From 172923e915054fb52e798645a316666f2e540f0d Mon Sep 17 00:00:00 2001 From: kljk345 Date: Wed, 28 Aug 2024 17:29:36 +0100 Subject: [PATCH] notebook typo fix --- .../doctrees/environment.pickle | Bin 1206687 -> 1206687 bytes .../notebooks/QSARtuna_Tutorial.ipynb | 30 +++++++++--------- .../notebooks/QPTUNA_Tutorial.doctree | Bin 10452537 -> 10452537 bytes .../notebooks/QSARtuna_Tutorial.doctree | Bin 10414592 -> 10414742 bytes docs/sphinx-builddir/doctrees/optunaz.doctree | Bin 790790 -> 790790 bytes .../notebooks/QSARtuna_Tutorial.ipynb.txt | 30 +++++++++--------- .../html/notebooks/QSARtuna_Tutorial.html | 30 +++++++++--------- .../html/notebooks/QSARtuna_Tutorial.ipynb | 30 +++++++++--------- docs/sphinx-builddir/html/optunaz.html | 2 +- docs/sphinx-builddir/html/searchindex.js | 2 +- notebooks/QSARtuna_Tutorial.ipynb | 30 +++++++++--------- 11 files changed, 77 insertions(+), 77 deletions(-) diff --git a/docs/sphinx-builddir/doctrees/environment.pickle 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b/docs/sphinx-builddir/doctrees/nbsphinx/notebooks/QSARtuna_Tutorial.ipynb @@ -893,7 +893,7 @@ "text": [ "[I 2024-08-27 14:01:27,262] A new study created in memory with name: my_study_stratified_split\n", "[I 2024-08-27 14:01:27,303] A new study created in memory with name: study_name_0\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n", " return self._cached_call(args, kwargs, shelving=False)[0]\n", "[I 2024-08-27 14:01:27,408] Trial 0 finished with value: -3999.9364276424735 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.11270803112210707, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 43.81076443656638, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3999.9364276424735.\n", "[I 2024-08-27 14:01:27,485] Trial 1 finished with value: -1856.4459752935309 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -1856.4459752935309.\n", @@ -1726,9 +1726,9 @@ "Traceback (most recent call last):\n", " File \"/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/optuna/study/_optimize.py\", line 196, in _run_trial\n", " value_or_values = func(trial)\n", - " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/objective.py\", line 128, in __call__\n", + " File \"/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/objective.py\", line 128, in __call__\n", " self._validate_algos()\n", - " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/objective.py\", line 270, in _validate_algos\n", + " File \"/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/objective.py\", line 270, in _validate_algos\n", " raise ValueError(\n", "ValueError: PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 \n", "[W 2024-08-27 14:02:47,253] Trial 0 failed with value None.\n" @@ -3388,7 +3388,7 @@ "[I 2024-08-27 14:09:24,367] A new study created in memory with name: my_study\n", "[I 2024-08-27 14:09:24,410] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__fd833c2dde0b7147e6516ea5eebb2657': 'ReLU', 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': 'mean', 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': 'none', 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657'}\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:859)\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:859)\n", " return self._cached_call(args, kwargs, shelving=False)[0]\n", "[I 2024-08-27 14:16:18,484] Trial 0 finished with value: 0.65625 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657', 'activation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': , 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n", " \r" @@ -4745,7 +4745,7 @@ "text": [ "[I 2024-08-27 15:09:26,977] A new study created in memory with name: non-transform_example\n", "[I 2024-08-27 15:09:26,979] A new study created in memory with name: study_name_0\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n", " return self._cached_call(args, kwargs, shelving=False)[0]\n", "[I 2024-08-27 15:09:27,144] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", "[I 2024-08-27 15:09:27,220] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", @@ -11399,7 +11399,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The AutoML daemon functionaility in Qptuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:\n", + "The AutoML daemon functionaility in QSARtuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:\n", "\n", "* __Automated Data Preparation__: Automated process of preparing data for model training, including cleaning, feature extraction, formatting and quorum checks, streamlining data preprocessing\n", "\n", @@ -11407,9 +11407,9 @@ "\n", "* __Scalable and Efficient with Dynamic Resource Allocation__: Workflow designed to handle large datasets (with multiple prediction tasks) and dynamically utilize CPU/GPU/memory HPC resources\n", "\n", - "* __Customizable SLURM and Qptuna Templates__: SLURM templates can be tailored for different use cases. Both initial training and retraining Qptuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate\n", + "* __Customizable SLURM and QSARtuna Templates__: SLURM templates can be tailored for different use cases. Both initial training and retraining QSARtuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate\n", "\n", - "* __Metadata, Prediction and Model Tracking__: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported Qptuna models, aiding monitoring and evaluating pseudo-prospective model performance over time\n", + "* __Metadata, Prediction and Model Tracking__: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported QSARtuna models, aiding monitoring and evaluating pseudo-prospective model performance over time\n", "\n", "* __Automatic Job Resubmission__: In case of SLURM job failures, the code provides functionality to automatically resubmit failed jobs with modified resource allocations, enhancing the robustness of the model training process\n", "\n", @@ -11417,7 +11417,7 @@ "\n", "* __Dry Run Mode__: Dry run mode option enables users to simulate the process without actually submitting jobs, useful for verifying configurations and testing the workflow\n", "\n", - "The following is an example from the Qptuna unit tests:" + "The