From 18edaeb1f349880cc0eebd4f8f6a419e3bc16d08 Mon Sep 17 00:00:00 2001 From: stephanbreimann Date: Tue, 19 Sep 2023 12:16:38 +0200 Subject: [PATCH] Update conf.py and CONTRIBUTING.rst and test --- CONTRIBUTING.rst | 68 +++-- README.rst | 2 + aaanalysis/__pycache__/utils.cpython-39.pyc | Bin 940 -> 1561 bytes aaanalysis/_utils/utils_dpulearn.py | 12 - .../__pycache__/data_loader.cpython-39.pyc | Bin 7473 -> 7435 bytes aaanalysis/data_loader/data_loader.py | 76 ++--- aaanalysis/utils.py | 22 +- .../doctrees/{index => _index}/badges.doctree | Bin 11526 -> 11663 bytes .../{_resources => _index}/overview.doctree | Bin 6430 -> 6426 bytes .../{_resources => _index}/tables.doctree | Bin 40493 -> 40489 bytes .../usage_principles/data_loading.doctree | Bin 0 -> 5624 bytes docs/build/doctrees/api.doctree | Bin 28450 -> 27016 bytes docs/build/doctrees/environment.pickle | Bin 25844344 -> 25846300 bytes .../generated/aaanalysis.AAclust.doctree | Bin 88759 -> 92103 bytes 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Bin 0 -> 7563 bytes .../doctrees/index/usage_principles.doctree | Bin 2794 -> 3801 bytes docs/build/html/.buildinfo | 2 +- ...png => summary__index_badges_577f5a73.png} | Bin ...g => summary__index_overview_2b433d77.png} | Bin ...png => summary__index_tables_88a5b382.png} | Bin ...usage_principles_data_loading_cc2c81b2.png | Bin 0 -> 53091 bytes ...summary_index_tables_template_5b05b8f2.png | Bin 0 -> 46926 bytes docs/build/html/{index => _index}/badges.html | 18 +- .../html/{_resources => _index}/overview.html | 8 +- .../html/{_resources => _index}/tables.html | 10 +- .../_index/usage_principles/data_loading.html | 253 ++++++++++++++++ .../_modules/aaanalysis/aaclust/aaclust.html | 4 +- .../html/_modules/aaanalysis/cpp/_split.html | 4 +- .../html/_modules/aaanalysis/cpp/cpp.html | 4 +- .../_modules/aaanalysis/cpp/cpp_plot.html | 4 +- .../html/_modules/aaanalysis/cpp/feature.html | 4 +- .../aaanalysis/data_loader/data_loader.html | 80 ++--- .../aaanalysis/dpulearn/dpulearn.html | 4 +- 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(100%) rename tests/{test_cpp/test_feature.py => unit/test_cpp_feature.py} (100%) create mode 100644 tests/unit/test_dataloader.py create mode 100644 tests/unit/test_dpulearn.py create mode 100644 tests/unit/test_plotting.py diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index 13012879..2f412a17 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -1,3 +1,9 @@ +.. Developer Notes: + This fill is a summary of sources and conventions for software development in Python used in this project. + Aims of the project and our naming conventions are defined in 'Vision' and 'Documentation', respectively. + Please modify only CONTRIBUTING.rst and just update by copy-pasting the /docs/source/index/CONTRIBUTING_COPY.rst, + which is used for the readthedocs documentation. + ============ Contributing ============ @@ -7,7 +13,7 @@ Contributing :depth: 1 Introduction ------------- +============ Welcome and thank you for considering a contribution to AAanalysis! We are an open-source project focusing on interpretable protein