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jcrist
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Feb 11, 2016
- Add diagonal extraction to da.diag
- Add da.diag support for numpy arrays
- Move da.diag from creation to core
- Add da.corrcoef
- Add diagonal extraction to da.diag - Add da.diag support for numpy arrays - Move da.diag from creation to core - Add da.corrcoef
dask/array/core.py
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| return c/c | ||
| d = diag(c) | ||
| d = d.reshape((d.shape[0], 1)) | ||
| return c / sqrt(d * d.T) |
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I wonder if we should change the order of operations here. Does the number of tasks or the need to coordinate intermediate results change significantly if we do something like c / sqrt(d[None, :]) / sqrt(d[:, None])? (this is almost certainly not correct, but might not be far off.
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The diagonal is always positive, so that actually would be equivalent. They both result in virtually the same graph though, just a reordering of sqrt and div calls. The way I did it results in slightly fewer tasks in the optimized graph, but the memory usage/execution time is virtually the same either way.
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My concern is that, under a poor scheduler, we now have a large low-rank matrix d * d.T in memory.
It's a bit like this problem:
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This might work fine in the single-machine scheduler. The other way with multiple divisions works more robustly I think, and so might be helpful to have in the distributed scheduler, which is in a bit more of a challenging environment.
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I'm not against changing this, but I really don't think it does. Both operations are fully chunk aligned elementwise operations. The way I currently have it results in:
out[i, j] = c[i, j]/sqrt(d[i] * d[j].T)
The way you suggest it would be:
out[i, j] = c[i, j]/(sqrt(d[i]) * sqrt(d[j].T))
where i and j are the chunk indices. Each output cell draws on the same intermediate results. Thinking about it just from a key-value perspective, the x*x^T matrix is never actually constructed. I don't know the internals of the distributed scheduler as well, but I'd assume it would also fair fine in either situation? Correct me if I'm wrong here.
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I think it would handle it ok, but I'm not confident, especially not in all cases that might arise. If it does poorly then this problem will run really poorly.
Actually, in this case we might not handle this well just because data transfer is no longer cheap. Even if we manage to compute related blocks nearby in time it's hard to guarantee that any two independent tasks will compute on the same machine. The computation of a block of d * d.T might very easily occur on a different worker from the corresponding block of c. The relative cost of moving d to moving d * d.T is large.
Generally distributed computing has made me wary of creating large intermediate results.
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Copying from an offline conversation, just so others might benefit. My confusion here was that I thought Matt was worried about the computation of the entire d * d.T array in memory, which shouldn't happen in either case. Instead, the complaint was that in the current way, the chunks are n x n, where in his way the chunks are 1 x n. Both result in the same shape graphs, with the same dependencies, but one way reduces the number of n x n arrays we send between workers.
dask/array/core.py
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| raise ValueError("Array must be 1d or 2d only") | ||
| return Array(dsk, name, chunks, dtype=v.dtype) | ||
| if not isinstance(v, Array): | ||
| raise TypeError("v must be a dask array or numpy array") |
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... got type %s" % str(type(v))
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+1, even without the nitpicky error message |
