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convertor.rs
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convertor.rs
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#[cfg(test)]
pub(crate) mod tests {
use crate::dataset::FSRSBatcher;
use burn::data::dataloader::batcher::Batcher;
use burn::tensor::Data;
use chrono::prelude::*;
use chrono_tz::Tz;
use itertools::Itertools;
use rusqlite::Connection;
use rusqlite::{Result, Row};
use crate::dataset::{FSRSItem, FSRSReview};
#[derive(Clone, Debug, Default)]
pub(crate) struct RevlogEntry {
id: i64,
cid: i64,
button_chosen: i32,
review_kind: i64,
delta_t: i32,
}
fn row_to_revlog_entry(row: &Row) -> Result<RevlogEntry> {
Ok(RevlogEntry {
id: row.get(0)?,
cid: row.get(1)?,
button_chosen: row.get(2)?,
review_kind: row.get(3).unwrap_or_default(),
delta_t: 0,
})
}
fn convert_to_date(timestamp: i64, next_day_starts_at: i64, timezone: Tz) -> chrono::NaiveDate {
let timestamp_seconds = timestamp - next_day_starts_at * 3600 * 1000; // 剪去指定小时数
let datetime = Utc
.timestamp_millis_opt(timestamp_seconds)
.unwrap()
.with_timezone(&timezone);
datetime.date_naive()
}
/// Given a list of revlog entries for a single card with length n, we create
/// n-1 FSRS items, where each item contains the history of the preceding reviews.
fn convert_to_fsrs_items(
mut entries: Vec<RevlogEntry>,
next_day_starts_at: i64,
timezone: Tz,
) -> Option<Vec<FSRSItem>> {
// Find the index of the first RevlogEntry in the last continuous group where review_kind = 0
// 寻找最后一组连续 review_kind = 0 的第一个 RevlogEntry 的索引
let mut index_to_keep = 0;
let mut i = entries.len();
while i > 0 {
i -= 1;
if entries[i].review_kind == 0 {
index_to_keep = i;
} else if index_to_keep != 0 {
// Found a continuous group of review_kind = 0, exit the loop
// 找到了连续的 review_kind = 0 的组,退出循环
break;
}
}
// Remove all entries before this RevlogEntry
// 删除此 RevlogEntry 之前的所有条目
entries.drain(..index_to_keep);
// we ignore cards that don't start in the learning state
if let Some(entry) = entries.first() {
if entry.review_kind != 0 {
return None;
}
} else {
// no revlog entries
return None;
}
// Increment review_kind of all entries by 1
// 将所有 review_kind + 1
entries.iter_mut().for_each(|entry| entry.review_kind += 1);
// Convert the timestamp and keep the first RevlogEntry for each date
// 转换时间戳并保留每个日期的第一个 RevlogEntry
let mut unique_dates = std::collections::HashSet::new();
entries.retain(|entry| {
let date = convert_to_date(entry.id, next_day_starts_at, timezone);
unique_dates.insert(date)
});
// Compute delta_t for the remaining RevlogEntries
// 计算其余 RevlogEntry 的 delta_t
for i in 1..entries.len() {
let date_current = convert_to_date(entries[i].id, next_day_starts_at, timezone);
let date_previous = convert_to_date(entries[i - 1].id, next_day_starts_at, timezone);
entries[i].delta_t = (date_current - date_previous).num_days() as i32;
}
// Find the RevlogEntry with review_kind = 0 where the preceding RevlogEntry has review_kind of 1 or 2, then remove it and all following RevlogEntries
// 找到 review_kind = 0 且前一个 RevlogEntry 的 review_kind 是 1 或 2 的 RevlogEntry,然后删除其及其之后的所有 RevlogEntry
if let Some(index_to_remove) = entries.windows(2).enumerate().find_map(|(i, window)| {
if (window[0].review_kind == 1 || window[0].review_kind == 2)
&& window[1].review_kind == 0
{
// Return the index of the first RevlogEntry that meets the condition
// 返回第一个符合条件的 RevlogEntry 的索引
Some(i + 1)
} else {
None
}
}) {
// Truncate from 0 to index_to_remove, removing all subsequent entries
// 截取从 0 到 index_to_remove 的部分,删除其后的所有条目
entries.truncate(index_to_remove);
}
// Compute i, r_history, t_history
// 计算 i, r_history, t_history
// Except for the first entry, the remaining entries add the preceding button_chosen and delta_t to r_history and t_history
// 除了第一个条目,其余条目将前面的 button_chosen 和 delta_t 加入 r_history 和 t_history
Some(
entries
.iter()
.enumerate()
.skip(1)
.map(|(idx, _)| {
let reviews = entries
.iter()
.take(idx + 1)
.map(|r| FSRSReview {
rating: r.button_chosen,
delta_t: r.delta_t,
})
.collect();
FSRSItem { reviews }
})
.collect(),
)
}
/// Convert a series of revlog entries sorted by card id into FSRS items.
