dmntk-recognizer 0.0.11

/*
 * DMNTK - Decision Model and Notation Toolkit
 *
 * Decision table recognizer
 *
 * Copyright 2018-2021 Dariusz Depta Engos Software <dariusz.depta@engos.software>
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

use crate::errors::*;
use crate::recognizer::Recognizer;
use dmntk_model::dmntk_feel_parser::dmntk_feel::dmntk_common::Result;
use dmntk_model::model::{AnnotationEntry, DecisionRule, DecisionTable, HitPolicy, InputClause, InputEntry, OutputClause, OutputEntry, RuleAnnotationClause};

struct Size {
  input_clauses_count: usize,
  input_values_count: usize,
  output_clauses_count: usize,
  output_components_count: usize,
  output_values_count: usize,
  annotation_clauses_count: usize,
  rule_count: usize,
}

fn size_err(details: &str) -> Result<Size> {
  Err(invalid_size(details))
}

/// Validates the sizes of the individual components of the decision table in relation to each other.
fn validate_size(recognizer: &Recognizer) -> Result<Size> {
  // decision table must have minimum one input clauses
  let input_clauses_count = recognizer.input_clause_count;
  if input_clauses_count == 0 {
    return size_err("decision table must have minimum one input clause");
  }
  // number of input expressions must be equal to the number of input clauses
  let input_expression_count = recognizer.input_expressions.len();
  if input_expression_count != input_clauses_count {
    return size_err(&format!(
      "number of input expressions ({}) must be equal to the number of input clauses ({})",
      input_expression_count, input_clauses_count
    ));
  }
  // when input values are present, then the number of input values must be equal to the number input expressions
  let input_values_count = recognizer.input_values.len();
  if input_values_count > 0 && input_values_count != input_clauses_count {
    return size_err(&format!(
      "number of input values ({}) must be equal to the number of input clauses ({})",
      input_values_count, input_clauses_count
    ));
  }
  // decision table must have minimum one output clause
  let output_clauses_count = recognizer.output_clause_count;
  if output_clauses_count == 0 {
    return size_err("decision table must have minimum one output clause");
  }
  // the number of the output components must be equal to the number of output clauses when more than one output clause is present
  let output_components_count = recognizer.output_components.len();
  if output_clauses_count > 1 {
    if output_components_count != output_clauses_count {
      return size_err(&format!(
        "number of output components ({}) must be equal to the number of output clauses ({})",
        output_components_count, output_clauses_count
      ));
    }
  } else if output_components_count != 0 {
    return size_err("number of output components must be zero");
  }
  // when output values are present, then the number of output values must be equal to the number of output clauses
  let output_values_count = recognizer.output_values.len();
  if output_values_count > 0 && output_values_count != output_clauses_count {
    return size_err(&format!(
      "number of output values ({}) must be equal to the number of output clauses ({})",
      output_values_count, output_clauses_count
    ));
  }
  // decision table must contain minimum one rule
  let rule_count = recognizer.rule_count;
  if rule_count == 0 {
    return size_err("decision table must contain minimum one rule");
  }
  // number of rows of input entries must be equal to the number of rules
  let input_entries_row_count = recognizer.input_entries.len();
  if input_entries_row_count != rule_count {
    return size_err(&format!(
      "number of input entries ({}) must be equal to the number of rules ({})",
      input_entries_row_count, rule_count
    ));
  }
  // number of input entries in each row must be equal to the number of input clauses
  for (row_index, row) in recognizer.input_entries.iter().enumerate() {
    if row.len() != input_clauses_count {
      return size_err(&format!(
        "number of input entries ({}) must be equal to the number of input clauses ({}) in row {}",
        row.len(),
        input_clauses_count,
        row_index
      ));
    }
  }
  // number of rows of output entries must be equal to the number of rules
  let output_entries_row_count = recognizer.output_entries.len();
  if output_entries_row_count != rule_count {
    return size_err(&format!(
      "number of output entries ({}) must be equal to the number of rules ({})",
      output_entries_row_count, rule_count
    ));
  }
  // number of output entries in each row must be equal to the number of output clauses
  for (row_index, row) in recognizer.output_entries.iter().enumerate() {
    if row.len() != output_clauses_count {
      return size_err(&format!(
        "number of output entries ({}) must be equal to the number of output clauses ({}) in row {}",
        row.len(),
        output_clauses_count,
        row_index
      ));
    }
  }
  // decision table may contain some annotations
  let annotation_clauses_count = recognizer.annotation_clause_count;
  if annotation_clauses_count > 0 {
    // number of rows of annotation entries must be equal to the number of rules
    let annotation_entries_row_count = recognizer.annotation_entries.len();
    if annotation_entries_row_count != rule_count {
      return size_err(&format!(
        "number of annotation entries ({}) must be equal to the number of rules ({})",
        annotation_entries_row_count, rule_count
      ));
    }
    // number of annotation entries in each row must be equal to the number of annotation clauses
    for (row_index, row) in recognizer.annotation_entries.iter().enumerate() {
      if row.len() != annotation_clauses_count {
        return size_err(&format!(
          "number of annotation entries ({}) must be equal to the number of annotation clauses ({}) in row {}",
          row.len(),
          annotation_clauses_count,
          row_index
        ));
      }
    }
  }
  Ok(Size {
    input_clauses_count,
    input_values_count,
    output_clauses_count,
    output_components_count,
    output_values_count,
    annotation_clauses_count,
    rule_count,
  })
}

