On line 251 in pdp_plot_utils.py, one of the parameters for _pdp_contour_plot() is contour_label_fontsize and this causes the following error:

TypeError: clabel() got an unexpected keyword argument 'contour_label_fontsize'

According to the documentation for matplotlib.pyplot.clabel(), the parameter should be called fontsize.

Source: clabel() documentation

This command works fine and produces the expected results:

fig, axes = pdp.pdp_interact_plot(
    pdp_interact_out = inter1,
    feature_names=['NOx', 'NO_2'],
    plot_type='grid'
)

However, changing only plot_type to contour gives an error related to the labels and the font size. The figure appears label-less at the bottom after this error. Any guess or help is appreciated.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-363-7b31c15b4793> in <module>()
      2     pdp_interact_out = inter1,
      3     feature_names=['NOx', 'NO_2'],
----> 4     plot_type='contour'
      5 )

/Users/jsg/Documents/DrivenData_Cold_Forecast/venv/lib/python3.6/site-packages/pdpbox/pdp.py in pdp_interact_plot(pdp_interact_out, feature_names, plot_type, x_quantile, plot_pdp, which_classes, figsize, ncols, plot_params)
    773             fig.add_subplot(inter_ax)
    774             _pdp_inter_one(pdp_interact_out=pdp_interact_plot_data[0], inter_ax=inter_ax, norm=None,
--> 775                            feature_names=feature_names_adj, **inter_params)
    776     else:
    777         wspace = 0.3

/Users/jsg/Documents/DrivenData_Cold_Forecast/venv/lib/python3.6/site-packages/pdpbox/pdp_plot_utils.py in _pdp_inter_one(pdp_interact_out, feature_names, plot_type, inter_ax, x_quantile, plot_params, norm, ticks)
    330             # for numeric not quantile
    331             X, Y = np.meshgrid(pdp_interact_out.feature_grids[0], pdp_interact_out.feature_grids[1])
--> 332         im = _pdp_contour_plot(X=X, Y=Y, **inter_params)
    333     elif plot_type == 'grid':
    334         im = _pdp_inter_grid(**inter_params)

/Users/jsg/Documents/DrivenData_Cold_Forecast/venv/lib/python3.6/site-packages/pdpbox/pdp_plot_utils.py in _pdp_contour_plot(X, Y, pdp_mx, inter_ax, cmap, norm, inter_fill_alpha, fontsize, plot_params)
    249     c1 = inter_ax.contourf(X, Y, pdp_mx, N=level, origin='lower', cmap=cmap, norm=norm, alpha=inter_fill_alpha)
    250     c2 = inter_ax.contour(c1, levels=c1.levels, colors=contour_color, origin='lower')
--> 251     inter_ax.clabel(c2, contour_label_fontsize=fontsize, inline=1)
    252     inter_ax.set_aspect('auto')
    253 

/Users/jsg/Documents/DrivenData_Cold_Forecast/venv/lib/python3.6/site-packages/matplotlib/axes/_axes.py in clabel(self, CS, *args, **kwargs)
   6221 
   6222     def clabel(self, CS, *args, **kwargs):
-> 6223         return CS.clabel(*args, **kwargs)
   6224     clabel.__doc__ = mcontour.ContourSet.clabel.__doc__
   6225 

TypeError: clabel() got an unexpected keyword argument 'contour_label_fontsize'

Thank you in advance. Awesome library by the way!

I am following your examples and getting a weird error.

"fig, axes, summary_df = info_plots.actual_plot(model=forest_reg, X=df_new, feature = '1', feature_name='1')"

Thanks a lot in advance!

I just recently started to use this excellent repository to fill in a much needed gap in scikit learn. A suggestion for clarity in the parameters of pdpbox.pdp.pdp_isolate is to require train_X to be a deduplicated pandas dataframe because it caused a bit of confusion on my part when I wasn't able to plot due to the indexing issues from duplicated values. It's really just as simple as df.drop_duplicates(). Thanks for all of your work!

