Fixing s3 support

.python-version

@@ -1 +1 @@
-triage-3.5.3
+triage-3.6.2

.travis.yml

@@ -1,6 +1,6 @@
 # This file was autogenerated and will overwrite each time you run travis_pypi_setup.py
 language: python
-python: 3.5
+python: 3.6
 addons:
   postgresql: '9.5'
 services:

requirement/main.txt

@@ -4,7 +4,7 @@ PyYAML
 psycopg2
 python-dateutil
 scipy
-scikit-learn==0.18.2
+scikit-learn
 tables==3.3.0
 matplotlib
 pandas
@@ -17,3 +17,4 @@ retrying
 Dickens==1.0.1
 signalled-timeout==1.0.0
 smart_open==1.5.3
+s3fs==0.1.2

src/tests/catwalk_tests/test_individual_importance_uniform.py

@@ -30,7 +30,7 @@ def test_uniform_distribution_entity_id_index():
         for imp in feature_importances:
             data_dict[imp.feature] = [0.5, 0.5]
         test_store = InMemoryMatrixStore(
-            matrix=pandas.DataFrame.from_dict(data_dict).set_index(['entity_id']),
+            matrix=pandas.DataFrame.from_dict(data_dict),
             metadata=sample_metadata()
         )
         session.commit()
@@ -68,9 +68,11 @@ def test_uniform_distribution_entity_date_index():
         data_dict = {'entity_id': [1, 1], 'as_of_date': ['2016-01-01', '2017-01-01']}
         for imp in feature_importances:
             data_dict[imp.feature] = [0.5, 0.5]
+        metadata = sample_metadata()
+        metadata['indices'] = ['entity_id', 'as_of_date']
         test_store = InMemoryMatrixStore(
-            matrix=pandas.DataFrame.from_dict(data_dict).set_index(['entity_id', 'as_of_date']),
-            metadata=sample_metadata()
+            matrix=pandas.DataFrame.from_dict(data_dict),
+            metadata=metadata
         )
         session.commit()
         results = uniform_distribution(

src/tests/catwalk_tests/test_integration.py

@@ -68,7 +68,7 @@ def test_integration():
                 for as_of_date in as_of_dates
             ]
 
-            model_storage_engine = S3ModelStorageEngine(s3_conn, project_path)
+            model_storage_engine = S3ModelStorageEngine(project_path)
 
             experiment_hash = save_experiment_and_get_hash({}, db_engine)
             # instantiate pipeline objects

src/tests/catwalk_tests/test_model_trainers.py

@@ -1,6 +1,5 @@
 import boto3
 import pandas
-import pickle
 import testing.postgresql
 import datetime
 import sqlalchemy
@@ -10,7 +9,6 @@
 from moto import mock_s3
 from sqlalchemy import create_engine
 from triage.component.catwalk.db import ensure_db
-from triage.component.catwalk.utils import model_cache_key
 
 from triage.component.catwalk.model_trainers import ModelTrainer
 from triage.component.catwalk.storage import InMemoryModelStorageEngine,\
@@ -51,7 +49,7 @@ def test_model_trainer():
                 'indices': ['entity_id'],
             }
             project_path = 'econ-dev/inspections'
-            model_storage_engine = S3ModelStorageEngine(s3_conn, project_path)
+            model_storage_engine = S3ModelStorageEngine(project_path)
             trainer = ModelTrainer(
                 project_path=project_path,
                 experiment_hash=None,
@@ -79,11 +77,7 @@ def test_model_trainer():
                 engine.execute('select model_hash from results.models')
             ]
             assert len(records) == 4
-
-            cache_keys = [
-                model_cache_key(project_path, model_row[0], s3_conn)
-                for model_row in records
-            ]
+            hashes = [row[0] for row in records]
 
             # 2. that the model groups are distinct
             records = [
@@ -94,8 +88,8 @@ def test_model_trainer():
 
             # 3. that all four models are cached
             model_pickles = [
-                pickle.loads(cache_key.get()['Body'].read())
-                for cache_key in cache_keys
+                model_storage_engine.get_store(model_hash).load()
+                for model_hash in hashes
             ]
             assert len(model_pickles) == 4
             assert len([x for x in model_pickles if x is not None]) == 4
@@ -203,7 +197,7 @@ def test_n_jobs_not_new_model():
             trainer = ModelTrainer(
                 project_path='econ-dev/inspections',
                 experiment_hash=None,
-                model_storage_engine=S3ModelStorageEngine(s3_conn, 'econ-dev/inspections'),
+                model_storage_engine=S3ModelStorageEngine('econ-dev/inspections'),
                 db_engine=engine,
                 model_group_keys=['label_name', 'label_timespan']
             )

