mirror of
https://github.com/chanzuckerberg/cellxgene.git
synced 2026-09-30 13:58:12 +08:00
Dataframe, part deux - add varData and summarize() (#608)
* initial dataframe commit * initial dataframe port of core app * rename variables for clarity * remove unused import * comment out unused code * fix array handling bug in crossfilter dimension creation * allow creation of empty dataframes * handle non-existent columns * handle non-existent columns * revise tests for new dataframe * comments for clarity * comments for clarity * generate bulk add placeholder with real gene names * fix bug in gene name adding * more dataframe unit tests * fix bug - subset from current world, not universe * put cut and pasted code into a single function * improve caching of crossfilter * remove cascading update bug from graph * more performance work * improve state handling for scatterplot * performance optimization of critical path * add column summarization * dataframe utils * add callOnceLazy * fix tests * minor updates found during review * fix misspelling * remove RESTv02 from function names * comment cleanup * cut/icut col parameter defaults to null * break up large test * improve tests and comments on dataframe at/has functions * add Dataframe withCol/dropCol * expression varData now stored in a dataframe * dead code cleanup * use dataframe.summarize() * test cases for Dataframe.col.summarize * update test cases for new dataframe summarize * improve naming * use new hasCol API * add comments * add more Dataframe.withCol tests * add ability to specify row index in cut operation * retire subsetVarData function * correctly handle expression subsetting * lint and improve comments * rename cut to subset * changes based on PR review
This commit is contained in:
Bruce Martin
@@ -129,7 +129,7 @@ describe("simple data access", () => {
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});
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describe("dataframe subsetting", () => {
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describe("cutByList", () => {
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describe("subset", () => {
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const sourceDf = new Dataframe.Dataframe(
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[3, 4],
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[
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@@ -143,7 +143,7 @@ describe("dataframe subsetting", () => {
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);
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test("all rows, one column", () => {
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const dfA = sourceDf.cutByList(null, ["colors"]);
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const dfA = sourceDf.subset(null, ["colors"]);
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expect(dfA).toBeDefined();
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expect(dfA.dims).toEqual([3, 1]);
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expect(dfA.iat(0, 0)).toEqual("red");
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@@ -158,7 +158,7 @@ describe("dataframe subsetting", () => {
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});
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test("all rows, two columns", () => {
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const dfB = sourceDf.cutByList(null, ["colors", "float32"]);
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const dfB = sourceDf.subset(null, ["colors", "float32"]);
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expect(dfB).toBeDefined();
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expect(dfB.dims).toEqual([3, 2]);
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expect(dfB.iat(0, 0)).toBeCloseTo(4.4);
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@@ -182,7 +182,7 @@ describe("dataframe subsetting", () => {
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});
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test("one row, all columns", () => {
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const dfC = sourceDf.cutByList([1], null);
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const dfC = sourceDf.subset([1], null);
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expect(dfC).toBeDefined();
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expect(dfC.dims).toEqual([1, 4]);
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expect(dfC.iat(0, 0)).toEqual(1);
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@@ -194,7 +194,7 @@ describe("dataframe subsetting", () => {
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});
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test("two rows, all columns", () => {
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const dfD = sourceDf.cutByList([0, 2], null);
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const dfD = sourceDf.subset([0, 2], null);
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expect(dfD).toBeDefined();
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expect(dfD.dims).toEqual([2, 4]);
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expect(dfD.icol(0).asArray()).toEqual(new Int32Array([0, 2]));
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@@ -206,7 +206,7 @@ describe("dataframe subsetting", () => {
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});
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test("all rows, all columns", () => {
