docstr: [This function generators the box plot for the cohorts. This is operated directly from the frontend.]
  
  Args:
      filter_variable ([string]): [This variable is x-axis value of the graph. It can be either probabilities or model prediction values]
  
  Returns:
      [figure]: [box plot graph]
docstr_tok: ["[","This","function","generators","the","box","plot","for","the","cohorts",".","This","is","operated","directly","from","the","frontend",".","]","Args",":","filter_variable","(","[","string","]",")",":","[","This","variable","is","x-axis","value","of","the","graph",".","It","can","be","either","probabilities","or","model","prediction","values","]","Returns",":","[","figure","]",":","[","box","plot","graph","]"]
code: def cohort_graph(self, filter_variable):
      """[This function generators the box plot for the cohorts. This is operated directly from the frontend.]
  
      Args:
          filter_variable ([string]): [This variable is x-axis value of the graph. It can be either probabilities or model prediction values]
  
      Returns:
          [figure]: [box plot graph]
      """
  
      X_Value = str(filter_variable)
      Y_Value = "Cohorts"
  
      fig = go.Figure()
  
      for k, v in self.cohorts.items():
          fig.add_trace(go.Box(x=v, name=k))
  
      fig.update_layout(
          yaxis_title=Y_Value,
          xaxis_title=X_Value,
          template="plotly_white",
          font=dict(
              size=8,
          ),
      )
      fig.update_layout(
          legend=dict(
              orientation="h",
              yanchor="bottom",
              y=1.02,
              xanchor="right",
              x=1,
          )
      )
  
      fig.update_layout(
          margin={"t": 0},
      )
      return fig
  
file_path: explainx/lib/cohort_analysis.py
imports: from imports import *
label_desc: Use image node_id 10 for calling the cohortAnalysis obj's underlying member method code with example usage: obj.cohort_graph(filter_variable) and returns: fig