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-.. parsed-literal::
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- Output()
-
-
-
-.. raw:: html
-
-
-
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-
-.. parsed-literal::
-
- Output()
-
-
-.. parsed-literal::
-
- Normal
-
-
-
-.. raw:: html
-
-
-
-
-
-.. parsed-literal::
-
- Estimating model 2 of 2
-
-
-
-.. raw:: html
-
-
-
-
-
-
-
-.. raw:: html
-
-
- Sampler Progress
-Total Chains: 1
-Active Chains: 0
-- Finished Chains: - 1 -
-Sampling for now
-- Estimated Time to Completion: - now -
- - -| Progress | -Draws | -Divergences | -Step Size | -Gradients/Draw | -
|---|---|---|---|---|
| - - | -1500 | -0 | -0.34 | -15 | -
-
-
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-.. parsed-literal::
-
- Output()
-
-
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-.. raw:: html
-
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-.. parsed-literal::
-
- Normal
-
-
-
-.. parsed-literal::
-
- Output()
-
-
-
-.. raw:: html
-
-
-
-
-
-.. parsed-literal::
-
- Saving model meta-data...
- Evaluating the model ...
- Writing outputs ...
-
+ ptk.normative.estimate(
+ covfile=covfile,
+ respfile=respfile,
+ tsbefile=tsbefile,
+ trbefile=trbefile,
+ inscaler="standardize",
+ outscaler="standardize",
+ linear_mu="True",
+ random_intercept_mu="True",
+ centered_intercept_mu="True",
+ alg="hbr",
+ log_path=log_dir,
+ binary=True,
+ output_path=output_path,
+ testcov=testcovfile_path,
+ testresp=testrespfile_path,
+ outputsuffix=outputsuffix,
+ savemodel=True,
+ nuts_sampler="nutpie",
+ )
Here some analyses can be done, there are also some error metrics that
could be of interest. This is covered in step 6 and in `Saige’s
@@ -1687,45 +284,49 @@ training and testing set of covariates, measures and batch effects:
.. code:: ipython3
- X_adapt = (icbm_tr['age']/100).to_numpy(dtype=float)
+ X_adapt = (icbm_tr["age"] / 100).to_numpy(dtype=float)
Y_adapt = icbm_tr[idps].to_numpy(dtype=float)
- #batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
- batch_effects_adapt = icbm_tr[['sitenum']].to_numpy(dtype=int)
+ # batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
+ batch_effects_adapt = icbm_tr[["sitenum"]].to_numpy(dtype=int)
- with open('X_adaptation.pkl', 'wb') as file:
+ with open("X_adaptation.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_adapt), file)
- with open('Y_adaptation.pkl', 'wb') as file:
+ with open("Y_adaptation.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_adapt), file)
- with open('adbefile.pkl', 'wb') as file:
+ with open("adbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_adapt), file)
# Test data (new dataset)
- X_test_txfr = (icbm_te['age']/100).to_numpy(dtype=float)
+ X_test_txfr = (icbm_te["age"] / 100).to_numpy(dtype=float)
Y_test_txfr = icbm_te[idps].to_numpy(dtype=float)
- #batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
- batch_effects_test_txfr = icbm_te[['sitenum']].to_numpy(dtype=int)
+ # batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
+ batch_effects_test_txfr = icbm_te[["sitenum"]].to_numpy(dtype=int)
- with open('X_test_txfr.pkl', 'wb') as file:
+ with open("X_test_txfr.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_test_txfr), file)
- with open('Y_test_txfr.pkl', 'wb') as file:
+ with open("Y_test_txfr.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_test_txfr), file)
- with open('txbefile.pkl', 'wb') as file:
+ with open("txbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_test_txfr), file)
.. code:: ipython3
- respfile = os.path.join(processing_dir, 'Y_adaptation.pkl')
- covfile = os.path.join(processing_dir, 'X_adaptation.pkl')
- testrespfile_path = os.path.join(processing_dir, 'Y_test_txfr.pkl')
- testcovfile_path = os.path.join(processing_dir, 'X_test_txfr.pkl')
- trbefile = os.path.join(processing_dir, 'adbefile.pkl')
- tsbefile = os.path.join(processing_dir, 'txbefile.pkl')
-
- log_dir = os.path.join(processing_dir, 'log_transfer/')
- output_path = os.path.join(processing_dir, 'Transfer/')
- model_path = os.path.join(processing_dir, 'Models/') # path to the previously trained models
- outputsuffix = '_transfer' # suffix added to the output files from the transfer function
+ respfile = os.path.join(processing_dir, "Y_adaptation.pkl")
+ covfile = os.path.join(processing_dir, "X_adaptation.pkl")
+ testrespfile_path = os.path.join(processing_dir, "Y_test_txfr.pkl")
+ testcovfile_path = os.path.join(processing_dir, "X_test_txfr.pkl")
+ trbefile = os.path.join(processing_dir, "adbefile.pkl")
+ tsbefile = os.path.join(processing_dir, "txbefile.pkl")
+
+ log_dir = os.path.join(processing_dir, "log_transfer/")
+ output_path = os.path.join(processing_dir, "Transfer/")
+ model_path = os.path.join(
+ processing_dir, "Models/"
+ ) # path to the previously trained models
+ outputsuffix = (
+ "_transfer" # suffix added to the output files from the transfer function
+ )
Here, the difference is that the transfer function needs a model path,
which points to the models we just trained, and new site data (training
@@ -1733,394 +334,37 @@ and testing). That is basically the only difference.
.. code:: ipython3
- yhat, s2, z_scores = ptk.normative.transfer(covfile=covfile,
- respfile=respfile,
- tsbefile=tsbefile,
- trbefile=trbefile,
- inscaler='standardize',
- outscaler='standardize',
- linear_mu='True',
- random_intercept_mu='True',
- centered_intercept_mu='True',
- model_path = model_path,
- alg='hbr',
- log_path=log_dir,
- binary=True,
- output_path=output_path,
- testcov= testcovfile_path,
- testresp = testrespfile_path,
- outputsuffix=outputsuffix,
- savemodel=True,
- nuts_sampler='nutpie')
-
-
-.. parsed-literal::
-
- Loading data ...
- Using HBR transform...
- Transferring model 1 of 2
-
-
-
-.. raw:: html
-
-
-
-
-
-
-
-.. raw:: html
-
-
- Sampler Progress
-Total Chains: 1
-Active Chains: 0
-- Finished Chains: - 1 -
-Sampling for now
-- Estimated Time to Completion: - now -
- - -| Progress | -Draws | -Divergences | -Step Size | -Gradients/Draw | -
|---|---|---|---|---|
| - - | -1500 | -0 | -0.33 | -15 | -
-
-
-
-
-
-
-
-
-.. parsed-literal::
-
- Output()
-
-
-
-.. raw:: html
-
-
-
-
-
-.. parsed-literal::
-
- Using HBR transform...
- Transferring model 2 of 2
-
-
-
-.. raw:: html
-
-
-
-
-
-
-
-.. raw:: html
-
-
- Sampler Progress
-Total Chains: 1
-Active Chains: 0
-- Finished Chains: - 1 -
-Sampling for now
-- Estimated Time to Completion: - now -
- - -| Progress | -Draws | -Divergences | -Step Size | -Gradients/Draw | -
|---|---|---|---|---|
| - - | -1500 | -2 | -0.47 | -7 | -
-
-
-
-
-
-
-
-
-.. parsed-literal::
-
- Output()
-
-
-
-.. raw:: html
-
-
-
-
-
-.. parsed-literal::
-
- Evaluating the model ...
- Writing outputs ...
-
+ yhat, s2, z_scores = ptk.normative.transfer(
+ covfile=covfile,
+ respfile=respfile,
+ tsbefile=tsbefile,
+ trbefile=trbefile,
+ inscaler="standardize",
+ outscaler="standardize",
+ linear_mu="True",
+ random_intercept_mu="True",
+ centered_intercept_mu="True",
+ model_path=model_path,
+ alg="hbr",
+ log_path=log_dir,
+ binary=True,
+ output_path=output_path,
+ testcov=testcovfile_path,
+ testresp=testrespfile_path,
+ outputsuffix=outputsuffix,
+ savemodel=True,
+ nuts_sampler="nutpie",
+ )
.. code:: ipython3
output_path
-
-
-
-.. parsed-literal::
-
- '/content/HBR_demo/Transfer/'
-
-
-
.. code:: ipython3
- EV = pd.read_pickle('EXPV_estimate.pkl')
+ EV = pd.read_pickle("EXPV_estimate.pkl")
print(EV)
-
-.. parsed-literal::
-
- 0
- 0 0.438215
- 1 0.439181
-
-
And that is it, you now have models that benefited from prior knowledge
about different scanner sites to learn on unseen sites.
diff --git a/doc/build/html/_sources/pages/normative_modelling_walkthrough.rst.txt b/doc/build/html/_sources/pages/normative_modelling_walkthrough.rst.txt
index 61a297a7..5bbc326e 100644
--- a/doc/build/html/_sources/pages/normative_modelling_walkthrough.rst.txt
+++ b/doc/build/html/_sources/pages/normative_modelling_walkthrough.rst.txt
@@ -42,109 +42,6 @@ bad if you cannot complete everything during the tutorial.
!pip install nutpie
-.. parsed-literal::
-
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- Downloading https://github.com/amarquand/PCNtoolkit/archive/dev.zip
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- [?25h Preparing metadata (setup.py) ... [?25l[?25hdone
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- [?25hBuilding wheels for collected packages: pcntoolkit, bspline
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- Attempting uninstall: matplotlib
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- Uninstalling matplotlib-3.8.0:
- Successfully uninstalled matplotlib-3.8.0
- Successfully installed bspline-0.1.1 matplotlib-3.9.2 pcntoolkit-0.31.0
- Collecting nutpie
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- Requirement already satisfied: h5netcdf>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from arviz>=0.15.0->nutpie) (1.4.1)
- Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from arviz>=0.15.0->nutpie) (4.12.2)
- Requirement already satisfied: xarray-einstats>=0.3 in /usr/local/lib/python3.10/dist-packages (from arviz>=0.15.0->nutpie) (0.8.0)
- Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0->nutpie) (2.8.2)
- Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0->nutpie) (2024.2)
- Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0->nutpie) (2024.2)
- Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from h5netcdf>=1.0.2->arviz>=0.15.0->nutpie) (3.12.1)
- Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (1.3.1)
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- Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (4.54.1)
- Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (1.4.7)
- Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (11.0.0)
- Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (3.2.0)
- Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=2.0->nutpie) (1.16.0)
- Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl (1.5 MB)
- [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m1.5/1.5 MB[0m [31m9.5 MB/s[0m eta [36m0:00:00[0m
- [?25hInstalling collected packages: nutpie
- Successfully installed nutpie-0.13.2
-
-
**Option 1:** Connect your Google Drive account, and load data from
Google Drive. Having Google Drive connected will allow you to save any
files created back to your Drive folder. This step will require you to
@@ -177,54 +74,6 @@ them to Google Drive.
# code by S. Rutherford
-.. parsed-literal::
-
- --2024-11-19 12:28:31-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics.csv
- Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
- Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
- HTTP request sent, awaiting response... 200 OK
- Length: 17484 (17K) [text/plain]
- Saving to: ‘camcan_demographics.csv’
-
- camcan_demographics 100%[===================>] 17.07K --.-KB/s in 0.003s
-
- 2024-11-19 12:28:31 (5.14 MB/s) - ‘camcan_demographics.csv’ saved [17484/17484]
-
- --2024-11-19 12:28:31-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics_nordan.csv
- Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
- Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
- HTTP request sent, awaiting response... 200 OK
- Length: 332 [text/plain]
- Saving to: ‘camcan_demographics_nordan.csv’
-
- camcan_demographics 100%[===================>] 332 --.-KB/s in 0s
-
- 2024-11-19 12:28:32 (4.10 MB/s) - ‘camcan_demographics_nordan.csv’ saved [332/332]
-
- --2024-11-19 12:28:32-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features.csv
- Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...
- Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
- HTTP request sent, awaiting response... 200 OK
- Length: 188944 (185K) [text/plain]
- Saving to: ‘camcan_features.csv’
-
- camcan_features.csv 100%[===================>] 184.52K --.-KB/s in 0.05s
-
- 2024-11-19 12:28:33 (3.58 MB/s) - ‘camcan_features.csv’ saved [188944/188944]
-
- --2024-11-19 12:28:33-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features_nordan.csv
- Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
- Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
- HTTP request sent, awaiting response... 200 OK
- Length: 1695 (1.7K) [text/plain]
- Saving to: ‘camcan_features_nordan.csv’
-
- camcan_features_nor 100%[===================>] 1.66K --.-KB/s in 0s
-
- 2024-11-19 12:28:33 (22.1 MB/s) - ‘camcan_features_nordan.csv’ saved [1695/1695]
-
-
-
**TASK 1:** Format input data
-----------------------------
@@ -268,203 +117,6 @@ between those two files.
# code by T. Wolfers
-.. parsed-literal::
-
- age sex_name sex IQ_random
- paricipants
- CC110033 24 MALE 1 73
- CC110037 18 MALE 1 103
- CC110045 24 FEMALE 0 124
- CC110056 22 FEMALE 0 124
- CC110062 20 MALE 1 126
- ... ... ... ... ...
- CC722542 79 MALE 1 116
- CC722651 79 FEMALE 0 128
- CC722891 84 FEMALE 0 129
- CC723197 80 FEMALE 0 96
- CC723395 86 FEMALE 0 145
-
- [707 rows x 4 columns]
- left_Hippocampal_tail left_subiculum left_CA1 \
- participants
- CC110033 482.768229 419.948094 666.496024
- CC110037 595.269259 502.320315 698.157779
- CC110045 655.847194 476.433625 654.215689
- CC110056 561.345626 447.258970 611.114561
- CC110062 756.521166 521.034681 716.391590
- ... ... ... ...
- CC722542 467.896808 440.794061 688.130914
- CC722651 406.326167 393.469843 613.794018
- CC722891 393.430481 303.049578 444.772656
- CC723197 475.929914 372.449778 525.739508
- CC723395 444.301617 330.688394 565.359058
-
- left_hippocampal-fissure left_presubiculum left_parasubiculum \
- participants
- CC110033 131.719049 285.535445 59.209377
- CC110037 156.304335 367.678385 60.817591
- CC110045 146.767569 346.347202 67.481121
- CC110056 126.615335 327.528926 70.901227
- CC110062 206.205818 384.356075 80.329689
- ... ... ... ...
- CC722542 184.300085 306.287030 72.629722
- CC722651 224.292557 254.786917 50.006651
- CC722891 158.987352 202.213773 46.418129
- CC723197 172.558200 222.384434 40.304889
- CC723395 206.235576 197.417773 34.240227
-
- left_molecular_layer_HP left_GC-ML-DG left_CA3 left_CA4 \
- participants
- CC110033 583.239022 313.144932 223.022262 266.801434
- CC110037 619.053381 311.428298 192.949211 260.551999
- CC110045 622.037178 322.315065 204.756048 275.428880
- CC110056 597.467595 323.777115 233.160437 278.133998
- CC110062 666.590397 364.130988 253.917949 311.079938
- ... ... ... ... ...
- CC722542 597.823380 322.236056 252.159707 275.293551
- CC722651 558.060369 322.176631 261.160474 282.029715
- CC722891 384.798819 204.562530 149.220194 184.259996
- CC723197 467.847632 262.950594 215.411133 232.938256
- CC723395 470.969863 269.963160 234.219152 241.325755
-
- ... right_hippocampal-fissure right_presubiculum \
- participants ...
- CC110033 ... 133.101613 263.829445
- CC110037 ... 148.099481 339.309772
- CC110045 ... 159.687619 324.398659
- CC110056 ... 123.262352 290.196432
- CC110062 ... 136.785201 406.323486
- ... ... ... ...
- CC722542 ... 147.391931 273.150743
- CC722651 ... 185.053756 232.752897
- CC722891 ... 140.980648 211.807774
- CC723197 ... 213.080235 258.567312
- CC723395 ... 205.934342 210.039976
-
- right_parasubiculum right_molecular_layer_HP right_GC-ML-DG \
- participants
- CC110033 47.651798 586.026640 328.057551
- CC110037 59.693186 617.448302 312.116795
- CC110045 55.738550 609.208671 314.460832
- CC110056 67.410418 569.389816 310.290805
- CC110062 80.403248 767.955163 383.194510
- ... ... ... ...