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const dfE = sourceDf.cutByList(null, null);
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const dfE = sourceDf.subset(null, null);
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expect(dfE).toBeDefined();
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expect(dfE.dims).toEqual([3, 4]);
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expect(dfE.icol(0).asArray()).toEqual(sourceDf.icol(0).asArray());
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@@ -218,7 +218,7 @@ describe("dataframe subsetting", () => {
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});
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test("two rows, two colums", () => {
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const dfF = sourceDf.cutByList([0, 2], ["int32", "float32"]);
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const dfF = sourceDf.subset([0, 2], ["int32", "float32"]);
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expect(dfF).toBeDefined();
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expect(dfF.dims).toEqual([2, 2]);
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expect(dfF.icol(0).asArray()).toEqual(new Int32Array([0, 2]));
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@@ -226,9 +226,32 @@ describe("dataframe subsetting", () => {
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expect(dfF.rowIndex.keys()).toEqual(new Int32Array([0, 2]));
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expect(dfF.colIndex.keys()).toEqual(["int32", "float32"]);
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});
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test("withRowIndex", () => {
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const df = sourceDf.subset(
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null,
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["int32", "float32"],
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new Dataframe.DenseInt32Index([3, 2, 1])
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);
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expect(df.colIndex).toBeInstanceOf(Dataframe.KeyIndex);
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expect(df.rowIndex).toBeInstanceOf(Dataframe.DenseInt32Index);
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expect(df.at(3, "int32")).toEqual(df.iat(0, 0));
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});
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test("withRowIndex error checks", () => {
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expect(() =>
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sourceDf.subset(null, ["red"], new Dataframe.IdentityInt32Index(1))
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).toThrow(RangeError);
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expect(() =>
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sourceDf.subset(null, ["red"], new Dataframe.DenseInt32Index([0, 1]))
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).toThrow(RangeError);
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expect(() =>
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sourceDf.subset(null, ["red"], new Dataframe.KeyIndex([0, 1, 2, 3]))
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).toThrow(RangeError);
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});
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});
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test("icutByMask", () => {
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test("isubsetMask", () => {
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const sourceDf = new Dataframe.Dataframe(
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[3, 4],
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[
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@@ -241,7 +264,7 @@ describe("dataframe subsetting", () => {
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new Dataframe.KeyIndex(["int32", "string", "float32", "colors"])
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);
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const dfA = sourceDf.icutByMask(
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const dfA = sourceDf.isubsetMask(
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new Uint8Array([0, 1, 1]),
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new Uint8Array([1, 0, 0, 1])
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);
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@@ -293,6 +316,222 @@ describe("dataframe factories", () => {
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expect(dfB.icol(i).asArray()).toEqual(dfA.icol(i).asArray());
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}
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});
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describe("withCol", () => {
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test("KeyIndex", () => {
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const df = new Dataframe.Dataframe(
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[2, 2],
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[["red", "blue"], [true, false]],
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null,
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new Dataframe.KeyIndex(["colors", "bools"])
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);
