PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
Home Page: https://selfexplainml.github.io/PiML-Toolbox
License: Apache License 2.0
Hello!
Thank you very much for providing this tool!
Is it possible to use any csv files that are more than 10MB in size? Is there a work around to handle larger datafiles (~500MB). Thank you very much in advance for your help!
Hi,
I am simply trying to run piml with an imported dataset. This is what I have:
from piml import Experiment exp=Experiment() exp.data_loader(data=data) exp.data_summary() exp.data_prepare()
Then I get the following error:
`---> 1 exp.data_prepare()
File piml/api.py:179, in piml.api.Experiment.data_prepare()
File piml/api.py:816, in piml.api.Experiment._data_prepare_panel()
File piml/workflow/data_api.py:112, in piml.workflow.data_api.DataAPI.prepare_model()
File piml/workflow/base.py:54, in piml.workflow.base.Model.init()
File piml/workflow/base.py:175, in piml.workflow.base.Model.model_prepare()
File piml/models/glm.py:17, in piml.models.glm.GLMRegressor.init()
File piml/models/glm.py:225, in piml.models.glm.PiLinearRegression.init()
TypeError: init() got an unexpected keyword argument 'normalize'`
How can I resolve this?
Thank you
Hi,
In order to learn the details of the different algorithms and how to implement them, I am wondering whether the source codes of the different algorithms such as GAMI-Net, Unwrapping Deep ReLu, fairess, etc are available? What programing language do you use to write the algorithms?
Thanks,
Is it possible to make a high-code example for random or Cartesian grid search? I could not find one after a brief search of the doc ... please let me know if there is an example already.
Is there a preferred approach for grid search in PiML that I am missing?
Hi, this project looks really interesting and I would love to try it out. However, I can't install it on 64-bit Ubuntu 20.04 under various Python versions (3.8, 3.9, and 3.10). The error message is just:
ERROR: Could not find a version that satisfies the requirement PiML
ERROR: No matching distribution found for PiML
Is this expected? Are there only some operating systems supported?
Would be a nice enhancement to support residual plots and analysis by feature and by level or bin of feature. This just allows for spotting of specific bugs, like in the image below.
Prototype code here: https://nbviewer.org/github/jphall663/interpretable_machine_learning_with_python/blob/master/debugging_resid_analysis_redux.ipynb, cell 18.
For categorical features, residuals can be plotted across top-k levels, for numeric features they will have to binned before plotting.
Hi,
Is there a way to save models generated in PIML so that I do not need to run the program and train the model each time?
Also, what is the best way to export results? Is there any way to export results such that the widgets are still interactive? I have just been saving the notebook as an html file in order to share results.
Thank you
Dear,
Is there somewhere extended documentation about the toolbox, its functionalities and different input possibilities? Now, I tried to deduce it from the examples. Furthermore, the interpretability results for the 10 most important features are given. But how could I get it for all features?
Many thanks.
Hi,
When training classification models, I get the following error:
UnboundLocalError Traceback (most recent call last)
File piml/dashboard/model_panel.py:419, in piml.dashboard.model_panel.ModelTrainingPanel.train_register_panel.train_data_f()
UnboundLocalError: local variable 'df' referenced before assignment
Do you know how to fix this?
Thank you
when will it be available for py 3.11?
