code: def test1():
      # compute median per num bedrooms
      df_median_price_per_bedrooms = (
          df.groupby(by="bedrooms")["price"].median().reset_index()
      )
      beds_to_median = df_median_price_per_bedrooms.to_dict(
          orient="dict"
      )["price"]
      df["median_price_per_bedrooms"] = df["bedrooms"].map(
          beds_to_median
      )
      # compute ratio of price to median price for that num of bedrooms
      df["price_to_median_beds"] = (
          df["price"] / df["median_price_per_bedrooms"]
      )
      # ratio of num bedrooms to price
      df["beds_per_price"] = df["bedrooms"] / df["price"]
      # total rooms (bed, bath)
      df["beds_baths"] = df["bedrooms"] + df["bathrooms"]
      del df[
          "median_price_per_bedrooms"
      ]  # don't need after computation
  
      df_train, df_test = train_test_split(df, test_size=0.15)
  
      X_train, y_train = (
          df_train.drop("interest_level", axis=1),
          df_train["interest_level"],
      )
      X_test, y_test = (
          df_test.drop("interest_level", axis=1),
          df_test["interest_level"],
      )
  
      rf = RandomForestClassifier(
          n_estimators=50,
          n_jobs=-1,
          max_features=1.0,
          min_samples_leaf=10,
          oob_score=True,
      )
      rf.fit(X_train, y_train)
  
      I = importances(rf, X_test, y_test)
      return I
  
file_path: random-forest-importances/src/play_plot.py
imports: import pandas
  import matplotlib.pyplot
  import numpy
  import seaborn
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_error
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import train_test_split
  from sklearn.base import clone
  from rfpimp import *
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
label_desc: Use image node_id 1 for calling a global function with example usage: test1() and returns: I