/// Builds a decision table from text.
pub fn build(text: &str) -> Result<DecisionTable> {
  // recognize the components of decision table
  let recognizer = Recognizer::recognize(text)?;
  // validate the sizes of the individual components of the decision table
  let size = validate_size(&recognizer)?;

  let information_item_name = recognizer.information_item_name.clone();
  let hit_policy = recognizer.hit_policy;
  let aggregation;
  if let HitPolicy::Collect(built_in_aggregator) = hit_policy {
    aggregation = Some(built_in_aggregator)
  } else {
    aggregation = None
  }
  let preferred_orientation = recognizer.orientation;
  let output_label = recognizer.output_label.clone();

  let mut inputs = vec![];
  for i in 0..size.input_clauses_count {
    inputs.push(InputClause {
      input_expression: recognizer.input_expressions[i].clone(),
      input_values: if size.input_values_count > 0 {
        Some(recognizer.input_values[i].clone())
      } else {
        None
      },
    });
  }

  let mut outputs = vec![];
  for i in 0..size.output_clauses_count {
    outputs.push(OutputClause {
      type_ref: None,
      name: if size.output_components_count > 0 {
        Some(recognizer.output_components[i].clone())
      } else {
        None
      },
      output_values: if size.output_values_count > 0 {
        Some(recognizer.output_values[i].clone())
      } else {
        None
      },
      default_output_entry: None,
    });
  }

  let mut annotations = vec![];
  for i in 0..recognizer.annotation_clause_count {
    annotations.push(RuleAnnotationClause {
      name: recognizer.annotations[i].clone(),
    });
  }

  let mut rules = vec![];
  for rule_index in 0..size.rule_count {
    let mut input_entries = vec![];
    for column_index in 0..size.input_clauses_count {
      let input_entry = InputEntry {
        text: recognizer.input_entries[rule_index][column_index].clone(),
      };
      input_entries.push(input_entry);
    }
    let mut output_entries = vec![];
    for column_index in 0..size.output_clauses_count {
      let output_entry = OutputEntry {
        text: recognizer.output_entries[rule_index][column_index].clone(),
      };
      output_entries.push(output_entry);
    }
    let mut annotation_entries = vec![];
    for column_index in 0..size.annotation_clauses_count {
      let annotation_entry = AnnotationEntry {
        text: recognizer.annotation_entries[rule_index][column_index].clone(),
      };
      annotation_entries.push(annotation_entry);
    }
    rules.push(DecisionRule {
      input_entries,
      output_entries,
      annotation_entries,
    });
  }

  let decision_table = DecisionTable {
    information_item_name,
    input_clauses: inputs,
    output_clauses: outputs,
    annotations,
    rules,
    hit_policy,
    aggregation,
    preferred_orientation,
    output_label,
  };
  Ok(decision_table)
}