From 45a9d9f38fd7ba0f74d470a3d9e37fec1bf40aec Mon Sep 17 00:00:00 2001 From: Jeroen Van Der Donckt Date: Wed, 24 May 2023 10:03:59 +0200 Subject: [PATCH 1/8] :construction: POC of parallel intra strided-rolling --- tsflex/features/function_wrapper.py | 3 +++ tsflex/features/segmenter/strided_rolling.py | 19 +++++++++++++++++++ 2 files changed, 22 insertions(+) diff --git a/tsflex/features/function_wrapper.py b/tsflex/features/function_wrapper.py index f400a5cb..1515f541 100644 --- a/tsflex/features/function_wrapper.py +++ b/tsflex/features/function_wrapper.py @@ -66,6 +66,7 @@ def __init__( output_names: Optional[Union[List[str], str]] = None, input_type: Optional[Union[np.array, pd.Series]] = np.array, vectorized: bool = False, + parallel: bool = False, **kwargs, ): """Create FuncWrapper instance.""" @@ -85,8 +86,10 @@ def __init__( assert not ( vectorized & (input_type is not np.array) ), "The input_type must be np.array if vectorized is True!" + assert not (vectorized & parallel), "vectorized and parallel cannot be both True!" self.input_type = input_type self.vectorized = vectorized + self.parallel = parallel self._freeze() diff --git a/tsflex/features/segmenter/strided_rolling.py b/tsflex/features/segmenter/strided_rolling.py index 3f55d8f6..1766ce00 100644 --- a/tsflex/features/segmenter/strided_rolling.py +++ b/tsflex/features/segmenter/strided_rolling.py @@ -18,6 +18,7 @@ from abc import ABC, abstractmethod from collections import namedtuple from typing import List, Optional, Tuple, TypeVar, Union +from multiprocess import Pool import numpy as np import pandas as pd @@ -444,6 +445,24 @@ def apply_func(self, func: FuncWrapper) -> pd.DataFrame: # when combining into an array out = out.T if out_type is tuple else out + elif func.parallel: + # Parallel function execution + with Pool() as pool: + out = np.array( + list( + pool.imap( + func, + *[ + [ + sc.values[sc.start_indexes[idx] : sc.end_indexes[idx]] + for idx in range(len(self.index)) + ] + for sc in self.series_containers + ], + ) + ) + ) + else: # Sequential function execution (default) out = np.array( From 2ba02bc6aaf45605704d05b6def87d2b21906adb Mon Sep 17 00:00:00 2001 From: jonasvdd Date: Wed, 24 May 2023 13:13:31 +0200 Subject: [PATCH 2/8] :dash: small fixes --- tsflex/features/segmenter/strided_rolling.py | 2 ++ tsflex/features/utils.py | 1 + 2 files changed, 3 insertions(+) diff --git a/tsflex/features/segmenter/strided_rolling.py b/tsflex/features/segmenter/strided_rolling.py index 1766ce00..d8e3c53b 100644 --- a/tsflex/features/segmenter/strided_rolling.py +++ b/tsflex/features/segmenter/strided_rolling.py @@ -459,6 +459,8 @@ def apply_func(self, func: FuncWrapper) -> pd.DataFrame: ] for sc in self.series_containers ], + # TODO -> make this configurable + chunksize=100, ) ) ) diff --git a/tsflex/features/utils.py b/tsflex/features/utils.py index 769c8f8e..e12c319b 100644 --- a/tsflex/features/utils.py +++ b/tsflex/features/utils.py @@ -121,6 +121,7 @@ def _get_funcwrapper_func_and_kwargs(func: FuncWrapper) -> Tuple[Callable, dict] func_wrapper_kwargs["output_names"] = func.output_names func_wrapper_kwargs["input_type"] = func.input_type func_wrapper_kwargs["vectorized"] = func.vectorized + func_wrapper_kwargs['parallel'] = func.parallel func_wrapper_kwargs.update(func.kwargs) return function, func_wrapper_kwargs From 3b67793afea147f3ce5c306ce8fe878a482147d6 Mon Sep 17 00:00:00 2001 From: Jeroen Van Der Donckt Date: Thu, 