parameters: ()
return_statement: return _load_dataset_pd(url, md5, dataset_name, train_file, test_file, sep='\t')
docstring: Dataset with monotonic constraints.
      Can be used for regression.
      The first column contains target values.
      Other columns contain contain numerical features, for which monotonic constraints must hold.
  
      For features in columns named MonotonicNeg*, if feature value decreases, then prediction
      value must not decrease. Thus, if there are two samples x1, x2 with all features being
      equal except for a monotonic negative feature MNeg, such that x1[MNeg] > x2[MNeg], then
      the following inequality must hold for predictions: f(x1) <= f(x2)
      For features in columns named MonotonicPos*, if feature value decreases, then prediction
      value must not increase. Thus, if there are two samples x1, x2 with all features being
      equal except for a monotonic positive feature MPos, such that x1[MPos] > x2[MPos],
      then the following inequality must hold for predictions: f(x1) >= f(x2)
docstring_summary: Dataset with monotonic constraints.
      Can be used for regression.
      The first column contains target values.
      Other columns contain contain numerical features, for which monotonic constraints must hold.
docstring_tokens: Dataset with monotonic constraints . Can be used for regression . The first column contains target values . Other columns contain contain numerical features for which monotonic constraints must hold .
function: def monotonic2():
      """
      Dataset with monotonic constraints.
      Can be used for regression.
      The first column contains target values.
      Other columns contain contain numerical features, for which monotonic constraints must hold.
  
      For features in columns named MonotonicNeg*, if feature value decreases, then prediction
      value must not decrease. Thus, if there are two samples x1, x2 with all features being
      equal except for a monotonic negative feature MNeg, such that x1[MNeg] > x2[MNeg], then
      the following inequality must hold for predictions: f(x1) <= f(x2)
      For features in columns named MonotonicPos*, if feature value decreases, then prediction
      value must not increase. Thus, if there are two samples x1, x2 with all features being
      equal except for a monotonic positive feature MPos, such that x1[MPos] > x2[MPos],
      then the following inequality must hold for predictions: f(x1) >= f(x2)
      """
      url = 'https://storage.mds.yandex.net/get-devtools-opensource/250854/monotonic2.tar.gz'
      md5 = 'ce559e212cb72c156269f6f9a641baca'
      dataset_name, train_file, test_file = 'monotonic2', 'train.tsv', 'test.tsv'
      return _load_dataset_pd(url, md5, dataset_name, train_file, test_file, sep='\t')
function_tokens: def monotonic2 ( ) : url = 'https://storage.mds.yandex.net/get-devtools-opensource/250854/monotonic2.tar.gz' md5 = 'ce559e212cb72c156269f6f9a641baca' dataset_name , train_file , test_file = 'monotonic2' , 'train.tsv' , 'test.tsv' return _load_dataset_pd ( url , md5 , dataset_name , train_file , test_file , sep = '\t' )