prediction. Your involvement is invaluable to us. Contributions can be made in the following ways: @@ -17,13 +23,13 @@ interpretable protein prediction. Your involvement is invaluable to us. Contribu - Participating in project discussions. Newcomers can start by tackling issues labeled `good first issue `_. - +Please email stephanbreimann@gmail.com for further questions or suggestions? Vision ------- +====== Objectives -^^^^^^^^^^ +---------- - Establish a comprehensive toolkit for interpretable, sequence-based protein prediction. - Enable robust learning from small and unbalanced datasets, common in life sciences. @@ -32,14 +38,14 @@ Objectives - Offer flexible interoperability with other Python packages like `biopython `_. Non-goals -^^^^^^^^^ +--------- - Reimplementation of existing solutions. - Ignoring the biological context. - Reliance on opaque, black-box models. Principles -^^^^^^^^^^ +---------- - Algorithms should be biologically inspired and combine empirical insights with cutting-edge computational methods. - We emphasize fair, accountable, and transparent machine learning, as detailed @@ -49,7 +55,7 @@ Principles Bug Reports ------------ +=========== For effective bug reports, please include a Minimal Reproducible Example (MRE): @@ -61,10 +67,10 @@ Further guidelines can be found `here `_, `pandas `_, `matplotlib `_, and `seaborn `_. @@ -162,7 +168,11 @@ We primarily use two class templates for organizing our codebase: - **Tool**: Standalone classes that focus on specialized tasks, such as feature engineering for protein prediction. They feature `.run` and `.eval` methods to carry out the complete processing pipeline and generate various evaluation metrics. -Both `Wrapper` and `Tool` classes come with supplementary plotting classes for visualization. +The remaining classes should fulfill two further purposes, without being directly implemented using class inheritance. + +- **Data visualization**: Supplementary plotting classes for `Wrapper` and `Tool` classes, named accordingly using a + `Plot` suffix (e.g., 'CPPPlot'). These classes implement an `.eval` method to visualize the key evaluation measures. +- **Analysis support**: Supportive pre-processing classes for `Wrapper` and `Tool` classes. Function and Method Naming """""""""""""""""""""""""" @@ -172,30 +182,38 @@ processing data values should correspond with the names specified in our primary `aaanalysis/_utils/_utils_constants.py`. Code Philosophy -^^^^^^^^^^^^^^^ +--------------- We aim for a modular, robust, and easily extendable codebase. Therefore, we adhere to flat class hierarchies (i.e., only inheriting from `Wrapper` or `Tool` is recommended) and functional programming principles, as outlined in `A Philosophy of Software Design `_. -We also prioritize user-friendly interfaces, complete with descriptive error messages and -`Python type hints `_, comprehensively described in -`Robust Python `_. +Our goal is to provide a user-friendly public interface using concise description and +`Python type hints `_ (see also this Python Enhancement Proposal +`PEP 484 `_ +or the `Robust Python `_ book). +For the validation of user inputs, we use comprehensive checking functions with descriptive error messages. Documentation Style -^^^^^^^^^^^^^^^^^^^^ +------------------- + +- **Docstring Style**: We use the `Numpy Docstring style `_ and + adhere to the `PEP 257 `_ docstring conventions. -- **Docstring Style**: We use Numpy-style docstrings. Learn more in the - `Numpy Docstring Guide `_. +- **Code Style**: Please follow the `PEP 8 `_ and + `PEP 20 `_ style guides for Python code. - **Markup Language**: Documentation is in reStructuredText (.rst). For an introduction, see this `reStructuredText Primer `_. -- **Autodoc**: We use `sphinx.ext.autodoc` for automatic inclusion of docstrings in the documentation. +- **Autodoc**: We use `Sphinx `_ + for automatic inclusion of docstrings in the documentation, including its + `autodoc `_ and + `napoleon `_ extensions. - **Further Details**: See `docs/source/conf.py` for more. Building the Docs -^^^^^^^^^^^^^^^^^ +----------------- To generate the documentation locally: diff --git a/README.rst b/README.rst index 1da72797..af7a6c4d 100644 --- a/README.rst +++ b/README.rst @@ -1,5 +1,7 @@ Welcome to the AAanalysis documentation ======================================= +.. Developer Notes: + Please update badges in README.rst and vice versa .. image:: https://github.com/breimanntools/aaanalysis/workflows/Build/badge.svg :target: https://github.com/breimanntools/aaanalysis/actions :alt: Build Status diff --git a/aaanalysis/__pycache__/utils.cpython-39.pyc b/aaanalysis/__pycache__/utils.cpython-39.pyc index eddbb645696d0b5c0d6a5f5084281dd5ad6b11ae..08432b57e2ab28cfbb7b3b8fbc67a9fe1cc5179e 100644 GIT binary patch delta 686 zcmb_Zy-ve05Vq5xmeQnc`U4UmS-?OA0^R|M4VEszQdzzPBHE45_- z+6GjihPDZH@X)rP0Zp`Ru)3tvx`*Ur^W=b?#!9nrFpQ=4fem69^?k)e9BCd#OkDfo zCXB9FFp7i@<4Ca}mRJU#-SK`1+$2B0p6FPZiLyd5IL51NR6x7r%}U9cEMl~%N|;M4 zGqN58J>}mr%qR;Lp|XZL7kx~mi+zj8GqxGLJBJ@~snrfP{}R;yLXd+$&de{k-p#;! z{{Wv>=i@8EaYg$Xf(H6L_lI&GkUDdQz~}lXgWp8IvpPMu+H}ny$%hA diff --git a/aaanalysis/_utils/utils_dpulearn.py b/aaanalysis/_utils/utils_dpulearn.py index 2463d63c..9060c601 100644 --- a/aaanalysis/_utils/utils_dpulearn.py +++ b/aaanalysis/_utils/utils_dpulearn.py @@ -17,15 +17,3 @@ # III Test/Caller Functions - - -# IV Main -def main(): - t0 = time.time() - - t1 = time.time() - print("Time:", t1 - t0) - - -if __name__ == "__main__": - main() diff --git a/aaanalysis/data_loader/__pycache__/data_loader.cpython-39.pyc b/aaanalysis/data_loader/__pycache__/data_loader.cpython-39.pyc index f9e4161c1c260416d6bc66ea0f6cfdbbf46b58f8..fe2fb6fbd585ed399574383a75159449f4bfc7d8 100644 GIT binary patch delta 2171 zcmaJ?OKclO7~Wa0H}=|2($t9^=h1BiaTZdi1c{dND9($Pwuz`MwMvAXjb|KhvffQ+ z*J+ZvGSHv|l_IJ+7Nquq012cj2aqZxE(i`>xPas%5_b+gAWnX#BA!E@OSlkcv@`q1~+p7v)GxCMtc&`bg+v4$hCO5$$PO+qBqEs-#e z0&T9FG_cr%V;`!x6}N2!XL@l8@4+2l)rWgn(KbCWn(YyZMwclnBnGV+cGO?)c zUvLlFWQEvz(KbELDp^I-w_Hab%w}E3cWrlJ&B$yQ(Fqy#vCjjaA)TF5enn5RmxDi` zP8Lyz>qVJwVCPX`4#OCO;jI9vNf<53G|J=S09H06U%G*6=q4JME~86wO{&Q&h*BKn zvYITo0jxgorNC+I%7VU(LpABTgu}G}&;wdcfhkf8UY9-!T}E#svDGL|L3El0GR59+ zwUY<==MzBIjbfP;mkN$qAqBU@!fh*Pn7!FnM2CQmvVr#f=oxmrz0_v}8nWv(T5QYn zu|??!SQ~0X;g${db;se^{oJV_2Vm9ce8_VFo4)B0-vE)<3q+DbNI`KFLapM}ko>nI z%YP{W`A_;9$Y~D{L#4-IVy~*hZTu*MFaj{V-zD~rI#Tx)II|#DugZv@ZVR0589#VK z1zsp2-9$Ob4=%RUf;A}tX9NQfbXC5jZpk=WMf8JBxrA^Gw{C=LsxK{ux+QVuEj6{) z(k+1=5_(^4BG7AlL~rW8q8|nm8Mj}R-<7rk!sw2#Z9#z-Bi+2XAxJB_DQyNyG60eX z4DnDm5$H<*#}4xwh5gcE)H9l!)eV#bQa>b-eo^Dd2V>o)tS*TiGnPA-lNa~b$ z7Os+Evtl`}ZWb-952mx(v@xV(w_;iV-t_qNY5omB*FAE+3O)&Na}%$p4PD?