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//! BigML dataset support.
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use super::id::*;
use super::source::Field;
use super::status::*;
use super::{Resource, ResourceCommon, Source};
/// A BigML dataset. Basically a table of data with named columns.
///
/// TODO: Still lots of missing fields.
#[derive(Clone, Debug, Deserialize, Resource, Serialize)]
#[api_name = "dataset"]
pub struct Dataset {
/// Common resource information. These fields will be serialized at the
/// top-level of this structure by `serde`.
#[serde(flatten)]
pub common: ResourceCommon,
/// The ID of this resource.
pub resource: Id<Dataset>,
/// The current status of this execution.
pub status: GenericStatus,
/// The number of columns in the dataset.
pub columns: usize,
/// Field IDs excluded when building this dataset.
pub excluded_fields: Vec<String>,
/// The number of fields of each type. This includes a few odd things
/// like "preferred", so we represent it as a string.
pub field_types: HashMap<String, u64>,
/// Metadata describing each field. Will be empty while object is being
/// created.
#[serde(default)]
pub fields: HashMap<String, Field>,
/// Field IDs included when building this dataset.
pub input_fields: Vec<String>,
/// The number of rows in this dataset.
pub rows: usize,
/// Placeholder to allow extensibility without breaking the API.
#[serde(skip)]
_placeholder: (),
}
/// Arguments used to create a dataset.
#[derive(Debug, Serialize)]
pub struct Args {
/// The ID of the BigML `Source` from which to import data.
pub source: Id<Source>,
/// The name of this dataset.
#[serde(skip_serializing_if = "Option::is_none")]
pub name: Option<String>,
/// User-defined tags.
#[serde(skip_serializing_if = "Vec::is_empty")]
pub tags: Vec<String>,
/// Placeholder to allow extensibility without breaking the API.
#[serde(skip)]
_placeholder: (),
}
impl Args {
/// Create a new `Args`.
pub fn from_source(source: Id<Source>) -> Args {
Args {
source,
name: None,
tags: vec![],
_placeholder: (),
}
}
}
impl super::Args for Args {
type Resource = Dataset;
}