following is an example from the QSARtuna unit tests:" ] }, { @@ -11534,7 +11534,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Qptuna AutoML expects temporal data (`--input-data`) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns. \n", + "QSARtuna AutoML expects temporal data (`--input-data`) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns. \n", "\n", "Exports are expected to be temporal in nature, with the naming convention `%Y-%m-%d` (see [here](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) for details). Data can be exported in two ways:\n", "\n", @@ -11605,7 +11605,7 @@ "Then our configuration would be:\n", "\n", "```\n", - "qptuna-automl \n", + "qsartuna-automl \n", " --input-data \"../tests/data/automl/*\" \\\n", " --email @astrazeneca.com --user_name \\ # username should be accurate to monitor jobs\n", " --input-smiles-csv-column canonical --input-activity-csv-column molwt \\\n", @@ -11690,7 +11690,7 @@ " \"--n-cores\",\n", " \"1\",\n", " \"--dry-run\", # The dry-run option is enabled, so the AutoML pipeline does not submit to SLURM\n", - " \"-vv\", # Use this CLI option to enable detailed debugging logging to observe Qptuna AutoML behaviour \n", + " \"-vv\", # Use this CLI option to enable detailed debugging logging to observe QSARtuna AutoML behaviour \n", " \"--slurm-al-pool\",\n", " \"../tests/data/DRD2/subset-1000/train.csv\",\n", " \"--slurm-al-smiles-csv-column\",\n", @@ -11733,11 +11733,11 @@ "* resulting folder `data/TID1` comprises the following processed data:\n", " * `TID1.csv` : molecular property data set ready for modelling\n", " * `TID1.json`: config for an initial round of model training\n", - " * `TID1.sh`: used to run run Qptuna AutoML via an `sbatch` command, though `-- dry-run` prevented this happening\n", + " * `TID1.sh`: used to run run QSARtuna AutoML via an `sbatch` command, though `-- dry-run` prevented this happening\n", " * `.24_01_01` lock file initiated to track the status of the training at this timepoint\n", "* `processed_timepoints.json` is created to track which timepoints are processed\n", "\n", - "The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the Qptuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using `cron` or similar).\n", + "The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the QSARtuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using `cron` or similar).\n", "\n", "Running the AutoML workflow does a dry-run check of the status of the run:" ] @@ -11817,7 +11817,7 @@ "ml Miniconda3\n", "conda activate my_env_with_qsartuna\n", "\n", - "qptuna-automl \n", + "qsartuna-automl \n", " --input-data \"/tests/data/automl/*\" \\\n", " --email @astrazeneca.com --user_name \\\n", " --input-smiles-csv-column canonical --input-activity-csv-column molwt \\\n", diff --git a/docs/sphinx-builddir/doctrees/notebooks/QPTUNA_Tutorial.doctree b/docs/sphinx-builddir/doctrees/notebooks/QPTUNA_Tutorial.doctree index ed11d0516d57829709cb24ad74c0ab76f6325938..650f5c250f6f7ba2453bcd88496b45449db0af05 100644 GIT binary patch delta 941 zcmaje*HRNv7{Ku;SU@AL!9t=YC^l>{CDp^1v3VpnXtXA>1G_J*Py2r7yw7&|_M zPKH~bz)6;c{F4Z&i zOzqdRbeW$0)u<^%9BJDs?K(~}ZJUXtE0eCH9HlJfNSR28d|ScjEcMKCKdeA7*H*d1S+zjaU&lZpYy#r98+#Qo=UmGwoNJ0GLsQ9Y6?pzGiKSb{6swPt;nnT zx5O{6D9dD&V=m@lJ{F(?L4>dni?A4BL{Nz(Sc)nv!*Z;^N~}UPR$~p;Vjb2aiWuTZ zz(f)j1SC?}fEsMXCTzwQ)S?dcXh0*jq6you9XrsBo!EsI?8YAKMJx7UKMvp^4&gA4 z;3$rv4aac;Cvgg=VZ(ufi!?GggR?k?cAUorT*M_@#uaqnDz4!=Zr~hNa?FQd z&Fa=CaNpeP!tx2SR)4<0e%Jcl{nvV)i{~Ai{98DNZkZ<2r9cX0hRl>%GF#?Ik<68O z;+OeSEDOFVmHCL9woJ#dEz{OjJ?>~`!gZXOsU|fw7LBP^t~sxC6?taapGF{LvG7M-IBpw;<-{%1&~?`_Q?6^fnq?Ttl;)=OWNuOod@t~- z{w?v_E6Ok#i?A3=uoTNsf*?Xzj#8{Z7!j<*Dy&8s)?h8xp&S)hj}6#}P1uYrh$03R z8g#^AAORB=wxSZ-P=)Q-ft}ceYSdsi_MjGfu@C!EhXXi>LpY2hIEs24!*QHI0~*nU zlQ@ObXvP_w#W}R#Jd#MkMj8%Ww4x0c(2k3^gv+>stGI>^T*nRE#4X&$9o)q|+(#!K zpbHQ22;F#$CwL0MGdxEIJ?O;?yhI=R@d~dofI+;$TMS_s?~ug^-s1y4;uAjOOKvZ}7YdY?qr0WIN)H4CMJTokLZQ3eLGN{0?Iwf#0mK;-*={C;gqP3+3h(u|~Mio3<1gMsOtD z6HJpu73akCKpH}tY<}^am=i-CJA|-D4c`|dwAsjY)ncVft3{EkSH^num2(Yp<1 zk-z)1X}h4+;y0Q5(XwAmWxux~el`8l?f$Ah@>LvHRWhMbPk9SW`Mm9}sqsOA!Q>ar zo?wF113h)-M~fsx{?u6I>PD>wbFna7e{Wd>w?Mdso7`?RzpAY*;Xbr929kd=4WcB$ zJV2aY5ZEmkkr+*gFo*r~3BLTnk>XCv^pyI1X)dQbb(q)Xc4cPzyl!uX%ai5yrDXAy zhNwDY->$-zI_N#Or5EK`qB^>qvp(4x#gna;&&N8_Qige4Dc-Eiv>DFS)C`};?eTbK zc-(1jue)?cMk!C2ZaJmvGK}teEZ4e@Lf)AAc&`PlJl-2UbtkKN%P*BJ5^jZ)ds}oY znNCUP$T`Ytrfa7p2W?&|g;T{SYaHcnk_M7#iKOSnOQbjSfv4W-?G3zE33T*Lsj>0; z9y_&+Hyil071BEKf1A2aq>GJG4f~lFG)e8^{fauj=vqYh#(v@GociMlU?kUE9UH$0=z-u$TwuLITIij1?atXoOgw{=Nh~p%=t} z4SMtR_hYZ#WCyvO#|>;3R_2rIazZS%t&l}Z&ar3FVxLW>j)nHS)V5OAj?}+Ap@r1Z zBpdjKGjc~x)YcG3r=#{?&X}LeB6&xs{c`YB?EEOQ15Y7G%TLHZl2n#TguHyc)#dB`(jXl&AQQ468y1s}|SnJ^1x!yG7sa;Sj0PzhB~4K*+iYGFPsfQ9f9EP}=06KsYp@G3My6KsWMAlL@mVF$L=Ym;xV znS9T|o#8(+Bem>S1_n+&`u26@tKfF~y_=)EPZ-PNk%yG-diEh*KdQuO@jZ?y)qy|U ze@q$1=;Beu%szPd@My+{kbFe3#Uj3T{#>`C&{t7a?ymLCOG;|Gx5XP0$#F#KrA-}u zTzP>JRUB8m!3?R4LQg20LQObql9I@LQc0jItxCNXU;dUdmG-wP=FrH8408-eGj@f( zZdFzX%PPwdu4;lcuRo z_UE2N#&?yfr?c5E@~G-}T42F0wNPXWX#ZYS)@}$LMP%EjHn2oGzE`ybJ1zX&KD9*0 zKH2du0dG$>p gxHld)`cn8)U47d$&AHG&B&4Cs&kMfk3$;4pH}W)t761SM delta 3042 zcmciEdrVVT90zdD?F%R{F4W6IY^%u7>7=FQA)AVBJ~y4{e1lM0@GTXJZ?PzjZ7S#l ze~x)CYNpOD3>5L`GWUqiKa7ftE?Kgfs56<1ZqY4sCdRpMshH}P5FZ)IC){)T`4{Id(T4^s}ibI2i2Au)}LEz)O#-q)n=-k9~DoQc8!LAdR3FlsPP-)aOS4{ zUMZO(P8i4Hdh<|I7DX;M4WjX_nsL;!ShtUo4r-!#eW~Vrg7^_*LB7A)y1csCgQq_I znCB8GxJ6ScobFBz9+mB{PC2F7$vO+GS!Rl&+6F1zU)?f}!gO(uk+2sDaa-Kh5K6zQ z$zz}M0+qHkaCqMuJaXR!^}QO3TBH%gR+Zg%r z31;N&S0X*4FiY)!I%bWirN(gacYfwh9~%ifqzHd47vXl9y6CC z*U5xhwSUDsXG|e)jv=1V)X{I@qLs{BL-lQLQ6+RvY*L8^+9imE_d7T`5}wsS-B`yX z`Y~9{6S7d6N^9`dqUBhOs)tJm4ijet)RaICVd5`Ol60A$5XDos%teKkLZ>}*pu_I6 z+MRZ%BhBeb%dn?9i|whljA92r5FE8Ov{zSQE6sG%5gkh<(NSl+9J4y#5XJKi(ch0T zTkUpBrlZ(uwPo1s#a2t9B{L(n*x{?mUS!Qo=Tq{d539NiqZDWKwXUNuZ(Q|6S2P&- zL|3=m?QD8mi!^VUa5IcHZ|jyor|Kjdot+;QM>`HnYp7s^L8ORUsSnRyA^oNHuYBi$ z!Et!Z(>`&kOG{FnR4Uxk`!^#d)bRE?sY=s1P2krbb7fIWh-nC||3Vr@H~usjX-BJM zCgW}?o!{LnZBo!DN^MQ(+oRhZ&Fyc`y_5p#U6E2u^T85fsBLm<@AaF3f}ZumBc93AkYqltLMJ zpd1#%5?Bh$U^%RSm9PpbU^RH564t<4sDgFy7F5IA@D98S>tO?Igc_)YO|Ti>gDp@8 z^{^EPw!!