Remove old feature-slicing pipeline implementation (is replaced by BigQuery) Add Confusion matrix magic. (#129)

qimingj · web-flow · commit eb46fd90ffe7 · 2017-01-18T18:12:25.000-08:00
* Remove old feature-slicing pipeline implementation (is replaced by BigQuery).
Add Confusion matrix magic.

* Follow up on code review comments. Also fix an inception issue that eval loss is nan when eval size is smaller than batch size.

* Fix set union.
diff --git a/datalab/mlalpha/__init__.py b/datalab/mlalpha/__init__.py
@@ -26,8 +26,7 @@
 from ._package import Packager
 from ._cloud_models import CloudModels, CloudModelVersions
 from ._confusion_matrix import ConfusionMatrix
-from ._analysis import CsvEvalResults, CsvEvalSource, EvalResultsCsvCoder, \
-                       AccuracyFn, FeatureSlicingPipeline
+from ._analysis import csv_to_dataframe
 from ._package_runner import PackageRunner
 
 from plotly.offline import init_notebook_mode
diff --git a/datalab/mlalpha/_analysis.py b/datalab/mlalpha/_analysis.py
@@ -10,206 +10,39 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 
-"""Implements Cloud ML Eval Results Analysis"""
+"""Implements Cloud ML Analysis Helpers"""
 
-import apache_beam as beam
-from collections import namedtuple
 
-"""Prepresents an eval results CSV file. For example, the content is like:
-     107,Iris-versicolor,1.64827824278e-07,0.999999880791,6.27104979056e-10
-     100,Iris-versicolor,3.5338824091e-05,0.99996471405,1.32811195375e-09
-     ...
-"""
-CsvEvalResults = namedtuple('CsvEvalResults', 'source, key_index predicted_index score_index_start num_scores')
+import google.cloud.ml as ml
+import numpy as np
+import pandas as pd
+import yaml
 
-"""Prepresents an eval source CSV file. For example, the content is like:
-     107,Iris-virginica,4.9,2.5,4.5,1.7
-     100,Iris-versicolor,5.7,2.8,4.1,1.3
-     ...
-   The metadata is generated in the preprocessing pipeline. It is used to describe the CSV file,
-   including schema, headers, etc.
-"""
-CsvEvalSource = namedtuple('CsvEvalSource', 'source metadata')
+import datalab.bigquery as bq
 
