Python uploads

df = pd.read_csv("https://storage.googleapis.com/ml_universities/california_housing_train.csv", sep=",")
pd.read_csv('nome_do_arquivo.csv', encoding='ISO-8859-1')
xlsx = pd.ExcelFile('seu_arquivo_excel.xlsx')
df = pd.read_excel(xlsx, 'Planilha 1')




df.head()
df.describe()
len(df)


#Now, split the data into two parts -- training and evaluation:

np.random.seed(seed=1) #makes result reproducible
msk = np.random.rand(len(df)) < 0.8
traindf = df[msk]
evaldf = df[~msk]

#80% do dataframe será treinamento e 20% será avaliação. A escolha da entidade será via random.

len(traindf)
len(evaldf)

#we'll be trying to predict median_house_value
#We divide total_rooms by households to get avg_rooms_per_house which we expect to positively correlate with median_house_value
#We also divide population by total_rooms to get avg_persons_per_room which we expect to negatively correlate with median_house_value

def add_more_features(df):
  df['avg_rooms_per_house'] = df['total_rooms'] / df['households'] #expect positive correlation
  df['avg_persons_per_room'] = df['population'] / df['total_rooms'] #expect negative correlation
  return df
  
 # Create pandas input function
 # entrando com o dataframe e a quantidade de épocas a serem treinadas
def make_input_fn(df, num_epochs):
 #utilizando a função pandas input
  return tf.estimator.inputs.pandas_input_fn(
 # adicionando novas colunas:
    x = add_more_features(df),
    y = df['median_house_value'] / 100000, # will talk about why later in the course
    batch_size = 128,
    num_epochs = num_epochs,
    shuffle = True,
    queue_capacity = 1000,
    num_threads = 1
  )
  
  # Define your feature columns
def create_feature_cols():
  return [
    tf.feature_column.numeric_column('housing_median_age'),
    tf.feature_column.bucketized_column(tf.feature_column.numeric_column('latitude'), boundaries = np.arange(32.0, 42, 1).tolist()),
    tf.feature_column.numeric_column('avg_rooms_per_house'),
    tf.feature_column.numeric_column('avg_persons_per_room'),
    tf.feature_column.numeric_column('median_income')
  ]
  
  # Create estimator train and evaluate function
def train_and_evaluate(output_dir, num_train_steps):
  estimator = tf.estimator.LinearRegressor(model_dir = output_dir, feature_columns = create_feature_cols())
  train_spec = tf.estimator.TrainSpec(input_fn = make_input_fn(traindf, None), 
                                      max_steps = num_train_steps)
  eval_spec = tf.estimator.EvalSpec(input_fn = make_input_fn(evaldf, 1), 
                                    steps = None, 
                                    start_delay_secs = 1, # start evaluating after N seconds, 
                                    throttle_secs = 5)  # evaluate every N seconds
  tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
  
  
  # Launch tensorboard
from google.datalab.ml import TensorBoard

OUTDIR = './trained_model'
TensorBoard().start(OUTDIR)

# Run the model
shutil.rmtree(OUTDIR, ignore_errors = True) # start fresh each time
tf.summary.FileWriterCache.clear() # ensure filewriter cache is clear for TensorBoard events file
train_and_evaluate(OUTDIR, 2000)