text: from pathlib import Path
  from os.path import dirname
  
  from sklearn.linear_model import LogisticRegression
  from sklearn.utils import shuffle
  from sklearn.metrics import f1_score, roc_auc_score
  from sklearn.pipeline import Pipeline
  
  from rich.console import Console
  
  from joblib import dump
  
  from ..extension.sklearn import TfidfVectorizer, Text2Doc
  from .util import load_model
  
  from ..dataset.customer_review import load_train, \
      load_test, load_test_label, CORPUS_SIZE
  
  console = Console()
  
  def empty_model():
      return Pipeline(
          [('text2doc', Text2Doc('icu')),
           ('tfidf', TfidfVectorizer(tf_method='log_norm', idf_method='probabilistic', drop_punct=True,
                                     lowercase=True, show_progress=True)),
           ('logreg', LogisticRegression(penalty='l2', C=0.02987625339246147, fit_intercept=False, solver='liblinear'))
           ]
      )
  
  def build(max_rows=-1, save=True):
      try:
          import pandas as pd
      except ImportError:
          console.log(("pandas package is not a general sadedegel dependency."
                       " But we do have a dependency on building our prebuilt models"))
  
      raw = load_train()
      df = pd.DataFrame.from_records(raw)
  
      df = shuffle(df, random_state=42)
  
      if max_rows > 0:
          df = df.sample(max_rows, random_state=42)
  
      console.log(f"Corpus Size: {CORPUS_SIZE}")
  
      pipeline = empty_model()
  
      pipeline.fit(df.text, df.review_class)
  
      if save:
          model_dir = Path(dirname(__file__)) / 'model'
  
          model_dir.mkdir(parents=True, exist_ok=True)
  
          pipeline.steps[0][1].Doc = None
  
          dump(pipeline, (model_dir / 'customer_review_classification.joblib').absolute(), compress=('gzip', 9))
  
      evaluate(pipeline, max_rows=int(round(max_rows*0.20)))
  
      console.log('Model build [green]DONE[/green]')
  
  def load(model_name='customer_review_classification'):
      return load_model(model_name)
  
  def evaluate(model=None, scoring='f1', max_rows=-1):
      try:
          import pandas as pd
      except ImportError:
          console.log(("pandas package is not a general sadedegel dependency."
                       " But we do have a dependency on building our prebuilt models"))
      model = load()
  
      raw_test = load_test()
      test = pd.DataFrame.from_records(raw_test)
      true_labels = pd.DataFrame.from_records(load_test_label())
  
      if max_rows > 0:
          test = test[:max_rows]
          true_labels = true_labels[:max_rows]
  
      if scoring=='f1':
          y_pred = model.predict(test.text)
          console.log(f"Model test accuracy (f1-macro): {f1_score(true_labels.review_class, y_pred, average='macro')}")
      if scoring=='auc':
          y_pred = model.predict_proba(test.text)
          console.log(f"Model test roc_auc score: {roc_auc_score(true_labels.review_class, y_pred, multi_class='ovr')}")
  
  
  if __name__ == "__main__":
      build(save=True)