DashAI Backend

This reference details all the backend components available in DashAI. For more information on how to add models, please refer to the user_guide.

Datasets

DashAI.back.dataloaders.classes.dashai_dataset.DashAIDataset(table)

DashAI dataset wrapper for Huggingface datasets with extra metadata.

Tasks

`DashAI.back.tasks.BaseTask`()	Base class for DashAI compatible tasks.
`DashAI.back.tasks.RegressionTask`()	Base class for regression tasks.
`DashAI.back.tasks.TabularClassificationTask`()	Base class for tabular classification tasks.
`DashAI.back.tasks.TextClassificationTask`()	Base class for Text Classification Task.
`DashAI.back.tasks.TranslationTask`()	Base class for translation task.

Models

`DashAI.back.models.BaseModel`()	Abstract class of all machine learning models.
`DashAI.back.models.SVC`(**kwargs)	Scikit-learn's Support Vector Machine (SVM) classifier wrapper for DashAI.
`DashAI.back.models.BagOfWordsTextClassificationModel`(...)	Text classification meta-model.
`DashAI.back.models.DecisionTreeClassifier`(...)	Scikit-learn's Decision Tree Classifier wrapper for DashAI.
`DashAI.back.models.DistilBertTransformer`([model])	Pre-trained transformer DistilBERT allowing English text classification.
`DashAI.back.models.DummyClassifier`(**kwargs)	Scikit-learn's DummyClassifier wrapper for DashAI.
`DashAI.back.models.GradientBoostingR`(**kwargs)	Scikit-learn's Ridge Regression wrapper for DashAI.
`DashAI.back.models.HistGradientBoostingClassifier`(...)	Scikit-learn's HistGradientBoostingRegressor wrapper for DashAI.
`DashAI.back.models.KNeighborsClassifier`(**kwargs)	Scikit-learn's K-Nearest Neighbors (KNN) classifier wrapper for DashAI.
`DashAI.back.models.LinearRegression`(**kwargs)	Scikit-learn's Linear Regression wrapper for DashAI.
`DashAI.back.models.LinearSVR`(**kwargs)	Scikit-learn's Linear Support Vector Regression (LinearSVR) wrapper for DashAI.
`DashAI.back.models.LogisticRegression`(**kwargs)	Scikit-learn's Logistic Regression wrapper for DashAI.
`DashAI.back.models.MLPRegression`(**kwargs)	Scikit-learn's MLP Regression wrapper for DashAI.
`DashAI.back.models.OpusMtEnESTransformer`([model])	Pre-trained transformer for english-spanish translation.
`DashAI.back.models.RandomForestClassifier`(...)	Scikit-learn's Random Forest classifier wrapper for DashAI.
`DashAI.back.models.RandomForestRegression`(...)	Scikit-learn's Ridge Regression wrapper for DashAI.
`DashAI.back.models.RidgeRegression`(**kwargs)	Scikit-learn's Ridge regression wrapper for DashAI.

Dataloaders

`DashAI.back.dataloaders.CSVDataLoader`()	Data loader for tabular data in CSV files.
`DashAI.back.dataloaders.ExcelDataLoader`()	Data loader for tabular data in Excel files.
`DashAI.back.dataloaders.JSONDataLoader`()	Data loader for tabular data in JSON files.

Metrics

`DashAI.back.metrics.BaseMetric`()	Abstract class of all metrics.
`DashAI.back.metrics.F1`()	F1 score to classification tasks.
`DashAI.back.metrics.Accuracy`()	Accuracy metric to classification tasks.
`DashAI.back.metrics.Precision`()	Precision metric to classification tasks.
`DashAI.back.metrics.Recall`()	Recall metric to classification tasks.
`DashAI.back.metrics.Bleu`()	A class for calculating BLEU scores between source and target sentences.
`DashAI.back.metrics.MAE`()	Mean Absolute Error metric for regression tasks.
`DashAI.back.metrics.RMSE`()	Root Mean Squared Error metric for regression tasks.

Optimizers

`DashAI.back.optimizers.BaseOptimizer`()	Abstract class of all hyperparameter's Optimizers.
`DashAI.back.optimizers.OptunaOptimizer`([...])
`DashAI.back.optimizers.HyperOptOptimizer`([...])