EDIT:

Another data checking step should be added at line 303 in pdp.py for using pdp.pdp_interact. If the feature grids are not specified and are defaulted to 10 and train_X.shape[0] is less than 100, then you will have an error on line 305 since data_chunk_size will round to 0. I just need to specify that num_grid_points=[5,5] so that it would run when train_X.shape[0] = 25.

I have a couple of features which are scaled between 0 and 1. For all of those I get a "ValueError: cannot reindex from a duplicate axis". I assume that in creating the columns for the different values of a feature, some rounding happens for their naming, which results in several columns having the same name, although I couldn't trace back the error in the code. Multiplying the column by 10 solves the problem but is of course unintended.

The error message below.

Thanks for this beautiful package.

/home/cdsw/.local/lib/python3.6/site-packages/pdpbox/pdp.py in pdp_plot(pdp_isolate_out, feature_name, center, plot_org_pts, plot_lines, frac_to_plot, cluster, n_cluster_centers, cluster_method, x_quantile, figsize, ncols, plot_params, multi_flag, which_class) 546 _pdp_plot(pdp_isolate_out=pdp_isolate_out, feature_name=feature_name, center=center, plot_org_pts=plot_org_pts, plot_lines=plot_lines, 547 frac_to_plot=frac_to_plot, cluster=cluster, n_cluster_centers=n_cluster_centers, cluster_method=cluster_method, x_quantile=x_quantile, --> 548 ax=ax2, plot_params=plot_params) 549 550

/home/cdsw/.local/lib/python3.6/site-packages/pdpbox/pdp.py in _pdp_plot(pdp_isolate_out, feature_name, center, plot_org_pts, plot_lines, frac_to_plot, cluster, n_cluster_centers, cluster_method, x_quantile, ax, plot_params) 616 pdp_y -= pdp_y[0] 617 for col in display_columns[1:]: --> 618 ice_lines[col] -= ice_lines[display_columns[0]] 619 ice_lines['actual_preds'] -= ice_lines[display_columns[0]] 620 ice_lines[display_columns[0]] = 0

/home/cdsw/.local/lib/python3.6/site-packages/pandas/core/ops.py in f(self, other) 895 896 def f(self, other): --> 897 result = method(self, other) 898 899 # this makes sure that we are aligned like the input

/home/cdsw/.local/lib/python3.6/site-packages/pandas/core/ops.py in f(self, other, axis, level, fill_value) 1552 return _combine_series_frame(self, other, na_op, 1553 fill_value=fill_value, axis=axis, -> 1554 level=level, try_cast=True) 1555 else: 1556 if fill_value is not None:

/home/cdsw/.local/lib/python3.6/site-packages/pandas/core/ops.py in _combine_series_frame(self, other, func, fill_value, axis, level, try_cast) 1437 # default axis is columns 1438 return self._combine_match_columns(other, func, level=level, -> 1439 try_cast=try_cast) 1440 1441

/home/cdsw/.local/lib/python3.6/site-packages/pandas/core/frame.py in _combine_match_columns(self, other, func, level, try_cast) 4767 def _combine_match_columns(self, other, func, level=None, try_cast=True): 4768 left, right = self.align(other, join='outer', axis=1, level=level, -> 4769 copy=False) 4770 4771 new_data = left._data.eval(func=func, other=right,

/home/cdsw/.local/lib/python3.6/site-packages/pandas/core/frame.py in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis) 3548 method=method, limit=limit, 3549 fill_axis=fill_axis, -> 3550 broadcast_axis=broadcast_axis) 3551 3552 @Appender(_shared_docs['reindex'] % _shared_doc_kwargs)