exp.data_prepare(target='target', task_type='Classification', test_ratio=0.2, random_state=0)
gave me this error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-119-03d607a1b01e>](https://localhost:8080/#) in <cell line: 1>()
----> 1 exp.data_prepare(target='target', task_type='Classification', test_ratio=0.2, random_state=0)
10 frames
/usr/local/lib/python3.10/dist-packages/piml/api.cpython-310-x86_64-linux-gnu.so in piml.api.Experiment.data_prepare()
/usr/local/lib/python3.10/dist-packages/piml/api.cpython-310-x86_64-linux-gnu.so in piml.api.Experiment._data_prepare_api()
/usr/local/lib/python3.10/dist-packages/piml/workflow/data_api.cpython-310-x86_64-linux-gnu.so in piml.workflow.data_api.DataAPI.prepare_data()
/usr/local/lib/python3.10/dist-packages/piml/data/base.cpython-310-x86_64-linux-gnu.so in piml.data.base.DataSet.data_prepare()
/usr/local/lib/python3.10/dist-packages/piml/data/base.cpython-310-x86_64-linux-gnu.so in piml.data.base.DataSet.fit_preprocessor()
/usr/local/lib/python3.10/dist-packages/piml/data/data_preprocessor.cpython-310-x86_64-linux-gnu.so in piml.data.data_preprocessor.DataPreprocessor.fit()
[/usr/local/lib/python3.10/dist-packages/sklearn/utils/_set_output.py](https://localhost:8080/#) in wrapped(self, X, *args, **kwargs)
138 @wraps(f)
139 def wrapped(self, X, *args, **kwargs):
--> 140 data_to_wrap = f(self, X, *args, **kwargs)
141 if isinstance(data_to_wrap, tuple):
142 # only wrap the first output for cross decomposition
/usr/local/lib/python3.10/dist-packages/piml/data/data_preprocessor.cpython-310-x86_64-linux-gnu.so in piml.data.data_preprocessor.DataPreprocessor.fit_transform()
/usr/local/lib/python3.10/dist-packages/numpy/core/overrides.py in unique(*args, **kwargs)
[/usr/local/lib/python3.10/dist-packages/numpy/lib/arraysetops.py](https://localhost:8080/#) in unique(ar, return_index, return_inverse, return_counts, axis, equal_nan)
272 ar = np.asanyarray(ar)
273 if axis is None:
--> 274 ret = _unique1d(ar, return_index, return_inverse, return_counts,
275 equal_nan=equal_nan)
276 return _unpack_tuple(ret)
[/usr/local/lib/python3.10/dist-packages/numpy/lib/arraysetops.py](https://localhost:8080/#) in _unique1d(ar, return_index, return_inverse, return_counts, equal_nan)
334 aux = ar[perm]
335 else:
--> 336 ar.sort()
337 aux = ar
338 mask = np.empty(aux.shape, dtype=np.bool_)
TypeError: '<' not supported between instances of 'str' and 'float'
Hi,
What exactly do the L1 and L2 values refer to in the GLM model? Is it the lambda value or the lambda value multiplied by the alpha (or 1-alpha)? I got a result where the elastic net model selected zero variables, which was a very unexpected result. Consequently, I am suspecting that I am misinterpreting what the L1 and L2 values are referring to in PiML.
Thank you
Hi,
I have been reading the "Example_ExternalModels.ipynb" and wanted to register a Tensorflow keras model.
However, after I converted the Keras model into an Estimator (please correct me if this is not right), the model registering step failed saying "'generator' object has no attribute 'shape'".
Below was the code:
# https://www.tensorflow.org/tutorials/estimator/keras_model_to_estimator
import tempfile
model_dir = tempfile.mkdtemp()
normalizer = layers.Normalization(axis=-1)
# normalizer.adapt(np.array(train_x)) # this causes model_to_estimator to fail
dnn_model = keras.Sequential([
normalizer,
layers.Dense(512, activation='relu', input_dim=train_x.shape[1]),
layers.Dense(512, activation='relu'),
layers.Dense(1, activation='sigmoid')
])
dnn_model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(learning_rate=0.0001), metrics=['accuracy'])
keras_estimator = tf.keras.estimator.model_to_estimator(keras_model=dnn_model, model_dir=model_dir)
from piml import Experiment
exp_2 = Experiment()
pipeline_1 = exp_2.make_pipeline(model=keras_estimator, train_x=train_x, train_y=train_y.ravel(),
test_x=test_x, test_y=test_y.ravel(),
feature_names=feature_names, target_name="latency")
exp_2.register(pipeline_1, "my-dnn")
Here is the error:
AttributeError Traceback (most recent call last)
[<ipython-input-34-505e7d52087b>](https://localhost:8080/#) in <module>
22 test_x=test_x, test_y=test_y.ravel(),
23 feature_names=feature_names, target_name="latency")
---> 24 exp_2.register(pipeline_1, "my-dnn")
1 frames
/usr/local/lib/python3.9/dist-packages/piml/workflow/base.cpython-39-x86_64-linux-gnu.so in piml.workflow.base.Model.register_model()
AttributeError: 'generator' object has no attribute 'shape'
I was running on Google Colab with the installation of
!pip install piml
!pip install numpy==1.23.5
For train and test data, I used sklearn's standard train_test_split. And make_pipline() seemed successful.