- CC722542 50.874375 572.634593 302.504826
- CC722651 44.493903 533.912687 308.141458
- CC722891 57.371362 387.195124 199.979009
- CC723197 50.846316 481.409074 267.190903
- CC723395 33.216529 493.194601 274.020748
-
- right_CA3 right_CA4 right_fimbria right_HATA \
- participants
- CC110033 283.392837 282.565685 87.127463 73.589184
- CC110037 212.605572 269.307660 99.657823 60.920924
- CC110045 237.869822 271.505300 69.436808 59.323542
- CC110056 218.809310 267.327199 60.505521 51.726283
- CC110062 268.227177 325.403040 92.215816 85.484454
- ... ... ... ... ...
- CC722542 236.946562 261.352283 46.144212 43.966509
- CC722651 247.810543 267.203989 68.730322 59.699644
- CC722891 146.836915 187.083211 27.913196 38.629828
- CC723197 211.618157 244.176755 51.893458 65.474967
- CC723395 232.592060 240.980896 68.335159 62.081225
-
- right_Whole_hippocampus
- participants
- CC110033 3531.764896
- CC110037 3835.426137
- CC110045 3681.494304
- CC110056 3461.373764
- CC110062 4782.407821
- ... ...
- CC722542 3284.108783
- CC722651 3210.272905
- CC722891 2423.675065
- CC723197 3043.146264
- CC723395 2988.001288
-
- [651 rows x 26 columns]
- age sex_name sex IQ_random left_Hippocampal_tail left_subiculum \
- CC110033 24 MALE 1 73 482.768229 419.948094
- CC110037 18 MALE 1 103 595.269259 502.320315
- CC110045 24 FEMALE 0 124 655.847194 476.433625
- CC110056 22 FEMALE 0 124 561.345626 447.258970
- CC110062 20 MALE 1 126 756.521166 521.034681
- ... ... ... ... ... ... ...
- CC722542 79 MALE 1 116 467.896808 440.794061
- CC722651 79 FEMALE 0 128 406.326167 393.469843
- CC722891 84 FEMALE 0 129 393.430481 303.049578
- CC723197 80 FEMALE 0 96 475.929914 372.449778
- CC723395 86 FEMALE 0 145 444.301617 330.688394
-
- left_CA1 left_hippocampal-fissure left_presubiculum \
- CC110033 666.496024 131.719049 285.535445
- CC110037 698.157779 156.304335 367.678385
- CC110045 654.215689 146.767569 346.347202
- CC110056 611.114561 126.615335 327.528926
- CC110062 716.391590 206.205818 384.356075
- ... ... ... ...
- CC722542 688.130914 184.300085 306.287030
- CC722651 613.794018 224.292557 254.786917
- CC722891 444.772656 158.987352 202.213773
- CC723197 525.739508 172.558200 222.384434
- CC723395 565.359058 206.235576 197.417773
-
- left_parasubiculum ... right_hippocampal-fissure \
- CC110033 59.209377 ... 133.101613
- CC110037 60.817591 ... 148.099481
- CC110045 67.481121 ... 159.687619
- CC110056 70.901227 ... 123.262352
- CC110062 80.329689 ... 136.785201
- ... ... ... ...
- CC722542 72.629722 ... 147.391931
- CC722651 50.006651 ... 185.053756
- CC722891 46.418129 ... 140.980648
- CC723197 40.304889 ... 213.080235
- CC723395 34.240227 ... 205.934342
-
- right_presubiculum right_parasubiculum right_molecular_layer_HP \
- CC110033 263.829445 47.651798 586.026640
- CC110037 339.309772 59.693186 617.448302
- CC110045 324.398659 55.738550 609.208671
- CC110056 290.196432 67.410418 569.389816
- CC110062 406.323486 80.403248 767.955163
- ... ... ... ...
- CC722542 273.150743 50.874375 572.634593
- CC722651 232.752897 44.493903 533.912687
- CC722891 211.807774 57.371362 387.195124
- CC723197 258.567312 50.846316 481.409074
- CC723395 210.039976 33.216529 493.194601
-
- right_GC-ML-DG right_CA3 right_CA4 right_fimbria right_HATA \
- CC110033 328.057551 283.392837 282.565685 87.127463 73.589184
- CC110037 312.116795 212.605572 269.307660 99.657823 60.920924
- CC110045 314.460832 237.869822 271.505300 69.436808 59.323542
- CC110056 310.290805 218.809310 267.327199 60.505521 51.726283
- CC110062 383.194510 268.227177 325.403040 92.215816 85.484454
- ... ... ... ... ... ...
- CC722542 302.504826 236.946562 261.352283 46.144212 43.966509
- CC722651 308.141458 247.810543 267.203989 68.730322 59.699644
- CC722891 199.979009 146.836915 187.083211 27.913196 38.629828
- CC723197 267.190903 211.618157 244.176755 51.893458 65.474967
- CC723395 274.020748 232.592060 240.980896 68.335159 62.081225
-
- right_Whole_hippocampus
- CC110033 3531.764896
- CC110037 3835.426137
- CC110045 3681.494304
- CC110056 3461.373764
- CC110062 4782.407821
- ... ...
- CC722542 3284.108783
- CC722651 3210.272905
- CC722891 2423.675065
- CC723197 3043.146264
- CC723395 2988.001288
-
- [650 rows x 30 columns]
-
-
**TASK 2:** Prepare the covariate_normsample and testresponse_normsample file.
------------------------------------------------------------------------------
diff --git a/doc/build/html/pages/HBR_NormativeModel_FCONdata_Tutorial.html b/doc/build/html/pages/HBR_NormativeModel_FCONdata_Tutorial.html
index 9c7cd389..6364c591 100644
--- a/doc/build/html/pages/HBR_NormativeModel_FCONdata_Tutorial.html
+++ b/doc/build/html/pages/HBR_NormativeModel_FCONdata_Tutorial.html
@@ -23,7 +23,7 @@
-
+
@@ -80,7 +80,7 @@
-Sampler Progress
-Total Chains: 1
-Active Chains: 0
-- Finished Chains: - 1 -
-Sampling for now
-- Estimated Time to Completion: - now -
- - -| Progress | -Draws | -Divergences | -Step Size | -Gradients/Draw | -
|---|---|---|---|---|
| - - | -1500 | -1 | -0.40 | -15 | -
Step 0: Install necessary libraries & grab data files!pip install nutpie
-Collecting https://github.com/amarquand/PCNtoolkit/archive/dev.zip
- Downloading https://github.com/amarquand/PCNtoolkit/archive/dev.zip
-[2K [32m[0m [32m64.9 MB[0m [31m15.9 MB/s[0m [33m0:00:05[0m
-[?25h Installing build dependencies ... [?25l[?25hdone
- Getting requirements to build wheel ... [?25l[?25hdone
- Preparing metadata (pyproject.toml) ... [?25l[?25hdone
-Collecting bspline<0.2.0,>=0.1.1 (from pcntoolkit==0.31.0)
- Downloading bspline-0.1.1.tar.gz (84 kB)
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-[?25h Preparing metadata (setup.py) ... [?25l[?25hdone
-Collecting matplotlib<4.0.0,>=3.9.2 (from pcntoolkit==0.31.0)
- Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
-Requirement already satisfied: nibabel<6.0.0,>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.3.2)
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-[?25hBuilding wheels for collected packages: pcntoolkit, bspline
- Building wheel for pcntoolkit (pyproject.toml) ... [?25l[?25hdone
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- Stored in directory: /root/.cache/pip/wheels/3c/ab/0a/70927853a6d9166bc777922736063a6f99c43a327c802f9326
-Successfully built pcntoolkit bspline
-Installing collected packages: bspline, matplotlib, pcntoolkit
- Attempting uninstall: matplotlib
- Found existing installation: matplotlib 3.8.0
- Uninstalling matplotlib-3.8.0:
- Successfully uninstalled matplotlib-3.8.0
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-Collecting nutpie
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-[?25hInstalling collected packages: nutpie
-Successfully installed nutpie-0.13.2
For this tutorial we will use data from the Functional Connectom
Project FCON1000 to create a
multi-site dataset.
@@ -263,7 +164,7 @@ Step 0: Install necessary libraries & grab data filesfrom matplotlib import pyplot as plt
-processing_dir = "HBR_demo" # replace with desired working directory
+processing_dir = "HBR_demo" # replace with desired working directory
if not os.path.isdir(processing_dir):
os.makedirs(processing_dir)
os.chdir(processing_dir)
@@ -276,402 +177,35 @@ Overview
assign some site id to the different scanner sites and print an overview
of the left hemisphere mean raw cortical thickness as a function of age,
color coded by the various sites:
-fcon = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000.csv')
+fcon = pd.read_csv(
+ "https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000.csv"
+)
# extract the ICBM site for transfer
-icbm = fcon.loc[fcon['site'] == 'ICBM']
-icbm['sitenum'] = 0
+icbm = fcon.loc[fcon["site"] == "ICBM"]
+icbm["sitenum"] = 0
# remove from the training set (also Pittsburgh because it only has 3 samples)
-fcon = fcon.loc[fcon['site'] != 'ICBM']
-fcon = fcon.loc[fcon['site'] != 'Pittsburgh']
+fcon = fcon.loc[fcon["site"] != "ICBM"]
+fcon = fcon.loc[fcon["site"] != "Pittsburgh"]
-sites = fcon['site'].unique()
-fcon['sitenum'] = 0
+sites = fcon["site"].unique()
+fcon["sitenum"] = 0
f, ax = plt.subplots(figsize=(12, 12))
-for i,s in enumerate(sites):
- idx = fcon['site'] == s
- fcon['sitenum'].loc[idx] = i
+for i, s in enumerate(sites):
+ idx = fcon["site"] == s
+ fcon["sitenum"].loc[idx] = i
- print('site',s, sum(idx))
- ax.scatter(fcon['age'].loc[idx], fcon['lh_MeanThickness_thickness'].loc[idx])
+ print("site", s, sum(idx))
+ ax.scatter(fcon["age"].loc[idx], fcon["lh_MeanThickness_thickness"].loc[idx])
ax.legend(sites)
-ax.set_ylabel('LH mean cortical thickness [mm]')
-ax.set_xlabel('age')
+ax.set_ylabel("LH mean cortical thickness [mm]")
+ax.set_xlabel("age")
-<ipython-input-4-a7d14b9f2beb>:5: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- icbm['sitenum'] = 0
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-site AnnArbor_a 24
-site AnnArbor_b 32
-site Atlanta 28
-site Baltimore 23
-site Bangor 20
-site Beijing_Zang 198
-site Berlin_Margulies 26
-site Cambridge_Buckner 198
-site Cleveland 31
-site Leiden_2180 12
-site Leiden_2200 19
-site Milwaukee_b 46
-site Munchen 15
-site NewYork_a 83
-site NewYork_a_ADHD 25
-site Newark 19
-site Oulu 102
-site Oxford 22
-site PaloAlto 17
-site Queensland 19
-site SaintLouis 31
-
-
-Text(0.5, 0, 'age')
-
-
-
@@ -694,48 +228,24 @@ Step 1: Prepare training and testing setsicbm_tr = icbm.loc[tr]
icbm_te = icbm.loc[te]
-print('sample size check')
-for i,s in enumerate(sites):
- idx = fcon_tr['site'] == s
- idxte = fcon_te['site'] == s
- print(i,s, sum(idx), sum(idxte))
+print("sample size check")
+for i, s in enumerate(sites):
+ idx = fcon_tr["site"] == s
+ idxte = fcon_te["site"] == s
+ print(i, s, sum(idx), sum(idxte))
-fcon_tr.to_csv(processing_dir + '/fcon1000_tr.csv')
-fcon_te.to_csv(processing_dir + '/fcon1000_te.csv')
-icbm_tr.to_csv(processing_dir + '/fcon1000_icbm_tr.csv')
-icbm_te.to_csv(processing_dir + '/fcon1000_icbm_te.csv')
-
-
-sample size check
-0 AnnArbor_a 10 14
-1 AnnArbor_b 19 13
-2 Atlanta 12 16
-3 Baltimore 12 11
-4 Bangor 10 10
-5 Beijing_Zang 91 107
-6 Berlin_Margulies 9 17
-7 Cambridge_Buckner 96 102
-8 Cleveland 13 18
-9 Leiden_2180 5 7
-10 Leiden_2200 11 8
-11 Milwaukee_b 18 28
-12 Munchen 9 6
-13 NewYork_a 38 45
-14 NewYork_a_ADHD 15 10
-15 Newark 9 10
-16 Oulu 50 52
-17 Oxford 9 13
-18 PaloAlto 8 9
-19 Queensland 10 9
-20 SaintLouis 18 13
+fcon_tr.to_csv(processing_dir + "/fcon1000_tr.csv")
+fcon_te.to_csv(processing_dir + "/fcon1000_te.csv")
+icbm_tr.to_csv(processing_dir + "/fcon1000_icbm_tr.csv")
+icbm_te.to_csv(processing_dir + "/fcon1000_icbm_te.csv")
Otherwise you can just load these pre defined subsets:
# Optional
-#fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
-#fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
-#icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
-#icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
+# fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
+# fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
+# icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
+# icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
@@ -743,7 +253,7 @@ Step 1: Prepare training and testing sets
We will here only use the mean cortical thickness for the Right and Left
hemisphere: two idps.
-idps = ['rh_MeanThickness_thickness','lh_MeanThickness_thickness']
+idps = ["rh_MeanThickness_thickness", "lh_MeanThickness_thickness"]
As input to the model, we need covariates (used to describe predictable
@@ -755,584 +265,77 @@
Step 2: Configure HBR inputs: covariates, measures and batch effectsbatch_effects to the random effects
We need these values both for the training (_train) and for the
testing set (_test).
-X_train = (fcon_tr['age']/100).to_numpy(dtype=float)
+X_train = (fcon_tr["age"] / 100).to_numpy(dtype=float)
Y_train = fcon_tr[idps].to_numpy(dtype=float)
# configure batch effects for site and sex
-#batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
+# batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
# or only site
-batch_effects_train = fcon_tr[['sitenum']].to_numpy(dtype=int)
+batch_effects_train = fcon_tr[["sitenum"]].to_numpy(dtype=int)
-with open('X_train.pkl', 'wb') as file:
+with open("X_train.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_train), file)
-with open('Y_train.pkl', 'wb') as file:
+with open("Y_train.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_train), file)
-with open('trbefile.pkl', 'wb') as file:
+with open("trbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_train), file)
-X_test = (fcon_te['age']/100).to_numpy(dtype=float)
+X_test = (fcon_te["age"] / 100).to_numpy(dtype=float)
Y_test = fcon_te[idps].to_numpy(dtype=float)
-#batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
-batch_effects_test = fcon_te[['sitenum']].to_numpy(dtype=int)
+# batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
+batch_effects_test = fcon_te[["sitenum"]].to_numpy(dtype=int)
-with open('X_test.pkl', 'wb') as file:
+with open("X_test.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_test), file)
-with open('Y_test.pkl', 'wb') as file:
+with open("Y_test.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_test), file)
-with open('tsbefile.pkl', 'wb') as file:
+with open("tsbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_test), file)
+
# a simple function to quickly load pickle files
def ldpkl(filename: str):
- with open(filename, 'rb') as f:
+ with open(filename, "rb") as f:
return pickle.load(f)
batch_effects_test
-array([[ 0],
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- [20],
- [20]])
-
-
Step 3: Files and Folders grooming
-respfile = os.path.join(processing_dir, 'Y_train.pkl') # measurements (eg cortical thickness) of the training samples (columns: the various features/ROIs, rows: observations or subjects)
-covfile = os.path.join(processing_dir, 'X_train.pkl') # covariates (eg age) the training samples (columns: covariates, rows: observations or subjects)
-
-testrespfile_path = os.path.join(processing_dir, 'Y_test.pkl') # measurements for the testing samples
-testcovfile_path = os.path.join(processing_dir, 'X_test.pkl') # covariate file for the testing samples
-
-trbefile = os.path.join(processing_dir, 'trbefile.pkl') # training batch effects file (eg scanner_id, gender) (columns: the various batch effects, rows: observations or subjects)
-tsbefile = os.path.join(processing_dir, 'tsbefile.pkl') # testing batch effects file
-
-output_path = os.path.join(processing_dir, 'Models/') # output path, where the models will be written
-log_dir = os.path.join(processing_dir, 'log/') #
+respfile = os.path.join(
+ processing_dir, "Y_train.pkl"
+) # measurements (eg cortical thickness) of the training samples (columns: the various features/ROIs, rows: observations or subjects)
+covfile = os.path.join(
+ processing_dir, "X_train.pkl"
+) # covariates (eg age) the training samples (columns: covariates, rows: observations or subjects)
+
+testrespfile_path = os.path.join(
+ processing_dir, "Y_test.pkl"
+) # measurements for the testing samples
+testcovfile_path = os.path.join(
+ processing_dir, "X_test.pkl"
+) # covariate file for the testing samples
+
+trbefile = os.path.join(
+ processing_dir, "trbefile.pkl"
+) # training batch effects file (eg scanner_id, gender) (columns: the various batch effects, rows: observations or subjects)
+tsbefile = os.path.join(processing_dir, "tsbefile.pkl") # testing batch effects file
+
+output_path = os.path.join(
+ processing_dir, "Models/"
+) # output path, where the models will be written
+log_dir = os.path.join(processing_dir, "log/") #
if not os.path.isdir(output_path):
os.mkdir(output_path)
if not os.path.isdir(log_dir):
os.mkdir(log_dir)
-outputsuffix = '_estimate' # a string to name the output files, of use only to you, so adapt it for your needs.