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const dfA = df.withCol("numbers", [1, 0]);
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expect(dfA).toBeDefined();
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expect(dfA.dims).toEqual([2, 3]);
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expect(dfA.icol(0).asArray()).toEqual(["red", "blue"]);
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expect(dfA.icol(1).asArray()).toEqual([true, false]);
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expect(dfA.icol(2).asArray()).toEqual([1, 0]);
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expect(dfA.col("numbers").asArray()).toEqual([1, 0]);
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expect(dfA.colIndex.keys()).toEqual(["colors", "bools", "numbers"]);
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expect(df.colIndex.keys()).toEqual(["colors", "bools"]);
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expect(df.rowIndex.keys()).toEqual(dfA.rowIndex.keys());
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});
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test("DenseInt32Index", () => {
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const df = new Dataframe.Dataframe(
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[2, 2],
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[["red", "blue"], [true, false]],
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null,
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new Dataframe.DenseInt32Index([74, 75])
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);
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const dfA = df.withCol(72, [1, 0]);
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expect(dfA).toBeDefined();
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expect(dfA.dims).toEqual([2, 3]);
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expect(dfA.icol(0).asArray()).toEqual(["red", "blue"]);
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expect(dfA.icol(1).asArray()).toEqual([true, false]);
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expect(dfA.icol(2).asArray()).toEqual([1, 0]);
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expect(dfA.col(74).asArray()).toEqual(["red", "blue"]);
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expect(dfA.col(75).asArray()).toEqual([true, false]);
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expect(dfA.col(72).asArray()).toEqual([1, 0]);
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expect(dfA.colIndex.keys()).toEqual(new Int32Array([74, 75, 72]));
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expect(df.colIndex.keys()).toEqual(new Int32Array([74, 75]));
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expect(df.rowIndex.keys()).toEqual(dfA.rowIndex.keys());
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});
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test("DenseInt32Index promote", () => {
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const df = new Dataframe.Dataframe(
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[2, 2],
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[["red", "blue"], [true, false]],
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null,
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new Dataframe.DenseInt32Index([74, 75])
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);
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const dfA = df.withCol(999, [1, 0]);
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expect(dfA).toBeDefined();
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expect(dfA.dims).toEqual([2, 3]);
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expect(dfA.icol(0).asArray()).toEqual(["red", "blue"]);
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expect(dfA.icol(1).asArray()).toEqual([true, false]);
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expect(dfA.icol(2).asArray()).toEqual([1, 0]);
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expect(dfA.col(74).asArray()).toEqual(["red", "blue"]);
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expect(dfA.col(75).asArray()).toEqual([true, false]);
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expect(dfA.col(999).asArray()).toEqual([1, 0]);
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expect(dfA.colIndex.keys()).toEqual(new Int32Array([74, 75, 999]));
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expect(df.colIndex.keys()).toEqual(new Int32Array([74, 75]));
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expect(df.rowIndex.keys()).toEqual(dfA.rowIndex.keys());
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});
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test("IdentityInt32Index with last", () => {
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const df = new Dataframe.Dataframe(
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[2, 2],
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[["red", "blue"], [true, false]],
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null,
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null
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);
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const dfA = df.withCol(2, [1, 0]);
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expect(dfA).toBeDefined();
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expect(dfA.dims).toEqual([2, 3]);
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expect(dfA.icol(0).asArray()).toEqual(["red", "blue"]);
