class_docstr: Igel is the base model to use the fit, evaluate and predict functions of the sklearn library
class_docstr_tok: ["Igel","is","the","base","model","to","use","the","fit",",","evaluate","and","predict","functions","of","the","sklearn","library"]
docstr: generate predictions and save them as csv. This is used as a command from cli
docstr_tok: ["generate","predictions","and","save","them","as","csv",".","This","is","used","as","a","command","from","cli"]
code: def predict(self):
      """
      generate predictions and save them as csv. This is used as a command from cli
      """
  
      df_pred = self._get_predictions()
      self.predictions = df_pred
      logger.info(f"saving the predictions to {self.prediction_file}")
      df_pred.to_csv(self.prediction_file, index=False)
  
imports: import json
  import logging
  import os
  import warnings
  import joblib
  import numpy
  import pandas
  from sklearn.model_selection import cross_validate, train_test_split
  from sklearn.multioutput import MultiOutputClassifier, MultiOutputRegressor
  from skl2onnx import convert_sklearn
  from skl2onnx.common.data_types import FloatTensorType
label_desc: Use image node_id 14 for calling the Igel obj's underlying member method code with example usage: obj.predict() without return types