+outputsuffix = "_estimate" # a string to name the output files, of use only to you, so adapt it for your needs.
@@ -1345,320 +348,26 @@ Step 4: Estimating the modelsptk.normative.estimate(covfile=covfile,
- respfile=respfile,
- tsbefile=tsbefile,
- trbefile=trbefile,
- inscaler='standardize',
- outscaler='standardize',
- linear_mu='True',
- random_intercept_mu='True',
- centered_intercept_mu='True',
- alg='hbr',
- log_path=log_dir,
- binary=True,
- output_path=output_path,
- testcov= testcovfile_path,
- testresp = testrespfile_path,
- outputsuffix=outputsuffix,
- savemodel=True,
- nuts_sampler='nutpie')
-
-
-inscaler: standardize
-outscaler: standardize
-Processing data in /content/HBR_demo/Y_train.pkl
-Estimating model 1 of 2
-
-
-
- Sampler Progress
- Total Chains: 1
- Active Chains: 0
-
- Finished Chains:
- 1
-
- Sampling for now
-
- Estimated Time to Completion:
- now
-
-
-
-
-
-
- Progress
- Draws
- Divergences
- Step Size
- Gradients/Draw
-
-
-
-
-
-
-
-
- 1500
- 0
- 0.34
- 15
-
-
-
-
-
-Output()
-
-
-Output()
-
-
-Normal
-
-
-Estimating model 2 of 2
-
-
-
- Sampler Progress
- Total Chains: 1
- Active Chains: 0
-
- Finished Chains:
- 1
-
- Sampling for now
-
- Estimated Time to Completion:
- now
-
-
-
-
-
-
- Progress
- Draws
- Divergences
- Step Size
- Gradients/Draw
-
-
-
-
-
-
-
-
- 1500
- 0
- 0.33
- 15
-
-
-
-
-
-Output()
-
-
-Normal
-
-
-Output()
-
-
-Saving model meta-data...
-Evaluating the model ...
-Writing outputs ...
+ptk.normative.estimate(
+ covfile=covfile,
+ respfile=respfile,
+ tsbefile=tsbefile,
+ trbefile=trbefile,
+ inscaler="standardize",
+ outscaler="standardize",
+ linear_mu="True",
+ random_intercept_mu="True",
+ centered_intercept_mu="True",
+ alg="hbr",
+ log_path=log_dir,
+ binary=True,
+ output_path=output_path,
+ testcov=testcovfile_path,
+ testresp=testrespfile_path,
+ outputsuffix=outputsuffix,
+ savemodel=True,
+ nuts_sampler="nutpie",
+)
Here some analyses can be done, there are also some error metrics that
@@ -1671,367 +380,82 @@
Step 5: Transfering the models to unseen sitesX_adapt = (icbm_tr['age']/100).to_numpy(dtype=float)
+X_adapt = (icbm_tr["age"] / 100).to_numpy(dtype=float)
Y_adapt = icbm_tr[idps].to_numpy(dtype=float)
-#batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
-batch_effects_adapt = icbm_tr[['sitenum']].to_numpy(dtype=int)
+# batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
+batch_effects_adapt = icbm_tr[["sitenum"]].to_numpy(dtype=int)
-with open('X_adaptation.pkl', 'wb') as file:
+with open("X_adaptation.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_adapt), file)
-with open('Y_adaptation.pkl', 'wb') as file:
+with open("Y_adaptation.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_adapt), file)
-with open('adbefile.pkl', 'wb') as file:
+with open("adbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_adapt), file)
# Test data (new dataset)
-X_test_txfr = (icbm_te['age']/100).to_numpy(dtype=float)
+X_test_txfr = (icbm_te["age"] / 100).to_numpy(dtype=float)
Y_test_txfr = icbm_te[idps].to_numpy(dtype=float)
-#batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
-batch_effects_test_txfr = icbm_te[['sitenum']].to_numpy(dtype=int)
+# batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
+batch_effects_test_txfr = icbm_te[["sitenum"]].to_numpy(dtype=int)
-with open('X_test_txfr.pkl', 'wb') as file:
+with open("X_test_txfr.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_test_txfr), file)
-with open('Y_test_txfr.pkl', 'wb') as file:
+with open("Y_test_txfr.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_test_txfr), file)
-with open('txbefile.pkl', 'wb') as file:
+with open("txbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_test_txfr), file)
-respfile = os.path.join(processing_dir, 'Y_adaptation.pkl')
-covfile = os.path.join(processing_dir, 'X_adaptation.pkl')
-testrespfile_path = os.path.join(processing_dir, 'Y_test_txfr.pkl')
-testcovfile_path = os.path.join(processing_dir, 'X_test_txfr.pkl')
-trbefile = os.path.join(processing_dir, 'adbefile.pkl')
-tsbefile = os.path.join(processing_dir, 'txbefile.pkl')
+respfile = os.path.join(processing_dir, "Y_adaptation.pkl")
+covfile = os.path.join(processing_dir, "X_adaptation.pkl")
+testrespfile_path = os.path.join(processing_dir, "Y_test_txfr.pkl")
+testcovfile_path = os.path.join(processing_dir, "X_test_txfr.pkl")
+trbefile = os.path.join(processing_dir, "adbefile.pkl")
+tsbefile = os.path.join(processing_dir, "txbefile.pkl")
-log_dir = os.path.join(processing_dir, 'log_transfer/')
-output_path = os.path.join(processing_dir, 'Transfer/')
-model_path = os.path.join(processing_dir, 'Models/') # path to the previously trained models
-outputsuffix = '_transfer' # suffix added to the output files from the transfer function
+log_dir = os.path.join(processing_dir, "log_transfer/")
+output_path = os.path.join(processing_dir, "Transfer/")
+model_path = os.path.join(
+ processing_dir, "Models/"
+) # path to the previously trained models
+outputsuffix = (
+ "_transfer" # suffix added to the output files from the transfer function
+)
Here, the difference is that the transfer function needs a model path,
which points to the models we just trained, and new site data (training
and testing). That is basically the only difference.
-yhat, s2, z_scores = ptk.normative.transfer(covfile=covfile,
- respfile=respfile,
- tsbefile=tsbefile,
- trbefile=trbefile,
- inscaler='standardize',
- outscaler='standardize',
- linear_mu='True',
- random_intercept_mu='True',
- centered_intercept_mu='True',
- model_path = model_path,
- alg='hbr',
- log_path=log_dir,
- binary=True,
- output_path=output_path,
- testcov= testcovfile_path,
- testresp = testrespfile_path,
- outputsuffix=outputsuffix,
- savemodel=True,
- nuts_sampler='nutpie')
-
-
-Loading data ...
-Using HBR transform...
-Transferring model 1 of 2
-
-
-
- Sampler Progress
- Total Chains: 1
- Active Chains: 0
-
- Finished Chains:
- 1
-
- Sampling for now
-
- Estimated Time to Completion:
- now
-
-
-
-
-
-
- Progress
- Draws
- Divergences
- Step Size
- Gradients/Draw
-
-
-
-
-
-
-
-
- 1500
- 2
- 0.47
- 7
-
-
-
-
-
-Output()
-
-
-Using HBR transform...
-Transferring model 2 of 2
-
-
-
- Sampler Progress
- Total Chains: 1
- Active Chains: 0
-
- Finished Chains:
- 1
-
- Sampling for now
-
- Estimated Time to Completion:
- now
-
-
-
-
-
-
- Progress
- Draws
- Divergences
- Step Size
- Gradients/Draw
-
-
-
-
-
-
-
-
- 1500
- 1
- 0.40
- 15
-
-
-
-
-
-Output()
-
-
-Evaluating the model ...
-Writing outputs ...
+yhat, s2, z_scores = ptk.normative.transfer(
+ covfile=covfile,
+ respfile=respfile,
+ tsbefile=tsbefile,
+ trbefile=trbefile,
+ inscaler="standardize",
+ outscaler="standardize",
+ linear_mu="True",
+ random_intercept_mu="True",
+ centered_intercept_mu="True",
+ model_path=model_path,
+ alg="hbr",
+ log_path=log_dir,
+ binary=True,
+ output_path=output_path,
+ testcov=testcovfile_path,
+ testresp=testrespfile_path,
+ outputsuffix=outputsuffix,
+ savemodel=True,
+ nuts_sampler="nutpie",
+)
output_path
-'/content/HBR_demo/Transfer/'
-
-
-EV = pd.read_pickle('EXPV_estimate.pkl')
+EV = pd.read_pickle("EXPV_estimate.pkl")
print(EV)
- 0
-0 0.438215
-1 0.439181
-
-
And that is it, you now have models that benefited from prior knowledge
about different scanner sites to learn on unseen sites.
@@ -2054,7 +478,7 @@ Step 6: Interpreting model performance
Previous
- Next
+ Next
diff --git a/doc/build/html/pages/normative_modelling_walkthrough.html b/doc/build/html/pages/normative_modelling_walkthrough.html
index 55c5f1ec..73b066f4 100644
--- a/doc/build/html/pages/normative_modelling_walkthrough.html
+++ b/doc/build/html/pages/normative_modelling_walkthrough.html
@@ -77,7 +77,7 @@
Predictive Clinical Neuroscience Toolkit
Hierarchical Bayesian Regression Normative Modelling and Transfer onto unseen site.
-Braincharts: transfer
+Using lifespan models to make predictions on new data
Predictive Clinical Neuroscience Toolkit
The Normative Modeling Framework for Computational Psychiatry Protocol
Data Preparation
@@ -158,105 +158,6 @@ Task 0: Load data and install PCNtoolkit!pip install nutpie
-Collecting https://github.com/amarquand/PCNtoolkit/archive/dev.zip
- Downloading https://github.com/amarquand/PCNtoolkit/archive/dev.zip
-[2K [32m[0m [32m64.9 MB[0m [31m16.9 MB/s[0m [33m0:00:05[0m
-[?25h Installing build dependencies ... [?25l[?25hdone
- Getting requirements to build wheel ... [?25l[?25hdone
- Preparing metadata (pyproject.toml) ... [?25l[?25hdone
-Collecting bspline<0.2.0,>=0.1.1 (from pcntoolkit==0.31.0)
- Downloading bspline-0.1.1.tar.gz (84 kB)
-[2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m84.2/84.2 kB[0m [31m2.0 MB/s[0m eta [36m0:00:00[0m
-[?25h Preparing metadata (setup.py) ... [?25l[?25hdone
-Collecting matplotlib<4.0.0,>=3.9.2 (from pcntoolkit==0.31.0)
- Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
-Requirement already satisfied: nibabel<6.0.0,>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.3.2)
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-Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)
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-[?25hBuilding wheels for collected packages: pcntoolkit, bspline
- Building wheel for pcntoolkit (pyproject.toml) ... [?25l[?25hdone
- Created wheel for pcntoolkit: filename=pcntoolkit-0.31.0-py3-none-any.whl size=114835 sha256=40635c10c24ccf2c319ee965aaf1038272cd5578f14d9cb3dd14598ddab31d00
- Stored in directory: /tmp/pip-ephem-wheel-cache-nl3hy35q/wheels/9e/c4/29/3bca3a5facf8ef69b8622461d8520d24a19d3745aefa093d1e
- Building wheel for bspline (setup.py) ... [?25l[?25hdone
- Created wheel for bspline: filename=bspline-0.1.1-py3-none-any.whl size=84482 sha256=1da013ad20b77d85515a6a2318eaf7cc4baaa7772eb1d26249f923ce8d779d7e
- Stored in directory: /root/.cache/pip/wheels/3c/ab/0a/70927853a6d9166bc777922736063a6f99c43a327c802f9326
-Successfully built pcntoolkit bspline
-Installing collected packages: bspline, matplotlib, pcntoolkit
- Attempting uninstall: matplotlib
- Found existing installation: matplotlib 3.8.0
- Uninstalling matplotlib-3.8.0:
- Successfully uninstalled matplotlib-3.8.0
-Successfully installed bspline-0.1.1 matplotlib-3.9.2 pcntoolkit-0.31.0
-Collecting nutpie
- Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (5.4 kB)
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-[?25hInstalling collected packages: nutpie
-Successfully installed nutpie-0.13.2
Option 1: Connect your Google Drive account, and load data from
Google Drive. Having Google Drive connected will allow you to save any
files created back to your Drive folder. This step will require you to
@@ -284,51 +185,6 @@
Task 0: Load data and install PCNtoolkit# code by S. Rutherford
---2024-11-19 12:28:31-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics.csv
-Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
-Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
-HTTP request sent, awaiting response... 200 OK
-Length: 17484 (17K) [text/plain]
-Saving to: ‘camcan_demographics.csv’
-
-camcan_demographics 100%[===================>] 17.07K --.-KB/s in 0.003s
-
-2024-11-19 12:28:31 (5.14 MB/s) - ‘camcan_demographics.csv’ saved [17484/17484]
-
---2024-11-19 12:28:31-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics_nordan.csv
-Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
-Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
-HTTP request sent, awaiting response... 200 OK
-Length: 332 [text/plain]
-Saving to: ‘camcan_demographics_nordan.csv’
-
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-
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-
---2024-11-19 12:28:32-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features.csv
-Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...
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-HTTP request sent, awaiting response... 200 OK
-Length: 188944 (185K) [text/plain]
-Saving to: ‘camcan_features.csv’
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-2024-11-19 12:28:33 (3.58 MB/s) - ‘camcan_features.csv’ saved [188944/188944]
-
---2024-11-19 12:28:33-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features_nordan.csv
-Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
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-HTTP request sent, awaiting response... 200 OK
-Length: 1695 (1.7K) [text/plain]
-Saving to: ‘camcan_features_nordan.csv’
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-
-
TASK 1: Format input data
@@ -369,188 +225,6 @@ TASK 1: Format input data# code by T. Wolfers
- age sex_name sex IQ_random
-paricipants
-CC110033 24 MALE 1 73
-CC110037 18 MALE 1 103
-CC110045 24 FEMALE 0 124
-CC110056 22 FEMALE 0 124
-CC110062 20 MALE 1 126
-... ... ... ... ...
-CC722542 79 MALE 1 116
-CC722651 79 FEMALE 0 128
-CC722891 84 FEMALE 0 129
-CC723197 80 FEMALE 0 96
-CC723395 86 FEMALE 0 145
-
-[707 rows x 4 columns]
- left_Hippocampal_tail left_subiculum left_CA1 participants
-CC110033 482.768229 419.948094 666.496024
-CC110037 595.269259 502.320315 698.157779
-CC110045 655.847194 476.433625 654.215689
-CC110056 561.345626 447.258970 611.114561
-CC110062 756.521166 521.034681 716.391590
-... ... ... ...
-CC722542 467.896808 440.794061 688.130914
-CC722651 406.326167 393.469843 613.794018
-CC722891 393.430481 303.049578 444.772656
-CC723197 475.929914 372.449778 525.739508
-CC723395 444.301617 330.688394 565.359058
-
- left_hippocampal-fissure left_presubiculum left_parasubiculum participants
-CC110033 131.719049 285.535445 59.209377
-CC110037 156.304335 367.678385 60.817591
-CC110045 146.767569 346.347202 67.481121
-CC110056 126.615335 327.528926 70.901227
-CC110062 206.205818 384.356075 80.329689
-... ... ... ...
