dependency_plot_classes.py

import plotly.io as pio
pio.templates.default = "plotly_white"
from plotly.subplots import make_subplots
import pandas as pd
import plotly.express as px

import matplotlib.pylab as pl
import numpy as np



def dependence_plot_classes(feature_name,X_background_display, y_background, shap_values) :
    """
    Create a plot showing relationship between a feature and the shapley values for this feature, colored by the class value.

        Parameters
        ----------
        feature_name : str or int
            Indicate name of the column or the index

        X_background_display : pandas.DataFrame 
            Data frame of feature values (# samples x # features) with non tranformed features values of preference

        y_background : list or np.array
            the classes - can be real or predicted
            
        shap_values : np.array
            shapley values matrx
            
    """
    if isinstance(feature_name, str):
        i = X_background_display.columns.get_loc(feature_name)
    else : 
        i = feature_name
    assert(i not in X_background_display.columns), "Column name not in data frame columns"
    
    a = shap_values[:,i]
    b = X_background_display.iloc[:,i].values
    res = y_background.astype(int).astype(str)

    assert len(a) == len(b)
    y_name = "Shapley values for "+ X_background_display.columns[i]
    x_name = X_background_display.columns[i]
    df = pd.DataFrame({y_name:a,x_name:b,"y_label": res})
    fig = px.scatter(df,  x=x_name, y=y_name, color="y_label", marginal_y="histogram", marginal_x="histogram",width=500, height=500,opacity=0.5)

    fig.show()

Hello author, I encountered this issue while using your code. Could you please take a look at where the problem lies.

TypeError Traceback (most recent call last)
Cell In[52], line 1
----> 1 dependence_plot_classes('Carbonation Time',training_feature_value, training_target_value, shap_values)

Cell In[49], line 42, in dependence_plot_classes(feature_name, X_background_display, y_background, shap_values)
39 df = pd.DataFrame({y_name:a,x_name:b,"y_label": res})
40 fig = px.scatter(df, x=x_name, y=y_name, color="y_label", marginal_y="histogram", marginal_x="histogram",width=500, height=500,opacity=0.5)
---> 42 fig.show()

File ~\anaconda3\Lib\site-packages\plotly\basedatatypes.py:3398, in BaseFigure.show(self, *args, **kwargs)
3365 """
3366 Show a figure using either the default renderer(s) or the renderer(s)
3367 specified by the renderer argument
(...)
3394 None
3395 """
3396 import plotly.io as pio
-> 3398 return pio.show(self, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\plotly\io_renderers.py:388, in show(fig, renderer, validate, **kwargs)
385 fig_dict = validate_coerce_fig_to_dict(fig, validate)
387 # Mimetype renderers
--> 388 bundle = renderers._build_mime_bundle(fig_dict, renderers_string=renderer, **kwargs)
389 if bundle:
390 if not ipython_display:

File ~\anaconda3\Lib\site-packages\plotly\io_renderers.py:296, in RenderersConfig._build_mime_bundle(self, fig_dict, renderers_string, **kwargs)
293 if hasattr(renderer, k):
294 setattr(renderer, k, v)
--> 296 bundle.update(renderer.to_mimebundle(fig_dict))
298 return bundle

File ~\anaconda3\Lib\site-packages\plotly\io_base_renderers.py:95, in PlotlyRenderer.to_mimebundle(self, fig_dict)
91 if config:
92 fig_dict["config"] = config
94 json_compatible_fig_dict = json.loads(
---> 95 to_json(fig_dict, validate=False, remove_uids=False)
96 )
98 return {"application/vnd.plotly.v1+json": json_compatible_fig_dict}

File ~\anaconda3\Lib\site-packages\plotly\io_json.py:199, in to_json(fig, validate, pretty, remove_uids, engine)
196 for trace in fig_dict.get("data", []):
197 trace.pop("uid", None)
--> 199 return to_json_plotly(fig_dict, pretty=pretty, engine=engine)

File ~\anaconda3\Lib\site-packages\plotly\io_json.py:123, in to_json_plotly(plotly_object, pretty, engine)
119 opts["separators"] = (",", ":")
121 from _plotly_utils.utils import PlotlyJSONEncoder
--> 123 return json.dumps(plotly_object, cls=PlotlyJSONEncoder, **opts)
124 elif engine == "orjson":
125 JsonConfig.validate_orjson()

File ~\anaconda3\Lib\json_init_.py:238, in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)
232 if cls is None:
233 cls = JSONEncoder
234 return cls(
235 skipkeys=skipkeys, ensure_ascii=ensure_ascii,
236 check_circular=check_circular, allow_nan=allow_nan, indent=indent,
237 separators=separators, default=default, sort_keys=sort_keys,
--> 238 **kw).encode(obj)

File ~\anaconda3\Lib\site-packages_plotly_utils\utils.py:59, in PlotlyJSONEncoder.encode(self, o)
52 """
53 Load and then dump the result using parse_constant kwarg
54
55 Note that setting invalid separators will cause a failure at this step.
56
57 """
58 # this will raise errors in a normal-expected way
---> 59 encoded_o = super(PlotlyJSONEncoder, self).encode(o)
60 # Brute force guessing whether NaN or Infinity values are in the string
61 # We catch false positive cases (e.g. strings such as titles, labels etc.)
62 # but this is ok since the intention is to skip the decoding / reencoding
63 # step when it's completely safe
65 if not ("NaN" in encoded_o or "Infinity" in encoded_o):

File ~\anaconda3\Lib\json\encoder.py:200, in JSONEncoder.encode(self, o)
196 return encode_basestring(o)
197 # This doesn't pass the iterator directly to ''.join() because the
198 # exceptions aren't as detailed. The list call should be roughly
199 # equivalent to the PySequence_Fast that ''.join() would do.
--> 200 chunks = self.iterencode(o, _one_shot=True)
201 if not isinstance(chunks, (list, tuple)):
202 chunks = list(chunks)

File ~\anaconda3\Lib\json\encoder.py:258, in JSONEncoder.iterencode(self, o, _one_shot)
253 else:
254 _iterencode = _make_iterencode(
255 markers, self.default, _encoder, self.indent, floatstr,
256 self.key_separator, self.item_separator, self.sort_keys,
257 self.skipkeys, _one_shot)
--> 258 return _iterencode(o, 0)

File ~\anaconda3\Lib\site-packages_plotly_utils\utils.py:136, in PlotlyJSONEncoder.default(self, obj)
134 except NotEncodable:
135 pass
--> 136 return _json.JSONEncoder.default(self, obj)

File ~\anaconda3\Lib\json\encoder.py:180, in JSONEncoder.default(self, o)
161 def default(self, o):
162 """Implement this method in a subclass such that it returns
163 a serializable object for o, or calls the base implementation
164 (to raise a TypeError).
(...)
178
179 """
--> 180 raise TypeError(f'Object of type {o.class.name} '
181 f'is not JSON serializable')

TypeError: Object of type Explanation is not JSON serializable