<*z`I}h;OFDa$yNKdhuvnu6uDFGgFThAOa6(ByJX#iE{?9AIu^sr_Q`rR z`B4^5BFPC#Ez6}G12Xy7pkVEoecA2KP$?K4bsA+nZK{K<( zgxqgND)W?O)%kyTZ16|w10wnGV{+moZ7j}-95;8z zDki2%%5f8}5;>1k+?m>$s-U1nZk{qhaj60tc`AN;s?s2^Y2fg z=#v9V2)DSDBUx%0(~z=589}y2Ww&oxb}N&(WruQ4%{*k;tr-1%m%1fqnaj(HTxL&+ zxwL#?2ULadXr8-ADNwPK{OCSqtcnSwZ&JKSt7%d;vR^5pNm=D@m2iEtl99kJa%;O{ v@SR7vg&us#DdmXpADssT9&=7P5i+W)Uk!D0zfB4X+R){TnLv&~P~w=+Xn^SKxBvx3CbCl`JZog69s1S~$;QAP~RXpWX? lkCtHsVkRJF24WT, 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': , 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n", " \r" @@ -4745,7 +4745,7 @@ "text": [ "[I 2024-08-27 15:09:26,977] A new study created in memory with name: non-transform_example\n", "[I 2024-08-27 15:09:26,979] A new study created in memory with name: study_name_0\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n", " return self._cached_call(args, kwargs, shelving=False)[0]\n", "[I 2024-08-27 15:09:27,144] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", "[I 2024-08-27 15:09:27,220] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", @@ -11399,7 +11399,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The AutoML daemon functionaility in Qptuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:\n", + "The AutoML daemon functionaility in QSARtuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:\n", "\n", "* __Automated Data Preparation__: Automated process of preparing data for model training, including cleaning, feature extraction, formatting and quorum checks, streamlining data preprocessing\n", "\n", @@ -11407,9 +11407,9 @@ "\n", "* __Scalable and Efficient with Dynamic Resource Allocation__: Workflow designed to handle large datasets (with multiple prediction tasks) and dynamically utilize CPU/GPU/memory HPC resources\n", "\n", - "* __Customizable SLURM and Qptuna Templates__: SLURM templates can be tailored for different use cases. Both initial training and retraining Qptuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate\n", + "* __Customizable SLURM and QSARtuna Templates__: SLURM templates can be tailored for different use cases. Both initial training and retraining QSARtuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate\n", "\n", - "* __Metadata, Prediction and Model Tracking__: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported Qptuna models, aiding monitoring and evaluating pseudo-prospective model performance over time\n", + "* __Metadata, Prediction and Model Tracking__: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported QSARtuna models, aiding monitoring and evaluating pseudo-prospective model performance over time\n", "\n", "* __Automatic Job Resubmission__: In case of SLURM job failures, the code provides functionality to automatically resubmit failed jobs with modified resource allocations, enhancing the robustness of the model training process\n", "\n", @@ -11417,7 +11417,7 @@ "\n", "* __Dry Run Mode__: Dry run mode option enables users to simulate the process without actually submitting jobs, useful for verifying configurations and testing the workflow\n", "\n", - "The following is an example from the Qptuna unit tests:" + "The following is an example from the QSARtuna unit tests:" ] }, { @@ -11534,7 +11534,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Qptuna AutoML expects temporal data (`--input-data`) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns. \n", + "QSARtuna AutoML expects temporal data (`--input-data`) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns. \n", "\n", "Exports are expected to be temporal in nature, with the naming convention `%Y-%m-%d` (see [here](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) for details). Data can be exported in two ways:\n", "\n", @@ -11605,7 +11605,7 @@ "Then our configuration would be:\n", "\n", "```\n", - "qptuna-automl \n", + "qsartuna-automl \n", " --input-data \"../tests/data/automl/*\" \\\n", " --email @astrazeneca.com --user_name \\ # username should be accurate to monitor jobs\n", " --input-smiles-csv-column canonical --input-activity-csv-column molwt \\\n", @@ -11690,7 +11690,7 @@ " \"--n-cores\",\n", " \"1\",\n", " \"--dry-run\", # The dry-run option is enabled, so the AutoML pipeline does not submit to SLURM\n", - " \"-vv\", # Use this CLI option to enable detailed debugging logging to observe Qptuna AutoML behaviour \n", + " \"-vv\", # Use this CLI option to enable detailed debugging logging to observe QSARtuna AutoML behaviour \n", " \"--slurm-al-pool\",\n", " \"../tests/data/DRD2/subset-1000/train.csv\",\n", " \"--slurm-al-smiles-csv-column\",\n", @@ -11733,11 +11733,11 @@ "* resulting folder `data/TID1` comprises the following processed data:\n", " * `TID1.csv` : molecular property data set ready for modelling\n", " * `TID1.json`: config for an initial round of model training\n", - " * `TID1.sh`: used to run run Qptuna AutoML via an `sbatch` command, though `-- dry-run` prevented this happening\n", + " * `TID1.sh`: used to run run QSARtuna AutoML via an `sbatch` command, though `-- dry-run` prevented this happening\n", " * `.24_01_01` lock file initiated to track the status of the training at this timepoint\n", "* `processed_timepoints.json` is created to track which timepoints are processed\n", "\n", - "The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the Qptuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using `cron` or similar).\n", + "The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the QSARtuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using `cron` or similar).\n", "\n", "Running the AutoML workflow does a dry-run check of the status of the run:" ] @@ -11817,7 +11817,7 @@ "ml Miniconda3\n", "conda activate my_env_with_qsartuna\n", "\n", - "qptuna-automl \n", + "qsartuna-automl \n", " --input-data \"/tests/data/automl/*\" \\\n", " --email @astrazeneca.com --user_name \\\n", " --input-smiles-csv-column canonical --input-activity-csv-column molwt \\\n", diff --git a/docs/sphinx-builddir/html/notebooks/QSARtuna_Tutorial.html b/docs/sphinx-builddir/html/notebooks/QSARtuna_Tutorial.html index 3ce50ef..a42f003 100644 --- a/docs/sphinx-builddir/html/notebooks/QSARtuna_Tutorial.html +++ b/docs/sphinx-builddir/html/notebooks/QSARtuna_Tutorial.html @@ -1094,7 +1094,7 @@