 
-class EvalResultsCsvCoder(beam.coders.Coder):
-  """A coder to read from Eval results CSV file. Note encode() is only needed in cloud run.
-  """
-  def __init__(self, eval_results):
-    self._eval_results = eval_results
-
-  def decode(self, csv_line):
-    import csv
-    source_elem = next(csv.reader([csv_line]))
-    key = source_elem[self._eval_results.key_index]
-    element = {
-        'predicted': source_elem[self._eval_results.predicted_index],
-        'scores': source_elem[self._eval_results.score_index_start: \
-            self._eval_results.score_index_start+self._eval_results.num_scores]
-    }
-    return (key, element)
-
-  def encode(self, element):
-    return str(element)
-
-
-class AccuracyFn(beam.CombineFn):
-  """A transform to compute accuracy for feature slices.
-  """
-  def __init__(self, target_column_name):
-    self._target_column_name = target_column_name
-
-  def create_accumulator(self):
-    return (0.0, 0)
-
-  def add_input(self, (sum, count), input):
-    new_sum = sum
-    if (input['predicted'] == input[self._target_column_name]):
-      new_sum += 1
-    return new_sum, count + 1
-
-  def merge_accumulators(self, accumulators):
-    sums, counts = zip(*accumulators)
-    return sum(sums), sum(counts)
-
-  def extract_output(self, (sum, count)):
-    accuracy = float(sum) / count if count else float('NaN')
-    return {'accuracy': accuracy, 'totalWeightedExamples': count}
-
-
-class FeatureSlicingPipeline(object):
-  """The pipeline to generate feature slicing stats. For example, accuracy values given 
-     "species = Iris-versicolor", "education = graduate", etc.
-     It is implemented with DataFlow.
-  """
-  @staticmethod
-  def _pair_source_with_key(element):
-    key = element['key']
-    del element['key']
-    return (key, element)
-
-  @staticmethod
-  def _join_info((key, info)):
-    value = info['source'][0]
-    value.update(info['results'][0])
-    return (key, value)
-
-  def _pipeline_def(self, p, eval_source, eval_results, features_to_slice, metrics, output_file,
-                    shard_name_template=None):
-    import datalab.mlalpha as mlalpha
-    import google.cloud.ml.io as io
-    import json
-
-    metadata = mlalpha.Metadata(eval_source.metadata)
-    target_name, _ = metadata.get_target_name_and_scenario()
-
-    # Load eval source.
-    eval_source_coder = io.CsvCoder(metadata.get_csv_headers(), metadata.get_numeric_columns())
-    eval_source_data = p | beam.io.ReadFromText(eval_source.source, coder=eval_source_coder) | \
-        beam.Map('pair_source_with_key', FeatureSlicingPipeline._pair_source_with_key)
-
-    # Load eval results.
-    eval_results_data = p | \
-        beam.Read('ReadEvalResults', beam.io.TextFileSource(eval_results.source,
-            coder=EvalResultsCsvCoder(eval_results)))
-
-    # Join source with results by key.
-    joined_results = {'source': eval_source_data, 'results': eval_results_data} | \
-        beam.CoGroupByKey() | beam.Map('join by key', FeatureSlicingPipeline._join_info)
-
-    feature_metrics_list = []
-    for feature_to_slice in features_to_slice:
-      feature_metrics = joined_results | \
-          beam.Map('slice_get_key_%s' % feature_to_slice,
-                   lambda (k,v),f=feature_to_slice: (v[f], v)) | \
-          beam.CombinePerKey('slice_combine_%s' % feature_to_slice,
-                             AccuracyFn(target_name)) | \
-          beam.Map('slice_prepend_feature_name_%s' % feature_to_slice,
-                   lambda (k,v),f=feature_to_slice: ('%s:%s' % (f, k), v))
-      feature_metrics_list.append(feature_metrics)
-
-    feature_metrics_list | beam.Flatten() | \
-        beam.Map('ToJsonFormat', lambda (k,v): json.dumps({'feature': k, 'metricValues': v})) | \
-        beam.io.WriteToText(output_file, shard_name_template=shard_name_template)
-    return p
-
-
-  def run_local(self, eval_source, eval_results, features_to_slice, metrics, output_file):
-    """Run the pipeline locally. Blocks execution until it finishes.
-
-    Args:
-      eval_source: The only supported format is CsvEvalResults now while we may add more.
-                   Note the source can be either a GCS path or a local path.
-      eval_results: The only supported format is CsvEvalSource now while we may add more.
-                    Note the source can be either a GCS path or a local path.
-      features_to_slice: A list of features to slice on. The features must exist in
-                         eval_source, and can be numeric, categorical, or target.
-      metrics: A list of metrics to compute. For classification, it supports "accuracy",
-               "logloss". For regression, it supports "RMSE".
-      output_file: The path to a local file holding the aggregated results.
-    """
-    p = beam.Pipeline('DirectPipelineRunner')
-    self._pipeline_def(p, eval_source, eval_results, features_to_slice, metrics, output_file,
-                       shard_name_template='')
-    p.run()
-
-
-  def default_pipeline_options(self, output_dir):
-    """Get default DataFlow options. Users can customize it further on top of it and then
-       send the option to run_cloud().
+def csv_to_dataframe(csv_path, schema_path):
+  """Given a CSV file together with its BigQuery schema file in yaml, load
+     content into a dataframe.
 
     Args:
-      output_dir: A GCS path which will be used as base path for tmp and staging dir.
+      csv_path: Input CSV path. Can be local or GCS.
+      schema_path: Input schema path. Can be local or GCS.
 