Appreciate any help on how to register a Tensorflow model.
Thanks in advance!
Hi, thanks for making this tool. I just wonder is there anyway to export the plots. Sometimes, our engineers don't have jupter, and we need to make a UI for them. I wonder if we can use PiML in the backend, save the plot in the backend and show the plots in the frond end. Thanks.
Hi,
I want to run GLM with cross validation and then view the interpretability results with PiML. I tried to do the following but got an error.
How can I do this?
Thank you
Hey guys, once the model are trained how can i save them to test on other dataset? I see that the model are registered in the experiment object but then i cannot retrieve them back. Thanks in advance
Would it be nice to link to the OpenTeams PiML videos/playlist (e.g., https://www.youtube.com/watch?v=R79JGIMf6PY) somewhere here?
Also, please just close if they are already linked! But I didn't see them ...
The SHAP value for each sample index can be drawn using the model however, I couldn't find a way to develop the SHAP value summary plot using the package. Is there a way to develop other SHAP plots like SHAP Dependence Contribution Plots and summary plot for the model developed using custom data?
Hi,
Is there a way to output all of the estimated coefficients for a trained GLM model? I see there is a plot displaying the coefficients but is there a way to output them in a list or something that can be saved?
Thank you
This is not installable on M1 architecture machines. Can this be installed from the git repo?
Hi, I really like this package you've built, especially the data preparation / eda.
I've been developing the imodels package, which provides access to a bunch of inherently interpretable models. It would be really cool to be able to use some of these models (e.g. RuleFit, FIGS) in this toolbox too!
Best,
Chandan
Hello, I am getting the following error while running installation of PiML. Please help me resolve. Thanks.
This is a follow up from issue #26 .
When using GLMRegressor, I get very different results than from when I use the method shown in the README, which gives a very poor R2 and says all coefficients are 0. I am certain that this is wrong because we have run elastic net on this data in R and python and have gotten similar results that show a much better R2. The results from the glmRegressor look more reasonable. I am trying to use piml to compare many models at once on the same train/test split (the elastic net models with specific parameters included). We would like to use the results from PiML in an upcoming paper, but this problem with PiML's elastic net is preventing us from being able to do so. Please let me know if there is a solution to this.
In summary, I want to find a way to run elastic net using the same train/test split as when I run all of the other models in PiML. Is there a way to do this?
Thank you
Hi,
I trained an elastic net model using sklearn in python (from sklearn.linear_model import ElasticNet). I used the following parameters: ElasticNet(alpha = 0.434, l1_ratio = 0.5) . What can I enter into the L1 and L2 input in the GLM model in PiML to replicate these results (assuming I am using the same data with the same random seed)?
Thanks
Hi there.
I want to use piml as a python module in a fastapi service. how to run it out of jupyter?
For example when I run this in python shell I get this error:
from piml import Experiment
exp = Experiment()
exp.data_loader()
------
AttributeError: 'NoneType' object has no attribute 'events'
thanks.