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expect(dfA.icol(1).asArray()).toEqual([true, false]);
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expect(dfA.icol(2).asArray()).toEqual([1, 0]);
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expect(dfA.col(0).asArray()).toEqual(["red", "blue"]);
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expect(dfA.col(1).asArray()).toEqual([true, false]);
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expect(dfA.col(2).asArray()).toEqual([1, 0]);
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expect(dfA.colIndex.keys()).toEqual(new Int32Array([0, 1, 2]));
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expect(df.colIndex.keys()).toEqual(new Int32Array([0, 1]));
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expect(df.rowIndex.keys()).toEqual(dfA.rowIndex.keys());
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});
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test("IdentityInt32Index promote", () => {
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const df = new Dataframe.Dataframe(
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[2, 2],
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[["red", "blue"], [true, false]],
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null,
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null
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);
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const dfA = df.withCol(99, [1, 0]);
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expect(dfA).toBeDefined();
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expect(dfA.dims).toEqual([2, 3]);
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expect(dfA.icol(0).asArray()).toEqual(["red", "blue"]);
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expect(dfA.icol(1).asArray()).toEqual([true, false]);
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expect(dfA.icol(2).asArray()).toEqual([1, 0]);
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expect(dfA.col(0).asArray()).toEqual(["red", "blue"]);
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expect(dfA.col(1).asArray()).toEqual([true, false]);
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expect(dfA.col(99).asArray()).toEqual([1, 0]);
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expect(dfA.colIndex.keys()).toEqual(new Int32Array([0, 1, 99]));
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expect(df.colIndex.keys()).toEqual(new Int32Array([0, 1]));
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expect(df.rowIndex.keys()).toEqual(dfA.rowIndex.keys());
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});
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describe("handle column dimensions correctly", () => {
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/*
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there are two conditions:
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- empty dataframe - will accept an add of any dimensionality
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- non-empty dataframe - added column must match row-count dimension
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*/
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test("empty.withCol", () => {
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const edf = Dataframe.Dataframe.empty();
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const df = edf.withCol("foo", [1, 2, 3]);
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expect(edf).toBeDefined();
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expect(df).toBeDefined();
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expect(edf).not.toEqual(df);
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expect(df.dims).toEqual([3, 1]);
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expect(df.icol(0).asArray()).toEqual([1, 2, 3]);
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});
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test("withCol dimension check", () => {
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const dfA = new Dataframe.Dataframe([1, 1], [["a"]]);
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expect(() => {
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dfA.withCol(1, []);
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}).toThrow(RangeError);
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});
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});
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});
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describe("dropCol", () => {
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test("KeyIndex", () => {
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const df = new Dataframe.Dataframe(
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[2, 3],
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[["red", "blue"], [true, false], [1, 0]],
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null,
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new Dataframe.KeyIndex(["colors", "bools", "numbers"])
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);
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const dfA = df.dropCol("colors");
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expect(dfA).toBeDefined();
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expect(dfA.dims).toEqual([2, 2]);