     Returns:
-      A dictionary of options.
-    """
-    import datalab.context as context
-    import datetime
-    import google.cloud.ml as ml
-    import os
-
-    options = {
-        'staging_location': os.path.join(output_dir, 'tmp', 'staging'),
-        'temp_location': os.path.join(output_dir, 'tmp'),
-        'job_name': 'feature-slicing-pipeline' + '-' + \
-             datetime.datetime.now().strftime('%y%m%d-%H%M%S'),
-        'project': context.Context.default().project_id,
-        'extra_packages': ['gs://cloud-datalab/dataflow/datalab.tar.gz', ml.sdk_location],
-        'teardown_policy': 'TEARDOWN_ALWAYS',
-        'no_save_main_session': True
-      }
-    return options
-  
-  def run_cloud(self, eval_source, eval_results, features_to_slice, metrics, output_file,
-                pipeline_option=None):
-    """Run the pipeline in cloud. Returns when the job is submitted.
-       Calling of this function may incur some cost since it runs a DataFlow job in Google Cloud.
-       If pipeline_option is not specified, make sure you are signed in (through Datalab)
-       and a default project is set so it can get credentials and projects from global context.
-
-    Args:
-      eval_source: The only supported format is CsvEvalResults now while we may add more.
-                   The source needs to be a GCS path and is readable to current signed in user.
-      eval_results: The only supported format is CsvEvalSource now while we may add more.
-                    The source needs to be a GCS path and is readable to current signed in user.
-      features_to_slice: A list of features to slice on. The features must exist in
-                         eval_source, and can be numeric, categorical, or target.
-      metrics: A list of metrics to compute. For classification, it supports "accuracy",
-               "logloss". For regression, it supports "RMSE".
-      pipeline_option: If not specified, use default options. Recommend customizing your options
-                       based on default one obtained from default_pipeline_options(). For example,
-                         options = fsp.default_pipeline_options()
-                         options['num_workers'] = 10
-                         ...
-      output_file: A GCS file prefix holding the aggregated results.
-    """
-    import os
-    if pipeline_option is None:
-      output_dir = os.path.dirname(output_file)
-      pipeline_option = self.default_pipeline_options(output_dir)
-    opts = beam.pipeline.PipelineOptions(flags=[], **pipeline_option)
-    p = beam.Pipeline('DataflowPipelineRunner', options=opts)
-    self._pipeline_def(p, eval_source, eval_results, features_to_slice, metrics, output_file)
-    p.run()
-
+      Loaded pandas dataframe.
+  """
+  with ml.util._file.open_local_or_gcs(schema_path, mode='r') as f:
+    schema = yaml.safe_load(f)
+  _MAPPINGS = {
+    'FLOAT': np.float64,
+    'INTEGER': np.int64,
+    'TIMESTAMP': np.datetime64,
+    'BOOLEAN': np.bool,
+  }
+  for item in schema:
+    item['type'] = _MAPPINGS.get(item['type'], object)
+  names = [x['name'] for x in schema]
+  dtype = {x['name']: x['type'] for x in schema}
+  with ml.util._file.open_local_or_gcs(csv_path, mode='r') as f:
+    return pd.read_csv(f, names=names, dtype=dtype)
diff --git a/datalab/mlalpha/commands/_ml.py b/datalab/mlalpha/commands/_ml.py
@@ -17,9 +17,15 @@
   raise Exception('This module can only be loaded in ipython.')
 
 import collections
+import google.cloud.ml as cloudml
+import matplotlib.pyplot as plt
+import numpy as np
 import os
+import pandas as pd
+from sklearn.metrics import confusion_matrix
 import yaml
 
+
 import datalab.context
 import datalab.mlalpha
 import datalab.utils.commands
@@ -87,6 +93,23 @@ def ml(line, cell=None):
                                     required=True)
   batch_predict_parser.set_defaults(func=_batch_predict)
 
+  confusion_matrix_parser = parser.subcommand('confusion_matrix',
+                                              'Plot confusion matrix. The source is provided ' +
+                                              'in one of "csv", "bqtable", and "sql" params.')
+  confusion_matrix_parser.add_argument('--csv',
+                                       help='GCS or local path of CSV file which contains ' +
+                                            '"target", "predicted" columns at least. The CSV ' +
+                                            'either comes with a schema file in the same dir, ' +
+                                            'or specify "headers: name1, name2..." in cell.')
+  confusion_matrix_parser.add_argument('--bqtable',
+                                       help='name of the BigQuery table in the form of ' + 
+                                            'dataset.table.')
+  confusion_matrix_parser.add_argument('--sql',
+                                       help='name of the sql module defined in previous cell ' + 
+                                            'which should return "target", "predicted", ' +
+                                            'and "count" columns at least in results.')
+  confusion_matrix_parser.set_defaults(func=_confusion_matrix)
+
   namespace = datalab.utils.commands.notebook_environment()
   return datalab.utils.commands.handle_magic_line(line, cell, parser, namespace=namespace)
 