/home/cdsw/.local/lib/python3.6/site-packages/pandas/core/generic.py in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis) 7364 copy=copy, fill_value=fill_value, 7365 method=method, limit=limit, -> 7366 fill_axis=fill_axis) 7367 else: # pragma: no cover 7368 raise TypeError('unsupported type: %s' % type(other))

/home/cdsw/.local/lib/python3.6/site-packages/pandas/core/generic.py in _align_series(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis) 7461 7462 if lidx is not None: -> 7463 fdata = fdata.reindex_indexer(join_index, lidx, axis=0) 7464 else: 7465 raise ValueError('Must specify axis=0 or 1')

/home/cdsw/.local/lib/python3.6/site-packages/pandas/core/internals.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy) 4412 # some axes don't allow reindexing with dups 4413 if not allow_dups: -> 4414 self.axes[axis]._can_reindex(indexer) 4415 4416 if axis >= self.ndim:

/home/cdsw/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py in _can_reindex(self, indexer) 3558 # trying to reindex on an axis with duplicates 3559 if not self.is_unique and len(indexer): -> 3560 raise ValueError("cannot reindex from a duplicate axis") 3561 3562 def reindex(self, target, method=None, level=None, limit=None,

ValueError: cannot reindex from a duplicate axis

If you try to pickle a pdp_isolate_obj you get a PicklingError:

PicklingError: Can't pickle <type 'instancemethod'>: attribute lookup __builtin__.instancemethod failed

The reason is that currently the model's predict (or predict_proba) method is added as a class member to the object, and pickling instance methods is verboten.

As far as I can tell, there's no reason to add the predict method to class. The pdp_isolate_obj.predict member isn't used anywhere in the code, and it could quite easily be reconstructed from the model, if it were needed. I'd propose to simply remove this member. Happy to submit a PR, if desired.

Hi - firstly I'd like to thank you for producing this package, it's really great! I was just reading the ICEBox paper recently and was considering building something, but was delighted to see somebody else already had :)

I'm having issues with calling pdp_isolate on a regression model - it throws the following exception:

usr/local/lib/python3.5/dist-packages/PDPbox-0.1-py3.5.egg/pdpbox/pdp.py in pdp_isolate(model, train_X, feature, num_grid_points, percentile_range)
    113     # store the ICE lines
    114     # for multi-classifier, a dictionary is created
--> 115     if n_classes > 2:
    116         ice_lines = {}
    117         for n_class in range(n_classes):

TypeError: unorderable types: NoneType() > int()

Even the 'Regression.ipynb' example in PDPbox/test/Regression/ does this. A cursory glance at the codebase seems to suggest that when we have a sklearn model without a classes property, n_classes gets set to None on pdp.py line 64. Then all subsequent comparisons of n_classes to an integer will throw this error. Any suggestions?

I am trying to execute the code:

from pdpbox import pdp, get_dataset, info_plots
test_titanic = get_dataset.titanic()

And I'm having the below error. PDP 0.2.0+13.g73c6966 XGBoost 1.1.0-SNAPSHOT conda environment

Stacktrace:

XGBoostError                              Traceback (most recent call last)
<ipython-input-2-931a5e8d7b9f> in <module>
----> 1 test_titanic = get_dataset.titanic()

~/anaconda3/lib/python3.6/site-packages/PDPbox-0.2.0+13.g73c6966-py3.6.egg/pdpbox/get_dataset.py in titanic()
      7 
      8 def titanic():
----> 9         dataset = joblib.load(os.path.join(DIR, 'datasets/test_titanic.pkl'))
     10         return dataset
     11 

~/anaconda3/lib/python3.6/site-packages/joblib/numpy_pickle.py in load(filename, mmap_mode)
    603                     return load_compatibility(fobj)
    604 
--> 605                 obj = _unpickle(fobj, filename, mmap_mode)
    606 
    607     return obj

~/anaconda3/lib/python3.6/site-packages/joblib/numpy_pickle.py in _unpickle(fobj, filename, mmap_mode)
    527     obj = None
    528     try:
--> 529         obj = unpickler.load()
    530         if unpickler.compat_mode:
    531             warnings.warn("The file '%s' has been generated with a "