-CC722542 184.300085 306.287030 72.629722
-CC722651 224.292557 254.786917 50.006651
-CC722891 158.987352 202.213773 46.418129
-CC723197 172.558200 222.384434 40.304889
-CC723395 206.235576 197.417773 34.240227
-
- left_molecular_layer_HP left_GC-ML-DG left_CA3 left_CA4 participants
-CC110033 583.239022 313.144932 223.022262 266.801434
-CC110037 619.053381 311.428298 192.949211 260.551999
-CC110045 622.037178 322.315065 204.756048 275.428880
-CC110056 597.467595 323.777115 233.160437 278.133998
-CC110062 666.590397 364.130988 253.917949 311.079938
-... ... ... ... ...
-CC722542 597.823380 322.236056 252.159707 275.293551
-CC722651 558.060369 322.176631 261.160474 282.029715
-CC722891 384.798819 204.562530 149.220194 184.259996
-CC723197 467.847632 262.950594 215.411133 232.938256
-CC723395 470.969863 269.963160 234.219152 241.325755
-
- ... right_hippocampal-fissure right_presubiculum participants ...
-CC110033 ... 133.101613 263.829445
-CC110037 ... 148.099481 339.309772
-CC110045 ... 159.687619 324.398659
-CC110056 ... 123.262352 290.196432
-CC110062 ... 136.785201 406.323486
-... ... ... ...
-CC722542 ... 147.391931 273.150743
-CC722651 ... 185.053756 232.752897
-CC722891 ... 140.980648 211.807774
-CC723197 ... 213.080235 258.567312
-CC723395 ... 205.934342 210.039976
-
- right_parasubiculum right_molecular_layer_HP right_GC-ML-DG participants
-CC110033 47.651798 586.026640 328.057551
-CC110037 59.693186 617.448302 312.116795
-CC110045 55.738550 609.208671 314.460832
-CC110056 67.410418 569.389816 310.290805
-CC110062 80.403248 767.955163 383.194510
-... ... ... ...
-CC722542 50.874375 572.634593 302.504826
-CC722651 44.493903 533.912687 308.141458
-CC722891 57.371362 387.195124 199.979009
-CC723197 50.846316 481.409074 267.190903
-CC723395 33.216529 493.194601 274.020748
-
- right_CA3 right_CA4 right_fimbria right_HATA participants
-CC110033 283.392837 282.565685 87.127463 73.589184
-CC110037 212.605572 269.307660 99.657823 60.920924
-CC110045 237.869822 271.505300 69.436808 59.323542
-CC110056 218.809310 267.327199 60.505521 51.726283
-CC110062 268.227177 325.403040 92.215816 85.484454
-... ... ... ... ...
-CC722542 236.946562 261.352283 46.144212 43.966509
-CC722651 247.810543 267.203989 68.730322 59.699644
-CC722891 146.836915 187.083211 27.913196 38.629828
-CC723197 211.618157 244.176755 51.893458 65.474967
-CC723395 232.592060 240.980896 68.335159 62.081225
-
- right_Whole_hippocampus
-participants
-CC110033 3531.764896
-CC110037 3835.426137
-CC110045 3681.494304
-CC110056 3461.373764
-CC110062 4782.407821
-... ...
-CC722542 3284.108783
-CC722651 3210.272905
-CC722891 2423.675065
-CC723197 3043.146264
-CC723395 2988.001288
-
-[651 rows x 26 columns]
- age sex_name sex IQ_random left_Hippocampal_tail left_subiculum CC110033 24 MALE 1 73 482.768229 419.948094
-CC110037 18 MALE 1 103 595.269259 502.320315
-CC110045 24 FEMALE 0 124 655.847194 476.433625
-CC110056 22 FEMALE 0 124 561.345626 447.258970
-CC110062 20 MALE 1 126 756.521166 521.034681
-... ... ... ... ... ... ...
-CC722542 79 MALE 1 116 467.896808 440.794061
-CC722651 79 FEMALE 0 128 406.326167 393.469843
-CC722891 84 FEMALE 0 129 393.430481 303.049578
-CC723197 80 FEMALE 0 96 475.929914 372.449778
-CC723395 86 FEMALE 0 145 444.301617 330.688394
-
- left_CA1 left_hippocampal-fissure left_presubiculum CC110033 666.496024 131.719049 285.535445
-CC110037 698.157779 156.304335 367.678385
-CC110045 654.215689 146.767569 346.347202
-CC110056 611.114561 126.615335 327.528926
-CC110062 716.391590 206.205818 384.356075
-... ... ... ...
-CC722542 688.130914 184.300085 306.287030
-CC722651 613.794018 224.292557 254.786917
-CC722891 444.772656 158.987352 202.213773
-CC723197 525.739508 172.558200 222.384434
-CC723395 565.359058 206.235576 197.417773
-
- left_parasubiculum ... right_hippocampal-fissure CC110033 59.209377 ... 133.101613
-CC110037 60.817591 ... 148.099481
-CC110045 67.481121 ... 159.687619
-CC110056 70.901227 ... 123.262352
-CC110062 80.329689 ... 136.785201
-... ... ... ...
-CC722542 72.629722 ... 147.391931
-CC722651 50.006651 ... 185.053756
-CC722891 46.418129 ... 140.980648
-CC723197 40.304889 ... 213.080235
-CC723395 34.240227 ... 205.934342
-
- right_presubiculum right_parasubiculum right_molecular_layer_HP CC110033 263.829445 47.651798 586.026640
-CC110037 339.309772 59.693186 617.448302
-CC110045 324.398659 55.738550 609.208671
-CC110056 290.196432 67.410418 569.389816
-CC110062 406.323486 80.403248 767.955163
-... ... ... ...
-CC722542 273.150743 50.874375 572.634593
-CC722651 232.752897 44.493903 533.912687
-CC722891 211.807774 57.371362 387.195124
-CC723197 258.567312 50.846316 481.409074
-CC723395 210.039976 33.216529 493.194601
-
- right_GC-ML-DG right_CA3 right_CA4 right_fimbria right_HATA CC110033 328.057551 283.392837 282.565685 87.127463 73.589184
-CC110037 312.116795 212.605572 269.307660 99.657823 60.920924
-CC110045 314.460832 237.869822 271.505300 69.436808 59.323542
-CC110056 310.290805 218.809310 267.327199 60.505521 51.726283
-CC110062 383.194510 268.227177 325.403040 92.215816 85.484454
-... ... ... ... ... ...
-CC722542 302.504826 236.946562 261.352283 46.144212 43.966509
-CC722651 308.141458 247.810543 267.203989 68.730322 59.699644
-CC722891 199.979009 146.836915 187.083211 27.913196 38.629828
-CC723197 267.190903 211.618157 244.176755 51.893458 65.474967
-CC723395 274.020748 232.592060 240.980896 68.335159 62.081225
-
- right_Whole_hippocampus
-CC110033 3531.764896
-CC110037 3835.426137
-CC110045 3681.494304
-CC110056 3461.373764
-CC110062 4782.407821
-... ...
-CC722542 3284.108783
-CC722651 3210.272905
-CC722891 2423.675065
-CC723197 3043.146264
-CC723395 2988.001288
-
-[650 rows x 30 columns]
TASK 2: Prepare the covariate_normsample and testresponse_normsample file.
diff --git a/doc/build/html/searchindex.js b/doc/build/html/searchindex.js
index f74e15f5..9a18253f 100644
--- a/doc/build/html/searchindex.js
+++ b/doc/build/html/searchindex.js
@@ -1 +1 @@
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"hbr": 3, "hierarch": 3, "hoc": [1, 13], "how": 6, "hyppocamp": 10, "i": 12, "idea": 1, "implement": 10, "index": 9, "input": [3, 10], "instal": [1, 3, 8, 10], "interpret": [1, 3], "intro": 12, "kei": 7, "l1": 11, "l2": 11, "lasso": 11, "librari": [1, 3], "lifespan": 5, "line": 12, "linear": [1, 11], "load": [5, 10, 11], "local": 8, "machin": 8, "make": 5, "map": 13, "marquand": 3, "mass": 13, "measur": 3, "model": [1, 3, 5, 10, 11, 12, 13, 14], "modul": 9, "multi": 1, "necessari": [1, 3], "neg": 14, "net": 11, "neurosci": [0, 1, 3], "new": 5, "nordan": 10, "norm": [1, 3, 5, 10, 12, 13, 14], "onto": 3, "option": [5, 10], "other": 0, "output": [1, 13, 14], "overview": 3, "paralellis": 12, "patient": 10, "pcntoolkit": [6, 10, 12], "percent": 10, "percentag": 10, "perform": [1, 3], "pierr": 3, "plot": [5, 14], "posit": 14, "post": [1, 13], "predict": [0, 1, 3, 5, 11], "prepar": [1, 3, 5, 10], "preprar": 1, "princip": 11, "protocol": 1, "psychiatri": 1, "question": 2, "quickstart": 8, 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\ No newline at end of file
diff --git a/doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.rst b/doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.rst
index 55d17eb8..b4fa2dfb 100644
--- a/doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.rst
+++ b/doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.rst
@@ -25,110 +25,6 @@ Step 0: Install necessary libraries & grab data files
!pip install pcntoolkit
!pip install nutpie
-
-.. parsed-literal::
-
- Collecting https://github.com/amarquand/PCNtoolkit/archive/dev.zip
- Downloading https://github.com/amarquand/PCNtoolkit/archive/dev.zip
- [2K [32m\[0m [32m64.9 MB[0m [31m15.9 MB/s[0m [33m0:00:05[0m
- [?25h Installing build dependencies ... [?25l[?25hdone
- Getting requirements to build wheel ... [?25l[?25hdone
- Preparing metadata (pyproject.toml) ... [?25l[?25hdone
- Collecting bspline<0.2.0,>=0.1.1 (from pcntoolkit==0.31.0)
- Downloading bspline-0.1.1.tar.gz (84 kB)
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- [?25h Preparing metadata (setup.py) ... [?25l[?25hdone
- Collecting matplotlib<4.0.0,>=3.9.2 (from pcntoolkit==0.31.0)
- Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
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- [?25hBuilding wheels for collected packages: pcntoolkit, bspline
- Building wheel for pcntoolkit (pyproject.toml) ... [?25l[?25hdone
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- Successfully built pcntoolkit bspline
- Installing collected packages: bspline, matplotlib, pcntoolkit
- Attempting uninstall: matplotlib
- Found existing installation: matplotlib 3.8.0
- Uninstalling matplotlib-3.8.0:
- Successfully uninstalled matplotlib-3.8.0
- Successfully installed bspline-0.1.1 matplotlib-3.9.2 pcntoolkit-0.31.0
- Collecting nutpie
- Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (5.4 kB)
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- [?25hInstalling collected packages: nutpie
- Successfully installed nutpie-0.13.2
-
-
For this tutorial we will use data from the `Functional Connectom
Project FCON1000 :5: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame.
- Try using .loc[row_indexer,col_indexer] = value instead
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- icbm['sitenum'] = 0
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-
-
-.. parsed-literal::
-
- site AnnArbor_a 24
- site AnnArbor_b 32
- site Atlanta 28
- site Baltimore 23
- site Bangor 20
- site Beijing_Zang 198
- site Berlin_Margulies 26
- site Cambridge_Buckner 198
- site Cleveland 31
- site Leiden_2180 12
- site Leiden_2200 19
- site Milwaukee_b 46
- site Munchen 15
- site NewYork_a 83
- site NewYork_a_ADHD 25
- site Newark 19
- site Oulu 102
- site Oxford 22
- site PaloAlto 17
- site Queensland 19
- site SaintLouis 31
-
-
-
-
-.. parsed-literal::
-
- Text(0.5, 0, 'age')
-
-
-
-
-.. image:: HBR_NormativeModel_FCONdata_Tutorial_files/HBR_NormativeModel_FCONdata_Tutorial_10_3.png
+ ax.set_ylabel("LH mean cortical thickness [mm]")
+ ax.set_xlabel("age")
Step 1: Prepare training and testing sets
@@ -601,53 +115,26 @@ then displayed.
icbm_tr = icbm.loc[tr]
icbm_te = icbm.loc[te]
- print('sample size check')
- for i,s in enumerate(sites):
- idx = fcon_tr['site'] == s
- idxte = fcon_te['site'] == s
- print(i,s, sum(idx), sum(idxte))
+ print("sample size check")
+ for i, s in enumerate(sites):
+ idx = fcon_tr["site"] == s
+ idxte = fcon_te["site"] == s
+ print(i, s, sum(idx), sum(idxte))
- fcon_tr.to_csv(processing_dir + '/fcon1000_tr.csv')
- fcon_te.to_csv(processing_dir + '/fcon1000_te.csv')
- icbm_tr.to_csv(processing_dir + '/fcon1000_icbm_tr.csv')
- icbm_te.to_csv(processing_dir + '/fcon1000_icbm_te.csv')
-
-
-.. parsed-literal::
-
- sample size check
- 0 AnnArbor_a 10 14
- 1 AnnArbor_b 19 13
- 2 Atlanta 12 16
- 3 Baltimore 12 11
- 4 Bangor 10 10
- 5 Beijing_Zang 91 107
- 6 Berlin_Margulies 9 17
- 7 Cambridge_Buckner 96 102
- 8 Cleveland 13 18
- 9 Leiden_2180 5 7
- 10 Leiden_2200 11 8
- 11 Milwaukee_b 18 28
- 12 Munchen 9 6
- 13 NewYork_a 38 45
- 14 NewYork_a_ADHD 15 10
- 15 Newark 9 10
- 16 Oulu 50 52
- 17 Oxford 9 13
- 18 PaloAlto 8 9
- 19 Queensland 10 9
- 20 SaintLouis 18 13
-
+ fcon_tr.to_csv(processing_dir + "/fcon1000_tr.csv")
+ fcon_te.to_csv(processing_dir + "/fcon1000_te.csv")
+ icbm_tr.to_csv(processing_dir + "/fcon1000_icbm_tr.csv")
+ icbm_te.to_csv(processing_dir + "/fcon1000_icbm_te.csv")
Otherwise you can just load these pre defined subsets:
.. code:: ipython3
# Optional
- #fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
- #fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
- #icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
- #icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
+ # fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
+ # fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
+ # icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
+ # icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
Step 2: Configure HBR inputs: covariates, measures and batch effects
--------------------------------------------------------------------
@@ -657,7 +144,7 @@ hemisphere: two idps.
.. code:: ipython3
- idps = ['rh_MeanThickness_thickness','lh_MeanThickness_thickness']
+ idps = ["rh_MeanThickness_thickness", "lh_MeanThickness_thickness"]
As input to the model, we need covariates (used to describe predictable
source of variability (fixed effects), here ‘age’), measures (here
@@ -675,592 +162,79 @@ testing set (``_test``).