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* Make column projections stricter (dask#881) * Simplify again after lowering (dask#884) * Visual EXPLAIN (dask#885) * Fix merge predicate pushdowns with weird predicates (dask#888) * Handle futures that are put into map_partitions (dask#892) * Remove eager divisions from indexing (dask#891) * Add shuffle if objects are not aligned and partitions are unknown in assign (dask#887) Co-authored-by: Hendrik Makait <hendrik@makait.com> * Add support for dd.Aggregation (dask#893) * Fix random_split for series (dask#894) * Update dask version * Use Aggregation from dask/dask (dask#895) * Fix meta calculation error in groupby (dask#897) * Revert "Use Aggregation from dask/dask" (dask#898) * Parquet reader using Pyarrow FileSystem (dask#882) Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> * Fix assign for empty indexer (dask#901) * Add dask.dataframe import at start (dask#903) * Add indicator support to merge (dask#902) * Fix detection of parquet filter pushdown (dask#910) * Speedup init of `ReadParquetPyarrowFS` (dask#909) * Don't rely on sets in are_co_aligned (dask#908) * Implement more efficient GroupBy.mean (dask#906) * Refactor GroupByReduction (dask#920) * Implement array inference in new_collection (dask#922) * Add support for convert string option (dask#912) * P2P shuffle drops partitioning column early (dask#899) * Avoid culling for SetIndexBlockwise with divisions (dask#925) * Re-run versioneer install to fix version number (tag_prefix) (dask#926) * Sort if split_out=1 in value_counts (dask#924) * Wrap fragments (dask#911) * Ensure that columns are copied in projection (dask#927) * Raise in map if pandas < 2.1 (dask#929) * Add _repr_html_ and updated __repr__ for FrameBase (dask#930) * Support token for map_partitions (dask#931) * Fix Copy-on-Write related bug in groupby.transform (dask#933) * Fix to_dask_dataframe test after switching to dask-expr by default (dask#935) * Use multi-column assign in groupby apply (dask#934) * Enable copy on write by default (dask#932) Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> * Avoid fusing from_pandas ops to avoid duplicating data (dask#938) * Adjust automatic split_out parameter (dask#940) * Revert "Add _repr_html_ and updated __repr__ for FrameBase (dask#930)" (dask#941) * Remove repartition from P2P shuffle (dask#942) * [Parquet] Calculate divisions from statistics (dask#917) * Accept user arguments for arrow_to_pandas (dask#936) * Add _repr_html_ and prettier __repr__ w/o graph materialization (dask#943) * Add dask tokenize for fragment wrapper (dask#948) * Warn if annotations are ignored (dask#947) * Require `pyarrow>=7` (dask#949) * Implement string conversion for from_array (dask#950) * Add dtype and columns type check for shuffle (dask#951) * Concat arrow tables before converting to pandas (dask#928) * MINOR: Avoid confusion around shuffle method (dask#956) Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> * Set pa cpu count (dask#954) Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> * Update for pandas nighlies (dask#953) * Fix bug with split_out in groupby aggregate (dask#957) * Fix default observed value (dask#960) * Ensure that we respect shuffle in context manager (dask#958) Co-authored-by: Hendrik Makait <hendrik@makait.com> * Fix 'Empty' prefix to non-empty Series repr (dask#963) * Update README.md (dask#964) * Adjust split_out values to be consistent with other methods (dask#961) * bump version to 1.0 * Raise an error if the optimizer cannot terminate (dask#966) * Fix non-converging optimizer (dask#967) * Fixup filter pushdown through merges with ands and column reuse (dask#969) * Fix unique with shuffle and strings (dask#971) * Implement custom reductions (dask#970) * Fixup set_index with one partition but more divisions by user (dask#972) * Fixup predicate pushdown for query 19 (dask#973) Co-authored-by: Miles <miles59923@gmail.com> * Revert enabling pandas cow (dask#974) * Update changelog for 1.0.2 * Fix set-index preserving divisions for presorted (dask#977) * Fixup reduction with split_every=False (dask#978) * Release for dask 2024.3.1 * Raise better error for repartition on divisions with unknown divisions (dask#980) * Fix concat of series objects with column projection (dask#981) * Fix some reset_index optimization issues (dask#982) * Remove keys() (dask#983) * Ensure wrapping an array when comparing to Series works if columns are empty (dask#984) * Version v1.0.4 * Visual ANALYZE (dask#889) Co-authored-by: fjetter <fjetter@users.noreply.github.com> * Support ``prefix`` argument in ``from_delayed`` (dask#991) * Ensure drop matches column names exactly (dask#992) * Fix SettingWithCopyWarning in _merge.py (dask#990) * Update pyproject.toml (dask#994) * Allow passing of boolean index for column index in loc (dask#995) * Ensure that repr doesn't raise if an operand is a pandas object (dask#996) * Version v1.0.5 * Reduce coverage target a little bit (dask#999) * Nicer read_parquet prefix (dask#998) Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> * Set