Configuration example
 [I 2024-08-27 14:01:27,262] A new study created in memory with name: my_study_stratified_split
 [I 2024-08-27 14:01:27,303] A new study created in memory with name: study_name_0
-/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)
+/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)
   return self._cached_call(args, kwargs, shelving=False)[0]
 [I 2024-08-27 14:01:27,408] Trial 0 finished with value: -3999.9364276424735 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.11270803112210707, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 43.81076443656638, 'descriptor': '{"name": "ECFP", "parameters": {"radius": 3, "nBits": 2048, "returnRdkit": false}}'}. Best is trial 0 with value: -3999.9364276424735.
 [I 2024-08-27 14:01:27,485] Trial 1 finished with value: -1856.4459752935309 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{"name": "MACCS_keys", "parameters": {}}'}. Best is trial 1 with value: -1856.4459752935309.
@@ -1807,9 +1807,9 @@ 
Interlude: Cautionary advice for PRF ∆y (response column) validityEnsemble uncertainty (ChemProp Only)
 [I 2024-08-27 15:09:26,977] A new study created in memory with name: non-transform_example
 [I 2024-08-27 15:09:26,979] A new study created in memory with name: study_name_0
-/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)
+/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)
   return self._cached_call(args, kwargs, shelving=False)[0]
 [I 2024-08-27 15:09:27,144] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{"name": "ECFP", "parameters": {"radius": 3, "nBits": 2048, "returnRdkit": false}}'}. Best is trial 0 with value: -3501.942111261296.
 [I 2024-08-27 15:09:27,220] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{"name": "ECFP", "parameters": {"radius": 3, "nBits": 2048, "returnRdkit": false}}'}. Best is trial 0 with value: -3501.942111261296.
@@ -5524,18 +5524,18 @@ 
Precomputed descriptors from a file example