25 May 2023 12:21:22 +0200 Subject: [PATCH 3/8] :broom: formatting --- tsflex/features/function_wrapper.py | 6 ++++-- tsflex/features/segmenter/strided_rolling.py | 6 ++++-- tsflex/features/utils.py | 2 +- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/tsflex/features/function_wrapper.py b/tsflex/features/function_wrapper.py index 1515f541..dd027c41 100644 --- a/tsflex/features/function_wrapper.py +++ b/tsflex/features/function_wrapper.py @@ -84,9 +84,11 @@ def __init__( assert input_type in SUPPORTED_STROLL_TYPES, "Invalid input_type!" assert not ( - vectorized & (input_type is not np.array) + vectorized and (input_type is not np.array) ), "The input_type must be np.array if vectorized is True!" - assert not (vectorized & parallel), "vectorized and parallel cannot be both True!" + assert not ( + vectorized and parallel + ), "vectorized and parallel cannot be both True!" self.input_type = input_type self.vectorized = vectorized self.parallel = parallel diff --git a/tsflex/features/segmenter/strided_rolling.py b/tsflex/features/segmenter/strided_rolling.py index d8e3c53b..190ef506 100644 --- a/tsflex/features/segmenter/strided_rolling.py +++ b/tsflex/features/segmenter/strided_rolling.py @@ -18,10 +18,10 @@ from abc import ABC, abstractmethod from collections import namedtuple from typing import List, Optional, Tuple, TypeVar, Union -from multiprocess import Pool import numpy as np import pandas as pd +from multiprocess import Pool from ...utils.attribute_parsing import AttributeParser, DataType from ...utils.data import SUPPORTED_STROLL_TYPES, to_list, to_series_list, to_tuple @@ -454,7 +454,9 @@ def apply_func(self, func: FuncWrapper) -> pd.DataFrame: func, *[ [ - sc.values[sc.start_indexes[idx] : sc.end_indexes[idx]] + sc.values[ + sc.start_indexes[idx] : sc.end_indexes[idx] + ] for idx in range(len(self.index)) ] for sc in self.series_containers diff --git a/tsflex/features/utils.py b/tsflex/features/utils.py index e12c319b..43ca3201 100644 --- a/tsflex/features/utils.py +++ b/tsflex/features/utils.py @@ -121,7 +121,7 @@ def _get_funcwrapper_func_and_kwargs(func: FuncWrapper) -> Tuple[Callable, dict] func_wrapper_kwargs["output_names"] = func.output_names func_wrapper_kwargs["input_type"] = func.input_type func_wrapper_kwargs["vectorized"] = func.vectorized - func_wrapper_kwargs['parallel'] = func.parallel + func_wrapper_kwargs["parallel"] = func.parallel func_wrapper_kwargs.update(func.kwargs) return function, func_wrapper_kwargs From 053ddc1fc6fe6568cca7e9a2dc5c2e640150dd44 Mon Sep 17 00:00:00 2001 From: Jeroen Van Der Donckt Date: Thu, 25 May 2023 15:11:39 +0200 Subject: [PATCH 4/8] :sparkles: support both parallel over series and intra series --- tsflex/features/feature_collection.py | 129 ++++++++++++++----- tsflex/features/segmenter/strided_rolling.py | 47 ++++--- 2 files changed, 129 insertions(+), 47 deletions(-) diff --git a/tsflex/features/feature_collection.py b/tsflex/features/feature_collection.py index b4ed870a..bf00f469 100644 --- a/tsflex/features/feature_collection.py +++ b/tsflex/features/feature_collection.py @@ -104,6 +104,31 @@ def get_required_series(self) -> List[str]: set(flatten([fr_key[0] for fr_key in self._feature_desc_dict.keys()])) ) + def _get_feature_desc_dict( + self, parallel: bool + ) -> Dict[ + Tuple[Tuple[str, ...], Union[float, pd.Timedelta]], List[FeatureDescriptor] + ]: + """Return the feature descriptor dictionary. + + Parameters + ---------- + parallel : bool + Whether to return the feature descriptors for the parallel functions or not. + + Returns + ------- + Dict[Tuple[Tuple[str, ...], Union[float, pd.Timedelta]], List[FeatureDescriptor]] + The feature descriptor dictionary. + + """ + res = {} + for k, fd_list in self._feature_desc_dict.items(): + fd_list = [fd for fd in fd_list if fd.function.parallel == parallel] + if fd_list: + res[k] = fd_list + return res + def get_nb_output_features(self) -> int: """Return the number of output features in this feature collection. @@ -260,11 +285,23 @@ def add( self._check_feature_descriptors(skip_none=True) @staticmethod - def _executor(idx: int): - # global get_stroll_func - stroll, function = get_stroll_func(idx) + def _non_parallel_func_executor(idx: int): + """Execute functions that are not parallelized (over the strided window). + + This executor can be called in a multiprocessing context. + """ + stroll, function = get_stroll_non_parallel_func(idx) return stroll.apply_func(function) + @staticmethod + def _parallel_func_executor(idx: int, n_jobs: int): + """Execute functions that are parallelized (over the strided window). + + This executor canNOT be called in a multiprocessing context. + """ + stroll, function = get_stroll_parallel_func(idx) + return stroll.apply_func(function, n_jobs) + # def _get_stroll(self, kwargs): # return StridedRollingFactory.get_segmenter(**kwargs) @@ -279,20 +316,21 @@ def _stroll_feat_generator( window_idx: str, include_final_window: bool, approve_sparsity: bool, + parallel: bool, ) -> Callable[[int], Tuple[StridedRolling, FuncWrapper]]: # --- Future work --- # We could also make the StridedRolling creation multithreaded # Very low priority because the STROLL __init__ is rather efficient! - keys_wins_strides = list(self._feature_desc_dict.keys()) - lengths = np.cumsum( - [len(self._feature_desc_dict[k]) for k in keys_wins_strides] - ) + feature_desc_dict = self._get_feature_desc_dict(parallel) + + keys_wins_strides = list(feature_desc_dict) + lengths = np.cumsum([len(feature_desc_dict[k]) for k in keys_wins_strides]) def get_stroll_function(idx) -> Tuple[StridedRolling, FuncWrapper]: key_idx = np.searchsorted(lengths, idx, "right") # right bc idx starts at 0 key, win = keys_wins_strides[key_idx] - feature = self._feature_desc_dict[keys_wins_strides[key_idx]][ + feature = feature_desc_dict[keys_wins_strides[key_idx]][ idx - lengths[key_idx] ] stride = feature.stride if calc_stride is None else calc_stride @@ -316,10 +354,9 @@ def get_stroll_function(idx) -> Tuple[StridedRolling, FuncWrapper]: return get_stroll_function - def _get_stroll_feat_length(self) -> int: - return sum( - len(self._feature_desc_dict[k]) for k in self._feature_desc_dict.keys() - ) + def _get_stroll_feat_length(self, parallel: bool) -> int: + feat_desc_dict = self._get_feature_desc_dict(parallel) + return sum(len(feat_desc_dict[k]) for k in feat_desc_dict.keys()) def _check_no_multiple_windows(self): assert ( @@ -599,11 +636,18 @@ def calculate( for n, s, in series_dict.items() } + if ( + os.name == "nt" + ): # On Windows no multiprocessing is supported, see https://github.com/predict-idlab/tsflex/issues/51 + n_jobs = 1 + elif n_jobs is None: + n_jobs = os.cpu_count() + # Note: this variable has a global scope so this is shared in multiprocessing # TODO: try to make this more efficient (but is not really the bottleneck) - global get_stroll_func - get_stroll_func = self._stroll_feat_generator( - series_dict, + global get_stroll_non_parallel_func # is not applied in parallel on the stroll + global get_stroll_parallel_func # is applied in parallel on the stroll + kwargs = dict( calc_stride=stride, segment_start_idxs=segment_start_idxs, segment_end_idxs=segment_end_idxs, @@ -613,30 +657,42 @@ def calculate( include_final_window=include_final_window, approve_sparsity=approve_sparsity, ) - nb_stroll_funcs = self._get_stroll_feat_length() - - if ( - os.name == "nt" - ): # On Windows no multiprocessing is supported, see https://github.com/predict-idlab/tsflex/issues/51 - n_jobs = 1 - elif n_jobs is None: - n_jobs = os.cpu_count() - n_jobs = min(n_jobs, nb_stroll_funcs) + get_stroll_non_parallel_func = self._stroll_feat_generator( + series_dict, + **kwargs, + parallel=False, + ) + nb_stroll_non_parallel_func = self._get_stroll_feat_length(parallel=False) + n_jobs_non_parallel_func = min(n_jobs, nb_stroll_non_parallel_func) + get_stroll_parallel_func = self._stroll_feat_generator( + series_dict, + **kwargs, + parallel=True, + ) + nb_stroll_parallel_func = self._get_stroll_feat_length(parallel=True) calculated_feature_list = None + + # 1. Calculate the features for the non-parallel functions + # -> these functions are calculated (possibly in parallel) over the functions if n_jobs in [0, 1]: - idxs = range(nb_stroll_funcs) + idxs = range(nb_stroll_non_parallel_func) if show_progress: idxs = tqdm(idxs) try: - calculated_feature_list = [self._executor(idx) for idx in idxs] + # 1. Calculate the features for the non-parallel functions + calculated_feature_list = [ + self._non_parallel_func_executor(i) for i in idxs + ] except Exception: traceback.print_exc() else: - with Pool(processes=n_jobs) as pool: - results = pool.imap_unordered(self._executor, range(nb_stroll_funcs)) + with Pool(processes=n_jobs_non_parallel_func) as pool: + results = pool.imap_unordered( + self._non_parallel_func_executor, range(nb_stroll_non_parallel_func) + ) if show_progress: - results = tqdm(results, total=nb_stroll_funcs) + results = tqdm(results, total=nb_stroll_non_parallel_func) try: calculated_feature_list = [f for f in results] except Exception: @@ -647,6 +703,21 @@ def calculate( pool.close() pool.join() + # 2. Calculate the features for the parallel functions + # -> these functions are calculated (possibly in parallel) within the functions + idxs = range(nb_stroll_parallel_func) + if show_progress: + idxs = tqdm(idxs) + try: + # We collect one by one the results of the parallel functions + # (because the parallelization is done within the function , i.e. over the + # strided windows) + calculated_feature_list += [ + self._parallel_func_executor(i, n_jobs) for i in idxs + ] + except Exception: + traceback.print_exc() + # Close the file handler (this avoids PermissionError: [WinError 32]) if logging_file_path: f_handler.close() diff --git a/tsflex/features/segmenter/strided_rolling.py b/tsflex/features/segmenter/strided_rolling.py index 190ef506..cbf643e0 100644 --- a/tsflex/features/segmenter/strided_rolling.py +++ b/tsflex/features/segmenter/strided_rolling.py @@ -14,6 +14,7 @@ __author__ = "Jonas Van Der Donckt, Jeroen Van Der Donckt" import time +import traceback import warnings from abc import ABC, abstractmethod from collections import namedtuple @@ -326,7 +327,9 @@ def _construct_series_containers( ) return series_containers - def apply_func(self, func: FuncWrapper) -> pd.DataFrame: + def