
 'validmind.model_validation.ModelMetadata',
 'validmind.model_validation.TokenDisparity',
 'validmind.model_validation.ToxicityScore',
 'validmind.model_validation.embeddings.CosineSimilarityComparison',
 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots',
 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots',
 'validmind.model_validation.embeddings.CosineSimilarityHeatmap',
 'validmind.model_validation.embeddings.EuclideanDistanceComparison',
 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap',
 'validmind.model_validation.ragas.ContextEntityRecall',
 'validmind.model_validation.ragas.Faithfulness',
 'validmind.model_validation.ragas.AspectCritique',
 'validmind.model_validation.ragas.AnswerSimilarity',
 'validmind.model_validation.ragas.AnswerCorrectness',
 'validmind.model_validation.ragas.ContextRecall',
 'validmind.model_validation.ragas.ContextRelevancy',
 'validmind.model_validation.ragas.ContextPrecision',
 'validmind.model_validation.ragas.AnswerRelevance',
 'validmind.data_validation.DatasetDescription',
 'validmind.data_validation.DatasetSplit',
 'validmind.data_validation.nlp.Punctuations',
 'validmind.data_validation.nlp.CommonWords',
 'validmind.data_validation.nlp.Hashtags',
 'validmind.data_validation.nlp.LanguageDetection',
 'validmind.data_validation.nlp.Mentions',
 'validmind.data_validation.nlp.TextDescription',
 'validmind.data_validation.nlp.StopWords']

Delving into Test Details with describe_test

After identifying a set of potential tests, you might want to explore the specifics of an individual test. The describe_test function provides a deep dive into the details of a test. It reveals the test name, description, ID, test type, and required inputs. Below, we showcase how to describe a test using its ID:

describe_test("validmind.model_validation.sklearn.OverfitDiagnosis")

Next steps

By harnessing the functionalities presented in this guide, you should be able to easily list and filter through all of ValidMind’s available tests and find those you are interested in running against your model and/or dataset. The next step is to take the IDs of the tests you’d like to run and either create a test suite for reuse or just run them directly to try them out. See the other notebooks for a tutorial on how to do both.

Discover more learning resources

We offer many interactive notebooks to help you document models:

• Run tests & test suites
• Code samples

Or, visit our documentation to learn more about ValidMind.