}#M`Nl zSI_8KM|W2UU9rdo-8biLq8CjEl7VPGwcv(eeSS@^dXS5bNy)6*K6v(dZu%g{!%OKC zQkxVh#53A%Eeu&`(i6ZJcWOP(w_DiN$gJAl zD*K{+HqLE#?*tu$PmBH0dW;?2lh)60;Q$Z=(bJDA+U%`8Tm3w_e|hjYi{D-&#?l=l zo#MJQL|j9mXSude6ecHG|K6_Q=Yi?*ETn;U#NQ~~=vLGPGzqs_P`SxVAv(9cw)ZeX z>+DwiHoC|voj>)hat9sUfgDGJvI6Zu6Qc`Ji_LZ2MpsyGqQ5?*dqtCj@V~qAknYkQ zEduqcioU&bksb>{faT-i_bB>O{|mpT*rmjxZ2H@G5~DJjV24vjA|==oUErk5)>CDD zo-cWwKSfl??l(yRh)?+QQxk>UL_Q}vES=%Dvz#2^w3g?0XpKOy-i?yaUfD6 zgyM1%%E~K~gwjGO&ja6(r)amBn|~D}*qjA(YRU*qO-{m%S_GBJys@v*QYc`zSSZj* z;55`_Xhx5>b;p^hc92@MEnc(!Kx8}me&Z6LwlH{&}HZx v7xUfI^X%u|f#>)yNl`fT_zQqk6qutZsc3PnT|ztWT?MJ))$T@*X>I=iXbc*a delta 2152 zcma)7O>7%Q6y8}o_Iho{ZsNrGD@=b>$8C~AK(rVT-Pj2-ICh$KTAHG|ThBPz#POQh zO=&Ba2wIe&s)%afLaCGU?J4yzJ89sOSV>$UJs^!c>|&e0CyC(30s9VeZ%iz>9cTcQD?f?U_v zrDv{(1V8kV5D8yyneHWV5+PBD>LXpGoy0)fNn~A0c3*I={eH5`td*;a#H`KWa@DYH zvtpJF$Fyo|Z=tM=`uMk=+vrh#!TU4X%TKp_gJOKpcQDL)A-kln>8%+Hl-~;+jD*1u zfX@S;eGKt-b$F>E5#PMb#vJczOQVJOqKtG2UGz1ihVLw5 z9}!jf8a~(RC&9nX6gbhgFW@L7+>owIq_yD(8EGgWqm96I>4tI%y@?#}+)|*?(vad3 z*#Tc0X}>C8R99pYTSV-aWx0Y#2P~*GR7aWv65TC>4Yd*MmH>wY?p?%U%LtI2|BiG- zj7;KJ<+r63&qmaw6Izi5Vc~GMI9~`DXkC_;Jrxj~^wIFZed6%s?Lis32FGD@*?zD{jy zRqz>FE6*+%Y#tNCF(B5)2BWyntg|MeB<044xIsKgZIdigyi{kDm}OD_6n<(uZqcSh zY`5XYYy;nKESNP58)cIuv0bNS(LQ2_I%SSY?X4}uHvI`rE2TAkLeoc|NDN>x9h;iW z<~Q_QW^!pm9Uq?<-cTp=+DJB^*_i2CBDuBbAkJ6|hFRN8%gy@n&8+dmNt1Q(KctEK zOA}MLY}J+=SkA_awW@K3R^iNox$Oqen~H_=#LW@F6B?Z9>nw?1)(=t54;|vYF9Mcdn4uirIoou>k0W+|)>J z%U#s6IUv+!bi!Z;PsA}V#rycJ&>r{f6$YJ#`$fM!x87^XAs1c4DgJ$ER>rBdBjF-~ zk-yiP?p=2-80Sh=3XrW*jT`6qYU^R1h}fPni}4$gq5J@}QH4UkTcYK$rolyIJGgE@ z^WYRN7BYntHVDC0voMkn|y$_P&>yU;XfWpt8%p;Y*R=*gH^fb9}uw-Egx$^hBPe~2b!)MO=h`y<>QHQ?8Sy%BVp~^jw)%o3dp${Z-R;h{@P6 z0C~8rr+)&JrVqLjF63mGjfhp;)t?obo2kfRnx_MY_CF)Q-UY*+Z~ENkwcR9=UZxAQ zo30y@=C8MP#>Bzw6bNa56p2?st^DIayhrhN$_na&xhkX_l2z1-dJsFc_Ep;Do{%q|;M`q1AB?~A|B?{u}UeHPbb z^b-HA`*4tm45<(m{&deQJ}p$i^DV?&--A4_@-KS^mcqHLUMyv_BHSiL-DQm(6M@Ht zI4ndFL^G@xCrf%-%Z#Uw817P{m1@#XsrW_vB-s jdOZphO}tSQ@_Jzmp)Ri)lhDR{--~?itKJX2s7C$*MYbg` diff --git a/aaanalysis/data_loader/data_loader.py b/aaanalysis/data_loader/data_loader.py index 35d9b321..0b498a50 100644 --- a/aaanalysis/data_loader/data_loader.py +++ b/aaanalysis/data_loader/data_loader.py @@ -1,5 +1,6 @@ """ -This is a script for loading protein sequence benchmarking datasets and amino acid scales including classification +This is a script for loading protein sequence benchmarking datasets and amino acid scales and +their two-level classification (AAontology). """ import os import pandas as pd @@ -12,8 +13,8 @@ # I Helper Functions STR_AA_GAP = "-" LIST_CANONICAL_AA = ['N', 'A', 'I', 'V', 'K', 'Q', 'R', 'M', 'H', 'F', 'E', 'D', 'C', 'G', 'L', 'T', 'S', 'Y', 'W', 