Hi,
Is there a way to tune the parameters for the built-in models in PiML? For example, the L1 and L2 penalties for the GLM model default to 0. Is there any built-in way to tune these parameters or would we need to use external methods?
Thank you!
Hi,
When I try to run elastic net in PiML, I can get an r2 value and other global interpretability metrics, but when I try to look at the influence of individual predictors, I see that the model has removed each predictor. When running code in sklearn that should give identical results, elastic net does not remove all of the predictors. I am not sure what is going on here. Perhaps it is some kind of bug?
Thank you
Python 3.8.8
ERROR: Could not find a version that satisfies the requirement PiML
ERROR: No matching distribution found for PiML
It looks like GAMI-Tree is an extension/variant of EBM and GAMI-Net. GAMI-Tree utilizes model-based trees and boosting for describing main and interaction effects instead of neural networks, and there are a few additional algorithms for interaction filtering and iterative fitting. Perhaps it is more computationally efficient, though there is no speed comparison benchmark in the paper. I wonder if there is any plan to implement this method in PiML-Toolbox.
Hey PiML team—we at SolasAI just ran into this issue today: Now that scikit-learn 1.3.0 is out, it appears to cause an import error in piml/data/base.py.
python3.8 -m venv .venv
source .venv/bin/activate
pip install piml
python -c 'from piml import Experiment'Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/mike-wilkerson/code/piml-test/.venv/lib/python3.8/site-packages/piml/__init__.py", line 1, in <module>
from .api import Experiment
File "piml/api.py", line 12, in init piml.api
File "/home/mike-wilkerson/code/piml-test/.venv/lib/python3.8/site-packages/piml/dashboard/__init__.py", line 1, in <module>
from .fairness_panel import FairnessPanel, FairnessComparePanel
File "piml/dashboard/fairness_panel.py", line 11, in init piml.dashboard.fairness_panel
File "/home/mike-wilkerson/code/piml-test/.venv/lib/python3.8/site-packages/piml/workflow/__init__.py", line 1, in <module>
from .base import Model
File "piml/workflow/base.py", line 10, in init piml.workflow.base
File "piml/workflow/pipeline.py", line 10, in init piml.workflow.pipeline
File "piml/data/base.py", line 7, in init piml.data.base
ImportError: cannot import name SCORERS
On a hunch, we checked to see if scikit-learn had been updated recently, and discovered that 1.3.0 was released on June 30. To temporarily work around this issue, we just used pip install scikit-learn==1.2.2 to roll back.
OS: Ubuntu 20.04
Python version: 3.8
Please let us know if you need any more information!
Hi,
I am running the PiML examples on Colab via the links in this Github Repo. Those example notebooks worked on Colab about 1 month ago, but they do not work now. Here are the errors:
!pip install piml
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you have numpy 1.24.2 which is incompatible.
!pip install numpy==1.23.5
!pip install xgboost==1.7.2
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pygam 0.9.0 requires numpy<2.0.0,>=1.24.2, but you have numpy 1.23.5 which is incompatible.
from piml import Experiment
exp = Experiment()
RuntimeError Traceback (most recent call last)
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
Would you please sort out how to fix the errors?
Thanks
In the user guide 6.6.2.1. Resilience Performance, the description of each figure seems to be wrong.
It could be that I understood it all wrong though. Please let me know if it's an error or that I should understand it all differently. Thank you.
It would be nice to have explicit model ranking for selection. I.e., something that answers the question of which is the "best" model between a group of trained models without human-eyeballing (of course human eye-balling is also great!). This would be in addition to Pareto-based selection, not a replacement for Pareto-based selection.
Consider Caruana et al. 2004 "b" - https://dl.acm.org/doi/10.1145/1046456.1046470.
I have a prototype here: https://jphall663.github.io/GWU_rml/, code: https://nbviewer.org/github/jphall663/GWU_rml/blob/master/assignments/eval.ipynb.
