fix: ensure 1p algos are compatible with forward-port (#1976)

Author: metrizable

buildspec-unittests.yml

@@ -7,8 +7,8 @@ phases:
       - TOX_PARALLEL_NO_SPINNER=1
       - PY_COLORS=0
       - start_time=`date +%s`
-      - tox -e flake8,pylint,twine,black-check --parallel all
-      - ./ci-scripts/displaytime.sh 'flake8,pylint,twine,black-check' $start_time
+      - tox -e flake8,pylint,docstyle,black-check,twine --parallel all
+      - ./ci-scripts/displaytime.sh 'flake8,pylint,docstyle,black-check,twine' $start_time
 
       - start_time=`date +%s`
       - tox -e sphinx,doc8 --parallel all

src/sagemaker/amazon/amazon_estimator.py

@@ -44,8 +44,8 @@ class AmazonAlgorithmEstimatorBase(EstimatorBase):
     def __init__(
         self,
         role,
-        instance_count,
-        instance_type,
+        instance_count=None,
+        instance_type=None,
         data_location=None,
         enable_network_isolation=False,
         **kwargs
@@ -59,9 +59,9 @@ def __init__(
                 artifacts. After the endpoint is created, the inference code
                 might use the IAM role, if it needs to access an AWS resource.
             instance_count (int): Number of Amazon EC2 instances to use
-                for training.
+                for training. Required.
             instance_type (str): Type of EC2 instance to use for training,
-                for example, 'ml.c4.xlarge'.
+                for example, 'ml.c4.xlarge'. Required.
             data_location (str or None): The s3 prefix to upload RecordSet
                 objects to, expressed as an S3 url. For example
                 "s3://example-bucket/some-key-prefix/". Objects will be saved in
@@ -86,7 +86,6 @@ def __init__(
             enable_network_isolation=enable_network_isolation,
             **kwargs
         )
-
         data_location = data_location or "s3://{}/sagemaker-record-sets/".format(
             self.sagemaker_session.default_bucket()
         )

src/sagemaker/amazon/factorization_machines.py

@@ -84,10 +84,10 @@ class FactorizationMachines(AmazonAlgorithmEstimatorBase):
     def __init__(
         self,
         role,
-        instance_count,
-        instance_type,
-        num_factors,
-        predictor_type,
+        instance_count=None,
+        instance_type=None,
+        num_factors=None,
+        predictor_type=None,
         epochs=None,
         clip_gradient=None,
         eps=None,

src/sagemaker/amazon/ipinsights.py

@@ -60,10 +60,10 @@ class IPInsights(AmazonAlgorithmEstimatorBase):
     def __init__(
         self,
         role,
-        instance_count,
-        instance_type,
-        num_entity_vectors,
-        vector_dim,
+        instance_count=None,
+        instance_type=None,
+        num_entity_vectors=None,
+        vector_dim=None,
         batch_metrics_publish_interval=None,
         epochs=None,
         learning_rate=None,

src/sagemaker/amazon/kmeans.py

@@ -57,9 +57,9 @@ class KMeans(AmazonAlgorithmEstimatorBase):
     def __init__(
         self,
         role,
-        instance_count,
-        instance_type,
-        k,
+        instance_count=None,
+        instance_type=None,
+        k=None,
         init_method=None,
         max_iterations=None,
         tol=None,

src/sagemaker/amazon/knn.py

@@ -70,11 +70,11 @@ class KNN(AmazonAlgorithmEstimatorBase):
     def __init__(
         self,
         role,
-        instance_count,
-        instance_type,
-        k,
-        sample_size,
-        predictor_type,
+        instance_count=None,
+        instance_type=None,
+        k=None,
+        sample_size=None,
+        predictor_type=None,
         dimension_reduction_type=None,
         dimension_reduction_target=None,
         index_type=None,

src/sagemaker/amazon/lda.py

@@ -45,8 +45,8 @@ class LDA(AmazonAlgorithmEstimatorBase):
     def __init__(
         self,
         role,
-        instance_type,
-        num_topics,
+        instance_type=None,
+        num_topics=None,
         alpha0=None,
         max_restarts=None,
         max_iterations=None,

src/sagemaker/amazon/linear_learner.py

@@ -129,9 +129,9 @@ class LinearLearner(AmazonAlgorithmEstimatorBase):
     def __init__(
         self,
         role,
-        instance_count,
-        instance_type,
-        predictor_type,
+        instance_count=None,
+        instance_type=None,
+        predictor_type=None,
         binary_classifier_model_selection_criteria=None,
         target_recall=None,
         target_precision=None,

src/sagemaker/amazon/ntm.py

@@ -65,9 +65,9 @@ class NTM(AmazonAlgorithmEstimatorBase):
     def __init__(
         self,
         role,
-        instance_count,
-        instance_type,
-        num_topics,
+        instance_count=None,
+        instance_type=None,
+        num_topics=None,
         encoder_layers=None,
         epochs=None,
         encoder_layers_activation=None,

src/sagemaker/amazon/object2vec.py

@@ -140,11 +140,11 @@ class Object2Vec(AmazonAlgorithmEstimatorBase):
     def __init__(
         self,
         role,
-        instance_count,
-        instance_type,
-        epochs,
-        enc0_max_seq_len,
-        enc0_vocab_size,
+        instance_count=None,
+        instance_type=None,
+        epochs=None,
+        enc0_max_seq_len=None,
+        enc0_vocab_size=None,
         enc_dim=None,
         mini_batch_size=None,
         early_stopping_patience=None,
@@ -271,7 +271,6 @@ def __init__(
         """
 
         super(Object2Vec, self).__init__(role, instance_count, instance_type, **kwargs)
-
         self.enc_dim = enc_dim
         self.mini_batch_size = mini_batch_size
         self.epochs = epochs