'P'] -LIST_SCALES = [ut.STR_SCALES, ut.STR_SCALES_RAW] -LIST_DATASETS = LIST_SCALES + [ut.STR_SCALE_CAT, ut.STR_SCALES_PC, ut.STR_TOP60, ut.STR_TOP60_EVAL] +NAME_SCALE_SETS_BASE = [ut.STR_SCALES, ut.STR_SCALES_RAW] +NAMES_SCALE_SETS = NAME_SCALE_SETS_BASE + [ut.STR_SCALE_CAT, ut.STR_SCALES_PC, ut.STR_TOP60, ut.STR_TOP60_EVAL] # II Main Functions @@ -36,6 +37,22 @@ def _adjust_non_canonical_aa(df=None, non_canonical_aa="remove"): df[ut.COL_SEQ] = [re.sub(f'[{"".join(list_non_canonical_aa)}]', STR_AA_GAP, x) for x in df[ut.COL_SEQ]] return df + +def check_name_of_dataset(name="INFO", folder_in=None): + """""" + if name == "INFO": + return + list_datasets = [x.split(".")[0] for x in os.listdir(folder_in) if "." in x] + if name not in list_datasets: + list_aa = [x for x in list_datasets if 'AA' in x] + list_seq = [x for x in list_datasets if 'SEQ' in x] + list_dom = [x for x in list_datasets if 'DOM' in x] + raise ValueError(f"'name' ({name}) is not valid." + f"\n Amino acid datasets: {list_aa}" + f"\n Sequence datasets: {list_seq}" + f"\n Domain datasets: {list_dom}") + + # TODO write test and check in READTHEDOCA (end-to-end solution) def load_dataset(name: str = "INFO", n: Optional[int] = None, @@ -43,30 +60,26 @@ def load_dataset(name: str = "INFO", min_len: Optional[int] = None, max_len: Optional[int] = None) -> pd.DataFrame: """ - Load protein benchmarking datasets or their general overview by setting 'name' to 'INFO'. + Load protein benchmarking datasets. - Three types of benchmark datasets are provided: - - Residue prediction: 6 datasets used to predict residue (amino acid) specific properties - ('AA_CASPASE3', 'AA_FURIN', 'AA_LDR', 'AA_MMP2', 'AA_RNABIND', 'AA_SA') - - Domain prediction: 1 dataset used to predict domain specific properties (_PU contains unlabeled data) - (DOM_GSEC, DOM_GSEC_PU) - - Sequence prediction: 6 datasets used to predict sequence specific properties - ('SEQ_AMYLO', 'SEQ_CAPSID', 'SEQ_DISULFIDE', 'SEQ_LOCATION', 'SEQ_SOLUBLE', 'SEQ_TAIL') + The benchmarks are distinguished into residue/amino acid ('AA'), domain ('DOM'), and sequence ('SEQ') level + datasets. An overview table can be retrieved by using default setting (name='INFO'). A through analysis of + the residue and sequence datasets can be found in TODO[Breimann23a]. Parameters ---------- - name : - Name of the dataset. See 'Dataset' column in overview dataframe (name='INFO'). - n : + name + Name of the dataset. See 'Dataset' column in overview table. + n Number of proteins per class. If None, the whole dataset will be returned. - non_canonical_aa : + non_canonical_aa Options for modifying non-canonical amino acids: - 'remove': Sequences containing non-canonical amino acids are removed. - 'keep': Sequences containing non-canonical amino acids are not removed. - 'gap': Sequences are kept and modified by replacing non-canonical amino acids by gap symbol ('X'). - min_len : - Minimum length of sequences for filtering. None to disable. - max_len : + min_len + Minimum length of sequences for filtering. None to disable + max_len Maximum length of sequences for filtering. None to disable