In addition to prototype, would be really cool for users to be able to:
(The current prototype is fixed at 5 folds, fixed with five quality assessment stats (no AIR, etc.), and does not perturb folds.)
Let me know if you'd like to discuss.
Hi,
I have asked about this in a previous, closed issue (#14), but the solution given is not working.
Has this solution worked for you with PiML? When I try this solution, this line:
with open('name_model.pkl', 'rb') as file: clf_load = dill.load(file)
results in a recursion error every time. I tried changing the recursion limit but even with a recursion limit pf 10000000 I still run into this error. Increasing the recursion limit indefinitely just causes the kernel to crash.
The error is as follows:
RecursionError Traceback (most recent call last)
Cell In [39], line 2
1 with open('name_model.pkl', 'rb') as file:
----> 2 clf_load = dill.load(file)
File /n/holylfs05/LABS/liang_lab_l3/Lab/piml_py39_shared/lib/python3.9/site-packages/dill/_dill.py:272, in load(file, ignore, **kwds)
266 def load(file, ignore=None, **kwds):
267 """
268 Unpickle an object from a file.
269
270 See :func:loads for keyword arguments.
271 """
--> 272 return Unpickler(file, ignore=ignore, **kwds).load()
File /n/holylfs05/LABS/liang_lab_l3/Lab/piml_py39_shared/lib/python3.9/site-packages/dill/_dill.py:419, in Unpickler.load(self)
418 def load(self): #NOTE: if settings change, need to update attributes
--> 419 obj = StockUnpickler.load(self)
420 if type(obj).module == getattr(_main_module, 'name', 'main'):
421 if not self._ignore:
422 # point obj class to main
File piml/models/glm.py:32, in piml.models.glm.GLMRegressor.getattr()
File piml/models/glm.py:32, in piml.models.glm.GLMRegressor.getattr()
[... skipping similar frames: piml.models.glm.GLMRegressor.getattr at line 32 (9999967 times)]
File piml/models/glm.py:32, in piml.models.glm.GLMRegressor.getattr()
RecursionError: maximum recursion depth exceeded while calling a Python object
Any help with this would be very appreciated. When using PiML for research purposes, being able to save a trained model is essential for reproducibility. Thank you!
Hi PiML team,
I cant wait to try out your toolbox, however I run into issues with the import itself.
from piml import Experiment
takes about two-three minutes. And I get the following warnings
[.venv/lib/python3.10/site-packages/pygam/pygam.py:276](https://file+.vscode-resource.vscode-cdn.net/Users/matejuhrin/repo-others/solar-generation/.venv/lib/python3.10/site-packages/pygam/pygam.py:276): SyntaxWarning: "is" with a literal. Did you mean "=="?
if self.terms is 'auto':
SyntaxWarning: "is not" with a literal. Did you mean "!="?
if (self.terms is not 'auto') and not (isinstance(self.terms, (TermList, Term, type(None)))):
...
Is it possible to not import pyGAM or in other ways speed up the whole import procedure ?
My python is
'3.10.11 (main, Jul 3 2023, 18:02:14) [Clang 14.0.3 (clang-1403.0.22.14.1)]'
i386
thank you and have a great day.
we are able to install successfully using pip, is it possible to enable conda forge so that we can install PiML using "conda install".
Hi,
The graphs for exp.eda() and the window for exp.model_train() displayed fine.
However, exp.model_interpret() and exp.mode l_explain()'s results do not show in the jupyter notebook.
Tried many times, such as restarting the kernel, which didn't solve the problem.
Is there a way to use commands to select the model that display the graphs? I think that after selecting an item, the HTML needs to be re-rendered, and the second re-rendered seemed to fail.
Below are my version:
MacOS: 13.2.1
VSCode: 1.76.2 (Universal)
ipywidgets: 8.0.5
ipykernel: 6.17.1
ipython: 8.11.0
In Colab, all the GUI worked fine. But would like to run the notebook locally.
Appreciate any help!
