Struct MetricsCalculator 

pub struct MetricsCalculator {
    pub thresholds: IsoQualityConfig,
    /* private fields */
}

Engine for calculating comprehensive data quality metrics.

Fields

thresholds: IsoQualityConfig

Configurable quality thresholds and score weights.

Implementations

impl MetricsCalculator

pub fn new() -> MetricsCalculator

Create a new calculator with default quality settings.

pub fn with_thresholds(thresholds: IsoQualityConfig) -> MetricsCalculator

Create a calculator with custom thresholds

pub fn strict() -> MetricsCalculator

Create a calculator with strict thresholds (finance, healthcare)

pub fn lenient() -> MetricsCalculator

Create a calculator with lenient thresholds (exploratory, marketing)

pub fn with_row_completeness( self, summary: Option<RowCompletenessSummary>, ) -> MetricsCalculator

Supply the engine’s exact complete-record count over the full stream.

Without it the completeness dimension can only report a lower bound for complete_records_ratio, because per-column null totals do not say whether two nulls shared a record.

pub fn validate_sample_size( sample_size: usize, metric_type: &str, ) -> StatisticalValidation

Validate statistical requirements for metric calculation

pub fn calculate_comprehensive_metrics( &self, data: &HashMap<String, Vec<String>>, column_profiles: &[ColumnProfile], requested_dimensions: Option<&[QualityDimension]>, ) -> Result<QualityMetrics, DataProfilerError>

Calculate comprehensive data quality metrics from column data.

When requested_dimensions is None, all dimensions are computed (backward-compatible default). When Some(&[...]), only the listed dimensions are computed; the rest are None in the returned struct.

pub fn calculate_comprehensive_metrics_with_hints( &self, data: &HashMap<String, Vec<String>>, column_profiles: &[ColumnProfile], requested_dimensions: Option<&[QualityDimension]>, semantic_hints: &SemanticHints, row_duplicates: Option<RowDuplicateSummary>, ) -> Result<QualityMetrics, DataProfilerError>

Calculate comprehensive metrics with explicit semantic column hints and, when the engine tracked whole rows, full-stream duplicate counts that supersede the sample-based duplicate scan.

pub fn calculate_bifurcated_metrics( &self, data: &HashMap<String, Vec<String>>, column_profiles: &[ColumnProfile], requested_dimensions: Option<&[QualityDimension]>, ) -> Result<BifurcatedResult, DataProfilerError>

Calculate quality metrics with bifurcated computation for streaming.

Phase A (exact from global counters): Completeness metrics are computed from ColumnProfile stats (null_count, total_count) which are exact even for infinite streams. Key uniqueness already uses ColumnProfile.

Phase B (sampled): Consistency, Accuracy, and Timeliness are computed from the bounded reservoir sample. Duplicate rows use a full-stream row tracker when supplied; otherwise they fall back to an aligned sample.

Returns a BifurcatedResult containing the metrics plus provenance for which dimensions are exact vs sampled.

pub fn calculate_bifurcated_metrics_with_hints( &self, data: &HashMap<String, Vec<String>>, column_profiles: &[ColumnProfile], requested_dimensions: Option<&[QualityDimension]>, semantic_hints: &SemanticHints, row_duplicates: Option<RowDuplicateSummary>, ) -> Result<BifurcatedResult, DataProfilerError>

Calculate bifurcated metrics with explicit semantic column hints and, when the engine tracked whole rows, full-stream duplicate counts that supersede the sample-based duplicate scan.

Trait Implementations

impl Default for MetricsCalculator

fn default() -> MetricsCalculator

Returns the “default value” for a type.