Adding evaluationanalysis API to generate evaluation stats from eval …

datalab/mlalpha/__init__.py

@@ -26,6 +26,8 @@
 from ._package import Packager
 from ._cloud_models import CloudModels, CloudModelVersions
 from ._confusion_matrix import ConfusionMatrix
+from ._analysis import CsvEvalResults, CsvEvalSource, EvalResultsCsvCoder, \
+                       AccuracyFn, FeatureSlicingPipeline
 
 from plotly.offline import init_notebook_mode
 

datalab/mlalpha/_analysis.py

@@ -0,0 +1,215 @@
+# Copyright 2016 Google Inc. All rights reserved.
+#
+# Licensed 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.
+
+"""Implements Cloud ML Eval Results Analysis"""
+
+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')
+
+"""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')
+
+
+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().
+
+    Args:
+      output_dir: A GCS path which will be used as base path for tmp and staging dir.
+
+    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()
+

datalab/mlalpha/_dataset.py

@@ -12,12 +12,11 @@
 
 
 import google.cloud.ml.features as features
-import matplotlib.pyplot as plt
 import numpy as np
 import os
 import pandas as pd
 import pandas_profiling
-from plotly.graph_objs import Bar, Figure, Histogram, Layout, Scatter, Scatter3d
+from plotly.graph_objs import Histogram, Layout, Scatter, Scatter3d
 from plotly.offline import iplot
 from plotly import tools
 import seaborn as sns
@@ -377,6 +376,7 @@ def to_dataframes(self):
     return self._dataframes
 
   def plot(self, names=None, columns=None):
+    import matplotlib.pyplot as plt
     """Plot correlation graphs on the specified columns, in n*n grids.
 
     Args:

datalab/mlalpha/_metadata.py

@@ -84,3 +84,26 @@ def get_target_name_and_scenario(self):
       if feature['columns'][0] == target_column_name:
         return feature_name, scenario
     raise Exception('Invalid metadata. No target found in features.')
+
+  def get_csv_headers(self):
+    """Get CSV headers from metadata.
+
+    Returns:
+      List of CSV headers if the data format is CSV, else None.
+    """
+    with ml.util._file.open_local_or_gcs(self._metadata_path, 'r') as f:
+      metadata = yaml.load(f)
+    if 'csv' not in metadata or 'headers' not in metadata['csv']:
+      return None
+    return metadata['csv']['headers']
+
+  def get_numeric_columns(self):
+    """Get names of numeric feature columns.
+
+    Returns:
+      List of numeric feature columns.
+    """
+    with ml.util._file.open_local_or_gcs(self._metadata_path, 'r') as f:
+      metadata = yaml.load(f)
+    numeric_columns = [k for k,v in metadata['columns'].iteritems() if v['type'] == 'numeric']
+    return numeric_columns

setup.py

@@ -82,6 +82,9 @@
     'scikit-learn==0.17.1',
     'scipy==0.18.0',
     'ipykernel==4.4.1',
+    'psutil==4.3.0',
+    'plotly==1.12.5',
+    'seaborn==0.7.0',
   ],
   package_data={
     'datalab.notebook': [