 

 
Overview

-
The AutoML daemon functionaility in Qptuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:

+
The AutoML daemon functionaility in QSARtuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:

 
    
 
  • Automated Data Preparation: Automated process of preparing data for model training, including cleaning, feature extraction, formatting and quorum checks, streamlining data preprocessing
    • 
 
  • Model Training with SLURM: Integration with SLURM to dispatch tasks, leveraging distributed computing resources for efficient and scalable model training
    • 
 
  • Scalable and Efficient with Dynamic Resource Allocation: Workflow designed to handle large datasets (with multiple prediction tasks) and dynamically utilize CPU/GPU/memory HPC resources
    • 
-
  • Customizable SLURM and Qptuna Templates: SLURM templates can be tailored for different use cases. Both initial training and retraining Qptuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate
    • 
-
  • Metadata, Prediction and Model Tracking: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported Qptuna models, aiding monitoring and evaluating pseudo-prospective model performance over time
    • 
+
  • Customizable SLURM and QSARtuna Templates: SLURM templates can be tailored for different use cases. Both initial training and retraining QSARtuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate
    • 
+
  • Metadata, Prediction and Model Tracking: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported QSARtuna models, aiding monitoring and evaluating pseudo-prospective model performance over time
    • 
 
  • Automatic Job Resubmission: In case of SLURM job failures, the code provides functionality to automatically resubmit failed jobs with modified resource allocations, enhancing the robustness of the model training process
    • 
 
  • Parallel Task Processing: Supports for parallel processing training tasks, allowing for efficient handling of multiple retraining tasks simultaneously, reducing overall processing time
    • 
 
  • Dry Run Mode: Dry run mode option enables users to simulate the process without actually submitting jobs, useful for verifying configurations and testing the workflow
    • 
 

-
The following is an example from the Qptuna unit tests:

+
The following is an example from the QSARtuna unit tests:

 

 
[102]:
 

@@ -5631,7 +5631,7 @@ 
Note on High-Performance Computing (HPC) Setup
 
Data extraction options

-
Qptuna AutoML expects temporal data (--input-data) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns.

+
QSARtuna AutoML expects temporal data (--input-data) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns.