apply_func( + self, func: FuncWrapper, n_jobs: Optional[int] = None + ) -> pd.DataFrame: """Apply a function to the segmented series. Parameters @@ -445,27 +448,35 @@ def apply_func(self, func: FuncWrapper) -> pd.DataFrame: # when combining into an array out = out.T if out_type is tuple else out - elif func.parallel: + elif func.parallel and n_jobs > 1: # Parallel function execution - with Pool() as pool: - out = np.array( - list( - pool.imap( - func, - *[ - [ - sc.values[ - sc.start_indexes[idx] : sc.end_indexes[idx] + with Pool(processes=n_jobs) as pool: + try: + out = np.array( + list( + pool.imap( + func, + *[ + [ + sc.values[ + sc.start_indexes[idx] : sc.end_indexes[idx] + ] + for idx in range(len(self.index)) ] - for idx in range(len(self.index)) - ] - for sc in self.series_containers - ], - # TODO -> make this configurable - chunksize=100, + for sc in self.series_containers + ], + # TODO -> make this configurable + chunksize=100, + ) ) ) - ) + except Exception: + traceback.print_exc() + pool.terminate() + finally: + # Close & join because: https://github.com/uqfoundation/pathos/issues/131 + pool.close() + pool.join() else: # Sequential function execution (default) From 736ba7dbc33653513c8713b48e0a34cbb0c3ef9d Mon Sep 17 00:00:00 2001 From: jvdd Date: Tue, 30 May 2023 10:57:17 +0200 Subject: [PATCH 5/8] :pray: update lock file --- poetry.lock | 407 ++++++++++++++++++++++++++++------------------------ 1 file changed, 216 insertions(+), 191 deletions(-) diff --git a/poetry.lock b/poetry.lock index 69d64312..1279f390 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. 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"typing-extensions" -version = "4.6.2" +version = "4.6.3" description = "Backported and Experimental Type Hints for Python 3.7+" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "typing_extensions-4.6.2-py3-none-any.whl", hash = "sha256:3a8b36f13dd5fdc5d1b16fe317f5668545de77fa0b8e02006381fd49d731ab98"}, - {file = "typing_extensions-4.6.2.tar.gz", hash = "sha256:06006244c70ac8ee83fa8282cb188f697b8db25bc8b4df07be1873c43897060c"}, + {file = "typing_extensions-4.6.3-py3-none-any.whl", hash = "sha256:88a4153d8505aabbb4e13aacb7c486c2b4a33ca3b3f807914a9b4c844c471c26"}, + {file = "typing_extensions-4.6.3.tar.gz", hash = "sha256:d91d5919357fe7f681a9f2b5b4cb2a5f1ef0a1e9f59c4d8ff0d3491e05c0ffd5"}, ] [[package]] @@ -3843,9 +3872,10 @@ files = [ ] [package.extras] -brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"] -secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"] -socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"] +brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"] +secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"] +socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] +zstd = ["zstandard (>=0.18.0)"] [[package]] name = "wcwidth" @@ -4034,4 +4064,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more [metadata] lock-version = "2.0" python-versions = ">=3.7.1,<3.12" # When deploying set this to 3.7 -content-hash = "494abacce212d8ad465388a7be9c9a0a798fecfd13cb5245ddf108c7ea157efc" +content-hash = "8c79c9148f612667a3e2d74d0ca5ee45db9d6b80dc8aaa743a39aa77d348deb8" diff --git a/pyproject.toml b/pyproject.toml index 025639b6..a6a89526 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,6 +45,10 @@ pycatch22 = "^0.4.2" # antropy = "^0.1.5" antropy = { git = "https://github.com/raphaelvallat/antropy.git", branch = "master" } # replace with