src/tests/catwalk_tests/test_predictors.py

@@ -33,7 +33,7 @@ def test_predictor():
             s3_conn = boto3.resource('s3')
             s3_conn.create_bucket(Bucket='econ-dev')
             project_path = 'econ-dev/inspections'
-            model_storage_engine = S3ModelStorageEngine(s3_conn, project_path)
+            model_storage_engine = S3ModelStorageEngine(project_path)
             _, model_id = \
                 fake_trained_model(project_path, model_storage_engine, db_engine)
             predictor = Predictor(project_path, model_storage_engine, db_engine)
@@ -148,7 +148,7 @@ def test_predictor_composite_index():
             'end_time': AS_OF_DATE,
             'label_timespan': '3month',
             'metta-uuid': '1234',
-            'indices': ['entity_id'],
+            'indices': ['entity_id', 'as_of_date'],
         }
         matrix_store = InMemoryMatrixStore(matrix, metadata)
         predict_proba = predictor.predict(
@@ -238,7 +238,7 @@ def test_predictor_retrieve():
             'end_time': AS_OF_DATE,
             'label_timespan': '3month',
             'metta-uuid': '1234',
-            'indices': ['entity_id'],
+            'indices': ['entity_id', 'as_of_date'],
         }
         matrix_store = InMemoryMatrixStore(matrix, metadata)
         predict_proba = predictor.predict(

src/tests/catwalk_tests/test_storage.py

@@ -4,8 +4,7 @@
 import yaml
 from collections import OrderedDict
 
-import boto
-import pandas
+import pandas as pd
 from moto import mock_s3, mock_s3_deprecated
 
 from triage.component.catwalk.storage import (
@@ -24,11 +23,11 @@ def __init__(self, val):
 
 
 def test_S3Store():
-    import boto3
     with mock_s3():
-        s3_conn = boto3.resource('s3')
-        s3_conn.create_bucket(Bucket='a-bucket')
-        store = S3Store(s3_conn.Object('a-bucket', 'a-path'))
+        import boto3
+        client = boto3.client('s3')
+        client.create_bucket(Bucket='test_bucket', ACL='public-read-write')
+        store = S3Store(path=f"s3://test_bucket/a_path")
         assert not store.exists()
         store.write(SomeClass('val'))
         assert store.exists()
@@ -71,7 +70,7 @@ def matrix_store(self):
             ('m_feature', [0.4, 0.5]),
             ('label', [0, 1])
         ])
-        df = pandas.DataFrame.from_dict(data_dict)
+        df = pd.DataFrame.from_dict(data_dict)
         metadata = {
             'label_name': 'label',
             'indices': ['entity_id'],
@@ -85,7 +84,7 @@ def matrix_store(self):
             tmphdf = os.path.join(tmpdir, 'df.h5')
             with open(tmpyaml, 'w') as outfile:
                 yaml.dump(metadata, outfile, default_flow_style=False)
-                df.to_csv(tmpcsv, index=False)
+                df.to_csv(tmpcsv)
                 df.to_hdf(tmphdf, 'matrix')
                 csv = CSVMatrixStore(matrix_path=tmpcsv, metadata_path=tmpyaml)
                 hdf = HDFMatrixStore(matrix_path=tmphdf, metadata_path=tmpyaml)
@@ -105,7 +104,8 @@ def matrix_store(self):
                 assert csv.labels().to_dict() == inmemory.labels().to_dict()
                 assert hdf.labels().to_dict() == inmemory.labels().to_dict()
 
-        matrix_store = [inmemory, hdf, csv]
+        matrix_store = [inmemory, csv, hdf]
+
         return matrix_store
 
     def test_MatrixStore_resort_columns(self):
@@ -176,7 +176,7 @@ def test_as_of_dates_entity_index(self):
             'feature_two': [0.5, 0.6],
         }
         matrix = InMemoryMatrixStore(
-            matrix=pandas.DataFrame.from_dict(data),
+            matrix=pd.DataFrame.from_dict(data),
             metadata={'end_time': '2016-01-01', 'indices': ['entity_id']}
         )
 