Would be nice to have a histogram for exp.model_interpret() -> LLM-summary
I know that this project is not currently open source, but would it be possible to know what packages the backend uses? Do you use scikit-learn to calculate most of the interpretability measurements? This would be useful to know because it tells us exactly how these metrics are calculated. For example, permutation feature importance can be calculated in multiple different ways. One can permute the observations of each feature n times, or one can swap the observations of one feature with those of another. Also, there are several different metrics one can use to quantify permutation feature importance (R2, MSE, or RMSE for regression models and accuracy, precision, etc. for classification models).
Thank you
Is there any way to upload a dataset of size greater than 10mb?
Thanks
Hi. I want to add different Feature Selectors and different Classifiers. I try from Arbitrary part, but my question is how can I select the one which I want to work with?
I have started to work with this tool, and I couldn't find any file which have a complete explanation of this tool.
Thanks
Hi,
I tried "Example_CaliforniaHousing.ipynb" for a long time but still couldn't get the initial importing of PiML working.
Tried on 2023-03-18 using Google Colab py39 runtime without a hardware accelerator.
Running the first cell !pip install piml, I got most all dependencies installed, but an error was thrown:
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
I ignored the error, restarted the runtime, and moved to the next cell from piml import Experiment. I got an error (trimmed to the end):
File /usr/local/lib/python3.9/dist-packages/numba/np/ufunc/decorators.py:3
1 import inspect
----> 3 from numba.np.ufunc import _internal
4 from numba.np.ufunc.parallel import ParallelUFuncBuilder, ParallelGUFuncBuilder
6 from numba.core.registry import TargetRegistry
SystemError: initialization of _internal failed without raising an exception
Is there a way to resolve this? Thanks!
Extra:
Full error of the first cell:
!pip install piml
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting piml
Downloading PiML-0.4.3-cp39-none-manylinux_2_5_x86_64.whl (9.8 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 9.8/9.8 MB 40.6 MB/s eta 0:00:00
Collecting natsort>=8.2.0
Downloading natsort-8.3.1-py3-none-any.whl (38 kB)
Collecting shap>=0.39.0
Downloading shap-0.41.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (572 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 572.4/572.4 KB 19.8 MB/s eta 0:00:00
Collecting pygam>=0.8.0
Downloading pygam-0.9.0-py3-none-any.whl (522 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 522.2/522.2 KB 25.7 MB/s eta 0:00:00
Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.9/dist-packages (from piml) (1.13.1+cu116)
Requirement already satisfied: numpy>=1.21.4 in /usr/local/lib/python3.9/dist-packages (from piml) (1.22.4)
Requirement already satisfied: pandas>=1.3.5 in /usr/local/lib/python3.9/dist-packages (from piml) (1.4.4)
Requirement already satisfied: scikit-learn>=0.24.2 in /usr/local/lib/python3.9/dist-packages (from piml) (1.2.2)
Requirement already satisfied: statsmodels>=0.12.2 in /usr/local/lib/python3.9/dist-packages (from piml) (0.13.5)
Requirement already satisfied: matplotlib>=3.1.2 in /usr/local/lib/python3.9/dist-packages (from piml) (3.7.1)
Requirement already satisfied: lightgbm>=3.1.1 in /usr/local/lib/python3.9/dist-packages (from piml) (3.3.5)
Requirement already satisfied: scipy>=1.5.3 in /usr/local/lib/python3.9/dist-packages (from piml) (1.10.1)
Collecting dcor>=0.5.3
