text: #
  # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  import sys
  import time
  
  import model_store
  import pandas as pd
  import numpy as np
  from model_store import ModelStore
  from sklearn import datasets
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  from sklearn import model_selection
  import os
  
  _CANDIDATE_MODEL_STORE = ModelStore(model_store.CANDIDATE)
  _PRODUCTION_MODEL_STORE = ModelStore(model_store.PRODUCTION)
  
  import numpy as np
  import csv
  import argparse
  
  def load_iris_from_csv_file(f):
      df = pd.read_csv(f, header=0).reset_index(drop=True)
      types = {col:np.float64 for col in df.columns[:-1]}
      types['label'] = np.int32
      df = df.astype(types)
      return df
  
  def get_dataset(d):
      print("searching for csv files in {}".format(d))
      for root, dirs, files in os.walk(d):
  
          for file in files:
              if file.endswith(".csv"):
                  return os.path.join(d, file)
      return None
  
  def load_and_split(input_uri):
      csv_file = get_dataset(input_uri)
      if csv_file:
          print("found {} dataset".format(csv_file))
  
      iris = load_iris_from_csv_file(csv_file)
      return train_test_split(iris, test_size=0.2, random_state=8)
  
  def train_model(input_uri):
      train, test = load_and_split(input_uri)
      y = train.pop("label")
      X = train.loc[:, train.columns]
  
      model = LogisticRegression(max_iter=500)
      model.fit(X, y)
  
      scoring = 'accuracy'
      results = model_selection.cross_val_score(model, X, y, cv=5, scoring=scoring)
      print(f'Accuracy in cross validation: {results.mean()*100} % ({results.std()} std)')
      version = round(time.time())
  
      print(f'Saving model with version {version} to candidate model store.')
      _CANDIDATE_MODEL_STORE.save_model(model, version)
  
  
  def validate_model(input_uri):
      model, version = _CANDIDATE_MODEL_STORE.load_latest_model()
      print(f'Validating model with version {version} to candidate model store.')
      if not isinstance(model.predict([[1,1,1,1]]), np.ndarray):
          raise ValueError('Invalid model')
      train, test = load_and_split(input_uri)
      y = test.pop("label")
      X = test.loc[:, test.columns]
      result = model.score(X, y)
      print(f'model accuracy {result*100}')
      if result < 0.85:
          raise ValueError('model accuracy under threshold (0.85  ). Model is not promoted to production')
      print(f'Deploying model with version {version} to production model store.')
      _PRODUCTION_MODEL_STORE.save_model(model, version)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument("--action", choices=['train', 'validate'])
      parser.add_argument("--input_uri")
      args = parser.parse_args()
      if args.action == 'train':
          train_model(args.input_uri)
      else:
          validate_model(args.input_uri)