~/anaconda3/lib/python3.6/pickle.py in load(self)
   1048                     raise EOFError
   1049                 assert isinstance(key, bytes_types)
-> 1050                 dispatch[key[0]](self)
   1051         except _Stop as stopinst:
   1052             return stopinst.value

~/anaconda3/lib/python3.6/site-packages/joblib/numpy_pickle.py in load_build(self)
    340         NDArrayWrapper is used for backward compatibility with joblib <= 0.9.
    341         """
--> 342         Unpickler.load_build(self)
    343 
    344         # For backward compatibility, we support NDArrayWrapper objects.

~/anaconda3/lib/python3.6/pickle.py in load_build(self)
   1505         setstate = getattr(inst, "__setstate__", None)
   1506         if setstate is not None:
-> 1507             setstate(state)
   1508             return
   1509         slotstate = None

~/anaconda3/lib/python3.6/site-packages/xgboost/core.py in __setstate__(self, state)
   1096             ptr = (ctypes.c_char * len(buf)).from_buffer(buf)
   1097             _check_call(
-> 1098                 _LIB.XGBoosterUnserializeFromBuffer(handle, ptr, length))
   1099             state['handle'] = handle
   1100         self.__dict__.update(state)

~/anaconda3/lib/python3.6/site-packages/xgboost/core.py in _check_call(ret)
    187     """
    188     if ret != 0:
--> 189         raise XGBoostError(py_str(_LIB.XGBGetLastError()))
    190 
    191 

XGBoostError: [18:53:06] /home/sergey/xgboost/src/learner.cc:834: Check failed: header == serialisation_header_: 

  If you are loading a serialized model (like pickle in Python) generated by older
  XGBoost, please export the model by calling `Booster.save_model` from that version
  first, then load it back in current version.  There's a simple script for helping
  the process. See:

    https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html

  for reference to the script, and more details about differences between saving model and
  serializing.


Stack trace:
  [bt] (0) /home/sergey/anaconda3/lib/python3.6/site-packages/xgboost/lib/libxgboost.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x64) [0x7fe81e08c784]
  [bt] (1) /home/sergey/anaconda3/lib/python3.6/site-packages/xgboost/lib/libxgboost.so(xgboost::LearnerIO::Load(dmlc::Stream*)+0x674) [0x7fe81e19f444]
  [bt] (2) /home/sergey/anaconda3/lib/python3.6/site-packages/xgboost/lib/libxgboost.so(XGBoosterUnserializeFromBuffer+0x5e) [0x7fe81e07f61e]
  [bt] (3) /home/sergey/anaconda3/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c) [0x7fe84c23d630]
  [bt] (4) /home/sergey/anaconda3/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call+0x22d) [0x7fe84c23cfed]
  [bt] (5) /home/sergey/anaconda3/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2ce) [0x7fe84b3c509e]
  [bt] (6) /home/sergey/anaconda3/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(+0x13ad5) [0x7fe84b3c5ad5]
  [bt] (7) /home/sergey/anaconda3/bin/python -m ipykernel -f /home/sergey/.local/share/jupyter/runtime/kernel-813f0269-7bc5-4ef8-b890-fb9b799698ce.json(_PyObject_FastCallDict+0x8b) [0x559094256f8b]
  [bt] (8) /home/sergey/anaconda3/bin/python -m ipykernel -f /home/sergey/.local/share/jupyter/runtime/kernel-813f0269-7bc5-4ef8-b890-fb9b799698ce.json(+0x1a162e) [0x5590942e562e]

I have a one hot encoded feature, with the resulting datatype of numeric. When I plot the PDP for this feature, I get a weird plot representing nothing, as below:

The plot works fine for other numeric feature columns. Only is not working fine for this OHE feature. Any suggestions?

when i execute the follow code just like binary_classification tutorial: """ fig, axes, summary_df = info_plots.actual_plot( model=titanic_model, X=titanic_data[titanic_features], feature=['Embarked_C', 'Embarked_S', 'Embarked_Q'], feature_name='embarked' ) """ and got follow error: """ TypeError: predict_proba() argument after ** must be a mapping, not NoneType """ i also tried lgb.LGBMClassifier and lgb raw model on my own data but got same error. is there anyone knows how to fix it?

