roy-cruz / refrunrank Goto Github PK

# Looking only at relevant and non-highly correlated features
reducedfeaturelist = ["init_lumi", "end_lumi", "delta_totallumi", "hlt_physics_rate"]
weights_reduced = rrr.get_weights(run_features[reducedfeaturelist], plot=True)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_531/1173954961.py in <module>
      1 # Looking only at relevant and non-highly correlated features
      2 reducedfeaturelist = ["init_lumi", "end_lumi", "delta_totallumi", "hlt_physics_rate"]
----> 3 weights_reduced = rrr.get_weights(run_features[reducedfeaturelist], plot=True)

/eos/home-i02/r/rcruzcan/SWAN_projects/RefRunRank/utils/refrank_utils.py in get_weights(df, standardize, plot)
     22         df = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
     23     if plot:
---> 24         sns.pairplot(df)
     25         plt.show()
     26     pca = PCA(n_components=1)

/cvmfs/sft.cern.ch/lcg/views/LCG_105_swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/seaborn/_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

/cvmfs/sft.cern.ch/lcg/views/LCG_105_swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/seaborn/axisgrid.py in pairplot(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)
   2124     diag_kws.setdefault("legend", False)
   2125     if diag_kind == "hist":
-> 2126         grid.map_diag(histplot, **diag_kws)
   2127     elif diag_kind == "kde":
   2128         diag_kws.setdefault("fill", True)

/cvmfs/sft.cern.ch/lcg/views/LCG_105_swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/seaborn/axisgrid.py in map_diag(self, func, **kwargs)
   1442                 group = diag_axes[0].get_shared_y_axes()
   1443                 for ax in diag_axes[1:]:
-> 1444                     group.join(ax, diag_axes[0])
   1445 
   1446             self.diag_vars = np.array(diag_vars, np.object_)

AttributeError: 'GrouperView' object has no attribute 'join'