@@ -190,29 +190,25 @@ def test_as_of_dates_entity_date_index(self):
             'as_of_date': ['2016-01-01', '2016-01-01', '2017-01-01', '2017-01-01']
         }
         matrix = InMemoryMatrixStore(
-            matrix=pandas.DataFrame.from_dict(data),
+            matrix=pd.DataFrame.from_dict(data),
             metadata={'indices': ['entity_id', 'as_of_date']}
         )
 
         self.assertEqual(matrix.as_of_dates, ['2016-01-01', '2017-01-01'])
 
     def test_s3_save(self):
-        with mock_s3_deprecated():
-            s3_conn = boto.connect_s3()
-            bucket_name = 'fake-matrix-bucket'
-            s3_conn.create_bucket(bucket_name)
+        with mock_s3():
+            import boto3
+            client = boto3.client('s3')
+            client.create_bucket(Bucket='fake-matrix-bucket', ACL='public-read-write')
 
             matrix_store_list = self.matrix_store()
 
             for matrix_store in matrix_store_list:
-                matrix_store.save(project_path='s3://fake-matrix-bucket', name='test')
-
-            # HDF
-            hdf = HDFMatrixStore(matrix_path='s3://fake-matrix-bucket/test.h5', metadata_path='s3://fake-matrix-bucket/test.yaml')
-            # CSV
-            csv = CSVMatrixStore(matrix_path='s3://fake-matrix-bucket/test.csv', metadata_path='s3://fake-matrix-bucket/test.yaml')
+                if isinstance(matrix_store, CSVMatrixStore):
+                    matrix_store.save(project_path='s3://fake-matrix-bucket', name='test')
+                    # CSV
+                    csv = CSVMatrixStore(matrix_path='s3://fake-matrix-bucket/test.csv', metadata_path='s3://fake-matrix-bucket/test.yaml')
 
-            assert csv.metadata == matrix_store_list[0].metadata
-            assert csv.matrix.to_dict() == matrix_store_list[0].matrix.to_dict()
-            assert hdf.metadata == matrix_store_list[0].metadata
-            assert hdf.matrix.to_dict() == matrix_store_list[0].matrix.to_dict()
+                    assert csv.metadata == matrix_store_list[0].metadata
+                    assert csv.matrix.to_dict() == matrix_store_list[0].matrix.to_dict()

src/triage/component/architect/builders.py

@@ -3,6 +3,9 @@
 import pandas
 import os
 
+import s3fs
+from urllib.parse import urlparse
+
 from triage.component import metta
 
 
@@ -25,8 +28,11 @@ def validate(self):
 
     def build_all_matrices(self, build_tasks):
         logging.info('Building %s matrices', len(build_tasks.keys()))
-        for matrix_uuid, task_arguments in build_tasks.items():
+
+        for i, (matrix_uuid, task_arguments) in enumerate(build_tasks.items()):
+            logging.info(f"Building matrix {matrix_uuid} ({i}/{len(build_tasks.keys())})")
             self.build_matrix(**task_arguments)
+            logging.debug(f"Matrix {matrix_uuid} built")
 
     def _outer_join_query(
         self,
@@ -224,9 +230,23 @@ def build_matrix(
             matrix_directory,
             '{}.csv'.format(matrix_uuid)
         )
-        if not self.replace and os.path.exists(matrix_filename):
-            logging.info('Skipping %s because matrix already exists', matrix_filename)
-            return
+
+        # The output directory is local or in s3
+        path_parsed = urlparse(matrix_filename)
+        scheme = path_parsed.scheme  # If '' of 'file' is a regular file or 's3'
+
+        if scheme in ('', 'file'):
+            if not self.replace and os.path.exists(matrix_filename):
+                logging.info('Skipping %s because matrix already exists', matrix_filename)
+                return
+        elif scheme == 's3':
+            if not self.replace and s3fs.S3FileSystem().exists(matrix_filename):
+                logging.info('Skipping %s because matrix already exists', matrix_filename)
+                return
+        else:
+            raise ValueError(f"""URL scheme not supported:
+              {scheme} (from {matrix_filename})
+            """)
 
         logging.info('Creating matrix %s > %s', matrix_metadata['matrix_id'], matrix_filename)
         # make the entity time table and query the labels and features tables
@@ -248,8 +268,9 @@ def build_matrix(
             entity_date_table_name,
             matrix_uuid
         )
+        logging.info(f"Feature data extracted for matrix {matrix_uuid}")
         try:
-            logging.info('Extracting label data frmo database into file for '
+            logging.info('Extracting label data from database into file for '
                          'matrix %s', matrix_uuid)
             labels_csv_name = self.write_labels_data(
                 label_name,
@@ -260,13 +281,15 @@ def build_matrix(
             )
             features_csv_names.insert(0, labels_csv_name)
 