@@ -158,3 +181,66 @@ def _predict(args, cell):
 
 def _batch_predict(args, cell):
   return _run_package(args, cell, 'batch_predict')
+
+
+def _plot_confusion_matrix(cm, labels):
+  plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
+  plt.title('Confusion matrix')
+  plt.colorbar()
+  tick_marks = np.arange(len(labels))
+  plt.xticks(tick_marks, labels, rotation=45)
+  plt.yticks(tick_marks, labels)
+  plt.tight_layout()
+  plt.ylabel('True label')
+  plt.xlabel('Predicted label')
+
+
+def _confusion_matrix_from_csv(input_csv, cell):
+  schema_file = input_csv + '.schema.yaml'
+  headers = None
+  if cell is not None:
+    env = datalab.utils.commands.notebook_environment()
+    config = datalab.utils.commands.parse_config(cell, env)
+    headers_str = config.get('headers', None)
+    if headers_str is not None:
+      headers = [x.strip() for x in headers_str.split(',')]
+  if headers is not None:
+    with cloudml.util._file.open_local_or_gcs(input_csv, mode='r') as f:
+      df = pd.read_csv(f, names=headers)
+  elif cloudml.util._file.file_exists(schema_file):
+    df = datalab.mlalpha.csv_to_dataframe(input_csv, schema_file)
+  else:
+    raise Exception('headers is missing from cell, ' +
+                    'and there is no schema file in the same dir as csv')
+  labels = sorted(set(df['target']) | set(df['predicted']))
+  cm = confusion_matrix(df['target'], df['predicted'], labels=labels)
+  return cm, labels
+
+
+def _confusion_matrix_from_query(sql_module_name, bq_table):
+  if sql_module_name is not None:
+    item = datalab.utils.commands.get_notebook_item(sql_module_name)
+    query, _ = datalab.data.SqlModule.get_sql_statement_with_environment(item, {})
+  else:
+    query = ('select target, predicted, count(*) as count from %s group by target, predicted'
+             % bq_table)
+  dfbq = datalab.bigquery.Query(query).results().to_dataframe()
+  labels = sorted(set(dfbq['target']) | set(dfbq['predicted']))
+  labels_count = len(labels)
+  dfbq['target'] = [labels.index(x) for x in dfbq['target']]
+  dfbq['predicted'] = [labels.index(x) for x in dfbq['predicted']]
+  cm = [[0]*labels_count for i in range(labels_count)]
+  for index, row in dfbq.iterrows():
+    cm[row['target']][row['predicted']] = row['count']
+  return cm, labels
+
+
+def _confusion_matrix(args, cell):
+  if args['csv'] is not None:
+    #TODO: Maybe add cloud run for large CSVs with federated table.
+    cm, labels = _confusion_matrix_from_csv(args['csv'], cell)
+  elif args['sql'] is not None or args['bqtable'] is not None:
+    cm, labels = _confusion_matrix_from_query(args['sql'], args['bqtable'])
+  else:
+    raise Exception('One of "csv", "bqtable", and "sql" param is needed.')
+  _plot_confusion_matrix(cm, labels)
diff --git a/solutionbox/inception/datalab_solutions/inception/_trainer.py b/solutionbox/inception/datalab_solutions/inception/_trainer.py
@@ -41,7 +41,10 @@ class Evaluator(object):
   """Loads variables from latest checkpoint and performs model evaluation."""
 
   def __init__(self, model, data_paths, batch_size, output_path, dataset='eval'):
-    self.num_eval_batches = self._data_size(data_paths) // batch_size
+    data_size = self._data_size(data_paths)
+    if data_size <= batch_size:
+      raise Exception('Data size is smaller than batch size.')
+    self.num_eval_batches = data_size // batch_size
     self.batch_of_examples = []
     self.checkpoint_path = os.path.join(output_path, 'train')
     self.output_path = os.path.join(output_path, dataset)