Here is my code to reproduce the problem:

from pdpbox import pdp, get_dataset, info_plots
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import matplotlib.pyplot as plt

%matplotlib inline

# Setup data
data = load_iris()
df = pd.DataFrame(data.data, columns = data.feature_names)
df.index = data.target

# Train basic model
estimator = RandomForestClassifier()
model = estimator.fit(df, df.index)

#  pdp_interactions
pdp_paid= pdp.pdp_interact(
    model=model, dataset=df, model_features=df.columns, features=df.columns, 
    num_grid_points=[5, 5, 5], 
    percentile_ranges=[None, None, None], 
    n_jobs=4
)

# plotting
fig, axes = pdp.pdp_interact_plot(
    pdp_paid, ['petal length (cm)', 'petal width (cm)'], plot_type='grid',x_quantile=True, ncols=2, plot_pdp=True, 
    which_classes=[0, 1, 2]
)

• pdpbox.version == 0.2.0
• matplotlib.version == 3.0.2

The problem is that in the reference docs you have, these subplots that show the dimensional values to the left and above each class plot, they are aligned with the grid of the figure. They seem to be squished. I can probably figure out how to reference to axis or figure directly and correct them but is this expected? Any easy fix?

Thanks! Great library!

Otherwise it fails to install:

#36 272.1   × python setup.py egg_info did not run successfully.
#36 272.1   │ exit code: 1
#36 272.1   ╰─> [18 lines of output]
#36 272.1       The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
#36 272.1       rather than 'sklearn' for pip commands.
#36 272.1       
#36 272.1       Here is how to fix this error in the main use cases:
#36 272.1       - use 'pip install scikit-learn' rather than 'pip install sklearn'
#36 272.1       - replace 'sklearn' by 'scikit-learn' in your pip requirements files
#36 272.1         (requirements.txt, setup.py, setup.cfg, Pipfile, etc ...)
#36 272.1       - if the 'sklearn' package is used by one of your dependencies,
#36 272.1         it would be great if you take some time to track which package uses
#36 272.1         'sklearn' instead of 'scikit-learn' and report it to their issue tracker
#36 272.1       - as a last resort, set the environment variable
#36 272.1         SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True to avoid this error
#36 272.1       
#36 272.1       More information is available at
#36 272.1       https://github.com/scikit-learn/sklearn-pypi-package

Have to admit that your package works really well, I really like the contour plots in it to see the impact of the two attribute features. I would like to ask if it is possible to make a 3D plot to observe the common influence of 3 attribute features?

While I'm trying to install pdpbox, it seems a conflict happened to my matplotlib: note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed building wheel for matplotlib Running setup.py clean for matplotlib Failed to build matplotlib Installing collected packages: matplotlib, sklearn, pdpbox Attempting uninstall: matplotlib Found existing installation: matplotlib 3.5.1 Uninstalling matplotlib-3.5.1: Successfully uninstalled matplotlib-3.5.1 Running setup.py install for matplotlib ... error error: subprocess-exited-with-error

so why it tried to uninstall the version of matplotlib I've installed? How could I fix the problem?

Hello,

would be good to replace the grid value calculation in pdp_calc_utils.py line 237: value_grids = np.percentile(feature_values, percentile_grids) with value_grids = np.nanpercentile(feature_values, percentile_grids) This will avoid returning an array with nans only if the amount of nans is high in the dataset.