update xgboost version to 1.5-1 in notebooks (aws#3439)

* update xgboost to 1.5-1 * add time stamp to endpoint configuration * fix typo * code formatting change
hchings · Jun 16, 2022 · 93163a8 · 93163a8
1 parent f0cda95
commit 93163a8
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Showing 11 changed files with 5,067 additions and 5,064 deletions.
diff --git a/...unctionality/multi_model_xgboost_home_value/xgboost_multi_model_endpoint_home_value.ipynb b/...unctionality/multi_model_xgboost_home_value/xgboost_multi_model_endpoint_home_value.ipynb
@@ -101,6 +101,7 @@
    ],
    "source": [
     "import sagemaker\n",
+    "\n",
     "print(sagemaker.__version__)"
    ]
   },
@@ -338,6 +339,7 @@
     "from sagemaker import get_execution_role\n",
     "from sagemaker.amazon.amazon_estimator import image_uris\n",
     "import boto3\n",
+    "from time import gmtime, strftime\n",
     "\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
@@ -350,7 +352,7 @@
     "\n",
     "# This is references the AWS managed XGBoost container\n",
     "XGBOOST_IMAGE = image_uris.retrieve(\n",
-    "    region=boto3.Session().region_name, framework=\"xgboost\", version=\"1.0-1\"\n",
+    "    region=boto3.Session().region_name, framework=\"xgboost\", version=\"1.5-1\"\n",
     ")\n",
     "\n",
     "DATA_PREFIX = \"XGBOOST_BOSTON_HOUSING\"\n",
@@ -359,7 +361,7 @@
     "TRAIN_INSTANCE_TYPE = \"ml.m4.xlarge\"\n",
     "ENDPOINT_INSTANCE_TYPE = \"ml.m4.xlarge\"\n",
     "\n",
-    "ENDPOINT_NAME = \"mme-xgboost-housing\"\n",
+    "ENDPOINT_NAME = f'mme-xgboost-housing-{strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())}'\n",
     "\n",
     "MODEL_NAME = ENDPOINT_NAME"
    ]
@@ -506,9 +508,9 @@
     "        gamma=4,\n",
     "        min_child_weight=6,\n",
     "        subsample=0.8,\n",
-    "        silent=0,\n",
+    "        verbosity=0,\n",
     "        early_stopping_rounds=5,\n",
-    "        objective=\"reg:linear\",\n",
+    "        objective=\"reg:squarederror\",\n",
     "        num_round=25,\n",
     "    )\n",
     "\n",

diff --git a/...maker_neo_compilation_jobs/xgboost_customer_churn/xgboost_customer_churn_neo_studio.ipynb b/...maker_neo_compilation_jobs/xgboost_customer_churn/xgboost_customer_churn_neo_studio.ipynb
@@ -316,7 +316,7 @@
     "from sagemaker import image_uris\n",
     "\n",
     "container = image_uris.retrieve(\n",
-    "    framework=\"xgboost\", region=boto3.Session().region_name, version=\"1.0-1\"\n",
+    "    framework=\"xgboost\", region=boto3.Session().region_name, version=\"1.5-1\"\n",
     ")"
    ]
   },
@@ -377,7 +377,7 @@
     "    gamma=4,\n",
     "    min_child_weight=6,\n",
     "    subsample=0.8,\n",
-    "    silent=0,\n",
+    "    verbosity=0,\n",
     "    objective=\"binary:logistic\",\n",
     "    num_round=100,\n",
     ")\n",
@@ -437,9 +437,9 @@
     "    split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))\n",
     "    predictions = \"\"\n",
     "    for array in split_array:\n",
-    "        predictions = \",\".join([predictions, xgb_predictor.predict(array).decode(\"utf-8\")])\n",
+    "        predictions = \"\".join([predictions, xgb_predictor.predict(array).decode(\"utf-8\")])\n",
     "\n",
-    "    return np.fromstring(predictions[1:], sep=\",\")\n",
+    "    return predictions.split(\"\\n\")[:-1]\n",
     "\n",
     "\n",
     "predictions = predict(test_data.values[:, 1:])"
@@ -458,6 +458,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "predictions = np.array([float(num) for num in predictions])\n",
     "pd.crosstab(\n",
     "    index=test_data.iloc[:, 0],\n",
     "    columns=np.round(predictions),\n",
@@ -621,7 +622,7 @@
     "    input_shape={\"data\": [1, 69]},\n",
     "    role=role,\n",
     "    framework=\"xgboost\",\n",
-    "    framework_version=\"latest\",\n",
+    "    framework_version=\"1.5-1\",\n",
     "    output_path=output_path,\n",
     ")"
    ]
@@ -670,9 +671,9 @@
     "    split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))\n",
     "    predictions = \"\"\n",
     "    for array in split_array:\n",
-    "        predictions = \",\".join([predictions, compiled_predictor.predict(array).decode(\"utf-8\")])\n",
+    "        predictions = \"\".join([predictions, xgb_predictor.predict(array).decode(\"utf-8\")])\n",
     "\n",
-    "    return np.fromstring(predictions[1:], sep=\",\")\n",
+    "    return predictions.split(\"\\n\")[:-1]\n",
     "\n",
     "\n",
     "predictions = optimized_predict(test_data.values[:, 1:])"
@@ -721,4 +722,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
diff --git a/...rameter_tuning/xgboost_direct_marketing/hpo_xgboost_direct_marketing_sagemaker_APIs.ipynb b/...rameter_tuning/xgboost_direct_marketing/hpo_xgboost_direct_marketing_sagemaker_APIs.ipynb
@@ -383,7 +383,7 @@
    "source": [
     "from sagemaker.image_uris import retrieve\n",
     "\n",
-    "training_image = retrieve(framework=\"xgboost\", region=region, version=\"latest\")\n",
+    "training_image = retrieve(framework=\"xgboost\", region=region, version=\"1.5-1\")\n",
     "\n",
     "s3_input_train = \"s3://{}/{}/train\".format(bucket, prefix)\n",
     "s3_input_validation = \"s3://{}/{}/validation/\".format(bucket, prefix)\n",