divisions with divisions already known (dask#997) * Start building and publishing conda nightlies (dask#986) * Fix zero division error when reading index from parquet (dask#1000) * Rename overloaded `to/from_dask_dataframe` API (dask#987) * Register json and orc APIs for "pandas" dispatch (dask#1004) * Fix pyarrow fs reads for list of directories (dask#1006) * Release for dask 2024.4.0 * Fix meta caclulation in drop_duplicates (dask#1007) * Release 1.0.7 * Support named aggregations in groupby.aggregate (dask#1009) * Make release 1.0.9 * Adjust version number in changes * Make setattr work (dask#1011) * Release for dask 2024.4.1 * Fix head for npartitions=-1 and optimizer step (dask#1014) * Deprecate ``to/from_dask_dataframe`` API (dask#1001) * Fix projection for rename if projection isn't renamed (dask#1016) * Fix unique with numeric columns (dask#1017) * Add changes for new version * Fix column projections in merge when suffixes are relevant (dask#1019) * Simplify dtype casting logic for shuffle (dask#1012) * Use implicit knowledge about divisions for efficient grouping (dask#946) Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> Co-authored-by: Hendrik Makait <hendrik@makait.com> * Fix assign after set index incorrect projections (dask#1020) * Fix read_parquet if directory is empty (dask#1023) * Rename uniuqe_partition_mapping property and add docs (dask#1022) * Add docs for usefule optimizer methods (dask#1025) * Fix doc build error (dask#1026) * Fix error in analyze for scalar (dask#1027) * Add nr of columns to explain output for projection (dask#1030) Co-authored-by: Hendrik Makait <hendrik@makait.com> * Fuse more aggressively if parquet files are tiny (dask#1029) * Move IO docstrings over (dask#1033) * Release for dask 2024.4.2 * Add cudf support to ``to_datetime`` and ``_maybe_from_pandas`` (dask#1035) * Fix backend dispatching for `read_csv` (dask#1028) * Fix loc accessing index for element wise op (dask#1037) * Fix loc slicing with Datetime Index (dask#1039) * Fix shuffle after set_index from 1 partition df (dask#1040) * Bugfix release * Fix bug in ``Series`` reductions (dask#1041) * Fix shape returning integer (dask#1043) * Fix xarray integration with scalar columns (dask#1046) * Fix None min/max statistics and missing statistics generally (dask#1045) * Fix drop with set (dask#1047) * Fix delayed in fusing with multipled dependencies (dask#1038) * Add bugfix release * Optimize when from-delayed is called (dask#1048) * Fix default name conversion in `ToFrame` (dask#1044) Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> * Add support for ``DataFrame.melt`` (dask#1049) * Fixup failing test (dask#1052) * Generalize ``get_dummies`` (dask#1053) * reduce pickle size of parquet fragments (dask#1050) * Add a bunch of docs (dask#1051) Co-authored-by: Hendrik Makait <hendrik@makait.com> * Release for dask 2024.5.0 * Fix to_parquet in append mode (dask#1057) * Fix sort_values for unordered categories (dask#1058) * Fix dropna before merge (dask#1062) * Fix non-integer divisions in FusedIO (dask#1063) * Add cache argument to ``lower_once`` (dask#1059) * Use ensure_deterministic kwarg instead of config (dask#1064) * Fix isin with strings (dask#1067) * Fix isin for head computation (dask#1068) * Fix read_csv with positional usecols (dask#1069) * Release for dask 2024.5.1 * Use `is_categorical_dtype` dispatch for `sort_values` (dask#1070) * Fix meta for string accessors (dask#1071) * Fix projection to empty from_pandas (dask#1072) * Release for dask 2024.5.2 * Fix categorize if columns are dropped (dask#1074) * Fix resample divisions propagation (dask#1075) * Release for dask 2024.6.0 * Fix get_group for multiple keys (dask#1080) * Skip distributed tests (dask#1081) * Fix cumulative aggregations for empty partitions (dask#1082) * Move another test to distributed folder (dask#1085) * Release 1.1.4 * Release for dask 2024.6.2 * Add minimal subset of interchange protocol (dask#1087) * Add from_map docstring (dask#1088) * Ensure 1 task group per from_delayed (dask#1084) * Advise against using from_delayed (dask#1089) * Refactor shuffle method to handle invalid columns (dask#1091) * Fix freq behavior on ci (dask#1092) * Add first array draft (dask#1090) * Fix array import stuff (dask#1094) * Add asarray (dask#1095) * Implement arange (dask#1097) * Implement linspace (dask#1098) * Implement zeros and ones (dask#1099) * Remvoe pandas 2 checks (dask#1100) * Add unify-chunks draft to arrays (dask#1101) Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> * Release for dask 2024.7.0 * Skip test if optional