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expect(dfA.icol(0).asArray()).toEqual([true, false]);
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expect(dfA.icol(1).asArray()).toEqual([1, 0]);
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expect(dfA.col("numbers").asArray()).toEqual([1, 0]);
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expect(dfA.colIndex.keys()).toEqual(["bools", "numbers"]);
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expect(df.colIndex.keys()).toEqual(["colors", "bools", "numbers"]);
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expect(df.rowIndex.keys()).toEqual(dfA.rowIndex.keys());
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});
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test("IdentityInt32Index drop first", () => {
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const df = new Dataframe.Dataframe(
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[2, 3],
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[["red", "blue"], [true, false], [1, 0]],
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null,
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null
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);
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const dfA = df.dropCol(0);
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expect(dfA).toBeDefined();
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expect(dfA.dims).toEqual([2, 2]);
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expect(dfA.icol(0).asArray()).toEqual([true, false]);
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expect(dfA.icol(1).asArray()).toEqual([1, 0]);
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expect(df.col(1).asArray()).toEqual(dfA.col(1).asArray());
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expect(df.col(2).asArray()).toEqual(dfA.col(2).asArray());
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expect(dfA.colIndex.keys()).toEqual(new Int32Array([1, 2]));
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expect(df.colIndex.keys()).toEqual(new Int32Array([0, 1, 2]));
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expect(df.rowIndex.keys()).toEqual(dfA.rowIndex.keys());
|
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});
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test("IdentityInt32Index drop last", () => {
|
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const df = new Dataframe.Dataframe(
|
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[2, 3],
|
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[["red", "blue"], [true, false], [1, 0]],
|
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null,
|
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null
|
||||
);
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const dfA = df.dropCol(2);
|
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|
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expect(dfA).toBeDefined();
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expect(dfA.dims).toEqual([2, 2]);
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expect(dfA.icol(0).asArray()).toEqual(["red", "blue"]);
|
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expect(dfA.icol(1).asArray()).toEqual([true, false]);
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expect(df.col(0).asArray()).toEqual(dfA.col(0).asArray());
|
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expect(df.col(1).asArray()).toEqual(dfA.col(1).asArray());
|
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expect(dfA.colIndex.keys()).toEqual(new Int32Array([0, 1]));
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expect(df.colIndex.keys()).toEqual(new Int32Array([0, 1, 2]));
|
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expect(df.rowIndex.keys()).toEqual(dfA.rowIndex.keys());
|
||||
});
|
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|
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test("DenseInt32Index", () => {
|
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const df = new Dataframe.Dataframe(
|
||||
[2, 3],
|
||||
[["red", "blue"], [true, false], [1, 0]],
|
||||
null,
|
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new Dataframe.DenseInt32Index([102, 101, 100])
|
||||
);
|
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const dfA = df.dropCol(101);
|
||||
|
||||
expect(dfA).toBeDefined();
|
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expect(dfA.dims).toEqual([2, 2]);
|
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expect(dfA.icol(0).asArray()).toEqual(["red", "blue"]);
|
||||
expect(dfA.icol(1).asArray()).toEqual([1, 0]);
|
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expect(dfA.col(100).asArray()).toEqual([1, 0]);
|
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expect(dfA.col(102).asArray()).toEqual(["red", "blue"]);
|
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expect(dfA.colIndex.keys()).toEqual(new Int32Array([102, 100]));
|
||||
expect(df.colIndex.keys()).toEqual(new Int32Array([102, 101, 100]));
|
||||
expect(df.rowIndex.keys()).toEqual(dfA.rowIndex.keys());
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
describe("dataframe col", () => {
|
||||
|
||||
@@ -0,0 +1,253 @@
|
||||
import * as Dataframe from "../../../src/util/dataframe";
|
||||
|
||||
function float32Conversion(f) {
|
||||
return new Float32Array([f])[0];
|
||||
}
|
||||
|
||||
describe("Dataframe column summary", () => {
|
||||
test("empty column test", () => {
|
||||