 
Exports are expected to be temporal in nature, with the naming convention %Y-%m-%d (see here for details). Data can be exported in two ways:

 
    
 
  • 1.) Multiple files: Each extraction date gets an distinct/unique file with %Y-%m-%d format within the filename, which denotes that point in temporal train time, like so:
    • 
@@ -5680,7 +5680,7 @@ 
    Walkthough running an AutoML pipeline../tests/data/DRD2/subset-1000/train.csv
    
 

 
Then our configuration would be:

-
qptuna-automl
+
qsartuna-automl
    --input-data "../tests/data/automl/*"  \
    --email <example>@astrazeneca.com  --user_name <example>  \ # username should be accurate to monitor jobs
    --input-smiles-csv-column canonical  --input-activity-csv-column molwt \
@@ -5724,7 +5724,7 @@ 
Walkthough running an AutoML pipeline"--n-cores",
     "1",
     "--dry-run", # The dry-run option is enabled, so the AutoML pipeline does not submit to SLURM
-    "-vv", # Use this CLI option to enable detailed debugging logging to observe Qptuna AutoML behaviour
+    "-vv", # Use this CLI option to enable detailed debugging logging to observe QSARtuna AutoML behaviour
     "--slurm-al-pool",
     "../tests/data/DRD2/subset-1000/train.csv",
     "--slurm-al-smiles-csv-column",
@@ -5796,13 +5796,13 @@ 
Walkthough running an AutoML pipelineTID1.csv : molecular property data set ready for modelling

 
  • TID1.json: config for an initial round of model training
    • 
-
  • TID1.sh: used to run run Qptuna AutoML via an sbatch command, though -- dry-run prevented this happening
    • 
+
  • TID1.sh: used to run run QSARtuna AutoML via an sbatch command, though -- dry-run prevented this happening
    • 
 
  • .24_01_01 lock file initiated to track the status of the training at this timepoint
    • 
 
 
 
  • processed_timepoints.json is created to track which timepoints are processed
    • 
 
-
    The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the Qptuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using cron or similar).
    
+
    The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the QSARtuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using cron or similar).
    
 
    Running the AutoML workflow does a dry-run check of the status of the run:
    
 
    
 
    [ ]:
@@ -5851,7 +5851,7 @@ 
    Schedule AutoML as a daemon for up-to-date models, 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': , 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n",
       "                                                                                                                                                                            \r"
@@ -4745,7 +4745,7 @@
      "text": [
       "[I 2024-08-27 15:09:26,977] A new study created in memory with name: non-transform_example\n",
       "[I 2024-08-27 15:09:26,979] A new study created in memory with name: study_name_0\n",
-      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
+      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
       "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
       "[I 2024-08-27 15:09:27,144] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n",
       "[I 2024-08-27 15:09:27,220] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n",
@@ -11399,7 +11399,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The AutoML daemon functionaility in Qptuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:\n",
+    "The AutoML daemon functionaility in QSARtuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:\n",
     "\n",
     "* __Automated Data Preparation__: Automated process of preparing data for model training, including cleaning, feature extraction, formatting and quorum checks, streamlining data preprocessing\n",
     "\n",
@@ -11407,9 +11407,9 @@
     "\n",
     "* __Scalable and Efficient with Dynamic Resource Allocation__: Workflow designed to handle large datasets (with multiple prediction tasks) and dynamically utilize CPU/GPU/memory HPC resources\n",
     "\n",
-    "* __Customizable SLURM and Qptuna Templates__: SLURM templates can be tailored for different use cases. Both initial training and retraining Qptuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate\n",
+    "* __Customizable SLURM and QSARtuna Templates__: SLURM templates can be tailored for different use cases. Both initial training and retraining QSARtuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate\n",
     "\n",
-    "* __Metadata, Prediction and Model Tracking__: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported Qptuna models, aiding monitoring and evaluating pseudo-prospective model performance over time\n",
+    "* __Metadata, Prediction and Model Tracking__: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported QSARtuna models, aiding monitoring and evaluating pseudo-prospective model performance over time\n",
     "\n",
     "* __Automatic Job Resubmission__: In case of SLURM job failures, the code provides functionality to automatically resubmit failed jobs with modified resource allocations, enhancing the robustness of the model training process\n",
     "\n",
@@ -11417,7 +11417,7 @@
     "\n",
     "* __Dry Run Mode__: Dry run mode option enables users to simulate the process without actually submitting jobs, useful for verifying configurations and testing the workflow\n",
     "\n",
-    "The following is an example from the Qptuna unit tests:"
+    "The following is an example from the QSARtuna unit tests:"
    ]
   },
   {
@@ -11534,7 +11534,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Qptuna AutoML expects temporal data (`--input-data`) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing  SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns. \n",
+    "QSARtuna AutoML expects temporal data (`--input-data`) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing  SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns. \n",
     "\n",
     "Exports are expected to be temporal in nature, with the naming convention `%Y-%m-%d` (see [here](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) for details). Data can be exported in two ways:\n",
     "\n",
@@ -11605,7 +11605,7 @@
     "Then our configuration would be:\n",
     "\n",
     "```\n",
-    "qptuna-automl \n",
+    "qsartuna-automl \n",
     "   --input-data \"../tests/data/automl/*\"  \\\n",
     "   --email @astrazeneca.com  --user_name   \\ # username should be accurate to monitor jobs\n",
     "   --input-smiles-csv-column canonical  --input-activity-csv-column molwt \\\n",
@@ -11690,7 +11690,7 @@
     "    \"--n-cores\",\n",
     "    \"1\",\n",
     "    \"--dry-run\", # The dry-run option is enabled, so the AutoML pipeline does not submit to SLURM\n",
-    "    \"-vv\", # Use this CLI option to enable detailed debugging logging to observe Qptuna AutoML behaviour \n",
+    "    \"-vv\", # Use this CLI option to enable detailed debugging logging to observe QSARtuna AutoML behaviour \n",
     "    \"--slurm-al-pool\",\n",
     "    \"../tests/data/DRD2/subset-1000/train.csv\",\n",
     "    \"--slurm-al-smiles-csv-column\",\n",
@@ -11733,11 +11733,11 @@
     "* resulting folder `data/TID1` comprises the following processed data:\n",
     "    * `TID1.csv` : molecular property data set ready for modelling\n",
     "    * `TID1.json`: config for an initial round of model training\n",
-    "    * `TID1.sh`: used to run run Qptuna AutoML via an `sbatch` command, though `-- dry-run` prevented this happening\n",
+    "    * `TID1.sh`: used to run run QSARtuna AutoML via an `sbatch` command, though `-- dry-run` prevented this happening\n",
     "    * `.24_01_01` lock file initiated to track the status of the training at this timepoint\n",
     "* `processed_timepoints.json` is created to track which timepoints are processed\n",
     "\n",
-    "The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the Qptuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using `cron` or similar).\n",
+    "The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the QSARtuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using `cron` or similar).\n",
     "\n",
     "Running the AutoML workflow does a dry-run check of the status of the run:"
    ]
@@ -11817,7 +11817,7 @@
     "ml Miniconda3\n",
     "conda activate my_env_with_qsartuna\n",
     "\n",
-    "qptuna-automl \n",
+    "qsartuna-automl \n",
     "   --input-data \"/tests/data/automl/*\"  \\\n",
     "   --email @astrazeneca.com  --user_name   \\\n",
     "   --input-smiles-csv-column canonical  --input-activity-csv-column molwt \\\n",
diff --git a/docs/sphinx-builddir/html/optunaz.html b/docs/sphinx-builddir/html/optunaz.html
index 9af66b0..33fec51 100644
--- a/docs/sphinx-builddir/html/optunaz.html
+++ b/docs/sphinx-builddir/html/optunaz.html
@@ -163,7 +163,7 @@ 
    Submodules
    optunaz.automl module
    