pip when > 0.1.5 release available nolds = "^0.5.2" +pyentrp = [ + { version = "^0.7.1", python = "<3.8" }, + { version = "^0.8.2", python = ">=3.8" } +] # Linting ruff = "^0.0.264" black = "^22.12.0" From 651e1627189c5a3ca503f5748331019041dc4147 Mon Sep 17 00:00:00 2001 From: Jeroen Van Der Donckt Date: Thu, 8 Jun 2023 12:34:34 +0200 Subject: [PATCH 7/8] :construction: --- tests/test_features_feature_collection.py | 133 ++++++++++++++++++- tsflex/features/feature_collection.py | 2 +- tsflex/features/segmenter/strided_rolling.py | 9 +- 3 files changed, 140 insertions(+), 4 deletions(-) diff --git a/tests/test_features_feature_collection.py b/tests/test_features_feature_collection.py index e04ce198..900adc9b 100644 --- a/tests/test_features_feature_collection.py +++ b/tests/test_features_feature_collection.py @@ -1730,7 +1730,7 @@ def sum_mean(x, axis): s = "EDA__" p = "__w=1000" assert np.all(res[s + "sum" + p].values == res[s + "sum_vect" + p].values) - assert np.all(res[s + "mean" + p].values == res[s + "mean_vect" + p].values) + assert np.allclose(res[s + "mean" + p].values, res[s + "mean_vect" + p].values) def test_multiple_inputs_vectorized_features(dummy_data): @@ -1759,6 +1759,137 @@ def windowed_diff(x1, x2): assert np.all(res["EDA|TMP__windowed_diff" + p].values == manual_diff) +### Test parallel features + + +def test_basic_parallel_features_different_fc(dummy_data): + fs = 4 # The sample frequency in Hz + fc1 = FeatureCollection( + feature_descriptors=[ + FeatureDescriptor(np.max, "EDA", 250 * fs, 75 * fs), + ] + ) + fc2 = FeatureCollection( + feature_descriptors=[ + FeatureDescriptor( + FuncWrapper(np.max, output_names="max_", parallel=True), + "EDA", + 250 * fs, + 75 * fs, + ), + ] + ) + res1 = fc1.calculate(dummy_data) + res2 = fc2.calculate(dummy_data) + + assert len(res1) == 1 + assert len(res2) == 1 + res1 = res1[0] + res2 = res2[0] + assert (len(res1) > 1) and (len(res2) > 1) + assert np.all(res1.index == res2.index) + assert np.all(res1.values == res2.values) + + +def test_basic_parallel_features_same_fc(dummy_data): + fs = 4 # The sample frequency in Hz + fc = FeatureCollection( + feature_descriptors=[ + FeatureDescriptor(np.max, "EDA", 250 * fs, 75 * fs), + FeatureDescriptor( + FuncWrapper(np.max, output_names="max_", parallel=True), + "EDA", + 250 * fs, + 75 * fs, + ), + ] + ) + res = fc.calculate(dummy_data) + + assert len(res) == 2 + assert (len(res[0]) > 1) and (len(res[1]) > 1) + assert np.all(res[0].index == res[1].index) + assert np.all(res[0].values == res[1].values) + + +def test_time_based_parallel_features(dummy_data): + fc = FeatureCollection( + feature_descriptors=[ + FeatureDescriptor(np.max, "EDA", "5min", "3min"), + FeatureDescriptor( + FuncWrapper(np.max, output_names="max_", parallel=True), + "EDA", + "5min", + "3min", + ), + ] + ) + res = fc.calculate(dummy_data) + + assert len(res) == 2 + assert (len(res[0]) > 1) and (len(res[1]) > 1) + assert np.all(res[0].index == res[1].index) + assert np.all(res[0].values == res[1].values) + + +def test_multiple_outputs_parallel_features(dummy_data): + def sum_mean(x): + s = np.sum(x) + return s, s / x.shape[0] + + fs = 4 # The sample frequency in Hz + fc = FeatureCollection( + feature_descriptors=[ + FeatureDescriptor(np.sum, "EDA", 250 * fs, 75 * fs), + FeatureDescriptor(np.mean, "EDA", 250 * fs, 75 * fs), + FeatureDescriptor( + FuncWrapper( + sum_mean, + output_names=["sum_par", "mean_par"], + parallel=True, + ), + "EDA", + 