class_docstr: Constructor for the Polymer class.
  :param dataset_path is the path to the dataset
  :param save_model: whether the model should be saved in a .sav file
class_docstr_tok: ["Constructor","for","the","Polymer","class",".",":","param","dataset_path","is","the","path","to","the","dataset",":","param","save_model",":","whether","the","model","should","be","saved","in","a",".sav","file"]
code: def runwithPCA(self, json_file_path):
      new_id = str(uuid.uuid4())
      self.latest_id = new_id
  
      self.runPCA = True
      self.df_trans = None
      self.pca_model = None
      self.column_names = None
  
      if self.model is not None:
          self.update_history(json_file_path)
  
      request_info = {
          "df": self.df_path,
          "json": json_file_path,
          "y": None,
          "model": "None",
          "analysis": None,
          "custom": self.custom_files,
          "pca": self.runPCA,
      }
  
      dataset_initializer(request_info)
  
      preprocess_module(request_info)
  
      pca_dict = request_info["info"]
      self.column_names = pca_dict["original_names"]
      self.pca_model = pca_dict["pca_model"]
      self.df = request_info["df"]
      self.df_trans = dict()
      self.df_trans["train"] = self.transformToPrinciple(
          self.df["train"]
      )
      self.df_trans["test"] = self.transformToPrinciple(self.df["test"])
      request_info["df"] = self.df_trans
  
      modeling_module(request_info)
      analysis_module(request_info)
  
      self.set_class_after_run(request_info)
  
      return self
  
file_path: nylon/nylon/query.py
imports: from pandas.core.algorithms import mode
  from nylon.supplementaries.main import dataset_initializer
  from nylon.supplementaries.handlers import preprocess_module, modeling_module, analysis_module, perform_inference
  import uuid
  import pandas
label_desc: Use image node_id 3 for calling the Polymer obj's underlying member method code with example usage: obj.runwithPCA(json_file_path) and returns: self