Returns @@ -76,23 +89,16 @@ def load_dataset(name: str = "INFO", Notes ----- - For further information on the benchmark datasets, refer to the AAclust paper : TODO: add link to AAclust paper + See further information on the benchmark datasets in """ ut.check_non_negative_number(name="n", val=n, accept_none=True) ut.check_non_negative_number(name="min_len", val=min_len, accept_none=True) folder_in = ut.FOLDER_DATA + "benchmarks" + ut.SEP + check_name_of_dataset(name=name, folder_in=folder_in) + # Load overview table if name == "INFO": return pd.read_excel(folder_in + "INFO_benchmarks.xlsx") - list_datasets = [x.split(".")[0] for x in os.listdir(folder_in) if "." in x] - if name not in list_datasets: - list_aa = [x for x in list_datasets if 'AA' in x] - list_seq = [x for x in list_datasets if 'SEQ' in x] - list_dom = [x for x in list_datasets if 'DOM' in x] - raise ValueError(f"'name' ({name}) is not valid." - f"\n Amino acid datasets: {list_aa}" - f"\n Sequence datasets: {list_seq}" - f"\n Domain datasets: {list_dom}") df = pd.read_csv(folder_in + name + ".tsv", sep="\t") # Filter Rdata if min_len is not None: @@ -128,12 +134,14 @@ def _filter_scales(df_cat=None, unclassified_in=False, just_aaindex=False): # Extend for AAclustTop60 def load_scales(name="scales", just_aaindex=False, unclassified_in=True): """ - Load amino acid scales or scale classification. + Load amino acid scales, scale classification (AAontology), or scale evaluation. + + A through analysis of the residue and sequence datasets can be found in TODO[Breimann23a]. Parameters ---------- name : str, default = 'scales' - Name of the dataset to load. Options are 'scales', 'scales_raw', 'scale_classification', + Name of the dataset to load. Options are 'scales', 'scales_raw', 'scale_cat', 'scales_pc', 'top60', and 'top60_eval'. unclassified_in : bool, optional Whether unclassified scales should be included. The 'Others' category counts as unclassified. @@ -147,15 +155,15 @@ def load_scales(name="scales", just_aaindex=False, unclassified_in=True): df : :class:`pandas.DataFrame` Dataframe for the selected scale dataset. """ - if name not in LIST_DATASETS: - raise ValueError(f"'name' ({name}) is not valid. Choose one of following: {LIST_DATASETS}") - # Load data + if name not in NAMES_SCALE_SETS: + raise ValueError(f"'name' ({name}) is not valid. Choose one of following: {NAMES_SCALE_SETS}") + # Load _data df_cat = pd.read_excel(ut.FOLDER_DATA + f"{ut.STR_SCALE_CAT}.xlsx") df_cat = _filter_scales(df_cat=df_cat, unclassified_in=unclassified_in, just_aaindex=just_aaindex) if name == ut.STR_SCALE_CAT: return df_cat df = pd.read_excel(ut.FOLDER_DATA + name + ".xlsx", index_col=0) # Filter scales - if name in LIST_SCALES: + if name in NAME_SCALE_SETS_BASE: df = df[[x for x in list(df) if x in list(df_cat[ut.COL_SCALE_ID])]] return df diff --git a/aaanalysis/utils.py b/aaanalysis/utils.py index c99cc4bb..720843dd 100644 --- a/aaanalysis/utils.py +++ b/aaanalysis/utils.py @@ -1,5 +1,5 @@ """ -Config with folder structure +Config with folder structure. 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