Downloading dcor-0.6-py3-none-any.whl (55 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 55.5/55.5 KB 1.2 MB/s eta 0:00:00
Requirement already satisfied: xlrd>=1.2.0 in /usr/local/lib/python3.9/dist-packages (from piml) (2.0.1)
Requirement already satisfied: xgboost>=1.4.2 in /usr/local/lib/python3.9/dist-packages (from piml) (1.7.4)
Collecting ipython>=7.12.0
Downloading ipython-8.11.0-py3-none-any.whl (793 kB)
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Building wheels for collected packages: lime
Building wheel for lime (setup.py) ... done
Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283859 sha256=977759970aaaf5e3c539ceb4ea229ba921e34294642260690da4c7fac6e0c3ca
Stored in directory: /root/.cache/pip/wheels/ed/d7/c9/5a0130d06d6310bc6cbe55220e6e72dcb8c4eff9a478717066
Successfully built lime
Installing collected packages: pure-eval, executing, slicer, prompt-toolkit, progressbar2, numpy, natsort, matplotlib-inline, llvmlite, jedi, dill, asttokens, stack-data, numba, pygam, momentchi2, ipython, dcor, shap, lime, piml
Attempting uninstall: prompt-toolkit
Found existing installation: prompt-toolkit 2.0.10
Uninstalling prompt-toolkit-2.0.10:
Successfully uninstalled prompt-toolkit-2.0.10
Attempting uninstall: progressbar2
Found existing installation: progressbar2 3.38.0
Uninstalling progressbar2-3.38.0:
Successfully uninstalled progressbar2-3.38.0
Attempting uninstall: numpy
Found existing installation: numpy 1.22.4
Uninstalling numpy-1.22.4:
Successfully uninstalled numpy-1.22.4
Attempting uninstall: natsort
Found existing installation: natsort 5.5.0
Uninstalling natsort-5.5.0:
Successfully uninstalled natsort-5.5.0
Attempting uninstall: llvmlite
Found existing installation: llvmlite 0.39.1
Uninstalling llvmlite-0.39.1:
Successfully uninstalled llvmlite-0.39.1
Attempting uninstall: numba
Found existing installation: numba 0.56.4
Uninstalling numba-0.56.4:
Successfully uninstalled numba-0.56.4
Attempting uninstall: ipython
Found existing installation: ipython 7.9.0
Uninstalling ipython-7.9.0:
Successfully uninstalled ipython-7.9.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires ipython~=7.9.0, but you have ipython 8.11.0 which is incompatible.
Successfully installed asttokens-2.2.1 dcor-0.6 dill-0.3.6 executing-1.2.0 ipython-8.11.0 jedi-0.18.2 lime-0.2.0.1 llvmlite-0.36.0 matplotlib-inline-0.1.6 momentchi2-0.1.8 natsort-8.3.1 numba-0.53.1 numpy-1.24.2 piml-0.4.3 progressbar2-4.2.0 prompt-toolkit-3.0.38 pure-eval-0.2.2 pygam-0.9.0 shap-0.41.0 slicer-0.0.7 stack-data-0.6.2
Full error of the seond cell:
from piml import Experiment
exp = Experiment()
---------------------------------------------------------------------------
SystemError Traceback (most recent call last)
Cell In[2], line 1
----> 1 from piml import Experiment
File /usr/local/lib/python3.9/dist-packages/piml/__init__.py:1
----> 1 from .api import Experiment
2 from .diagnoser.utils import twosample_test
4 __all__ = ['Experiment']
File piml/api.py:16, in init piml.api()
File piml/dashboard/data_panel.py:12, in init piml.dashboard.data_panel()
File piml/workflow/data_api.py:8, in init piml.workflow.data_api()
File piml/data/base.py:14, in init piml.data.base()
File piml/data/data_selector.py:11, in init piml.data.data_selector()
File /usr/local/lib/python3.9/dist-packages/dcor/__init__.py:13
10 import os as _os
11 import pathlib as _pathlib
---> 13 from . import distances, homogeneity, independence
14 from ._dcor import (
15 DistanceCovarianceMethod as DistanceCovarianceMethod,
16 Stats as Stats,
(...)
27 u_distance_stats_sqr as u_distance_stats_sqr,
28 )
29 from ._dcor_internals import (
30 double_centered as double_centered,
31 mean_product as mean_product,
(...)