diff --git a/introduction_to_amazon_algorithms/xgboost_mnist/xgboost_mnist.ipynb b/introduction_to_amazon_algorithms/xgboost_mnist/xgboost_mnist.ipynb
@@ -255,7 +255,7 @@
    "source": [
     "from sagemaker.image_uris import retrieve\n",
     "\n",
-    "container = retrieve(framework=\"xgboost\", region=region, version=\"1.0-1\")"
+    "container = retrieve(framework=\"xgboost\", region=region, version=\"1.5-1\")"
    ]
   },
   {
@@ -275,7 +275,7 @@
     "        \"eta\": \"0.2\",\n",
     "        \"gamma\": \"4\",\n",
     "        \"min_child_weight\": \"6\",\n",
-    "        \"silent\": \"0\",\n",
+    "        \"verbosity\": \"0\",\n",
     "        \"objective\": \"multi:softmax\",\n",
     "        \"num_class\": \"10\",\n",
     "        \"num_round\": \"10\",\n",
@@ -636,7 +636,7 @@
     "        EndpointName=endpoint_name, ContentType=content_type, Body=payload\n",
     "    )\n",
     "    result = response[\"Body\"].read().decode(\"ascii\")\n",
-    "    preds = [float(num) for num in result.split(\",\")]\n",
+    "    preds = [float(num) for num in result.split(\"\\n\")[:-1]]\n",
     "    return preds\n",
     "\n",
     "\n",

diff --git a/...oduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.ipynb b/...oduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.ipynb
@@ -677,7 +677,7 @@
    },
    "outputs": [],
    "source": [
-    "container = sagemaker.image_uris.retrieve(\"xgboost\", sess.boto_region_name, \"latest\")\n",
+    "container = sagemaker.image_uris.retrieve(\"xgboost\", sess.boto_region_name, \"1.5-1\")\n",
     "display(container)"
    ]
   },
@@ -790,7 +790,7 @@
     "    gamma=4,\n",
     "    min_child_weight=6,\n",
     "    subsample=0.8,\n",
-    "    silent=0,\n",
+    "    verbosity=0,\n",
     "    objective=\"binary:logistic\",\n",
     "    num_round=100,\n",
     ")\n",
@@ -900,9 +900,9 @@
     "    split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))\n",
     "    predictions = \"\"\n",
     "    for array in split_array:\n",
-    "        predictions = \",\".join([predictions, xgb_predictor.predict(array).decode(\"utf-8\")])\n",
+    "        predictions = \"\".join([predictions, xgb_predictor.predict(array).decode(\"utf-8\")])\n",
     "\n",
-    "    return np.fromstring(predictions[1:], sep=\",\")\n",
+    "    return predictions.split(\"\\n\")[:-1]\n",
     "\n",
     "\n",
     "predictions = predict(test_data.to_numpy()[:, 1:])"
@@ -915,6 +915,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "predictions = np.array([float(num) for num in predictions])\n",
     "print(predictions)"
    ]
   },