Post Release Fixes (#3008)

Author: TobyRoseman

src/python/turicreate/_scripts/_pylambda_worker.py

@@ -28,7 +28,7 @@ def _write_log(s, error=False):
                 try:
                     error_log_function(s)
                 except Exception as e:
-                    print("Error setting exception: repr(e)")
+                    print("Error setting exception: " + repr(e))
                     print("Error: %s" % str(s))
         else:
             if info_log_function is not None:

src/python/turicreate/toolkits/_data_zoo.py

@@ -7,7 +7,6 @@
 from __future__ import division as _
 from __future__ import absolute_import as _
 
-import hashlib as _hashlib
 import os as _os
 import turicreate as _tc
 import shutil as _shutil

src/python/turicreate/toolkits/_internal_utils.py

@@ -719,7 +719,7 @@ def _validate_data(dataset, target, features=None, validation_set="auto"):
     if not hasattr(features, "__iter__"):
         raise TypeError("Input 'features' must be a list.")
     if not all([isinstance(x, str) for x in features]):
-        raise TypeError("Invalid feature %s: Feature names must be of type str" % x)
+        raise TypeError("Invalid 'features' value. Feature names must all be of type str.")
 
     # Check validation_set argument
     if isinstance(validation_set, str):

src/python/turicreate/toolkits/activity_classifier/_activity_classifier.py

@@ -10,25 +10,17 @@
 from __future__ import print_function as _
 from __future__ import division as _
 
-import numpy as _np
 import time as _time
 import six as _six
 
-from turicreate import SArray as _SArray, SFrame as _SFrame
-from turicreate import aggregate as _agg
+from turicreate import SFrame as _SFrame
 
 import turicreate.toolkits._internal_utils as _tkutl
 from turicreate.toolkits import _coreml_utils
 import turicreate.toolkits._feature_engineering._internal_utils as _fe_tkutl
 from turicreate.toolkits._main import ToolkitError as _ToolkitError
-from turicreate.toolkits import evaluation as _evaluation
 
-from turicreate.toolkits._model import CustomModel as _CustomModel
 from turicreate.toolkits._model import Model as _Model
-from turicreate.toolkits._model import PythonProxy as _PythonProxy
-
-from .util import random_split_by_session as _random_split_by_session
-from .util import _MIN_NUM_SESSIONS_FOR_SPLIT
 
 
 def create(
@@ -163,7 +155,7 @@ def create(
     if not hasattr(features, "__iter__"):
         raise TypeError("Input 'features' must be a list.")
     if not all([isinstance(x, str) for x in features]):
-        raise TypeError("Invalid feature %s: Feature names must be of type str." % x)
+        raise TypeError('Invalid member of "features": feature names must be of type str.')
     if len(features) == 0:
         raise TypeError("Input 'features' must contain at least one column name.")
 

src/python/turicreate/toolkits/classifier/boosted_trees_classifier.py

@@ -9,15 +9,13 @@
 from __future__ import print_function as _
 from __future__ import division as _
 from __future__ import absolute_import as _
-import turicreate as _turicreate
+
 from turicreate.toolkits._supervised_learning import Classifier as _Classifier
 import turicreate.toolkits._supervised_learning as _sl
-import turicreate.toolkits._main as _toolkits_main
 
 from turicreate.toolkits._internal_utils import _toolkit_repr_print
 from turicreate.toolkits._internal_utils import _raise_error_evaluation_metric_is_valid
 from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
-from turicreate.toolkits._internal_utils import _raise_error_if_column_exists
 from turicreate.toolkits._internal_utils import _check_categorical_option_type
 from turicreate.toolkits._tree_model_mixin import TreeModelMixin as _TreeModelMixin
 from turicreate.util import _make_internal_url

src/python/turicreate/toolkits/classifier/random_forest_classifier.py

@@ -9,7 +9,7 @@
 from __future__ import print_function as _
 from __future__ import division as _
 from __future__ import absolute_import as _
-import turicreate as _turicreate
+
 from turicreate.toolkits._supervised_learning import Classifier as _Classifier
 import turicreate.toolkits._supervised_learning as _sl
 

src/python/turicreate/toolkits/drawing_classifier/_tf_drawing_classifier.py

@@ -239,8 +239,6 @@ def predict(self, feed_dict):
         for key in feed_dict.keys():
             feed_dict[key] = _utils.convert_shared_float_array_to_numpy(feed_dict[key])
 
-        one_hot_labels = _np.zeros((int(self.batch_size), self.num_classes))
-
         feed_dict_for_session = {self.input: feed_dict["input"]}
 
         if is_train:

src/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py

@@ -7,20 +7,14 @@
 from __future__ import print_function as _
 from __future__ import division as _
 from __future__ import absolute_import as _
+
 import turicreate as _tc
-import numpy as _np
-import time as _time
 from turicreate.toolkits import _coreml_utils
-from turicreate.toolkits._model import CustomModel as _CustomModel
 from turicreate.toolkits._model import Model as _Model
-from turicreate.toolkits._model import PythonProxy as _PythonProxy
-from turicreate.toolkits import evaluation as _evaluation
 import turicreate.toolkits._internal_utils as _tkutl
 from turicreate.toolkits._main import ToolkitError as _ToolkitError
 from ..image_classifier._evaluation import Evaluation as _Evaluation
-from turicreate import extensions as _extensions
 from .. import _pre_trained_models
-from six.moves import reduce as _reduce
 
 BITMAP_WIDTH = 28
 BITMAP_HEIGHT = 28
@@ -170,6 +164,7 @@ def create(
         warm_start = warm_start.replace("auto", "quickdraw_245_v0")
 
     if "_advanced_parameters" in kwargs:
+        # XXX: this code will not work. Tracking in https://github.com/apple/turicreate/issues/3007
         # Make sure no additional parameters are provided
         new_keys = set(kwargs["_advanced_parameters"].keys())
         set_keys = set(params.keys())

src/python/turicreate/toolkits/image_classifier/_evaluation.py

@@ -208,8 +208,6 @@ def _filter_sframe(filters, row_type, mat_type, sf, evaluation):
 
 
 def __get_incorrects(label, sf, evaluation):
-    conf_metric = evaluation._data["confidence_metric_for_threshold"]
-
     sf = sf.filter_by([False], "correct")
 
     if sf["target_label"].dtype == int:
@@ -232,8 +230,6 @@ def __get_incorrects(label, sf, evaluation):
 
 
 def __get_corrects(sf, evaluation):
-    conf_metric = evaluation._data["confidence_metric_for_threshold"]
-
     sf = sf.filter_by([True], "correct")
 
     unique_predictions = list(sf["target_label"].unique())

src/python/turicreate/toolkits/image_classifier/image_classifier.py

@@ -22,7 +22,6 @@
 from .. import _pre_trained_models
 from .. import _image_feature_extractor
 from ._evaluation import Evaluation as _Evaluation
-from turicreate.toolkits import _coreml_utils
 
 _DEFAULT_SOLVER_OPTIONS = {
     "convergence_threshold": 1e-2,
@@ -359,9 +358,8 @@ def _load_version(cls, state, version):
         instance.
         """
         _tkutl._model_version_check(version, cls._PYTHON_IMAGE_CLASSIFIER_VERSION)
-        from turicreate.toolkits.classifier.logistic_classifier import (
-            LogisticClassifier,
-        )
+        from turicreate.toolkits._main import ToolkitError
+        from turicreate.toolkits.classifier.logistic_classifier import LogisticClassifier
 
         state["classifier"] = LogisticClassifier(state["classifier"])
         state["classes"] = state["classifier"].classes