.. code:: ipython3
- X_train = (fcon_tr['age']/100).to_numpy(dtype=float)
+ X_train = (fcon_tr["age"] / 100).to_numpy(dtype=float)
Y_train = fcon_tr[idps].to_numpy(dtype=float)
# configure batch effects for site and sex
- #batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
+ # batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
# or only site
- batch_effects_train = fcon_tr[['sitenum']].to_numpy(dtype=int)
+ batch_effects_train = fcon_tr[["sitenum"]].to_numpy(dtype=int)
- with open('X_train.pkl', 'wb') as file:
+ with open("X_train.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_train), file)
- with open('Y_train.pkl', 'wb') as file:
+ with open("Y_train.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_train), file)
- with open('trbefile.pkl', 'wb') as file:
+ with open("trbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_train), file)
- X_test = (fcon_te['age']/100).to_numpy(dtype=float)
+ X_test = (fcon_te["age"] / 100).to_numpy(dtype=float)
Y_test = fcon_te[idps].to_numpy(dtype=float)
- #batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
- batch_effects_test = fcon_te[['sitenum']].to_numpy(dtype=int)
+ # batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
+ batch_effects_test = fcon_te[["sitenum"]].to_numpy(dtype=int)
- with open('X_test.pkl', 'wb') as file:
+ with open("X_test.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_test), file)
- with open('Y_test.pkl', 'wb') as file:
+ with open("Y_test.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_test), file)
- with open('tsbefile.pkl', 'wb') as file:
+ with open("tsbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_test), file)
+
# a simple function to quickly load pickle files
def ldpkl(filename: str):
- with open(filename, 'rb') as f:
+ with open(filename, "rb") as f:
return pickle.load(f)
.. code:: ipython3
batch_effects_test
-
-
-
-.. parsed-literal::
-
- array([[ 0],
- [ 0],
- [ 0],
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- [20],
- [20],
- [20],
- [20],
- [20],
- [20]])
-
-
-
Step 3: Files and Folders grooming
----------------------------------
.. code:: ipython3
- respfile = os.path.join(processing_dir, 'Y_train.pkl') # measurements (eg cortical thickness) of the training samples (columns: the various features/ROIs, rows: observations or subjects)
- covfile = os.path.join(processing_dir, 'X_train.pkl') # covariates (eg age) the training samples (columns: covariates, rows: observations or subjects)
-
- testrespfile_path = os.path.join(processing_dir, 'Y_test.pkl') # measurements for the testing samples
- testcovfile_path = os.path.join(processing_dir, 'X_test.pkl') # covariate file for the testing samples
-
- trbefile = os.path.join(processing_dir, 'trbefile.pkl') # training batch effects file (eg scanner_id, gender) (columns: the various batch effects, rows: observations or subjects)
- tsbefile = os.path.join(processing_dir, 'tsbefile.pkl') # testing batch effects file
-
- output_path = os.path.join(processing_dir, 'Models/') # output path, where the models will be written
- log_dir = os.path.join(processing_dir, 'log/') #
+ respfile = os.path.join(
+ processing_dir, "Y_train.pkl"
+ ) # measurements (eg cortical thickness) of the training samples (columns: the various features/ROIs, rows: observations or subjects)
+ covfile = os.path.join(
+ processing_dir, "X_train.pkl"
+ ) # covariates (eg age) the training samples (columns: covariates, rows: observations or subjects)
+
+ testrespfile_path = os.path.join(
+ processing_dir, "Y_test.pkl"
+ ) # measurements for the testing samples
+ testcovfile_path = os.path.join(
+ processing_dir, "X_test.pkl"
+ ) # covariate file for the testing samples
+
+ trbefile = os.path.join(
+ processing_dir, "trbefile.pkl"
+ ) # training batch effects file (eg scanner_id, gender) (columns: the various batch effects, rows: observations or subjects)
+ tsbefile = os.path.join(processing_dir, "tsbefile.pkl") # testing batch effects file
+
+ output_path = os.path.join(
+ processing_dir, "Models/"
+ ) # output path, where the models will be written
+ log_dir = os.path.join(processing_dir, "log/") #
if not os.path.isdir(output_path):
os.mkdir(output_path)
if not os.path.isdir(log_dir):
os.mkdir(log_dir)
- outputsuffix = '_estimate' # a string to name the output files, of use only to you, so adapt it for your needs.
+ outputsuffix = "_estimate" # a string to name the output files, of use only to you, so adapt it for your needs.
Step 4: Estimating the models
-----------------------------
@@ -1275,403 +249,26 @@ and output files will be written and how they will be named.
.. code:: ipython3
- ptk.normative.estimate(covfile=covfile,
- respfile=respfile,
- tsbefile=tsbefile,
- trbefile=trbefile,
- inscaler='standardize',
- outscaler='standardize',
- linear_mu='True',
- random_intercept_mu='True',
- centered_intercept_mu='True',
- alg='hbr',
- log_path=log_dir,
- binary=True,
- output_path=output_path,
- testcov= testcovfile_path,
- testresp = testrespfile_path,
- outputsuffix=outputsuffix,
- savemodel=True,
- nuts_sampler='nutpie')
-
-
-.. parsed-literal::
-
- inscaler: standardize
- outscaler: standardize
- Processing data in /content/HBR_demo/Y_train.pkl
- Estimating model 1 of 2
-
-
-
-.. raw:: html
-
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-
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-.. raw:: html
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- Estimating model 2 of 2
-
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-
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- Total Chains: 1
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-.. raw:: html
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-
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-.. raw:: html
-
-
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-
-.. parsed-literal::
-
- Saving model meta-data...
- Evaluating the model ...
- Writing outputs ...
-
+ ptk.normative.estimate(
+ covfile=covfile,
+ respfile=respfile,
+ tsbefile=tsbefile,
+ trbefile=trbefile,
+ inscaler="standardize",
+ outscaler="standardize",
+ linear_mu="True",
+ random_intercept_mu="True",
+ centered_intercept_mu="True",
+ alg="hbr",
+ log_path=log_dir,
+ binary=True,
+ output_path=output_path,
+ testcov=testcovfile_path,
+ testresp=testrespfile_path,
+ outputsuffix=outputsuffix,
+ savemodel=True,
+ nuts_sampler="nutpie",
+ )
Here some analyses can be done, there are also some error metrics that
could be of interest. This is covered in step 6 and in `Saige’s
@@ -1687,45 +284,49 @@ training and testing set of covariates, measures and batch effects:
.. code:: ipython3
- X_adapt = (icbm_tr['age']/100).to_numpy(dtype=float)
+ X_adapt = (icbm_tr["age"] / 100).to_numpy(dtype=float)
Y_adapt = icbm_tr[idps].to_numpy(dtype=float)
- #batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
- batch_effects_adapt = icbm_tr[['sitenum']].to_numpy(dtype=int)
+ # batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
+ batch_effects_adapt = icbm_tr[["sitenum"]].to_numpy(dtype=int)
- with open('X_adaptation.pkl', 'wb') as file:
+ with open("X_adaptation.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_adapt), file)
- with open('Y_adaptation.pkl', 'wb') as file:
+ with open("Y_adaptation.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_adapt), file)
- with open('adbefile.pkl', 'wb') as file:
+ with open("adbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_adapt), file)
# Test data (new dataset)
- X_test_txfr = (icbm_te['age']/100).to_numpy(dtype=float)
+ X_test_txfr = (icbm_te["age"] / 100).to_numpy(dtype=float)
Y_test_txfr = icbm_te[idps].to_numpy(dtype=float)
- #batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
- batch_effects_test_txfr = icbm_te[['sitenum']].to_numpy(dtype=int)
+ # batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
+ batch_effects_test_txfr = icbm_te[["sitenum"]].to_numpy(dtype=int)
- with open('X_test_txfr.pkl', 'wb') as file:
+ with open("X_test_txfr.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_test_txfr), file)
- with open('Y_test_txfr.pkl', 'wb') as file:
+ with open("Y_test_txfr.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_test_txfr), file)
- with open('txbefile.pkl', 'wb') as file:
+ with open("txbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_test_txfr), file)
.. code:: ipython3
- respfile = os.path.join(processing_dir, 'Y_adaptation.pkl')
- covfile = os.path.join(processing_dir, 'X_adaptation.pkl')
- testrespfile_path = os.path.join(processing_dir, 'Y_test_txfr.pkl')
- testcovfile_path = os.path.join(processing_dir, 'X_test_txfr.pkl')
- trbefile = os.path.join(processing_dir, 'adbefile.pkl')
- tsbefile = os.path.join(processing_dir, 'txbefile.pkl')
-
- log_dir = os.path.join(processing_dir, 'log_transfer/')
- output_path = os.path.join(processing_dir, 'Transfer/')
- model_path = os.path.join(processing_dir, 'Models/') # path to the previously trained models
- outputsuffix = '_transfer' # suffix added to the output files from the transfer function
+ respfile = os.path.join(processing_dir, "Y_adaptation.pkl")
+ covfile = os.path.join(processing_dir, "X_adaptation.pkl")
+ testrespfile_path = os.path.join(processing_dir, "Y_test_txfr.pkl")
+ testcovfile_path = os.path.join(processing_dir, "X_test_txfr.pkl")
+ trbefile = os.path.join(processing_dir, "adbefile.pkl")
+ tsbefile = os.path.join(processing_dir, "txbefile.pkl")
+
+ log_dir = os.path.join(processing_dir, "log_transfer/")
+ output_path = os.path.join(processing_dir, "Transfer/")
+ model_path = os.path.join(
+ processing_dir, "Models/"
+ ) # path to the previously trained models
+ outputsuffix = (
+ "_transfer" # suffix added to the output files from the transfer function
+ )
Here, the difference is that the transfer function needs a model path,
which points to the models we just trained, and new site data (training
@@ -1733,394 +334,37 @@ and testing). That is basically the only difference.
.. code:: ipython3
- yhat, s2, z_scores = ptk.normative.transfer(covfile=covfile,
- respfile=respfile,
- tsbefile=tsbefile,
- trbefile=trbefile,
- inscaler='standardize',
- outscaler='standardize',
- linear_mu='True',
- random_intercept_mu='True',
- centered_intercept_mu='True',
- model_path = model_path,
- alg='hbr',
- log_path=log_dir,
- binary=True,
- output_path=output_path,
- testcov= testcovfile_path,
- testresp = testrespfile_path,
- outputsuffix=outputsuffix,
- savemodel=True,
- nuts_sampler='nutpie')
-
-
-.. parsed-literal::
-
- Loading data ...
- Using HBR transform...
- Transferring model 1 of 2
-
-
-
-.. raw:: html
-
-
-
-
-
-
-
-.. raw:: html
-
-
-
- Sampler Progress
- Total Chains: 1
- Active Chains: 0
-
- Finished Chains:
- 1
-
- Sampling for now
-
- Estimated Time to Completion:
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- 0.47
- 7
-
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-.. parsed-literal::
-
- Output()
-
-
-
-.. raw:: html
-
-
-
-
-
-.. parsed-literal::
-
- Using HBR transform...
- Transferring model 2 of 2
-
-
-
-.. raw:: html
-
-
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-.. raw:: html
-
-
-
- Sampler Progress
- Total Chains: 1
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-.. parsed-literal::
-
- Output()
-
-
-
-.. raw:: html
-
-
-
-
-
-.. parsed-literal::
-
- Evaluating the model ...
- Writing outputs ...
-
+ yhat, s2, z_scores = ptk.normative.transfer(
+ covfile=covfile,
+ respfile=respfile,
+ tsbefile=tsbefile,
+ trbefile=trbefile,
+ inscaler="standardize",
+ outscaler="standardize",
+ linear_mu="True",
+ random_intercept_mu="True",
+ centered_intercept_mu="True",
+ model_path=model_path,
+ alg="hbr",
+ log_path=log_dir,
+ binary=True,
+ output_path=output_path,
+ testcov=testcovfile_path,
+ testresp=testrespfile_path,
+ outputsuffix=outputsuffix,
+ savemodel=True,
+ nuts_sampler="nutpie",
+ )
.. code:: ipython3
output_path
-
-
-
-.. parsed-literal::
-
- '/content/HBR_demo/Transfer/'
-
-
-
.. code:: ipython3
- EV = pd.read_pickle('EXPV_estimate.pkl')
+ EV = pd.read_pickle("EXPV_estimate.pkl")
print(EV)
-
-.. parsed-literal::
-
- 0
- 0 0.438215
- 1 0.439181
-
-
And that is it, you now have models that benefited from prior knowledge
about different scanner sites to learn on unseen sites.
processing_dir = "HBR_demo" # replace with desired working directory
+processing_dir = "HBR_demo" # replace with desired working directory
if not os.path.isdir(processing_dir):
os.makedirs(processing_dir)
os.chdir(processing_dir)
@@ -276,402 +177,35 @@ Overview
assign some site id to the different scanner sites and print an overview
of the left hemisphere mean raw cortical thickness as a function of age,
color coded by the various sites:
-fcon = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000.csv')
+fcon = pd.read_csv(
+ "https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000.csv"
+)
# extract the ICBM site for transfer
-icbm = fcon.loc[fcon['site'] == 'ICBM']
-icbm['sitenum'] = 0
+icbm = fcon.loc[fcon["site"] == "ICBM"]
+icbm["sitenum"] = 0
# remove from the training set (also Pittsburgh because it only has 3 samples)
-fcon = fcon.loc[fcon['site'] != 'ICBM']
-fcon = fcon.loc[fcon['site'] != 'Pittsburgh']
+fcon = fcon.loc[fcon["site"] != "ICBM"]
+fcon = fcon.loc[fcon["site"] != "Pittsburgh"]
-sites = fcon['site'].unique()
-fcon['sitenum'] = 0
+sites = fcon["site"].unique()
+fcon["sitenum"] = 0
f, ax = plt.subplots(figsize=(12, 12))
-for i,s in enumerate(sites):
- idx = fcon['site'] == s
- fcon['sitenum'].loc[idx] = i
+for i, s in enumerate(sites):
+ idx = fcon["site"] == s
+ fcon["sitenum"].loc[idx] = i
- print('site',s, sum(idx))
- ax.scatter(fcon['age'].loc[idx], fcon['lh_MeanThickness_thickness'].loc[idx])
+ print("site", s, sum(idx))
+ ax.scatter(fcon["age"].loc[idx], fcon["lh_MeanThickness_thickness"].loc[idx])
ax.legend(sites)
-ax.set_ylabel('LH mean cortical thickness [mm]')
-ax.set_xlabel('age')
+ax.set_ylabel("LH mean cortical thickness [mm]")
+ax.set_xlabel("age")
-<ipython-input-4-a7d14b9f2beb>:5: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- icbm['sitenum'] = 0
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
-You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
-A typical example is when you are setting values in a column of a DataFrame, like:
-
-df["col"][row_indexer] = value
-
-Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
-<ipython-input-4-a7d14b9f2beb>:18: SettingWithCopyWarning:
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-site AnnArbor_a 24
-site AnnArbor_b 32
-site Atlanta 28
-site Baltimore 23
-site Bangor 20
-site Beijing_Zang 198
-site Berlin_Margulies 26
-site Cambridge_Buckner 198
-site Cleveland 31
-site Leiden_2180 12
-site Leiden_2200 19
-site Milwaukee_b 46
-site Munchen 15
-site NewYork_a 83
-site NewYork_a_ADHD 25
-site Newark 19
-site Oulu 102
-site Oxford 22
-site PaloAlto 17
-site Queensland 19
-site SaintLouis 31
-
-
-Text(0.5, 0, 'age')
-
-
-
@@ -694,48 +228,24 @@ Step 1: Prepare training and testing setsicbm_tr = icbm.loc[tr]
icbm_te = icbm.loc[te]
-print('sample size check')
-for i,s in enumerate(sites):
- idx = fcon_tr['site'] == s
- idxte = fcon_te['site'] == s
- print(i,s, sum(idx), sum(idxte))
+print("sample size check")
+for i, s in enumerate(sites):
+ idx = fcon_tr["site"] == s
+ idxte = fcon_te["site"] == s
+ print(i, s, sum(idx), sum(idxte))
-fcon_tr.to_csv(processing_dir + '/fcon1000_tr.csv')
-fcon_te.to_csv(processing_dir + '/fcon1000_te.csv')
-icbm_tr.to_csv(processing_dir + '/fcon1000_icbm_tr.csv')
-icbm_te.to_csv(processing_dir + '/fcon1000_icbm_te.csv')
-
-
-sample size check
-0 AnnArbor_a 10 14
-1 AnnArbor_b 19 13
-2 Atlanta 12 16
-3 Baltimore 12 11
-4 Bangor 10 10
-5 Beijing_Zang 91 107
-6 Berlin_Margulies 9 17
-7 Cambridge_Buckner 96 102
-8 Cleveland 13 18
-9 Leiden_2180 5 7
-10 Leiden_2200 11 8
-11 Milwaukee_b 18 28
-12 Munchen 9 6
-13 NewYork_a 38 45
-14 NewYork_a_ADHD 15 10
-15 Newark 9 10
-16 Oulu 50 52
-17 Oxford 9 13
-18 PaloAlto 8 9
-19 Queensland 10 9
-20 SaintLouis 18 13
+fcon_tr.to_csv(processing_dir + "/fcon1000_tr.csv")
+fcon_te.to_csv(processing_dir + "/fcon1000_te.csv")
+icbm_tr.to_csv(processing_dir + "/fcon1000_icbm_tr.csv")
+icbm_te.to_csv(processing_dir + "/fcon1000_icbm_te.csv")
Otherwise you can just load these pre defined subsets:
# Optional
-#fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
-#fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
-#icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
-#icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
+# fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
+# fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
+# icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
+# icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
@@ -743,7 +253,7 @@ Step 1: Prepare training and testing sets
We will here only use the mean cortical thickness for the Right and Left
hemisphere: two idps.
-idps = ['rh_MeanThickness_thickness','lh_MeanThickness_thickness']
+idps = ["rh_MeanThickness_thickness", "lh_MeanThickness_thickness"]
As input to the model, we need covariates (used to describe predictable
@@ -755,584 +265,77 @@
Step 2: Configure HBR inputs: covariates, measures and batch effectsbatch_effects to the random effects
We need these values both for the training (_train) and for the
testing set (_test).
