structured data is done

solutionbox/structured_data/datalab_solutions/structured_data/preprocess/preprocess.py

@@ -62,15 +62,15 @@ def parse_arguments(argv):
                       type=int,
                       help='Percent of input data for test dataset.')
   parser.add_argument('--output_dir',
-                      type=str,    
+                      type=str,
                       required=True,
                       help=('Google Cloud Storage or Local directory in which '
                             'to place outputs.'))
-  parser.add_argument('--transforms_config_file',
-                      type=str,    
+  parser.add_argument('--schema_file',
+                      type=str,
                       required=True,
-                      help=('File describing the schema and transforms of '
-                            'each column in the csv data files.'))
+                      help=('File describing the schema of each column in the '
+                            'csv data files.'))
   parser.add_argument('--job_name',
                       type=str,
                       help=('If using --cloud, the job name as listed in'
@@ -93,17 +93,47 @@ def parse_arguments(argv):
 
   # args.job_name will not be used unless --cloud is used.
   if not args.job_name:
-    args.job_name = ('structured-data-' + 
+    args.job_name = ('structured-data-' +
                      datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
 
   return args
 
 
+def load_and_check_config(schema_file_path):
+  """Checks the sschema file is well formatted."""
+
+  try:
+    json_str = ml.util._file.load_file(schema_file_path)
+    config = json.loads(json_str)
+  except:
+    print('ERROR reading schema file.')
+    sys.exit(1)
+
+  model_columns = (config.get('numerical_columns', [])
+                   + config.get('categorical_columns', []))
+  if config['target_column'] not in model_columns:
+    print('ERROR: target not listed as a numerical or categorial column.')
+    sys.exit(1)
+
+  if set(config['column_names']) != set(model_columns + [config['key_column']]):
+    print('ERROR: column_names do not match what was listed other fields')
+    sys.exit(1)
+
+  if set(config['numerical_columns']) & set(config['categorical_columns']):
+    print('ERROR: numerical_columns and categorical_columns must be disjoint.')
+    sys.exit(1)
+
+  if config['key_column'] in model_columns:
+    print('ERROR: kye_column should not be listed in numerical_columns or categorical_columns')
+    sys.exit(1)
+
+  return config
+
+
 def preprocessing_features(args):
 
   # Read the config file.
-  json_str = ml.util._file.load_file(args.transforms_config_file)
-  config = json.loads(json_str)
+  config = load_and_check_config(args.schema_file)
 
   column_names = config['column_names']
 
@@ -114,48 +144,38 @@ def preprocessing_features(args):
 
   # Extract target feature
   target_name = config['target_column']
-  if config['problem_type'] == 'regression':
+  key_name = config['key_column']
+  if target_name in config.get('numerical_columns', []):
     feature_set[target_name] = features.target(target_name).continuous()
   else:
     feature_set[target_name] = features.target(target_name).discrete()
 
 
   # Extract numeric features
-  if 'numerical' in config:
-    for name, transform_config in config['numerical'].iteritems():
-      transform = transform_config['transform']
-      default = transform_config.get('default', None)
-      if transform == 'scale':
-        feature_set[name] = features.numeric(name, default=default).scale()
-      elif transform == 'max_abs_scale':
-        feature_set[name] = features.numeric(name, default=default).max_abs_scale(transform_config['value'])
-      elif transform == 'identity':
-        feature_set[name] = features.numeric(name, default=default).identity()
-      else:
-        print('Error: unkown numerical transform name %s in %s' % (transform, str(transform_config)))
-        sys.exit(1)
+  for name in config.get('numerical_columns', []):
+    if name == target_name or name == key_name:
+      continue
+    # apply identity to all numerical features.
+    default = config.get('defaults', {}).get(name, None)
+    feature_set[name] = features.numeric(name, default=default).identity()
 
   # Extract categorical features
-  if 'categorical' in config:
-    for name, transform_config in config['categorical'].iteritems():
-      transform = transform_config['transform']
-      default = transform_config.get('default', None)
-      frequency_threshold = transform_config.get('frequency_threshold', 5)
-      if transform == 'one_hot' or transform == 'embedding':
-        feature_set[name] = features.categorical(
-            name, 
-            default=default,
-            frequency_threshold=frequency_threshold)
-      else:
-        print('Error: unkown categorical transform name %s in %s' % (transform, str(transform_config)))
-        sys.exit(1)
+  for name in config.get('categorical_columns', []):
+    if name == target_name or name == key_name:
+      continue
+    # apply sparse transform to all categorical features.
+    default = config.get('defaults', {}).get(name, None)
+    feature_set[name] = features.categorical(
+          name,
+          default=default,
+          frequency_threshold=1).sparse(use_counts=True)
 
   return feature_set, column_names
 
 
-
 def preprocess(pipeline, feature_set, column_names, input_file_path,
-               train_percent, eval_percent, test_percent, output_dir):
+               schema_file, train_percent, eval_percent, test_percent,
+               output_dir):
   """Builds the preprocessing Dataflow pipeline.
 
