text: import pandas as pd
  
  from examples.example_utils import add_AR_cols, create_binary_box
  from TinyAutoML import MetaPipeline
  from TinyAutoML.Models import *
  
  """iris = load_breast_cancer()
  X = pd.DataFrame(data=iris.data, columns=iris.feature_names)
  y = iris.target
  """
  
  
  df = pd.read_csv("examples/database.csv")
  df["Date"] = pd.to_datetime(df["Date"])
  
  df = (
      create_binary_box(df, relative_threshold=0.05, box_length=5)
      .set_index("Date")
      .dropna(axis=0)
  )
  df = add_AR_cols(df, 7).dropna(axis=0)
  
  # X,y = df.drop('Box', axis=1)[:'2020'], df['Box'][:'2020']
  X, y = df.drop("Box", axis=1)[:"2020"].values, df["Box"][:"2020"].values
  
  cut = int(len(y) * 0.8)
  
  X_train, X_test = X[:cut], X[cut:]
  y_train, y_test = y[:cut], y[cut:]
  
  
  model = BestModel(comprehensiveSearch=False, parameterTuning=False, metrics="accuracy")
  mp = MetaPipeline(model=model)
  mp.fit(X_train, y_train)
  pool = mp.get_pool()
  
  model_2 = DemocraticModel(
      comprehensiveSearch=False, parameterTuning=False, metrics="accuracy"
  )
  mp_2 = MetaPipeline(model=model_2)
  mp_2.fit(X_train, y_train, pool=pool)
  
  model_3 = OneRulerForAll(
      comprehensiveSearch=False, parameterTuning=False, metrics="accuracy"
  )
  mp_3 = MetaPipeline(model=model_3)
  mp_3.fit(X_train, y_train, pool=pool)
  
  mp_2.classification_report(X_test, y_test)
  mp.classification_report(X_test, y_test)
  mp_3.classification_report(X_test, y_test)
  
  
  # mp.transform(X,y)
  # mp.predict(X_test)
  # mp.classification_report(X_test, y_test)
  