const df = Dataframe.Dataframe.create([0, 1], [[]]);
|
||||
const summary = df.icol(0).summarize();
|
||||
expect(summary).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: [],
|
||||
categoryCounts: new Map(),
|
||||
numCategories: 0
|
||||
})
|
||||
);
|
||||
});
|
||||
|
||||
test("simple test", () => {
|
||||
const df = new Dataframe.Dataframe(
|
||||
[1, 6],
|
||||
[
|
||||
["n1"],
|
||||
["hi"],
|
||||
[true],
|
||||
new Float32Array([39.3]),
|
||||
new Int32Array([99]),
|
||||
[1]
|
||||
],
|
||||
null,
|
||||
new Dataframe.KeyIndex([
|
||||
"name",
|
||||
"nameString",
|
||||
"nameBoolean",
|
||||
"nameFloat32",
|
||||
"nameInt32",
|
||||
"nameCategorical"
|
||||
])
|
||||
);
|
||||
|
||||
expect(df.icol(0).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: ["n1"],
|
||||
categoryCounts: new Map([["n1", 1]]),
|
||||
numCategories: 1
|
||||
})
|
||||
);
|
||||
expect(df.icol(1).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: ["hi"],
|
||||
categoryCounts: new Map([["hi", 1]]),
|
||||
numCategories: 1
|
||||
})
|
||||
);
|
||||
expect(df.icol(2).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: [true],
|
||||
categoryCounts: new Map([[true, 1]]),
|
||||
numCategories: 1
|
||||
})
|
||||
);
|
||||
expect(df.icol(3).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: false,
|
||||
min: float32Conversion(39.3),
|
||||
max: float32Conversion(39.3),
|
||||
nan: 0,
|
||||
ninf: 0,
|
||||
pinf: 0
|
||||
})
|
||||
);
|
||||
expect(df.icol(4).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: false,
|
||||
min: 99,
|
||||
max: 99,
|
||||
nan: 0,
|
||||
ninf: 0,
|
||||
pinf: 0
|
||||
})
|
||||
);
|
||||
expect(df.icol(5).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: [1],
|
||||
categoryCounts: new Map([[1, 1]]),
|
||||
numCategories: 1
|
||||
})
|
||||
);
|
||||
});
|
||||
|
||||
test("multi test", () => {
|
||||
const df = new Dataframe.Dataframe(
|
||||
[3, 6],
|
||||
[
|
||||
["n0", "n1", "n2"],
|
||||
["hi", "hi", "bye"],
|
||||
[false, true, true],
|
||||
new Float32Array([39.3, 39.3, 0]),
|
||||
new Int32Array([99, 99, 99]),
|
||||
[1, false, "0"]
|
||||
],
|
||||
null,
|
||||
new Dataframe.KeyIndex([
|
||||
"name",
|
||||
"nameString",
|
||||
"nameBoolean",
|
||||
"nameFloat32",
|
||||
"nameInt32",
|
||||
"nameCategorical"
|
||||
])
|
||||
);
|
||||
|
||||
expect(df.icol(0).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining(["n0", "n1", "n2"]),
|
||||
categoryCounts: new Map([["n0", 1], ["n1", 1], ["n2", 1]]),
|
||||
numCategories: 3
|
||||
})
|
||||
);
|
||||
expect(df.icol(1).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining(["hi", "bye"]),
|
||||
categoryCounts: new Map([["hi", 2], ["bye", 1]]),
|
||||
numCategories: 2
|
||||
})
|
||||
);
|
||||
expect(df.icol(2).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining([true, false]),
|
||||
categoryCounts: new Map([[true, 2], [false, 1]]),
|
||||
numCategories: 2
|
||||
})
|
||||
);
|
||||
expect(df.icol(3).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: false,
|
||||
min: 0,
|
||||
max: float32Conversion(39.3),
|
||||
nan: 0,
|
||||
ninf: 0,
|
||||
pinf: 0
|
||||
})
|
||||
);
|
||||
expect(df.icol(4).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: false,
|
||||
min: 99,
|
||||
max: 99,
|
||||
nan: 0,
|
||||
ninf: 0,
|
||||
pinf: 0
|
||||
})
|
||||
);
|
||||
expect(df.icol(5).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining([1, false, "0"]),
|
||||
categoryCounts: new Map([[1, 1], [false, 1], ["0", 1]]),
|
||||
numCategories: 3
|
||||
})
|
||||
);
|
||||
});
|
||||
|
||||
test("non-finite numbers", () => {
|
||||
const df = new Dataframe.Dataframe(
|
||||
[4, 6],
|
||||
[
|
||||
["n0", "n1", "n2", "n2"],
|
||||
["hi", "hi", "bye", "bye"],
|
||||
[false, true, true, true],
|
||||
new Float32Array([
|
||||
39.3,
|
||||
Number.NEGATIVE_INFINITY,
|
||||
Number.NaN,
|
||||
Number.POSITIVE_INFINITY
|
||||
]),
|
||||
new Int32Array([99, 99, 99, 99]),
|
||||
[1, false, "0", "0"]
|
||||
],
|
||||
null,
|
||||
new Dataframe.KeyIndex([
|
||||
"name",
|
||||
"nameString",
|
||||
"nameBoolean",
|
||||
"nameFloat32",
|
||||
"nameInt32",
|
||||
"nameCategorical"
|
||||
])
|
||||
);
|
||||
|
||||
expect(df.icol(0).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining(["n0", "n1", "n2"]),
|
||||
categoryCounts: new Map([["n0", 1], ["n1", 1], ["n2", 2]]),
|
||||
numCategories: 3
|
||||
})
|
||||
);
|
||||
expect(df.icol(1).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining(["hi", "bye"]),
|
||||
categoryCounts: new Map([["hi", 2], ["bye", 1]]),
|
||||
numCategories: 2
|
||||
})
|
||||
);
|
||||
expect(df.icol(2).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining([true, false]),
|
||||
categoryCounts: new Map([[true, 2], [false, 1]]),
|
||||
numCategories: 2
|
||||
})
|
||||
);
|
||||
expect(df.icol(3).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: false,
|
||||
min: float32Conversion(39.3),
|
||||
max: float32Conversion(39.3),
|
||||
nan: 1,
|
||||
ninf: 1,
|
||||
pinf: 1
|
||||
})
|
||||
);
|
||||
expect(df.icol(4).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: false,
|
||||
min: 99,
|
||||
max: 99,
|
||||
nan: 0,
|
||||
ninf: 0,
|
||||
pinf: 0
|
||||
})
|
||||
);
|
||||
expect(df.icol(5).summarize()).toEqual(
|
||||
expect.objectContaining({
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining([1, false, "0"]),
|
||||
categoryCounts: new Map([[1, 1], [false, 1], ["0", 1]]),
|
||||
numCategories: 3
|
||||
})
|
||||
);
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user