pandrs 0.3.0

//! Machine learning pipelines
//!
//! This module provides functionality for creating pipelines of data
//! transformations and machine learning models.

use crate::core::error::{Error, Result};
use crate::dataframe::DataFrame;
use std::collections::HashMap;

/// Trait for pipeline stages that transform DataFrames
pub trait PipelineTransformer {
    /// Transform DataFrame according to this pipeline stage
    fn transform(&self, df: &DataFrame) -> Result<DataFrame>;

    /// Fit this transformer to the data (if needed)
    fn fit(&mut self, df: &DataFrame) -> Result<()>;

    /// Fit and transform in one step
    fn fit_transform(&mut self, df: &DataFrame) -> Result<DataFrame> {
        self.fit(df)?;
        self.transform(df)
    }
}

/// Enum for different types of pipeline stages
#[derive(Debug)]
pub enum PipelineStage {
    /// Standard scaler
    StandardScaler {
        /// Columns to scale
        columns: Option<Vec<String>>,
        /// Internal storage for fit parameters
        _means: Option<HashMap<String, f64>>,
        _stds: Option<HashMap<String, f64>>,
    },

    /// Min-max scaler
    MinMaxScaler {
        /// Columns to scale
        columns: Option<Vec<String>>,
        /// Feature range (min, max)
        feature_range: (f64, f64),
        /// Internal storage for fit parameters
        _min_values: Option<HashMap<String, f64>>,
        _max_values: Option<HashMap<String, f64>>,
    },

    /// One-hot encoder
    OneHotEncoder {
        /// Columns to encode
        columns: Option<Vec<String>>,
        /// Whether to drop the first category
        drop_first: bool,
        /// Prefix for new column names
        prefix: Option<String>,
        /// Internal storage for fit parameters
        _categories: Option<HashMap<String, Vec<String>>>,
    },

    /// Imputer for missing values
    Imputer {
        /// Columns to impute
        columns: Option<Vec<String>>,
        /// Imputation strategy
        strategy: String,
        /// Constant value for constant strategy
        fill_value: Option<f64>,
        /// Internal storage for fit parameters
        _fill_values: Option<HashMap<String, f64>>,
    },

    /// Feature selector
    FeatureSelector {
        /// Columns to select
        columns: Vec<String>,
    },
    // Custom transformer (commented out since we can't implement Debug for it)
    /*
    Custom {
        // Custom transformation function
        transform_fn: Box<dyn Fn(&DataFrame) -> Result<DataFrame>>,
    },
    */
}

impl PipelineTransformer for PipelineStage {
    fn transform(&self, df: &DataFrame) -> Result<DataFrame> {
        match self {
            PipelineStage::StandardScaler { .. } => {
                // Placeholder implementation for StandardScaler
                Ok(df.clone())
            }

            PipelineStage::MinMaxScaler { .. } => {
                // Placeholder implementation for MinMaxScaler
                Ok(df.clone())
            }

            PipelineStage::OneHotEncoder { .. } => {
                // Placeholder implementation for OneHotEncoder
                Ok(df.clone())
            }

            PipelineStage::Imputer { .. } => {
                // Placeholder implementation for Imputer
                Ok(df.clone())
            }

            PipelineStage::FeatureSelector { columns } => {
                let mut result = DataFrame::new();

                for col_name in columns {
                    if !df.contains_column(col_name) {
                        return Err(Error::InvalidValue(format!(
                            "Column '{}' not found",
                            col_name
                        )));
                    }

                    let col: &crate::series::Series<String> = df.get_column(col_name)?;
                    result.add_column(col_name.clone(), col.clone())?;
                }

                Ok(result)
            } // PipelineStage::Custom { transform_fn } => {
              //     (transform_fn)(df)
              // },
        }
    }

    fn fit(&mut self, df: &DataFrame) -> Result<()> {
        match self {
            PipelineStage::StandardScaler {
                columns,
                _means,
                _stds,
            } => {
                // Placeholder implementation for StandardScaler fit
                let cols: Vec<String> = match columns {
                    Some(cols) => cols.clone(),
                    None => df.column_names(),
                };

                let mut means = HashMap::new();
                let mut stds = HashMap::new();

                for col_name in &cols {
                    if !df.contains_column(col_name) {
                        return Err(Error::InvalidInput(format!(
                            "Column '{}' not found",
                            col_name
                        )));
                    }

                    let col: &crate::series::Series<String> = df.get_column(col_name)?;

                    // Skip non-numeric columns
                    // Stub implementation for now
                    means.insert(col_name.clone(), 0.0);
                    stds.insert(col_name.clone(), 1.0);
                }

                *_means = Some(means);
                *_stds = Some(stds);

                Ok(())
            }

            // Placeholders for other stages
            PipelineStage::MinMaxScaler { .. } => Ok(()),
            PipelineStage::OneHotEncoder { .. } => Ok(()),
            PipelineStage::Imputer { .. } => Ok(()),
            PipelineStage::FeatureSelector { .. } => Ok(()),
            // PipelineStage::Custom { .. } => Ok(()),
        }
    }
}

/// Pipeline for chaining multiple data transformation steps
#[derive(Debug)]
pub struct Pipeline {
    /// Pipeline stages
    pub stages: Vec<PipelineStage>,
}

impl Pipeline {
    /// Create a new empty pipeline
    pub fn new() -> Self {
        Pipeline { stages: Vec::new() }
    }

    /// Add a stage to the pipeline
    pub fn add_stage(&mut self, stage: PipelineStage) -> &mut Self {
        self.stages.push(stage);
        self
    }

    /// Fit the pipeline to the data
    pub fn fit(&mut self, df: &DataFrame) -> Result<()> {
        let mut current_df = df.clone();

        for stage in &mut self.stages {
            stage.fit(&current_df)?;
            current_df = stage.transform(&current_df)?;
        }

        Ok(())
    }

    /// Transform data using the fitted pipeline
    pub fn transform(&self, df: &DataFrame) -> Result<DataFrame> {
        let mut current_df = df.clone();

        for stage in &self.stages {
            current_df = stage.transform(&current_df)?;
        }

        Ok(current_df)
    }

    /// Fit the pipeline and transform data in one step
    pub fn fit_transform(&mut self, df: &DataFrame) -> Result<DataFrame> {
        let mut current_df = df.clone();

        for stage in &mut self.stages {
            stage.fit(&current_df)?;
            current_df = stage.transform(&current_df)?;
        }

        Ok(current_df)
    }
}

// No need for re-exports here as the types are already defined in this module