   The input files are split into a training, eval and test sets, and the SDK
@@ -206,11 +226,14 @@ def _partition_fn(row_unused, num_partitions_unused):  # pylint: disable=unused-
                       >> io.SaveFeatures(
                           os.path.join(output_dir, 'features_test')))
   # pylint: enable=expression-not-assigned
+  # Put a copy of the schema file in the output folder. Datalab will look for 
+  # it there.
+  ml.util._file.copy_file(schema_file, os.path.join(output_dir, 'schema.json'))
 
 
-def run_dataflow(feature_set, column_names, input_file_path, train_percent,
-                 eval_percent, test_percent, output_dir, cloud, project_id,
-                 job_name):
+def run_dataflow(feature_set, column_names, input_file_path, schema_file,
+                 train_percent, eval_percent, test_percent, output_dir, cloud,
+                 project_id, job_name):
   """Run Preprocessing as a Dataflow pipeline."""
 
   # Configure the pipeline.
@@ -233,6 +256,7 @@ def run_dataflow(feature_set, column_names, input_file_path, train_percent,
       feature_set=feature_set,
       column_names=column_names,
       input_file_path=input_file_path,
+      schema_file=schema_file,
       train_percent=train_percent,
       eval_percent=eval_percent,
       test_percent=test_percent,
@@ -251,6 +275,7 @@ def main(argv=None):
       feature_set=feature_set,
       column_names=column_names,
       input_file_path=args.input_file_path,
+      schema_file=args.schema_file,
       train_percent=args.train_percent,
       eval_percent=args.eval_percent,
       test_percent=args.test_percent,

solutionbox/structured_data/datalab_solutions/structured_data/setup.py

@@ -24,4 +24,6 @@
       version=VERSION,
       packages=['trainer', 'preprocess'],
       author='Google',
-      author_email='cloudml-feedback@google.com',)
+      author_email='cloudml-feedback@google.com',
+      test_suite='nose.collector',
+      tests_require=['nose'])

solutionbox/structured_data/datalab_solutions/structured_data/test/e2e_functions.py

@@ -13,9 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
+
+import os
 import random
 import subprocess
-import os
+
 
 def make_csv_data(filename, num_rows, problem_type):
   random.seed(12321)
@@ -29,9 +31,9 @@ def make_csv_data(filename, num_rows, problem_type):
       str2 = random.choice(['abc', 'def', 'ghi', 'jkl', 'mno', 'pqr'])
       str3 = random.choice(['car', 'truck', 'van', 'bike', 'train', 'drone'])
 
-      map1 = {'red':2, 'blue':6, 'green':4, 'pink':-5, 'yellow':-6, 'brown':-1, 'black':7}
-      map2 = {'abc':10, 'def':1, 'ghi':1, 'jkl':1, 'mno':1, 'pqr':1}
-      map3 = {'car':5, 'truck':10, 'van':15, 'bike':20, 'train':25, 'drone': 30}
+      map1 = {'red': 2, 'blue': 6, 'green': 4, 'pink': -5, 'yellow': -6, 'brown': -1, 'black': 7}
+      map2 = {'abc': 10, 'def': 1, 'ghi': 1, 'jkl': 1, 'mno': 1, 'pqr': 1}
+      map3 = {'car': 5, 'truck': 10, 'van': 15, 'bike': 20, 'train': 25, 'drone': 30}
 
       # Build some model.
       t = 0.5 + 0.5*num1 -2.5*num2 + num3
@@ -56,67 +58,83 @@ def make_csv_data(filename, num_rows, problem_type):
           str3=str3)
       f1.write(csv_line)
 