-X_train = (fcon_tr['age']/100).to_numpy(dtype=float)
+X_train = (fcon_tr["age"] / 100).to_numpy(dtype=float)
Y_train = fcon_tr[idps].to_numpy(dtype=float)
# configure batch effects for site and sex
-#batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
+# batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
# or only site
-batch_effects_train = fcon_tr[['sitenum']].to_numpy(dtype=int)
+batch_effects_train = fcon_tr[["sitenum"]].to_numpy(dtype=int)
-with open('X_train.pkl', 'wb') as file:
+with open("X_train.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_train), file)
-with open('Y_train.pkl', 'wb') as file:
+with open("Y_train.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_train), file)
-with open('trbefile.pkl', 'wb') as file:
+with open("trbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_train), file)
-X_test = (fcon_te['age']/100).to_numpy(dtype=float)
+X_test = (fcon_te["age"] / 100).to_numpy(dtype=float)
Y_test = fcon_te[idps].to_numpy(dtype=float)
-#batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
-batch_effects_test = fcon_te[['sitenum']].to_numpy(dtype=int)
+# batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
+batch_effects_test = fcon_te[["sitenum"]].to_numpy(dtype=int)
-with open('X_test.pkl', 'wb') as file:
+with open("X_test.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_test), file)
-with open('Y_test.pkl', 'wb') as file:
+with open("Y_test.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_test), file)
-with open('tsbefile.pkl', 'wb') as file:
+with open("tsbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_test), file)
+
# a simple function to quickly load pickle files
def ldpkl(filename: str):
- with open(filename, 'rb') as f:
+ with open(filename, "rb") as f:
return pickle.load(f)
batch_effects_test
-array([[ 0],
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- [20],
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- [20],
- [20]])
-
-
Step 3: Files and Folders grooming
-respfile = os.path.join(processing_dir, 'Y_train.pkl') # measurements (eg cortical thickness) of the training samples (columns: the various features/ROIs, rows: observations or subjects)
-covfile = os.path.join(processing_dir, 'X_train.pkl') # covariates (eg age) the training samples (columns: covariates, rows: observations or subjects)
-
-testrespfile_path = os.path.join(processing_dir, 'Y_test.pkl') # measurements for the testing samples
-testcovfile_path = os.path.join(processing_dir, 'X_test.pkl') # covariate file for the testing samples
-
-trbefile = os.path.join(processing_dir, 'trbefile.pkl') # training batch effects file (eg scanner_id, gender) (columns: the various batch effects, rows: observations or subjects)
-tsbefile = os.path.join(processing_dir, 'tsbefile.pkl') # testing batch effects file
-
-output_path = os.path.join(processing_dir, 'Models/') # output path, where the models will be written
-log_dir = os.path.join(processing_dir, 'log/') #
+respfile = os.path.join(
+ processing_dir, "Y_train.pkl"
+) # measurements (eg cortical thickness) of the training samples (columns: the various features/ROIs, rows: observations or subjects)
+covfile = os.path.join(
+ processing_dir, "X_train.pkl"
+) # covariates (eg age) the training samples (columns: covariates, rows: observations or subjects)
+
+testrespfile_path = os.path.join(
+ processing_dir, "Y_test.pkl"
+) # measurements for the testing samples
+testcovfile_path = os.path.join(
+ processing_dir, "X_test.pkl"
+) # covariate file for the testing samples
+
+trbefile = os.path.join(
+ processing_dir, "trbefile.pkl"
+) # training batch effects file (eg scanner_id, gender) (columns: the various batch effects, rows: observations or subjects)
+tsbefile = os.path.join(processing_dir, "tsbefile.pkl") # testing batch effects file
+
+output_path = os.path.join(
+ processing_dir, "Models/"
+) # output path, where the models will be written
+log_dir = os.path.join(processing_dir, "log/") #
if not os.path.isdir(output_path):
os.mkdir(output_path)
if not os.path.isdir(log_dir):
os.mkdir(log_dir)
-outputsuffix = '_estimate' # a string to name the output files, of use only to you, so adapt it for your needs.
+outputsuffix = "_estimate" # a string to name the output files, of use only to you, so adapt it for your needs.
@@ -1345,320 +348,26 @@ Step 4: Estimating the modelsptk.normative.estimate(covfile=covfile,
- respfile=respfile,
- tsbefile=tsbefile,
- trbefile=trbefile,
- inscaler='standardize',
- outscaler='standardize',
- linear_mu='True',
- random_intercept_mu='True',
- centered_intercept_mu='True',
- alg='hbr',
- log_path=log_dir,
- binary=True,
- output_path=output_path,
- testcov= testcovfile_path,
- testresp = testrespfile_path,
- outputsuffix=outputsuffix,
- savemodel=True,
- nuts_sampler='nutpie')
-
-
-inscaler: standardize
-outscaler: standardize
-Processing data in /content/HBR_demo/Y_train.pkl
-Estimating model 1 of 2
-
-
-
- Sampler Progress
- Total Chains: 1
- Active Chains: 0
-
- Finished Chains:
- 1
-
- Sampling for now
-
- Estimated Time to Completion:
- now
-
-
-
-
-
-
- Progress
- Draws
- Divergences
- Step Size
- Gradients/Draw
-
-
-
-
-
-
-
-
- 1500
- 0
- 0.34
- 15
-
-
-
-
-
-Output()
-
-
-Output()
-
-
-Normal
-
-
-Estimating model 2 of 2
-
-
-
- Sampler Progress
- Total Chains: 1
- Active Chains: 0
-
- Finished Chains:
- 1
-
- Sampling for now
-
- Estimated Time to Completion:
- now
-
-
-
-
-
-
- Progress
- Draws
- Divergences
- Step Size
- Gradients/Draw
-
-
-
-
-
-
-
-
- 1500
- 0
- 0.33
- 15
-
-
-
-
-
-Output()
-
-
-Normal
-
-
-Output()
-
-
-Saving model meta-data...
-Evaluating the model ...
-Writing outputs ...
+ptk.normative.estimate(
+ covfile=covfile,
+ respfile=respfile,
+ tsbefile=tsbefile,
+ trbefile=trbefile,
+ inscaler="standardize",
+ outscaler="standardize",
+ linear_mu="True",
+ random_intercept_mu="True",
+ centered_intercept_mu="True",
+ alg="hbr",
+ log_path=log_dir,
+ binary=True,
+ output_path=output_path,
+ testcov=testcovfile_path,
+ testresp=testrespfile_path,
+ outputsuffix=outputsuffix,
+ savemodel=True,
+ nuts_sampler="nutpie",
+)
Here some analyses can be done, there are also some error metrics that
@@ -1671,367 +380,82 @@
Step 5: Transfering the models to unseen sitesX_adapt = (icbm_tr['age']/100).to_numpy(dtype=float)
+X_adapt = (icbm_tr["age"] / 100).to_numpy(dtype=float)
Y_adapt = icbm_tr[idps].to_numpy(dtype=float)
-#batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
-batch_effects_adapt = icbm_tr[['sitenum']].to_numpy(dtype=int)
+# batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
+batch_effects_adapt = icbm_tr[["sitenum"]].to_numpy(dtype=int)
-with open('X_adaptation.pkl', 'wb') as file:
+with open("X_adaptation.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_adapt), file)
-with open('Y_adaptation.pkl', 'wb') as file:
+with open("Y_adaptation.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_adapt), file)
-with open('adbefile.pkl', 'wb') as file:
+with open("adbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_adapt), file)
# Test data (new dataset)
-X_test_txfr = (icbm_te['age']/100).to_numpy(dtype=float)
+X_test_txfr = (icbm_te["age"] / 100).to_numpy(dtype=float)
Y_test_txfr = icbm_te[idps].to_numpy(dtype=float)
-#batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
-batch_effects_test_txfr = icbm_te[['sitenum']].to_numpy(dtype=int)
+# batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
+batch_effects_test_txfr = icbm_te[["sitenum"]].to_numpy(dtype=int)
-with open('X_test_txfr.pkl', 'wb') as file:
+with open("X_test_txfr.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_test_txfr), file)
-with open('Y_test_txfr.pkl', 'wb') as file:
+with open("Y_test_txfr.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_test_txfr), file)
-with open('txbefile.pkl', 'wb') as file:
+with open("txbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_test_txfr), file)
-respfile = os.path.join(processing_dir, 'Y_adaptation.pkl')
-covfile = os.path.join(processing_dir, 'X_adaptation.pkl')
-testrespfile_path = os.path.join(processing_dir, 'Y_test_txfr.pkl')
-testcovfile_path = os.path.join(processing_dir, 'X_test_txfr.pkl')
-trbefile = os.path.join(processing_dir, 'adbefile.pkl')
-tsbefile = os.path.join(processing_dir, 'txbefile.pkl')
+respfile = os.path.join(processing_dir, "Y_adaptation.pkl")
+covfile = os.path.join(processing_dir, "X_adaptation.pkl")
+testrespfile_path = os.path.join(processing_dir, "Y_test_txfr.pkl")
+testcovfile_path = os.path.join(processing_dir, "X_test_txfr.pkl")
+trbefile = os.path.join(processing_dir, "adbefile.pkl")
+tsbefile = os.path.join(processing_dir, "txbefile.pkl")
-log_dir = os.path.join(processing_dir, 'log_transfer/')
-output_path = os.path.join(processing_dir, 'Transfer/')
-model_path = os.path.join(processing_dir, 'Models/') # path to the previously trained models
-outputsuffix = '_transfer' # suffix added to the output files from the transfer function
+log_dir = os.path.join(processing_dir, "log_transfer/")
+output_path = os.path.join(processing_dir, "Transfer/")
+model_path = os.path.join(
+ processing_dir, "Models/"
+) # path to the previously trained models
+outputsuffix = (
+ "_transfer" # suffix added to the output files from the transfer function
+)
Here, the difference is that the transfer function needs a model path,
which points to the models we just trained, and new site data (training
and testing). That is basically the only difference.
-yhat, s2, z_scores = ptk.normative.transfer(covfile=covfile,
- respfile=respfile,
- tsbefile=tsbefile,
- trbefile=trbefile,
- inscaler='standardize',
- outscaler='standardize',
- linear_mu='True',
- random_intercept_mu='True',
- centered_intercept_mu='True',
- model_path = model_path,
- alg='hbr',
- log_path=log_dir,
- binary=True,
- output_path=output_path,
- testcov= testcovfile_path,
- testresp = testrespfile_path,
- outputsuffix=outputsuffix,
- savemodel=True,
- nuts_sampler='nutpie')
-
-
-Loading data ...
-Using HBR transform...
-Transferring model 1 of 2
-
-
-
- Sampler Progress
- Total Chains: 1
- Active Chains: 0
-
- Finished Chains:
- 1
-
- Sampling for now
-
- Estimated Time to Completion:
- now
-
-
-
-
-
-
- Progress
- Draws
- Divergences
- Step Size
- Gradients/Draw
-
-
-
-
-
-
-
-
- 1500
- 2
- 0.47
- 7
-
-
-
-
-
-Output()
-
-
-Using HBR transform...
-Transferring model 2 of 2
-
-
-
- Sampler Progress
- Total Chains: 1
- Active Chains: 0
-
- Finished Chains:
- 1
-
- Sampling for now
-
- Estimated Time to Completion:
- now
-
-
-
-
-
-
- Progress
- Draws
- Divergences
- Step Size
- Gradients/Draw
-
-
-
-
-
-
-
-
- 1500
- 1
- 0.40
- 15
-
-
-
-
-
-Output()
-
-
-Evaluating the model ...
-Writing outputs ...
+yhat, s2, z_scores = ptk.normative.transfer(
+ covfile=covfile,
+ respfile=respfile,
+ tsbefile=tsbefile,
+ trbefile=trbefile,
+ inscaler="standardize",
+ outscaler="standardize",
+ linear_mu="True",
+ random_intercept_mu="True",
+ centered_intercept_mu="True",
+ model_path=model_path,
+ alg="hbr",
+ log_path=log_dir,
+ binary=True,
+ output_path=output_path,
+ testcov=testcovfile_path,
+ testresp=testrespfile_path,
+ outputsuffix=outputsuffix,
+ savemodel=True,
+ nuts_sampler="nutpie",
+)
output_path
-'/content/HBR_demo/Transfer/'
-
-
-EV = pd.read_pickle('EXPV_estimate.pkl')
+EV = pd.read_pickle("EXPV_estimate.pkl")
print(EV)
- 0
-0 0.438215
-1 0.439181
-
-
And that is it, you now have models that benefited from prior knowledge
about different scanner sites to learn on unseen sites.
@@ -2054,7 +478,7 @@ Step 6: Interpreting model performance
Previous
- Next
+ Next
diff --git a/doc/build/html/pages/normative_modelling_walkthrough.html b/doc/build/html/pages/normative_modelling_walkthrough.html
index 55c5f1ec..73b066f4 100644
--- a/doc/build/html/pages/normative_modelling_walkthrough.html
+++ b/doc/build/html/pages/normative_modelling_walkthrough.html
@@ -77,7 +77,7 @@
Predictive Clinical Neuroscience Toolkit
Hierarchical Bayesian Regression Normative Modelling and Transfer onto unseen site.
-Braincharts: transfer
+Using lifespan models to make predictions on new data
Predictive Clinical Neuroscience Toolkit
The Normative Modeling Framework for Computational Psychiatry Protocol
Data Preparation
@@ -158,105 +158,6 @@ Task 0: Load data and install PCNtoolkit!pip install nutpie
-Collecting https://github.com/amarquand/PCNtoolkit/archive/dev.zip
- Downloading https://github.com/amarquand/PCNtoolkit/archive/dev.zip
-[2K [32m[0m [32m64.9 MB[0m [31m16.9 MB/s[0m [33m0:00:05[0m
-[?25h Installing build dependencies ... [?25l[?25hdone
- Getting requirements to build wheel ... [?25l[?25hdone
- Preparing metadata (pyproject.toml) ... [?25l[?25hdone
-Collecting bspline<0.2.0,>=0.1.1 (from pcntoolkit==0.31.0)
- Downloading bspline-0.1.1.tar.gz (84 kB)
-[2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m84.2/84.2 kB[0m [31m2.0 MB/s[0m eta [36m0:00:00[0m
-[?25h Preparing metadata (setup.py) ... [?25l[?25hdone
-Collecting matplotlib<4.0.0,>=3.9.2 (from pcntoolkit==0.31.0)
- Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
-Requirement already satisfied: nibabel<6.0.0,>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.3.2)
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-Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)
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-[?25hBuilding wheels for collected packages: pcntoolkit, bspline
- Building wheel for pcntoolkit (pyproject.toml) ... [?25l[?25hdone
- Created wheel for pcntoolkit: filename=pcntoolkit-0.31.0-py3-none-any.whl size=114835 sha256=40635c10c24ccf2c319ee965aaf1038272cd5578f14d9cb3dd14598ddab31d00
- Stored in directory: /tmp/pip-ephem-wheel-cache-nl3hy35q/wheels/9e/c4/29/3bca3a5facf8ef69b8622461d8520d24a19d3745aefa093d1e
- Building wheel for bspline (setup.py) ... [?25l[?25hdone
- Created wheel for bspline: filename=bspline-0.1.1-py3-none-any.whl size=84482 sha256=1da013ad20b77d85515a6a2318eaf7cc4baaa7772eb1d26249f923ce8d779d7e
- Stored in directory: /root/.cache/pip/wheels/3c/ab/0a/70927853a6d9166bc777922736063a6f99c43a327c802f9326
-Successfully built pcntoolkit bspline
-Installing collected packages: bspline, matplotlib, pcntoolkit
- Attempting uninstall: matplotlib
- Found existing installation: matplotlib 3.8.0
- Uninstalling matplotlib-3.8.0:
- Successfully uninstalled matplotlib-3.8.0
-Successfully installed bspline-0.1.1 matplotlib-3.9.2 pcntoolkit-0.31.0
-Collecting nutpie
- Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (5.4 kB)
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-[?25hInstalling collected packages: nutpie
-Successfully installed nutpie-0.13.2
Option 1: Connect your Google Drive account, and load data from
Google Drive. Having Google Drive connected will allow you to save any
files created back to your Drive folder. This step will require you to
@@ -284,51 +185,6 @@
Task 0: Load data and install PCNtoolkit# code by S. Rutherford
---2024-11-19 12:28:31-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics.csv
-Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
-Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
-HTTP request sent, awaiting response... 200 OK
-Length: 17484 (17K) [text/plain]
-Saving to: ‘camcan_demographics.csv’
-
-camcan_demographics 100%[===================>] 17.07K --.-KB/s in 0.003s
-
-2024-11-19 12:28:31 (5.14 MB/s) - ‘camcan_demographics.csv’ saved [17484/17484]
-
---2024-11-19 12:28:31-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics_nordan.csv
-Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
-Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
-HTTP request sent, awaiting response... 200 OK
-Length: 332 [text/plain]
-Saving to: ‘camcan_demographics_nordan.csv’
-
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-
---2024-11-19 12:28:32-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features.csv
-Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...