xarray cannot be imported (dask#1104) * Fix deepcopying FromPandas class (dask#1105) * Fix from_pandas with chunksize and empty df (dask#1106) * Link fix in readme (dask#1107) * Fix shuffle blowing up the task graph (dask#1108) Co-authored-by: Hendrik Makait <hendrik@makait.com> * Release for dask 2024.7.1 * Fix some things for pandas 3 (dask#1110) * Fixup remaining upstream failures (dask#1111) * Release for dask 2024.8.0 * Drop support for Python 3.9 (dask#1109) Co-authored-by: James Bourbeau <jrbourbeau@gmail.com> * Fix tuples as on argument in merge (dask#1117) * Fix merging when index name in meta missmatches actual name (dask#1119) Co-authored-by: Hendrik Makait <hendrik@makait.com> * Register `read_parquet` and `read_csv` as "dispatchable" (dask#1114) * Fix projection for Index class in read_parquet (dask#1120) * Fix result index of merge (dask#1121) * Introduce `ToBackend` expression (dask#1115) * Avoid calling ``array`` attribute on ``cudf.Series`` (dask#1122) * Make split_out for categorical default smarter (dask#1124) * Release for dask 2024.8.1 * Fix scalar detection of columns coming from sql (dask#1125) * Bump `pyarrow>=14.0.1` minimum versions (dask#1127) Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> * Fix concat axis 1 bug in divisions (dask#1128) * Release for dask 2024.8.2 * Use task-based rechunking as default (dask#1131) * Improve performance of `DelayedsExpr` through caching (dask#1132) * Import from tokenize (dask#1133) * Release for dask 2024.9.0 * Add concatenate flag to .compute() (dask#1138) * Release for dask 2024.9.1 * Fix displaying timestamp scalar (dask#1141) * Fix alignment issue with groupby index accessors (dask#1142) * Improve handling of optional dependencies in `analyze` and `explain` (dask#1146) * Switch from mambaforge to miniforge in CI (dask#1147) * Fix merge_asof for single partition (dask#1145) * Raise exception when calculating divisons (dask#1149) * Fix binary operations with scalar on the left (dask#1150) * Explicitly list setuptools as a build dependency in conda recipe (dask#1151) * Version v1.1.16 * Fix ``Merge`` divisions after filtering partitions (dask#1152) * Fix meta calculation for to_datetime (dask#1153) * Internal cleanup of P2P code (dask#1154) * Migrate P2P shuffle and merge to TaskSpec (dask#1155) * Improve Aggregation docstring explicitly mentionning SeriesGroupBy (dask#1156) * Migrate shuffle and merge to `P2PBarrierTask` (dask#1157) * Migrate Blockwise to use taskspec (dask#1159) * Add support for Python 3.13 (dask#1160) * Release for dask 2024.11.0 * Fix fusion calling things multiple times (dask#1161) * Version 1.1.18 * Version 1.1.19 * Fix orphaned dependencies in Fused expression (dask#1163) * Use Taskspec fuse implementation (dask#1162) Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> * Introduce more caching when walking the expression (dask#1165) * Avoid exponentially growing graph for Assign-Projection combinations (dask#1164) * Remove ``from_dask_dataframe`` (dask#1167) * Deprecated and remove from_legacy_dataframe usage (dask#1168) Co-authored-by: James Bourbeau <jrbourbeau@users.noreply.github.com> * Remove recursion in task spec (dask#1158) * Fix value_counts with split_out != 1 (dask#1170) * Release 2024.12.0 * Use new blockwise unpack collection in array (dask#1173) * Propagate group_keys in DataFrameGroupBy (dask#1174) * Fix assign optimization when overwriting columns (dask#1176) * Remove custom read-csv stuff (dask#1178) * Fixup install paths (dask#1179) * Version 1.1.21 * Remove legacy conversion functions (dask#1177) * Remove duplicated files * Move repository * Clean up docs and imports * Clean up docs and imports --------- Co-authored-by: Hendrik Makait <hendrik@makait.com> Co-authored-by: Florian Jetter <fjetter@users.noreply.github.com> Co-authored-by: Miles <miles59923@gmail.com> Co-authored-by: Joris Van den Bossche <jorisvandenbossche@gmail.com> Co-authored-by: Richard (Rick) Zamora <rzamora217@gmail.com> Co-authored-by: Charles Blackmon-Luca <20627856+charlesbluca@users.noreply.github.com> Co-authored-by: James Bourbeau <jrbourbeau@gmail.com> Co-authored-by: alex-rakowski <alexrakowski90@gmail.com> Co-authored-by: Matthew Rocklin <mrocklin@gmail.com> Co-authored-by: Sandro <shfu29r4bu@liamekaens.com> Co-authored-by: Ben <55319792+benrutter@users.noreply.github.com> Co-authored-by: James Bourbeau <jrbourbeau@users.noreply.github.com> Co-authored-by: Guillaume Eynard-Bontemps <g.eynard.bontemps@gmail.com> Co-authored-by: Tom Augspurger <tom.augspurger88@gmail.com>
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