-  config = {'column_names': ['key', 'target', 'num1', 'num2', 'num3', 
+  schema = {'column_names': ['key', 'target', 'num1', 'num2', 'num3',
                              'str1', 'str2', 'str3'],
             'key_column': 'key',
             'target_column': 'target',
-            'problem_type': problem_type,
-            'model_type': '',
-            'numerical': {'num1': {'transform': 'identity'},
-                          'num2': {'transform': 'identity'},
-                          'num3': {'transform': 'identity'}},
-            'categorical': {'str1': {'transform': 'one_hot'},
-                            'str2': {'transform': 'one_hot'},
-                            'str3': {'transform': 'one_hot'}}
+            'numerical_columns': ['num1', 'num2', 'num3'],
+            'categorical_columns': ['str1', 'str2', 'str3']
   }
-  return config
-
+  if problem_type == 'classification':
+    schema['categorical_columns'] += ['target']
+  else:
+    schema['numerical_columns'] += ['target']
 
+  # use defaults for num3 and str3
+  transforms = {'num1': {'transform': 'identity'},
+                'num2': {'transform': 'identity'},
+  #              'num3': {'transform': 'identity'},
+                'str1': {'transform': 'one_hot'},
+                'str2': {'transform': 'one_hot'},
+  #              'str3': {'transform': 'one_hot'}
+  }
+  return schema, transforms
 
 
-def run_preprocess(output_dir, csv_filename, config_filename,
+def run_preprocess(output_dir, csv_filename, schema_filename,
                    train_percent='80', eval_percent='10', test_percent='10'):
-  cmd = ['python', './preprocess/preprocess.py', 
+  preprocess_script = os.path.abspath(
+      os.path.join(os.path.dirname(__file__), '../preprocess/preprocess.py'))
+  cmd = ['python', preprocess_script,
          '--output_dir', output_dir,
-         '--input_file_path', csv_filename, 
-         '--transforms_config_file', config_filename,
+         '--input_file_path', csv_filename,
+         '--schema_file', schema_filename,
          '--train_percent', train_percent,
          '--eval_percent', eval_percent,
          '--test_percent', test_percent,
   ]
-  print('Current working directoyr: %s' % os.getcwd())
   print('Going to run command: %s' % ' '.join(cmd))
   subprocess.check_call(cmd, stderr=open(os.devnull, 'wb'))
 
-def run_training(output_dir, input_dir, config_filename, extra_args=[]):
-  """Runs Training via gcloud alpha ml local train.
+
+def run_training(output_dir, input_dir, schema_filename, transforms_filename,
+                 max_steps, extra_args=[]):
+  """Runs Training via gcloud beta ml local train.
 
   Args:
     output_dir: the trainer's output folder
-    input_folder: should contain features_train*, features_eval*, and
+    input_dir: should contain features_train*, features_eval*, and
         mmetadata.json.
-    config_filename: path to the config file
+    schema_filename: path to the schema file
+    transforms_filename: path to the transforms file.
+    max_steps: int. max training steps.
     extra_args: array of strings, passed to the trainer.
+
+  Returns:
+    The stderr of training as one string. TF writes to stderr, so basically, the
+    output of training.
   """
   train_filename = os.path.join(input_dir, 'features_train*')
   eval_filename = os.path.join(input_dir, 'features_eval*')
   metadata_filename = os.path.join(input_dir, 'metadata.json')
-  cmd = ['gcloud alpha ml local train',
+
+  # Gcloud has the fun bug that you have to be in the parent folder of task.py
+  # when you call it. So cd there first.
+  task_parent_folder = os.path.abspath(
+      os.path.join(os.path.dirname(__file__), '..'))
+  cmd = ['cd %s &&' % task_parent_folder,
+         'gcloud beta ml local train',
          '--module-name=trainer.task',
          '--package-path=trainer',
          '--',
          '--train_data_paths=%s' % train_filename,
          '--eval_data_paths=%s' % eval_filename,
          '--metadata_path=%s' % metadata_filename,
          '--output_path=%s' % output_dir,
-         '--transforms_config_file=%s' % config_filename,
-         '--max_steps=2500'] + extra_args
-  print('Current working directoyr: %s' % os.getcwd())
+         '--schema_file=%s' % schema_filename,
+         '--transforms_file=%s' % transforms_filename,
+         '--max_steps=%s' % max_steps] + extra_args
   print('Going to run command: %s' % ' '.join(cmd))
-  sp = subprocess.Popen(' '.join(cmd), shell=True, stderr=subprocess.PIPE) #open(os.devnull, 'wb'))
+  sp = subprocess.Popen(' '.join(cmd), shell=True, stderr=subprocess.PIPE)
   _, err = sp.communicate()
-  err = err.splitlines()
-  print 'last line'
-  print err[len(err)-1]
-
-  stderr=subprocess.PIPE
+  return err