+            logging.info(f"Label data extracted for matrix {matrix_uuid}")
             # stitch together the csvs
             logging.info('Merging feature files for matrix %s', matrix_uuid)
             output = self.merge_feature_csvs(
                 features_csv_names,
                 matrix_directory,
                 matrix_uuid
             )
+            logging.info(f"Features data merged for matrix {matrix_uuid}")
         finally:
             # clean up files and database before finishing
             for csv_name in features_csv_names:
@@ -281,6 +304,7 @@ def build_matrix(
                 directory=self.matrix_directory,
                 format='csv'
             )
+            logging.info("Matrix {matrix_uuid} archived (using metta)")
         finally:
             if isinstance(output, str):
                 os.remove(output)

src/triage/component/architect/planner.py

@@ -221,5 +221,5 @@ def build_all_matrices(self, *args, **kwargs):
         self.builder.build_all_matrices(*args, **kwargs)
 
     def build_matrix(self, *args, **kwargs):
-        logging.info('Building matrix with args %s', args)
+        logging.info(f"Building matrix with {args}, {kwargs}")
         self.builder.build_matrix(*args, **kwargs)

src/triage/component/catwalk/README.rst

@@ -35,7 +35,7 @@ Below is a complete sample usage of the three Catwalk components::
     from sqlalchemy import create_engine
 
     from triage.component import metta
-    from triage.component.catwalk.storage import FSModelStorageEngine, MettaCSVMatrixStore
+    from triage.component.catwalk.storage import FSModelStorageEngine, CSVMatrixStore
     from triage.component.catwalk.model_trainers import ModelTrainer
     from triage.component.catwalk.predictors import Predictor
     from triage.component.catwalk.evaluation import ModelEvaluator
@@ -66,9 +66,9 @@ Below is a complete sample usage of the three Catwalk components::
     }
     train_matrix_uuid = metta.archive_matrix(train_metadata, train_matrix, format='csv')
 
-    # The MettaCSVMatrixStore bundles the matrix and metadata together
+    # The CSVMatrixStore bundles the matrix and metadata together
     # for catwalk to use
-    train_matrix_store = MettaCSVMatrixStore(
+    train_matrix_store = CSVMatrixStore(
         matrix_path='{}.csv'.format(train_matrix_uuid),
         metadata_path='{}.yaml'.format(train_matrix_uuid)
     )
@@ -91,9 +91,9 @@ Below is a complete sample usage of the three Catwalk components::
     }
     test_matrix_uuid = metta.archive_matrix(test_metadata, test_matrix, format='csv')
 
-    # The MettaCSVMatrixStore bundles the matrix and metadata together
+    # The CSVMatrixStore bundles the matrix and metadata together
     # for catwalk to use
-    test_matrix_store = MettaCSVMatrixStore(
+    test_matrix_store = CSVMatrixStore(
         matrix_path='{}.csv'.format(test_matrix_uuid),
         metadata_path='{}.yaml'.format(test_matrix_uuid)
     )

src/triage/component/catwalk/model_trainers.py

@@ -422,7 +422,8 @@ def process_train_task(
         elif not saved_model_id:
             reason = 'model metadata not found'
 
-        logging.info('Training %s/%s: %s', class_path, parameters, reason)
+        logging.info(f"Training {class_path} with parameters {parameters}"
+                     f"(reason to train: {reason})")
         model_id = self._train_and_store_model(
             matrix_store,
             class_path,
@@ -464,7 +465,8 @@ def generate_train_tasks(
 
         for class_path, parameters in self._generate_model_configs(grid_config):
             model_hash = self._model_hash(matrix_store.metadata, class_path, parameters)
-            logging.info('Computed model hash %s', model_hash)
+            logging.info(f"Computed model hash for {class_path} "
+                         f"with parameters {parameters}: {model_hash}")
 
             if any(task['model_hash'] == model_hash for task in tasks):
                 logging.info('Skipping model_hash %s because another'
@@ -480,4 +482,5 @@ def generate_train_tasks(
                 'misc_db_parameters': misc_db_parameters,
             })
         logging.info('Found %s unique model training tasks', len(tasks))
+
         return tasks