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-HTTP request sent, awaiting response... 200 OK
-Length: 188944 (185K) [text/plain]
-Saving to: ‘camcan_features.csv’
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-2024-11-19 12:28:33 (3.58 MB/s) - ‘camcan_features.csv’ saved [188944/188944]
-
---2024-11-19 12:28:33-- https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features_nordan.csv
-Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
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-HTTP request sent, awaiting response... 200 OK
-Length: 1695 (1.7K) [text/plain]
-Saving to: ‘camcan_features_nordan.csv’
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-
-
TASK 1: Format input data
@@ -369,188 +225,6 @@ TASK 1: Format input data# code by T. Wolfers
- age sex_name sex IQ_random
-paricipants
-CC110033 24 MALE 1 73
-CC110037 18 MALE 1 103
-CC110045 24 FEMALE 0 124
-CC110056 22 FEMALE 0 124
-CC110062 20 MALE 1 126
-... ... ... ... ...
-CC722542 79 MALE 1 116
-CC722651 79 FEMALE 0 128
-CC722891 84 FEMALE 0 129
-CC723197 80 FEMALE 0 96
-CC723395 86 FEMALE 0 145
-
-[707 rows x 4 columns]
- left_Hippocampal_tail left_subiculum left_CA1 participants
-CC110033 482.768229 419.948094 666.496024
-CC110037 595.269259 502.320315 698.157779
-CC110045 655.847194 476.433625 654.215689
-CC110056 561.345626 447.258970 611.114561
-CC110062 756.521166 521.034681 716.391590
-... ... ... ...
-CC722542 467.896808 440.794061 688.130914
-CC722651 406.326167 393.469843 613.794018
-CC722891 393.430481 303.049578 444.772656
-CC723197 475.929914 372.449778 525.739508
-CC723395 444.301617 330.688394 565.359058
-
- left_hippocampal-fissure left_presubiculum left_parasubiculum participants
-CC110033 131.719049 285.535445 59.209377
-CC110037 156.304335 367.678385 60.817591
-CC110045 146.767569 346.347202 67.481121
-CC110056 126.615335 327.528926 70.901227
-CC110062 206.205818 384.356075 80.329689
-... ... ... ...
-CC722542 184.300085 306.287030 72.629722
-CC722651 224.292557 254.786917 50.006651
-CC722891 158.987352 202.213773 46.418129
-CC723197 172.558200 222.384434 40.304889
-CC723395 206.235576 197.417773 34.240227
-
- left_molecular_layer_HP left_GC-ML-DG left_CA3 left_CA4 participants
-CC110033 583.239022 313.144932 223.022262 266.801434
-CC110037 619.053381 311.428298 192.949211 260.551999
-CC110045 622.037178 322.315065 204.756048 275.428880
-CC110056 597.467595 323.777115 233.160437 278.133998
-CC110062 666.590397 364.130988 253.917949 311.079938
-... ... ... ... ...
-CC722542 597.823380 322.236056 252.159707 275.293551
-CC722651 558.060369 322.176631 261.160474 282.029715
-CC722891 384.798819 204.562530 149.220194 184.259996
-CC723197 467.847632 262.950594 215.411133 232.938256
-CC723395 470.969863 269.963160 234.219152 241.325755
-
- ... right_hippocampal-fissure right_presubiculum participants ...
-CC110033 ... 133.101613 263.829445
-CC110037 ... 148.099481 339.309772
-CC110045 ... 159.687619 324.398659
-CC110056 ... 123.262352 290.196432
-CC110062 ... 136.785201 406.323486
-... ... ... ...
-CC722542 ... 147.391931 273.150743
-CC722651 ... 185.053756 232.752897
-CC722891 ... 140.980648 211.807774
-CC723197 ... 213.080235 258.567312
-CC723395 ... 205.934342 210.039976
-
- right_parasubiculum right_molecular_layer_HP right_GC-ML-DG participants
-CC110033 47.651798 586.026640 328.057551
-CC110037 59.693186 617.448302 312.116795
-CC110045 55.738550 609.208671 314.460832
-CC110056 67.410418 569.389816 310.290805
-CC110062 80.403248 767.955163 383.194510
-... ... ... ...
-CC722542 50.874375 572.634593 302.504826
-CC722651 44.493903 533.912687 308.141458
-CC722891 57.371362 387.195124 199.979009
-CC723197 50.846316 481.409074 267.190903
-CC723395 33.216529 493.194601 274.020748
-
- right_CA3 right_CA4 right_fimbria right_HATA participants
-CC110033 283.392837 282.565685 87.127463 73.589184
-CC110037 212.605572 269.307660 99.657823 60.920924
-CC110045 237.869822 271.505300 69.436808 59.323542
-CC110056 218.809310 267.327199 60.505521 51.726283
-CC110062 268.227177 325.403040 92.215816 85.484454
-... ... ... ... ...
-CC722542 236.946562 261.352283 46.144212 43.966509
-CC722651 247.810543 267.203989 68.730322 59.699644
-CC722891 146.836915 187.083211 27.913196 38.629828
-CC723197 211.618157 244.176755 51.893458 65.474967
-CC723395 232.592060 240.980896 68.335159 62.081225
-
- right_Whole_hippocampus
-participants
-CC110033 3531.764896
-CC110037 3835.426137
-CC110045 3681.494304
-CC110056 3461.373764
-CC110062 4782.407821
-... ...
-CC722542 3284.108783
-CC722651 3210.272905
-CC722891 2423.675065
-CC723197 3043.146264
-CC723395 2988.001288
-
-[651 rows x 26 columns]
- age sex_name sex IQ_random left_Hippocampal_tail left_subiculum CC110033 24 MALE 1 73 482.768229 419.948094
-CC110037 18 MALE 1 103 595.269259 502.320315
-CC110045 24 FEMALE 0 124 655.847194 476.433625
-CC110056 22 FEMALE 0 124 561.345626 447.258970
-CC110062 20 MALE 1 126 756.521166 521.034681
-... ... ... ... ... ... ...
-CC722542 79 MALE 1 116 467.896808 440.794061
-CC722651 79 FEMALE 0 128 406.326167 393.469843
-CC722891 84 FEMALE 0 129 393.430481 303.049578
-CC723197 80 FEMALE 0 96 475.929914 372.449778
-CC723395 86 FEMALE 0 145 444.301617 330.688394
-
- left_CA1 left_hippocampal-fissure left_presubiculum CC110033 666.496024 131.719049 285.535445
-CC110037 698.157779 156.304335 367.678385
-CC110045 654.215689 146.767569 346.347202
-CC110056 611.114561 126.615335 327.528926
-CC110062 716.391590 206.205818 384.356075
-... ... ... ...
-CC722542 688.130914 184.300085 306.287030
-CC722651 613.794018 224.292557 254.786917
-CC722891 444.772656 158.987352 202.213773
-CC723197 525.739508 172.558200 222.384434
-CC723395 565.359058 206.235576 197.417773
-
- left_parasubiculum ... right_hippocampal-fissure CC110033 59.209377 ... 133.101613
-CC110037 60.817591 ... 148.099481
-CC110045 67.481121 ... 159.687619
-CC110056 70.901227 ... 123.262352
-CC110062 80.329689 ... 136.785201
-... ... ... ...
-CC722542 72.629722 ... 147.391931
-CC722651 50.006651 ... 185.053756
-CC722891 46.418129 ... 140.980648
-CC723197 40.304889 ... 213.080235
-CC723395 34.240227 ... 205.934342
-
- right_presubiculum right_parasubiculum right_molecular_layer_HP CC110033 263.829445 47.651798 586.026640
-CC110037 339.309772 59.693186 617.448302
-CC110045 324.398659 55.738550 609.208671
-CC110056 290.196432 67.410418 569.389816
-CC110062 406.323486 80.403248 767.955163
-... ... ... ...
-CC722542 273.150743 50.874375 572.634593
-CC722651 232.752897 44.493903 533.912687
-CC722891 211.807774 57.371362 387.195124
-CC723197 258.567312 50.846316 481.409074
-CC723395 210.039976 33.216529 493.194601
-
- right_GC-ML-DG right_CA3 right_CA4 right_fimbria right_HATA CC110033 328.057551 283.392837 282.565685 87.127463 73.589184
-CC110037 312.116795 212.605572 269.307660 99.657823 60.920924
-CC110045 314.460832 237.869822 271.505300 69.436808 59.323542
-CC110056 310.290805 218.809310 267.327199 60.505521 51.726283
-CC110062 383.194510 268.227177 325.403040 92.215816 85.484454
-... ... ... ... ... ...
-CC722542 302.504826 236.946562 261.352283 46.144212 43.966509
-CC722651 308.141458 247.810543 267.203989 68.730322 59.699644
-CC722891 199.979009 146.836915 187.083211 27.913196 38.629828
-CC723197 267.190903 211.618157 244.176755 51.893458 65.474967
-CC723395 274.020748 232.592060 240.980896 68.335159 62.081225
-
- right_Whole_hippocampus
-CC110033 3531.764896
-CC110037 3835.426137
-CC110045 3681.494304
-CC110056 3461.373764
-CC110062 4782.407821
-... ...
-CC722542 3284.108783
-CC722651 3210.272905
-CC722891 2423.675065
-CC723197 3043.146264
-CC723395 2988.001288
-
-[650 rows x 30 columns]
TASK 2: Prepare the covariate_normsample and testresponse_normsample file.
diff --git a/doc/build/html/searchindex.js b/doc/build/html/searchindex.js
index f74e15f5..9a18253f 100644
--- a/doc/build/html/searchindex.js
+++ b/doc/build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"(Optional) Load adaptation data": [[5, "optional-load-adaptation-data"]], "Accuracy of Predictions": [[11, "accuracy-of-predictions"]], "Acknowledgements": [[4, null]], "Add variable to model site/scanner effects": [[1, "add-variable-to-model-site-scanner-effects"]], "Algorithm & Modeling": [[1, "algorithm-modeling"]], "Alternative installation (on a shared resource)": [[8, "alternative-installation-on-a-shared-resource"]], "BBS Cross Validation": [[11, "bbs-cross-validation"]], "Background": [[0, null]], "Background Story": [[10, "background-story"]], "Basic installation (on a local machine)": [[8, "basic-installation-on-a-local-machine"]], "Basic usage (command line)": [[12, "basic-usage-command-line"]], "Basic usage (scripted)": [[12, "basic-usage-scripted"]], "Basis expansion using B-Splines": [[1, "basis-expansion-using-b-splines"]], "Brain space extreme deviation counts": [[14, "brain-space-extreme-deviation-counts"]], "CPM Cross Validation": [[11, "cpm-cross-validation"]], "Centile visualization": [[14, "centile-visualization"]], "Classical case-control testing": [[13, "classical-case-control-testing"]], "Combine covariate & cortical thickness dataframes": [[1, "combine-covariate-cortical-thickness-dataframes"]], "Configure covariates": [[5, "configure-covariates"]], "Configure which models to fit": [[5, "configure-which-models-to-fit"]], "Connectome Predictive Modelling": [[11, "connectome-predictive-modelling"]], "Create Train/Test Splits": [[11, "create-train-test-splits"]], "Created by Saige Rutherford": [[3, "created-by-saige-rutherford"]], "DEMO ON NORMATIVE MODELING": [[10, null]], "Data Preparation": [[1, "data-preparation"]], "Data formats": [[12, "data-formats"]], "Describe the normative model performance": [[1, "describe-the-normative-model-performance"]], "Elastic Net (Linear Regression + L1/L2 Regularization)": [[11, "elastic-net-linear-regression-l1-l2-regularization"]], "Estimate normative model": [[1, 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Regularization)": [[11, "lasso-linear-regression-l1-regularization"]], "Load Data": [[11, null]], "Load test data": [[5, "load-test-data"]], "Make predictions": [[5, "make-predictions"]], "Mass univariate two sample t-tests on deviation score maps": [[13, "mass-univariate-two-sample-t-tests-on-deviation-score-maps"]], "Mass univariate two sample t-tests on true cortical thickness data": [[13, "mass-univariate-two-sample-t-tests-on-true-cortical-thickness-data"]], "Module Index": [[9, null]], "Other Useful Stuff": [[0, null]], "Overview": [[3, "overview"]], "PCNtoolkit Background": [[12, null]], "Paralellising estimation to speed things up": [[12, "paralellising-estimation-to-speed-things-up"]], "Plotting the normative models": [[5, "plotting-the-normative-models"]], "Post-Hoc analysis ideas": [[1, "post-hoc-analysis-ideas"]], "Post-hoc analysis on normative modeling outputs": [[13, null]], "Predictive Clinical Neuroscience Toolkit": [[1, null], [3, null]], "Predictive Clinical 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"hbr": 3, "hierarch": 3, "hoc": [1, 13], "how": 6, "hyppocamp": 10, "i": 12, "idea": 1, "implement": 10, "index": 9, "input": [3, 10], "instal": [1, 3, 8, 10], "interpret": [1, 3], "intro": 12, "kei": 7, "l1": 11, "l2": 11, "lasso": 11, "librari": [1, 3], "lifespan": 5, "line": 12, "linear": [1, 11], "load": [5, 10, 11], "local": 8, "machin": 8, "make": 5, "map": 13, "marquand": 3, "mass": 13, "measur": 3, "model": [1, 3, 5, 10, 11, 12, 13, 14], "modul": 9, "multi": 1, "necessari": [1, 3], "neg": 14, "net": 11, "neurosci": [0, 1, 3], "new": 5, "nordan": 10, "norm": [1, 3, 5, 10, 12, 13, 14], "onto": 3, "option": [5, 10], "other": 0, "output": [1, 13, 14], "overview": 3, "paralellis": 12, "patient": 10, "pcntoolkit": [6, 10, 12], "percent": 10, "percentag": 10, "perform": [1, 3], "pierr": 3, "plot": [5, 14], "posit": 14, "post": [1, 13], "predict": [0, 1, 3, 5, 11], "prepar": [1, 3, 5, 10], "preprar": 1, "princip": 11, "protocol": 1, "psychiatri": 1, "question": 2, "quickstart": 8, 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\ No newline at end of file
diff --git a/doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.rst b/doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.rst
index 55d17eb8..b4fa2dfb 100644
--- a/doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.rst
+++ b/doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.rst
@@ -25,110 +25,6 @@ Step 0: Install necessary libraries & grab data files
!pip install pcntoolkit
!pip install nutpie
-
-.. parsed-literal::
-
- Collecting https://github.com/amarquand/PCNtoolkit/archive/dev.zip
- Downloading https://github.com/amarquand/PCNtoolkit/archive/dev.zip
- [2K [32m\[0m [32m64.9 MB[0m [31m15.9 MB/s[0m [33m0:00:05[0m
- [?25h Installing build dependencies ... [?25l[?25hdone
- Getting requirements to build wheel ... [?25l[?25hdone
- Preparing metadata (pyproject.toml) ... [?25l[?25hdone
- Collecting bspline<0.2.0,>=0.1.1 (from pcntoolkit==0.31.0)
- Downloading bspline-0.1.1.tar.gz (84 kB)
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- [?25h Preparing metadata (setup.py) ... [?25l[?25hdone
- Collecting matplotlib<4.0.0,>=3.9.2 (from pcntoolkit==0.31.0)
- Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
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- [?25hBuilding wheels for collected packages: pcntoolkit, bspline
- Building wheel for pcntoolkit (pyproject.toml) ... [?25l[?25hdone
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- Successfully built pcntoolkit bspline
- Installing collected packages: bspline, matplotlib, pcntoolkit
- Attempting uninstall: matplotlib
- Found existing installation: matplotlib 3.8.0
- Uninstalling matplotlib-3.8.0:
- Successfully uninstalled matplotlib-3.8.0
- Successfully installed bspline-0.1.1 matplotlib-3.9.2 pcntoolkit-0.31.0
- Collecting nutpie
- Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (5.4 kB)
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- [?25hInstalling collected packages: nutpie
- Successfully installed nutpie-0.13.2
-
-
For this tutorial we will use data from the `Functional Connectom
Project FCON1000 :5: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame.
