fix get_execution_role error (aws#1835)

Author: jakemraz

introduction_to_applying_machine_learning/ensemble_modeling/EnsembleLearnerCensusIncome.ipynb

@@ -73,7 +73,7 @@
  "import time\n",
  "import re\n",
  "import sagemaker \n",
- "role = get_execution_role()\n",
+ "role = sagemaker.get_execution_role()\n",
  "\n",
  "# Now let's define the S3 bucket we'll used for the remainder of this example.\n",
  "\n",
@@ -340,8 +340,8 @@
  },
  "outputs": [],
  "source": [
- "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
- "container = get_image_uri(boto3.Session().region_name, 'xgboost')"
+ "from sagemaker.amazon.amazon_estimator import image_uris\n",
+ "container = image_uris.retrieve(region=boto3.Session().region_name, framework='xgboost', version='1')"
  ]
  },
  {
@@ -645,11 +645,11 @@
  " \n",
  " for offset in range(0, items, batch_size):\n",
  " if offset+batch_size < items:\n",
- " datav = data.iloc[offset:(offset+batch_size),:].as_matrix()\n",
+ " datav = data.iloc[offset:(offset+batch_size),:].values\n",
  " results = do_predict(datav, endpoint_name, content_type)\n",
  " arrs.extend(results)\n",
  " else:\n",
- " datav = data.iloc[offset:items,:].as_matrix()\n",
+ " datav = data.iloc[offset:items,:].values\n",
  " arrs.extend(do_predict(datav, endpoint_name, content_type))\n",
  " sys.stdout.write('.')\n",
  " return(arrs)"
@@ -739,14 +739,14 @@
  "data_test = pd.read_csv(\"formatted_test.csv\", sep=',', header=None) \n",
  "data_val = pd.read_csv(\"formatted_val.csv\", sep=',', header=None) \n",
  "\n",
- "train_y = data_train.iloc[:,0].as_matrix();\n",
- "train_X = data_train.iloc[:,1:].as_matrix();\n",
+ "train_y = data_train.iloc[:,0].values;\n",
+ "train_X = data_train.iloc[:,1:].values;\n",
  "\n",
- "val_y = data_val.iloc[:,0].as_matrix();\n",
- "val_X = data_val.iloc[:,1:].as_matrix();\n",
+ "val_y = data_val.iloc[:,0].values;\n",
+ "val_X = data_val.iloc[:,1:].values;\n",
  "\n",
- "test_y = data_test.iloc[:,0].as_matrix();\n",
- "test_X = data_test.iloc[:,1:].as_matrix();\n"
+ "test_y = data_test.iloc[:,0].values;\n",
+ "test_X = data_test.iloc[:,1:].values;\n"
  ]
  },
  {
@@ -826,8 +826,8 @@
  },
  "outputs": [],
  "source": [
- "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
- "container = get_image_uri(boto3.Session().region_name, 'linear-learner')"
+ "from sagemaker.amazon.amazon_estimator import image_uris\n",
+ "container = image_uris.retrieve(region=boto3.Session().region_name, framework='linear-learner', version='1')"
  ]
  },
  {
@@ -1097,11 +1097,11 @@
  " \n",
  " for offset in range(0, items, batch_size):\n",
  " if offset+batch_size < items:\n",
- " datav = data.iloc[offset:(offset+batch_size),:].as_matrix()\n",
+ " datav = data.iloc[offset:(offset+batch_size),:].values\n",
  " results = do_predict_linear(datav, endpoint_name, content_type)\n",
  " arrs.extend(results)\n",
  " else:\n",
- " datav = data.iloc[offset:items,:].as_matrix()\n",
+ " datav = data.iloc[offset:items,:].values\n",
  " arrs.extend(do_predict_linear(datav, endpoint_name, content_type))\n",
  " sys.stdout.write('.')\n",
  " return(arrs)"