 
    
 
    
-class optunaz.automl.ModelAutoML(output_path=None, input_data=None, n_cores=- 1, email=None, user_name=None, smiles_col=None, activity_col=None, task_col=None, dry_run=False, timestr='20240828-171643')[source]
    
+class optunaz.automl.ModelAutoML(output_path=None, input_data=None, n_cores=- 1, email=None, user_name=None, smiles_col=None, activity_col=None, task_col=None, dry_run=False, timestr='20240828-172746')[source]
 
    Bases: object
    
 
    Prepares the data ready for the model training with ModelDispatcher.
 The ModelAutoML will also store activity for new tasks pending enough data.
    
diff --git a/docs/sphinx-builddir/html/searchindex.js b/docs/sphinx-builddir/html/searchindex.js
index bb29867..c28ffe8 100644
--- a/docs/sphinx-builddir/html/searchindex.js
+++ b/docs/sphinx-builddir/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["README", "algorithms", "deduplicator", "descriptors", "index", "modules", "notebooks/QPTUNA_Tutorial", "notebooks/QSARtuna_Tutorial", "notebooks/preprocess_data", "optunaz", "optunaz.config", "optunaz.utils", "optunaz.utils.enums", "optunaz.utils.preprocessing", "splitters", "transform"], "filenames": ["README.md", "algorithms.rst", "deduplicator.rst", "descriptors.rst", "index.rst", "modules.rst", "notebooks/QPTUNA_Tutorial.ipynb", "notebooks/QSARtuna_Tutorial.ipynb", "notebooks/preprocess_data.ipynb", "optunaz.rst", "optunaz.config.rst", "optunaz.utils.rst", "optunaz.utils.enums.rst", "optunaz.utils.preprocessing.rst", "splitters.rst", "transform.rst"], "titles": ["QSARtuna \ud80c\udd9b: QSAR using Optimization for Hyperparameter Tuning (formerly Optuna AZ and QPTUNA)", "Available algorithms", "Available deduplicators", "Available descriptors", "Welcome to QSARtuna Documentation!", "optunaz", "QPTUNA CLI Tutorial", "QSARtuna CLI Tutorial", "Preprocessing data for QSARtuna", "optunaz package", "optunaz.config package", "optunaz.utils package", "optunaz.utils.enums package", "optunaz.utils.preprocessing package", "Available splitters", "Available transform"], "terms": {"build": [0, 4, 8, 9, 10, 11, 12], "predict": [0, 1, 3, 5, 6, 7, 8, 10, 11, 12], "compchem": 0, "develop": [0, 3, 7, 9], "uncertainti": [0, 1, 4, 8, 9, 10, 13, 15], "quantif": 0, "explain": [0, 1, 4, 5, 10], "mind": 0, "thi": [0, 1, 3, 4, 8, 9, 10, 11, 12, 13, 14], "librari": [0, 6, 7, 8], "search": [0, 1, 3, 9, 10], "best": [0, 1, 9, 10], "ml": [0, 4, 7, 10], "molecular": [0, 1, 3, 4, 6, 7, 8, 9, 10], "given": [0, 1, 3, 6, 7, 8, 9, 10, 11], "data": [0, 1, 3, 4, 9, 10, 11, 12, 13, 14, 15], "itself": [0, 6, 7], "done": [0, 6, 7], "emploi": [0, 3, 6, 7, 8, 9], "latest": [0, 9, 10], "state": [0, 6, 7, 13], "art": [0, 6, 7], "estim": [0, 1, 3, 4, 9, 10, 12], "packag": [0, 1, 3, 5, 6, 7, 8], "further": [0, 10], "document": [0, 6, 7, 8], "github": [0, 6, 7, 9, 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[11, 5, 1, "", "test_scores"]], "optunaz.utils.tracking.Datapoint": [[11, 5, 1, "", "expected"], [11, 5, 1, "", "predicted"], [11, 5, 1, "", "smiles"]], "optunaz.utils.tracking.InternalTrackingCallback": [[11, 5, 1, "", "optconfig"], [11, 5, 1, "", "trial_number_offset"]], "optunaz.utils.tracking.TrackingData": [[11, 5, 1, "", "all_cv_test_scores"], [11, 5, 1, "", "buildconfig"], [11, 5, 1, "", "scoring"], [11, 5, 1, "", "trial_number"], [11, 5, 1, "", "trial_state"], [11, 5, 1, "", "trial_value"]], "optunaz.visualizer": [[9, 1, 1, "", "Visualizer"]], "optunaz.visualizer.Visualizer": [[9, 3, 1, "", "plot_by_configuration"], [9, 3, 1, "", "plot_contour"], [9, 3, 1, "", "plot_history"], [9, 3, 1, "", "plot_parallel_coordinate"], [9, 3, 1, "", "plot_slice"]]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:property", "3": "py:method", "4": "py:function", "5": "py:attribute", "6": "py:exception"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "property", "Python property"], "3": ["py", "method", "Python method"], "4": ["py", "function", "Python function"], "5": ["py", "attribute", "Python attribute"], "6": ["py", "exception", "Python exception"]}, "titleterms": {"qsartuna": [0, 4, 7, 8], "\ud80c\udd9b": 0, "qsar": 0, "us": 0, "optim": [0, 6, 7], "hyperparamet": 0, "tune": 0, "formerli": 0, "optuna": 0, "az": 0, "qptuna": [0, 6], "background": [0, 6, 7], "The": [0, 6, 7], "three": [0, 6, 7], "step": [0, 6, 7], "process": [0, 6, 7], "json": 0, "base": [0, 8], "command": 0, "line": 0, "interfac": 0, "configur": [0, 6, 7], "file": [0, 6, 7], "run": [0, 6, 7], "from": [0, 6, 7, 8], "python": 0, "jupyt": 0, "notebook": 0, "via": 0, "cli": [0, 6, 7], "submit": 0, "slurm": 0, "model": [0, 6, 7], "option": [0, 6, 7], "inspect": 0, "automl": [0, 6, 7, 9], "automat": 0, "machin": 0, "learn": [0, 6, 7], "ad": 0, "descriptor": [0, 3, 6, 7, 9], "algorithm": [0, 1, 6, 7], "avail": [1, 2, 3, 14, 15], "adaboostclassifi": 1, "lasso": 1, "kneighborsclassifi": 1, "kneighborsregressor": 1, "logisticregress": 1, "plsregress": 1, "randomforestclassifi": 1, "randomforestregressor": 1, "ridg": 1, "svc": 1, "svr": 1, "xgbregressor": 1, "prfclassifi": 1, "chempropregressor": 1, "chempropclassifi": 1, "chemprophyperoptclassifi": 1, "chemprophyperoptregressor": 1, "chempropregressorpretrain": 1, "calibratedclassifiercvwithva": 1, "mapi": [1, 6, 7], "dedupl": [2, 8, 13], "keepfirst": 2, "keeplast": 2, "keeprandom": 2, "keepmin": 2, "keepmax": 2, "keepavg": 2, "keepmedian": 2, "keepkeepallnodedupl": 2, "avalon": 3, "ecfp": 3, "ecfp_count": 3, "pathfp": 3, "maccs_kei": 3, "unscaledphyschemdescriptor": 3, "unscaledjazzydescriptor": 3, "unscaledzscalesdescriptor": 3, "physchemdescriptor": 3, "jazzydescriptor": 3, "precomputeddescriptorfromfil": 3, "zscale": [3, 15], "smilesfromfil": 3, "smilesandsideinfofromfil": 3, "scaleddescriptor": 3, "compositedescriptor": 3, "welcom": 4, "document": 4, "develop": 4, "optunaz": [5, 9, 10, 11, 12, 13], "tutori": [6, 7], "thi": [6, 7], "prepar": [6, 7], "regress": [6, 7], "exampl": [6, 7], "creat": [6, 7], "visual": [6, 7, 9], "progress": [6, 7], "pick": [6, 7], "best": [6, 7, 8], "trial": [6, 7], "build": [6, 7], "merg": [6, 7], "preprocess": [6, 7, 8, 13], "split": [6, 7, 8], "data": [6, 7, 8], "train": [6, 7], "test": [6, 7], "set": [6, 7], "remov": [6, 7], "duplic": [6, 7, 8], "dataset": [6, 7], "choos": [6, 7], "score": [6, 7], "function": [6, 7], "advanc": [6, 7], "functoinail": [6, 7], "probabilist": [6, 7, 8], "random": [6, 7, 8, 14], "forest": [6, 7], "prf": [6, 7], "interlud": [6, 7, 8], "cautionari": [6, 7], "advic": [6, 7], "y": [6, 7], "respons": [6, 7], "column": [6, 7], "valid": [6, 7], "chemprop": [6, 7], "simpl": [6, 7], "separ": [6, 7], "shallow": [6, 7], "method": [6, 7], "default": [6, 7], "behavior": [6, 7], "turn": [6, 7], "hyperopt": [6, 7], "within": [6, 7, 8], "functionail": [6, 7], "veri": [6, 7], "larg": [6, 7], "comput": [6, 7], "cost": [6, 7], "A": [6, 7], "note": [6, 7], "mpnn": [6, 7], "search": [6, 7], "space": [6, 7], "side": [6, 7], "inform": [6, 7], "multi": [6, 7], "task": [6, 7], "mtl": [6, 7], "combin": [6, 7], "onli": [6, 7], "recommend": [6, 7], "long": [6, 7], "time": [6, 7], "pre": [6, 7], "adapt": [6, 7], "transfer": [6, 7], "fingerprint": [6, 7], "encod": [6, 7], "latent": [6, 7], "represent": [6, 7, 8], "probabl": [6, 7], "calibr": [6, 7], "classif": [6, 7], "uncertainti": [6, 7], "estim": [6, 7], "vennab": [6, 7], "ensembl": [6, 7], "dropout": [6, 7], "explain": [6, 7, 9], "shap": [6, 7], "interpret": [6, 7], "log": [6, 7, 8], "transform": [6, 7, 8, 13, 15], "covari": [6, 7], "one": [6, 7], "e": [6, 7], "g": [6, 7], "dose": [6, 7], "point": [6, 7], "co": [6, 7], "variat": [6, 7], "proteochemometr": [6, 7], "pcm": [6, 7], "more": [6, 7], "vectorfromsmil": [6, 7], "z": [6, 7], "scale": [6, 7], "object": [6, 7, 9], "priorit": [6, 7], "perform": [6, 7, 8], "standard": [6, 7], "deviat": [6, 7], "further": [6, 7], "precomput": [6, 7], "autom": [6, 7], "retrain": [6, 7, 11], "overview": [6, 7], "high": [6, 7], "hpc": [6, 7], "setup": [6, 7], "extract": [6, 7], "walkthough": [6, 7], "an": [6, 7], "pipelin": 7, "schedul": 7, "daemon": 7, "up": 7, "date": 7, "introduct": 8, "translat": 8, "sdf": 8, "csv": 8, "need": 8, "deal": 8, "compar": 8, "differ": 8, "strategi": 8, "tempor": [8, 14], "stratifi": [8, 14], "scaffold": 8, "input": 8, "user": 8, "import": 8, "logarithm": 8, "dataread": [8, 9], "threshold": 8, "ptr": [8, 15], "experiment": 8, "error": 8, "definit": 8, "implement": 8, "conclus": 8, "calcul": 8, "evalu": [8, 9], "reproduc": 8, "practic": 8, "packag": [9, 10, 11, 12, 13], "subpackag": [9, 11], "submodul": [9, 10, 11, 12, 13], "modul": [9, 10, 11, 12, 13], "builder": 9, "metirc": 9, "model_writ": 9, "optbuild": 9, "predict": 9, "schemagen": 9, "three_step_opt_build_merg": 9, "content": [9, 10, 11, 12, 13], "config": 10, "build_from_opt": 10, "buildconfig": 10, "optconfig": 10, "util": [11, 12, 13], "files_path": 11, "load_json": 11, "mlflow": 11, "schema": 11, "track": 11, "enum": 12, "building_configuration_enum": 12, "configuration_enum": 12, "interface_enum": 12, "model_runner_enum": 12, "objective_enum": 12, "optimization_configuration_enum": 12, "prediction_configuration_enum": 12, "return_values_enum": 12, "visualization_enum": 12, "splitter": [13, 14], "predefin": 14, "scaffoldsplit": 14, "modeldatatransform": 15, "vectorfromcolumn": 15}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.todo": 2, "sphinx.ext.viewcode": 1, "sphinx": 56}})