35 u_projection as u_projection,
36 )
File /usr/local/lib/python3.9/dist-packages/dcor/independence.py:14
11 import numpy as np
12 import scipy.stats
---> 14 from ._dcor import u_distance_correlation_sqr
15 from ._dcor_internals import (
16 _check_same_n_elements,
17 _distance_matrix_generic,
(...)
22 u_product,
23 )
24 from ._hypothesis import HypothesisTest, _permutation_test_with_sym_matrix
File /usr/local/lib/python3.9/dist-packages/dcor/_dcor.py:35
32 from dcor._dcor_internals import _af_inv_scaled
34 from ._dcor_internals import _dcov_from_terms, _dcov_terms_naive
---> 35 from ._fast_dcov_avl import _distance_covariance_sqr_terms_avl
36 from ._fast_dcov_mergesort import _distance_covariance_sqr_terms_mergesort
37 from ._utils import ArrayType, CompileMode, _sqrt, get_namespace
File /usr/local/lib/python3.9/dist-packages/dcor/_fast_dcov_avl.py:18
7 import warnings
8 from typing import (
9 TYPE_CHECKING,
10 Any,
(...)
15 overload,
16 )
---> 18 import numba
19 import numpy as np
20 from numba import boolean, float64, int64
File /usr/local/lib/python3.9/dist-packages/numba/__init__.py:43
39 from numba.core.decorators import (cfunc, generated_jit, jit, njit, stencil,
40 jit_module)
42 # Re-export vectorize decorators and the thread layer querying function
---> 43 from numba.np.ufunc import (vectorize, guvectorize, threading_layer,
44 get_num_threads, set_num_threads)
46 # Re-export Numpy helpers
47 from numba.np.numpy_support import carray, farray, from_dtype
File /usr/local/lib/python3.9/dist-packages/numba/np/ufunc/__init__.py:3
1 # -*- coding: utf-8 -*-
----> 3 from numba.np.ufunc.decorators import Vectorize, GUVectorize, vectorize, guvectorize
4 from numba.np.ufunc._internal import PyUFunc_None, PyUFunc_Zero, PyUFunc_One
5 from numba.np.ufunc import _internal, array_exprs
File /usr/local/lib/python3.9/dist-packages/numba/np/ufunc/decorators.py:3
1 import inspect
----> 3 from numba.np.ufunc import _internal
4 from numba.np.ufunc.parallel import ParallelUFuncBuilder, ParallelGUFuncBuilder
6 from numba.core.registry import TargetRegistry
SystemError: initialization of _internal failed without raising an exception
I am trying to apply PiML for my custom data but unable to add target variable which is multiclass problem. I am receiving this error and dont know how to fix it ! please help me in this regard ! Please find the attached link of notebook (html) and snapshot of error. Thank you
file:///C:/Users/Zahid/Downloads/Montreal_subsample.html
I am applying PiML models to my custom data in Jupyter notebook and receiving the attached error. Please help and guid me in this regard. Thank you
Spearman correlation may better inform monotonicity constraints vs. Pearson correlation.
Would just be nice to see in addition to Pearson correlation in feature_select().
Just had to re-install conda and am now running the newest version (23.1.0) and Python 3.10.4.
pip install seems to be working. For instance, pip install torch worked and showed up in conda list --show-channel-urls, after I verified that it was not there before.
pip install PiML gives the following:
(base) Williams-2018-MacBook-Pro:~ william$ pip install PiML
ERROR: Could not find a version that satisfies the requirement PiML (from versions: none)
ERROR: No matching distribution found for PiML
Is it possible to use the method for a multiple target regression problem (Input1 + Input2 + Input3 = Target1 + Target2 for instance)?
The examples you provide considers only one target. Is it possible to reconfigure the output layer structure for multiple targets? My targets are highly correlated, so fitting separate single response models would ignore the correlations between them.
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