250 * fs, + 75 * fs, + ), + ] + ) + + res = fc.calculate(dummy_data, return_df=True) + + assert res.shape[1] == 4 + s = "EDA__" + p = "__w=1000" + assert np.all(res[s + "sum" + p].values == res[s + "sum_par" + p].values) + assert np.allclose(res[s + "mean" + p].values, res[s + "mean_par" + p].values) + + +def test_multiple_inputs_parallel_features(dummy_data): + def windowed_diff(x1, x2): + return np.sum(x1) - np.sum(x2) + + fc = FeatureCollection( + feature_descriptors=[ + FeatureDescriptor(np.sum, "EDA", "5min", "2.5min"), + FeatureDescriptor(np.sum, "TMP", "5min", "2.5min"), + FeatureDescriptor( + FuncWrapper(windowed_diff, parallel=True), + ("EDA", "TMP"), + "5min", + "2.5min", + ), + ] + ) + + res = fc.calculate(dummy_data, return_df=True) + + assert res.shape[1] == 3 + assert res.shape[0] > 1 + p = "__w=5m" + manual_diff = res["EDA__sum" + p].values - res["TMP__sum" + p].values + assert np.all(res["EDA|TMP__windowed_diff" + p].values == manual_diff) + + ### Test feature extraction length diff --git a/tsflex/features/feature_collection.py b/tsflex/features/feature_collection.py index bf00f469..73739447 100644 --- a/tsflex/features/feature_collection.py +++ b/tsflex/features/feature_collection.py @@ -675,7 +675,7 @@ def calculate( # 1. Calculate the features for the non-parallel functions # -> these functions are calculated (possibly in parallel) over the functions - if n_jobs in [0, 1]: + if n_jobs_non_parallel_func in [0, 1]: idxs = range(nb_stroll_non_parallel_func) if show_progress: idxs = tqdm(idxs) diff --git a/tsflex/features/segmenter/strided_rolling.py b/tsflex/features/segmenter/strided_rolling.py index cbf643e0..c4328fca 100644 --- a/tsflex/features/segmenter/strided_rolling.py +++ b/tsflex/features/segmenter/strided_rolling.py @@ -336,6 +336,9 @@ def apply_func( ---------- func : FuncWrapper The Callable wrapped function which will be applied. + n_jobs : Optional[int], optional + The number of processes to use for parallel FuncWrapper execution, by + default None. Returns ------- @@ -716,9 +719,11 @@ def __init__( # we want to assure that the window-stride arguments are integers (samples) assert all(isinstance(p, int) for p in [self.window] + self.strides) - def apply_func(self, func: FuncWrapper) -> pd.DataFrame: + def apply_func( + self, func: FuncWrapper, n_jobs: Optional[int] = None + ) -> pd.DataFrame: # Apply the function and stitch back the time-index - df = super().apply_func(func) + df = super().apply_func(func, n_jobs) df.index = self._series_index[df.index] return df From 17c5a23ef696a8a4def31d241aae86e8e434d3da Mon Sep 17 00:00:00 2001 From: jvdd Date: Thu, 8 Jun 2023 14:14:09 +0200 Subject: [PATCH 8/8] :bug: allow parallel multi-input funcwrapper --- tsflex/features/segmenter/strided_rolling.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tsflex/features/segmenter/strided_rolling.py b/tsflex/features/segmenter/strided_rolling.py index c4328fca..3c4b93d6 100644 --- a/tsflex/features/segmenter/strided_rolling.py +++ b/tsflex/features/segmenter/strided_rolling.py @@ -457,19 +457,19 @@ def apply_func( try: out = np.array( list( - pool.imap( + pool.starmap( func, - *[ + [ [ sc.values[ sc.start_indexes[idx] : sc.end_indexes[idx] ] - for idx in range(len(self.index)) + for sc in self.series_containers ] - for sc in self.series_containers + for idx in range(len(self.index)) ], # TODO -> make this configurable - chunksize=100, + # chunksize=100, ) ) )