\ No newline at end of file
diff --git a/notebooks/QSARtuna_Tutorial.ipynb b/notebooks/QSARtuna_Tutorial.ipynb
index 3968fe6..030a871 100644
--- a/notebooks/QSARtuna_Tutorial.ipynb
+++ b/notebooks/QSARtuna_Tutorial.ipynb
@@ -893,7 +893,7 @@
      "text": [
       "[I 2024-08-27 14:01:27,262] A new study created in memory with name: my_study_stratified_split\n",
       "[I 2024-08-27 14:01:27,303] A new study created in memory with name: study_name_0\n",
-      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
+      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
       "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
       "[I 2024-08-27 14:01:27,408] Trial 0 finished with value: -3999.9364276424735 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.11270803112210707, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 43.81076443656638, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3999.9364276424735.\n",
       "[I 2024-08-27 14:01:27,485] Trial 1 finished with value: -1856.4459752935309 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -1856.4459752935309.\n",
@@ -1726,9 +1726,9 @@
       "Traceback (most recent call last):\n",
       "  File \"/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/optuna/study/_optimize.py\", line 196, in _run_trial\n",
       "    value_or_values = func(trial)\n",
-      "  File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/objective.py\", line 128, in __call__\n",
+      "  File \"/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/objective.py\", line 128, in __call__\n",
       "    self._validate_algos()\n",
-      "  File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/objective.py\", line 270, in _validate_algos\n",
+      "  File \"/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/objective.py\", line 270, in _validate_algos\n",
       "    raise ValueError(\n",
       "ValueError: PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 \n",
       "[W 2024-08-27 14:02:47,253] Trial 0 failed with value None.\n"
@@ -3388,7 +3388,7 @@
       "[I 2024-08-27 14:09:24,367] A new study created in memory with name: my_study\n",
       "[I 2024-08-27 14:09:24,410] A new study created in memory with name: study_name_0\n",
       "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__fd833c2dde0b7147e6516ea5eebb2657': 'ReLU', 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': 'mean', 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': 'none', 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657'}\n",
-      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:859)\n",
+      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:859)\n",
       "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
       "[I 2024-08-27 14:16:18,484] Trial 0 finished with value: 0.65625 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657', 'activation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': , 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n",
       "                                                                                                                                                                            \r"
@@ -4745,7 +4745,7 @@
      "text": [
       "[I 2024-08-27 15:09:26,977] A new study created in memory with name: non-transform_example\n",
       "[I 2024-08-27 15:09:26,979] A new study created in memory with name: study_name_0\n",
-      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
+      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
       "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
       "[I 2024-08-27 15:09:27,144] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n",
       "[I 2024-08-27 15:09:27,220] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n",
@@ -11399,7 +11399,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The AutoML daemon functionaility in Qptuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:\n",
+    "The AutoML daemon functionaility in QSARtuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:\n",
     "\n",
     "* __Automated Data Preparation__: Automated process of preparing data for model training, including cleaning, feature extraction, formatting and quorum checks, streamlining data preprocessing\n",
     "\n",
@@ -11407,9 +11407,9 @@
     "\n",
     "* __Scalable and Efficient with Dynamic Resource Allocation__: Workflow designed to handle large datasets (with multiple prediction tasks) and dynamically utilize CPU/GPU/memory HPC resources\n",
     "\n",
-    "* __Customizable SLURM and Qptuna Templates__: SLURM templates can be tailored for different use cases. Both initial training and retraining Qptuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate\n",
+    "* __Customizable SLURM and QSARtuna Templates__: SLURM templates can be tailored for different use cases. Both initial training and retraining QSARtuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate\n",
     "\n",
-    "* __Metadata, Prediction and Model Tracking__: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported Qptuna models, aiding monitoring and evaluating pseudo-prospective model performance over time\n",
+    "* __Metadata, Prediction and Model Tracking__: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported QSARtuna models, aiding monitoring and evaluating pseudo-prospective model performance over time\n",
     "\n",
     "* __Automatic Job Resubmission__: In case of SLURM job failures, the code provides functionality to automatically resubmit failed jobs with modified resource allocations, enhancing the robustness of the model training process\n",
     "\n",
@@ -11417,7 +11417,7 @@
     "\n",
     "* __Dry Run Mode__: Dry run mode option enables users to simulate the process without actually submitting jobs, useful for verifying configurations and testing the workflow\n",
     "\n",
-    "The following is an example from the Qptuna unit tests:"
+    "The following is an example from the QSARtuna unit tests:"
    ]
   },
   {
@@ -11534,7 +11534,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Qptuna AutoML expects temporal data (`--input-data`) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing  SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns. \n",
+    "QSARtuna AutoML expects temporal data (`--input-data`) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing  SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns. \n",
     "\n",
     "Exports are expected to be temporal in nature, with the naming convention `%Y-%m-%d` (see [here](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) for details). Data can be exported in two ways:\n",
     "\n",
@@ -11605,7 +11605,7 @@
     "Then our configuration would be:\n",
     "\n",
     "```\n",
-    "qptuna-automl \n",
+    "qsartuna-automl \n",
     "   --input-data \"../tests/data/automl/*\"  \\\n",
     "   --email @astrazeneca.com  --user_name   \\ # username should be accurate to monitor jobs\n",
     "   --input-smiles-csv-column canonical  --input-activity-csv-column molwt \\\n",
@@ -11690,7 +11690,7 @@
     "    \"--n-cores\",\n",
     "    \"1\",\n",
     "    \"--dry-run\", # The dry-run option is enabled, so the AutoML pipeline does not submit to SLURM\n",
-    "    \"-vv\", # Use this CLI option to enable detailed debugging logging to observe Qptuna AutoML behaviour \n",
+    "    \"-vv\", # Use this CLI option to enable detailed debugging logging to observe QSARtuna AutoML behaviour \n",
     "    \"--slurm-al-pool\",\n",
     "    \"../tests/data/DRD2/subset-1000/train.csv\",\n",
     "    \"--slurm-al-smiles-csv-column\",\n",
@@ -11733,11 +11733,11 @@
     "* resulting folder `data/TID1` comprises the following processed data:\n",
     "    * `TID1.csv` : molecular property data set ready for modelling\n",
     "    * `TID1.json`: config for an initial round of model training\n",
-    "    * `TID1.sh`: used to run run Qptuna AutoML via an `sbatch` command, though `-- dry-run` prevented this happening\n",
+    "    * `TID1.sh`: used to run run QSARtuna AutoML via an `sbatch` command, though `-- dry-run` prevented this happening\n",
     "    * `.24_01_01` lock file initiated to track the status of the training at this timepoint\n",
     "* `processed_timepoints.json` is created to track which timepoints are processed\n",
     "\n",
-    "The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the Qptuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using `cron` or similar).\n",
+    "The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the QSARtuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using `cron` or similar).\n",
     "\n",
     "Running the AutoML workflow does a dry-run check of the status of the run:"
    ]
@@ -11817,7 +11817,7 @@
     "ml Miniconda3\n",
     "conda activate my_env_with_qsartuna\n",
     "\n",
-    "qptuna-automl \n",
+    "qsartuna-automl \n",
     "   --input-data \"/tests/data/automl/*\"  \\\n",
     "   --email @astrazeneca.com  --user_name   \\\n",
     "   --input-smiles-csv-column canonical  --input-activity-csv-column molwt \\\n",