pub(crate) fn anki_to_fsrs(revlogs: Vec<RevlogEntry>) -> Vec<FSRSItem> {
let mut revlogs = revlogs
.into_iter()
.group_by(|r| r.cid)
.into_iter()
.filter_map(|(_cid, entries)| {
convert_to_fsrs_items(entries.collect(), 4, Tz::Asia__Shanghai)
})
.flatten()
.collect_vec();
revlogs.sort_by_cached_key(|r| r.reviews.len());
revlogs
}
pub(crate) fn anki21_sample_file_converted_to_fsrs() -> Vec<FSRSItem> {
anki_to_fsrs(read_collection().expect("read error"))
}
fn read_collection() -> Result<Vec<RevlogEntry>> {
let db = Connection::open("tests/data/collection.anki21")?;
let filter_out_suspended_cards = false;
let filter_out_flags = [];
let flags_str = if !filter_out_flags.is_empty() {
format!(
"AND flags NOT IN ({})",
filter_out_flags
.iter()
.map(|x: &i32| x.to_string())
.collect::<Vec<_>>()
.join(", ")
)
} else {
"".to_string()
};
let suspended_cards_str = if filter_out_suspended_cards {
"AND queue != -1"
} else {
""
};
let current_timestamp = Utc::now().timestamp() * 1000;
// This sql query will be remove in the futrue. See https://github.com/open-spaced-repetition/fsrs-optimizer-burn/pull/14#issuecomment-1685895643
let revlogs = db
.prepare_cached(&format!(
"SELECT id, cid, ease, type
FROM revlog
WHERE (type != 4 OR ivl <= 0)
AND (factor != 0 or type != 3)
AND id < ?1
AND cid < ?2
AND cid IN (
SELECT id
FROM cards
WHERE queue != 0
{suspended_cards_str}
{flags_str}
)
order by cid"
))?
.query_and_then((current_timestamp, current_timestamp), row_to_revlog_entry)?
.collect::<Result<Vec<_>>>()?;
Ok(revlogs)
}
// This test currently expects the following .anki21 file to be placed in tests/data/:
// https://github.com/open-spaced-repetition/fsrs-optimizer-burn/files/12394182/collection.anki21.zip
#[test]
fn conversion_works() {
let revlogs = read_collection().unwrap();
let single_card_revlog = vec![revlogs
.iter()
.filter(|r| r.cid == 1528947214762)
.cloned()
.collect_vec()];
assert_eq!(revlogs.len(), 24394);
let fsrs_items = anki_to_fsrs(revlogs);
assert_eq!(fsrs_items.len(), 14290);
assert_eq!(
fsrs_items.iter().map(|x| x.reviews.len()).sum::<usize>(),
49382 + 14290
);
// convert a subset and check it matches expectations
let mut fsrs_items = single_card_revlog
.into_iter()
.filter_map(|entries| convert_to_fsrs_items(entries, 4, Tz::Asia__Shanghai))
.flatten()
.collect_vec();
assert_eq!(
&fsrs_items,
&[
FSRSItem {
reviews: vec![
FSRSReview {
rating: 3,
delta_t: 0
},
FSRSReview {
rating: 3,
delta_t: 5
}
],
},
FSRSItem {
reviews: vec![
FSRSReview {
rating: 3,
delta_t: 0
},
FSRSReview {
rating: 3,
delta_t: 5
},
FSRSReview {
rating: 3,
delta_t: 10
}
],
},
FSRSItem {
reviews: vec![
FSRSReview {
rating: 3,
delta_t: 0
},
FSRSReview {
rating: 3,
delta_t: 5
},
FSRSReview {
rating: 3,
delta_t: 10
},
FSRSReview {
rating: 3,
delta_t: 22
}
],
},
FSRSItem {
reviews: vec![
FSRSReview {
rating: 3,
delta_t: 0
},
FSRSReview {
rating: 3,
delta_t: 5
},
FSRSReview {
rating: 3,
delta_t: 10
},
FSRSReview {
rating: 3,
delta_t: 22
},
FSRSReview {
rating: 2,
delta_t: 56
}
],
},
FSRSItem {
reviews: vec![
FSRSReview {
rating: 3,
delta_t: 0
},
FSRSReview {
rating: 3,
delta_t: 5
},
FSRSReview {
rating: 3,
delta_t: 10
},
FSRSReview {
rating: 3,
delta_t: 22
},
FSRSReview {
rating: 2,
delta_t: 56
},
FSRSReview {
rating: 3,
delta_t: 64
}
],
}
]
);
use burn_ndarray::NdArrayDevice;
let device = NdArrayDevice::Cpu;
use burn_ndarray::NdArrayBackend;
type Backend = NdArrayBackend<f32>;
let batcher = FSRSBatcher::<Backend>::new(device);
let res = batcher.batch(vec![fsrs_items.pop().unwrap()]);
assert_eq!(res.delta_ts.into_scalar(), 64.0);
assert_eq!(
res.r_historys.squeeze(1).to_data(),
Data::from([3.0, 3.0, 3.0, 3.0, 2.0])
);
assert_eq!(
res.t_historys.squeeze(1).to_data(),
Data::from([0.0, 5.0, 10.0, 22.0, 56.0])
);
assert_eq!(res.labels.to_data(), Data::from([1]));
}
#[test]
fn ordering_of_inputs_should_not_change() {
let revlogs = anki21_sample_file_converted_to_fsrs();
assert_eq!(
revlogs[0],
FSRSItem {
reviews: vec![
FSRSReview {
rating: 4,
delta_t: 0
},
FSRSReview {
rating: 3,
delta_t: 3
}
]
}
);
}
}