Fixed the data path (removed scikit-learn).
"## The data\n",
"The [Auto MPG Data Set](https://archive.ics.uci.edu/ml/datasets/Auto+MPG) that this sample\n",
"uses for training is provided by the [UC Irvine Machine Learning\n",
- "Repository](https://archive.ics.uci.edu/ml/datasets/). We have hosted the data on a public GCS bucket `gs://cloud-samples-data/ml-engine/sklearn/auto_mpg_data/`. The data has been pre-processed to remove rows with incomplete data so as not to create additional steps for this notebook.\n",
+ "Repository](https://archive.ics.uci.edu/ml/datasets/). We have hosted the data on a public GCS bucket `gs://cloud-samples-data/ml-engine/auto_mpg/`. The data has been pre-processed to remove rows with incomplete data so as not to create additional steps for this notebook.\n",
"\n",
" * Training file is `auto-mpg.data`\n",
"\n",
"# Public bucket holding the auto mpg data\n",
"bucket = storage.Client().bucket('cloud-samples-data')\n",
"# Path to the data inside the public bucket\n",
- "blob = bucket.blob('ml-engine/sklearn/auto_mpg_data/auto-mpg.data')\n",
+ "blob = bucket.blob('ml-engine/auto_mpg/auto-mpg.data')\n",
"# Download the data\n",
"blob.download_to_filename('auto-mpg.data')\n",
"\n",
"You can view the logs for your training job:\n",
"1. Go to https://console.cloud.google.com/\n",
"1. Select \"Logging\" in left-hand pane\n",
- "1. Select \"Cloud ML Job\" resource from the drop-down\n",
+ "1. In left-hand pane, go to \"ML Engine\" and select Jobs\n",
"1. In filter by prefix, use the value of $JOB_NAME to view the logs\n",
"\n",
"On the logging page of your model, you can view the different results for each HP tuning job. \n",
"Example:\n",
"```\n",
"{\n",
- " \"trialId\": \"2\",\n",
+ " \"trialId\": \"15\",\n",
" \"hyperparameters\": {\n",
- " \"selection\": \"random\",\n",
- " \"max_iter\": \"1892\",\n",
- " \"tol\": \"0.0609819896050862\",\n",
- " \"alpha\": \"4.3704164028167725\"\n",
+ " \"booster\": \"dart\",\n",
+ " \"max_depth\": \"7\",\n",
+ " \"n_estimators\": \"102\"\n",
" },\n",
" \"finalMetric\": {\n",
" \"trainingStep\": \"1000\",\n",
- " \"objectiveValue\": 0.8658283435394591\n",
+ " \"objectiveValue\": 0.9259230441279733\n",
" }\n",
"}\n",
"```"