- Try using .loc[row_indexer,col_indexer] = value instead
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- icbm['sitenum'] = 0
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
- :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
- You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
- A typical example is when you are setting values in a column of a DataFrame, like:
-
- df["col"][row_indexer] = value
-
- Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-
- fcon['sitenum'].loc[idx] = i
- :18: SettingWithCopyWarning:
- A value is trying to be set on a copy of a slice from a DataFrame
-
- See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- fcon['sitenum'].loc[idx] = i
-
-
-.. parsed-literal::
-
- site AnnArbor_a 24
- site AnnArbor_b 32
- site Atlanta 28
- site Baltimore 23
- site Bangor 20
- site Beijing_Zang 198
- site Berlin_Margulies 26
- site Cambridge_Buckner 198
- site Cleveland 31
- site Leiden_2180 12
- site Leiden_2200 19
- site Milwaukee_b 46
- site Munchen 15
- site NewYork_a 83
- site NewYork_a_ADHD 25
- site Newark 19
- site Oulu 102
- site Oxford 22
- site PaloAlto 17
- site Queensland 19
- site SaintLouis 31
-
-
-
-
-.. parsed-literal::
-
- Text(0.5, 0, 'age')
-
-
-
-
-.. image:: HBR_NormativeModel_FCONdata_Tutorial_files/HBR_NormativeModel_FCONdata_Tutorial_10_3.png
+ ax.set_ylabel("LH mean cortical thickness [mm]")
+ ax.set_xlabel("age")
Step 1: Prepare training and testing sets
@@ -601,53 +115,26 @@ then displayed.
icbm_tr = icbm.loc[tr]
icbm_te = icbm.loc[te]
- print('sample size check')
- for i,s in enumerate(sites):
- idx = fcon_tr['site'] == s
- idxte = fcon_te['site'] == s
- print(i,s, sum(idx), sum(idxte))
+ print("sample size check")
+ for i, s in enumerate(sites):
+ idx = fcon_tr["site"] == s
+ idxte = fcon_te["site"] == s
+ print(i, s, sum(idx), sum(idxte))
- fcon_tr.to_csv(processing_dir + '/fcon1000_tr.csv')
- fcon_te.to_csv(processing_dir + '/fcon1000_te.csv')
- icbm_tr.to_csv(processing_dir + '/fcon1000_icbm_tr.csv')
- icbm_te.to_csv(processing_dir + '/fcon1000_icbm_te.csv')
-
-
-.. parsed-literal::
-
- sample size check
- 0 AnnArbor_a 10 14
- 1 AnnArbor_b 19 13
- 2 Atlanta 12 16
- 3 Baltimore 12 11
- 4 Bangor 10 10
- 5 Beijing_Zang 91 107
- 6 Berlin_Margulies 9 17
- 7 Cambridge_Buckner 96 102
- 8 Cleveland 13 18
- 9 Leiden_2180 5 7
- 10 Leiden_2200 11 8
- 11 Milwaukee_b 18 28
- 12 Munchen 9 6
- 13 NewYork_a 38 45
- 14 NewYork_a_ADHD 15 10
- 15 Newark 9 10
- 16 Oulu 50 52
- 17 Oxford 9 13
- 18 PaloAlto 8 9
- 19 Queensland 10 9
- 20 SaintLouis 18 13
-
+ fcon_tr.to_csv(processing_dir + "/fcon1000_tr.csv")
+ fcon_te.to_csv(processing_dir + "/fcon1000_te.csv")
+ icbm_tr.to_csv(processing_dir + "/fcon1000_icbm_tr.csv")
+ icbm_te.to_csv(processing_dir + "/fcon1000_icbm_te.csv")
Otherwise you can just load these pre defined subsets:
.. code:: ipython3
# Optional
- #fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
- #fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
- #icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
- #icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
+ # fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
+ # fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
+ # icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
+ # icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
Step 2: Configure HBR inputs: covariates, measures and batch effects
--------------------------------------------------------------------
@@ -657,7 +144,7 @@ hemisphere: two idps.
.. code:: ipython3
- idps = ['rh_MeanThickness_thickness','lh_MeanThickness_thickness']
+ idps = ["rh_MeanThickness_thickness", "lh_MeanThickness_thickness"]
As input to the model, we need covariates (used to describe predictable
source of variability (fixed effects), here ‘age’), measures (here
@@ -675,592 +162,79 @@ testing set (``_test``).
.. code:: ipython3
- X_train = (fcon_tr['age']/100).to_numpy(dtype=float)
+ X_train = (fcon_tr["age"] / 100).to_numpy(dtype=float)
Y_train = fcon_tr[idps].to_numpy(dtype=float)
# configure batch effects for site and sex
- #batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
+ # batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
# or only site
- batch_effects_train = fcon_tr[['sitenum']].to_numpy(dtype=int)
+ batch_effects_train = fcon_tr[["sitenum"]].to_numpy(dtype=int)
- with open('X_train.pkl', 'wb') as file:
+ with open("X_train.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_train), file)
- with open('Y_train.pkl', 'wb') as file:
+ with open("Y_train.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_train), file)
- with open('trbefile.pkl', 'wb') as file:
+ with open("trbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_train), file)
- X_test = (fcon_te['age']/100).to_numpy(dtype=float)
+ X_test = (fcon_te["age"] / 100).to_numpy(dtype=float)
Y_test = fcon_te[idps].to_numpy(dtype=float)
- #batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
- batch_effects_test = fcon_te[['sitenum']].to_numpy(dtype=int)
+ # batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
+ batch_effects_test = fcon_te[["sitenum"]].to_numpy(dtype=int)
- with open('X_test.pkl', 'wb') as file:
+ with open("X_test.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_test), file)
- with open('Y_test.pkl', 'wb') as file:
+ with open("Y_test.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_test), file)
- with open('tsbefile.pkl', 'wb') as file:
+ with open("tsbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_test), file)
+
# a simple function to quickly load pickle files
def ldpkl(filename: str):
- with open(filename, 'rb') as f:
+ with open(filename, "rb") as f:
return pickle.load(f)
.. code:: ipython3
batch_effects_test
-
-
-
-.. parsed-literal::
-
- array([[ 0],
- [ 0],
- [ 0],
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- [20],
- [20],
- [20],
- [20],
- [20],
- [20]])
-
-
-
Step 3: Files and Folders grooming
----------------------------------
.. code:: ipython3
- respfile = os.path.join(processing_dir, 'Y_train.pkl') # measurements (eg cortical thickness) of the training samples (columns: the various features/ROIs, rows: observations or subjects)
- covfile = os.path.join(processing_dir, 'X_train.pkl') # covariates (eg age) the training samples (columns: covariates, rows: observations or subjects)
-
- testrespfile_path = os.path.join(processing_dir, 'Y_test.pkl') # measurements for the testing samples
- testcovfile_path = os.path.join(processing_dir, 'X_test.pkl') # covariate file for the testing samples
-
- trbefile = os.path.join(processing_dir, 'trbefile.pkl') # training batch effects file (eg scanner_id, gender) (columns: the various batch effects, rows: observations or subjects)
- tsbefile = os.path.join(processing_dir, 'tsbefile.pkl') # testing batch effects file
-
- output_path = os.path.join(processing_dir, 'Models/') # output path, where the models will be written
- log_dir = os.path.join(processing_dir, 'log/') #
+ respfile = os.path.join(
+ processing_dir, "Y_train.pkl"
+ ) # measurements (eg cortical thickness) of the training samples (columns: the various features/ROIs, rows: observations or subjects)
+ covfile = os.path.join(
+ processing_dir, "X_train.pkl"
+ ) # covariates (eg age) the training samples (columns: covariates, rows: observations or subjects)
+
+ testrespfile_path = os.path.join(
+ processing_dir, "Y_test.pkl"
+ ) # measurements for the testing samples
+ testcovfile_path = os.path.join(
+ processing_dir, "X_test.pkl"
+ ) # covariate file for the testing samples
+
+ trbefile = os.path.join(
+ processing_dir, "trbefile.pkl"
+ ) # training batch effects file (eg scanner_id, gender) (columns: the various batch effects, rows: observations or subjects)
+ tsbefile = os.path.join(processing_dir, "tsbefile.pkl") # testing batch effects file
+
+ output_path = os.path.join(
+ processing_dir, "Models/"
+ ) # output path, where the models will be written
+ log_dir = os.path.join(processing_dir, "log/") #
if not os.path.isdir(output_path):
os.mkdir(output_path)
if not os.path.isdir(log_dir):
os.mkdir(log_dir)
- outputsuffix = '_estimate' # a string to name the output files, of use only to you, so adapt it for your needs.
+ outputsuffix = "_estimate" # a string to name the output files, of use only to you, so adapt it for your needs.
Step 4: Estimating the models
-----------------------------
@@ -1275,403 +249,26 @@ and output files will be written and how they will be named.
.. code:: ipython3
- ptk.normative.estimate(covfile=covfile,
- respfile=respfile,
- tsbefile=tsbefile,
- trbefile=trbefile,
- inscaler='standardize',
- outscaler='standardize',
- linear_mu='True',
- random_intercept_mu='True',
- centered_intercept_mu='True',
- alg='hbr',
- log_path=log_dir,
- binary=True,
- output_path=output_path,
- testcov= testcovfile_path,
- testresp = testrespfile_path,
- outputsuffix=outputsuffix,
- savemodel=True,
- nuts_sampler='nutpie')
-
-
-.. parsed-literal::
-
- inscaler: standardize
- outscaler: standardize
- Processing data in /content/HBR_demo/Y_train.pkl
- Estimating model 1 of 2
-
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-.. raw:: html
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-
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-
- Saving model meta-data...
- Evaluating the model ...
- Writing outputs ...
-
+ ptk.normative.estimate(
+ covfile=covfile,
+ respfile=respfile,
+ tsbefile=tsbefile,
+ trbefile=trbefile,
+ inscaler="standardize",
+ outscaler="standardize",
+ linear_mu="True",
+ random_intercept_mu="True",
+ centered_intercept_mu="True",
+ alg="hbr",
+ log_path=log_dir,
+ binary=True,
+ output_path=output_path,
+ testcov=testcovfile_path,
+ testresp=testrespfile_path,
+ outputsuffix=outputsuffix,
+ savemodel=True,
+ nuts_sampler="nutpie",
+ )
Here some analyses can be done, there are also some error metrics that
could be of interest. This is covered in step 6 and in `Saige’s
@@ -1687,45 +284,49 @@ training and testing set of covariates, measures and batch effects:
.. code:: ipython3
- X_adapt = (icbm_tr['age']/100).to_numpy(dtype=float)
+ X_adapt = (icbm_tr["age"] / 100).to_numpy(dtype=float)
Y_adapt = icbm_tr[idps].to_numpy(dtype=float)
- #batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
- batch_effects_adapt = icbm_tr[['sitenum']].to_numpy(dtype=int)
+ # batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
+ batch_effects_adapt = icbm_tr[["sitenum"]].to_numpy(dtype=int)
- with open('X_adaptation.pkl', 'wb') as file:
+ with open("X_adaptation.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_adapt), file)
- with open('Y_adaptation.pkl', 'wb') as file:
+ with open("Y_adaptation.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_adapt), file)
- with open('adbefile.pkl', 'wb') as file:
+ with open("adbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_adapt), file)
# Test data (new dataset)
- X_test_txfr = (icbm_te['age']/100).to_numpy(dtype=float)
+ X_test_txfr = (icbm_te["age"] / 100).to_numpy(dtype=float)
Y_test_txfr = icbm_te[idps].to_numpy(dtype=float)
- #batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
- batch_effects_test_txfr = icbm_te[['sitenum']].to_numpy(dtype=int)
+ # batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
+ batch_effects_test_txfr = icbm_te[["sitenum"]].to_numpy(dtype=int)
- with open('X_test_txfr.pkl', 'wb') as file:
+ with open("X_test_txfr.pkl", "wb") as file:
pickle.dump(pd.DataFrame(X_test_txfr), file)
- with open('Y_test_txfr.pkl', 'wb') as file:
+ with open("Y_test_txfr.pkl", "wb") as file:
pickle.dump(pd.DataFrame(Y_test_txfr), file)
- with open('txbefile.pkl', 'wb') as file:
+ with open("txbefile.pkl", "wb") as file:
pickle.dump(pd.DataFrame(batch_effects_test_txfr), file)
.. code:: ipython3
- respfile = os.path.join(processing_dir, 'Y_adaptation.pkl')
- covfile = os.path.join(processing_dir, 'X_adaptation.pkl')
- testrespfile_path = os.path.join(processing_dir, 'Y_test_txfr.pkl')
- testcovfile_path = os.path.join(processing_dir, 'X_test_txfr.pkl')
- trbefile = os.path.join(processing_dir, 'adbefile.pkl')
- tsbefile = os.path.join(processing_dir, 'txbefile.pkl')
-
- log_dir = os.path.join(processing_dir, 'log_transfer/')
- output_path = os.path.join(processing_dir, 'Transfer/')
- model_path = os.path.join(processing_dir, 'Models/') # path to the previously trained models
- outputsuffix = '_transfer' # suffix added to the output files from the transfer function
+ respfile = os.path.join(processing_dir, "Y_adaptation.pkl")
+ covfile = os.path.join(processing_dir, "X_adaptation.pkl")
+ testrespfile_path = os.path.join(processing_dir, "Y_test_txfr.pkl")
+ testcovfile_path = os.path.join(processing_dir, "X_test_txfr.pkl")
+ trbefile = os.path.join(processing_dir, "adbefile.pkl")
+ tsbefile = os.path.join(processing_dir, "txbefile.pkl")
+
+ log_dir = os.path.join(processing_dir, "log_transfer/")
+ output_path = os.path.join(processing_dir, "Transfer/")
+ model_path = os.path.join(
+ processing_dir, "Models/"
+ ) # path to the previously trained models
+ outputsuffix = (
+ "_transfer" # suffix added to the output files from the transfer function
+ )
Here, the difference is that the transfer function needs a model path,
which points to the models we just trained, and new site data (training
@@ -1733,394 +334,37 @@ and testing). That is basically the only difference.
.. code:: ipython3
- yhat, s2, z_scores = ptk.normative.transfer(covfile=covfile,
- respfile=respfile,
- tsbefile=tsbefile,
- trbefile=trbefile,
- inscaler='standardize',
- outscaler='standardize',
- linear_mu='True',
- random_intercept_mu='True',
- centered_intercept_mu='True',
- model_path = model_path,
- alg='hbr',
- log_path=log_dir,
- binary=True,
- output_path=output_path,
- testcov= testcovfile_path,
- testresp = testrespfile_path,
- outputsuffix=outputsuffix,
- savemodel=True,
- nuts_sampler='nutpie')
-
-
-.. parsed-literal::
-
- Loading data ...
- Using HBR transform...
- Transferring model 1 of 2
-
-
-
-.. raw:: html
-
-
-
-
-
-
-
-.. raw:: html
-
-
-
- Sampler Progress
- Total Chains: 1
- Active Chains: 0
-
- Finished Chains:
- 1
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- Sampling for now
-
- Estimated Time to Completion:
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-.. parsed-literal::
-
- Output()
-
-
-
-.. raw:: html
-
-
-
-
-
-.. parsed-literal::
-
- Using HBR transform...
- Transferring model 2 of 2
-
-
-
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-
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-
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-
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-
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-
-
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-.. parsed-literal::
-
- Evaluating the model ...
- Writing outputs ...
-
+ yhat, s2, z_scores = ptk.normative.transfer(
+ covfile=covfile,
+ respfile=respfile,
+ tsbefile=tsbefile,
+ trbefile=trbefile,
+ inscaler="standardize",
+ outscaler="standardize",
+ linear_mu="True",
+ random_intercept_mu="True",
+ centered_intercept_mu="True",
+ model_path=model_path,
+ alg="hbr",
+ log_path=log_dir,
+ binary=True,
+ output_path=output_path,
+ testcov=testcovfile_path,
+ testresp=testrespfile_path,
+ outputsuffix=outputsuffix,
+ savemodel=True,
+ nuts_sampler="nutpie",
+ )
.. code:: ipython3
output_path
-
-
-
-.. parsed-literal::
-
- '/content/HBR_demo/Transfer/'
-
-
-
.. code:: ipython3
- EV = pd.read_pickle('EXPV_estimate.pkl')
+ EV = pd.read_pickle("EXPV_estimate.pkl")
print(EV)
-
-.. parsed-literal::
-
- 0
- 0 0.438215
- 1 0.439181
-
-
And that is it, you now have models that benefited from prior knowledge
about different scanner sites to learn on unseen sites.