Jobs

`DashAI.back.job.ExplainerJob`(**kwargs)	ExplainerJob class to calculate explanations.
`DashAI.back.job.DatasetJob`(**kwargs)	Job for processing and uploading datasets using streaming data processing.
`DashAI.back.job.ModelJob`(**kwargs)	ModelJob class to run the model training.
`DashAI.back.job.PredictJob`(**kwargs)	PredictJob class to run the prediction.
`DashAI.back.job.ExplorerJob`(**kwargs)	ExplorerJob class to launch explorations.
`DashAI.back.job.ConverterListJob`(**kwargs)	ConverterListJob class to modify a dataset by applying a sequence of converters.

Explainers

`DashAI.back.explainability.KernelShap`(model)	Kernel SHAP is a model-agnostic explainability method for approximating SHAP values to explain the output of machine learning model by attributing contributions of each feature to the model's prediction.
`DashAI.back.explainability.PartialDependence`(model)	PartialDependence is a model-agnostic explainability method that shows the average prediction of a machine learning model for each possible value of a feature.
`DashAI.back.explainability.PermutationFeatureImportance`(model)	Permutation Feature Importance is a explanation method to asses the importance of each feature in a model by evaluating how much the model's performance decreases when the values of a specific feature are randomly shuffled.

Converters

`DashAI.back.converters.base_converter.BaseConverter`()	Base class for all converters
`DashAI.back.converters.sklearn_wrapper.SklearnWrapper`(...)	Abstract class to define generic rules for sklearn transformers
`DashAI.back.converters.hugging_face_wrapper.HuggingFaceWrapper`(...)	Abstract base wrapper for HuggingFace transformers.
`DashAI.back.converters.scikit_learn.sklearn_like_converter.SklearnLikeConverter`()	Abstract class of a sklearn transformer.
`DashAI.back.converters.scikit_learn.converter_chain.ConverterChain`(steps)	Chain of converters.

Explorers

`DashAI.back.exploration.BoxPlotExplorer`(**kwargs)	BoxPlotExplorer is an explorer that returns a box plot of selected columns of a dataset.
`DashAI.back.exploration.CorrelationMatrixExplorer`(...)	CorrelationMatrixExplorer is an explorer that returns a correlation matrix of a dataset.
`DashAI.back.exploration.CovarianceMatrixExplorer`(...)	CovarianceExplorer is an explorer that returns the covariance matrix of the dataset.
`DashAI.back.exploration.DensityHeatmapExplorer`(...)	DensityHeatmapExplorer is an explorer that returns a density heatmap of selected columns of a dataset.
`DashAI.back.exploration.DescribeExplorer`(...)	DescribeExplorer is an explorer that uses the pandas describe method to describe the dataset.
`DashAI.back.exploration.ECDFPlotExplorer`(...)	ECDFPlotExplorer is an explorer that creates an Empirical Cumulative Distribution Plot.
`DashAI.back.exploration.HistogramPlotExplorer`(...)	HistogramPlotExplorer is an explorer that returns a density heatmap of a selected column of a dataset.
`DashAI.back.exploration.MultiColumnBoxPlotExplorer`(...)	MultiColumnBoxPlotExplorer is an explorer that returns a figure with a box plot of multiple columns of a dataset in a single axis.
`DashAI.back.exploration.ParallelCategoriesExplorer`(...)	Parallel Categories Explorer is a class that generates a parallel categories plot for a given dataset.
`DashAI.back.exploration.ParallelCordinatesExplorer`(...)	Parallel Cordinates Explorer is a class that generates a parallel cordinates plot for a given dataset.
`DashAI.back.exploration.RowExplorer`(**kwargs)	RowExplorer is an explorer that takes a number of rows from the dataset to display them on tabular format.
`DashAI.back.exploration.ScatterMatrixExplorer`(...)	ScatterMatrixExplorer is an explorer that returns a scatter matrix plot of selected columns of a dataset.
`DashAI.back.exploration.ScatterPlotExplorer`(...)	ScatterPlotExplorer is an explorer that returns a scatter plot of selected columns of a dataset.
`DashAI.back.exploration.WordcloudExplorer`(...)	WordcloudExplorer is an explorer that generates a wordcloud from the concatenated strings of all selected columns in the dataset.