From 9130a0b4ed6d17979fcae4e5a49bca7796847b63 Mon Sep 17 00:00:00 2001 From: Snowflake Provisioner <58576687+snowflake-provisioner@users.noreply.github.com> Date: Fri, 13 Oct 2023 16:34:33 -0700 Subject: [PATCH] Update OSS repo (#56) Co-authored-by: Snowflake Authors --- requirements.txt | 52 ++++ .../customer_demo/Basic_Feature_Demo.ipynb | 211 +++++++++++---- .../customer_demo/Basic_Feature_Demo.pdf | Bin 175105 -> 205679 bytes .../Time_Series_Feature_Demo.ipynb | 256 +++++++++++------- .../Time_Series_Feature_Demo.pdf | Bin 226950 -> 245915 bytes .../tests/feature_store_large_scale_test.py | 2 +- 6 files changed, 375 insertions(+), 146 deletions(-) create mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..b9c04935 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,52 @@ +# DO NOT EDIT! +# Generate by running 'bazel run --config=pre_build //bazel/requirements:sync_requirements' + +absl-py==1.3.0 +accelerate==0.22.0 +anyio==3.5.0 +boto3==1.24.28 +cachetools==4.2.2 +cloudpickle==2.0.0 +coverage==6.3.2 +cryptography==39.0.1 +flask-cors==3.0.10 +flask==2.1.3 +fsspec[http]==2023.3.0 +httpx==0.23.0 +inflection==0.5.1 +joblib==1.1.1 +jsonschema==3.2.0 +lightgbm==3.3.5 +mlflow==2.3.1 +moto==4.0.11 +mypy==1.5.1 +networkx==2.8.4 +numpy==1.24.3 +packaging==23.0 +pandas==1.5.3 +protobuf==3.20.3 +pytest==7.4.0 +pytimeparse==1.1.8 +pyyaml==6.0 +ruamel.yaml==0.17.21 +s3fs==2023.3.0 +scikit-learn==1.3.0 +scipy==1.9.3 +sentencepiece==0.1.99 +shap==0.42.1 +snowflake-connector-python[pandas]==3.2.0 +snowflake-snowpark-python==1.6.1 +sphinx==5.0.2 +sqlparse==0.4.4 +starlette==0.27.0 +tensorflow==2.13.1 +tokenizers==0.13.2 +torch==2.0.1 +torchdata==0.6.1 +transformers==4.32.1 +types-PyYAML==6.0.12 +types-cachetools==4.2.2 +types-protobuf==4.23.0.1 +types-requests==2.30.0.0 +typing-extensions==4.5.0 +xgboost==1.7.3 diff --git a/snowflake/ml/feature_store/notebooks/customer_demo/Basic_Feature_Demo.ipynb b/snowflake/ml/feature_store/notebooks/customer_demo/Basic_Feature_Demo.ipynb index d8bb805a..7f6af1b0 100644 --- a/snowflake/ml/feature_store/notebooks/customer_demo/Basic_Feature_Demo.ipynb +++ b/snowflake/ml/feature_store/notebooks/customer_demo/Basic_Feature_Demo.ipynb @@ -5,9 +5,20 @@ "id": "0bb54abc", "metadata": {}, "source": [ - "Version: 0.1.0\n", + "Version: 0.1.1\n", "\n", - "Updated date: 10/10/2023" + "Updated date: 10/11/2023" + ] + }, + { + "cell_type": "markdown", + "id": "144c09b2", + "metadata": {}, + "source": [ + "## Before getting started\n", + "\n", + "### Watch out object name case sensitivity\n", + "The Model Registry and Feature Store are not consistent with each other in the way they case names for databases, schemas, and other SQL objects. (Keep in mind that the objects in both APIs are Snowflake objects on the back end.) The model registry preserves the case of names for these objects, while the feature store converts names to uppercase unless you enclose them in double quotes. The way the feature store handles names is consistent with Snowflake’s identifier requirements. We are working to make this more consistent. In the meantime, we suggest using uppercase names in both APIs to ensure correct interoperation between the feature store and the model registry." ] }, { @@ -43,7 +54,7 @@ "id": "9f16e6a8", "metadata": {}, "source": [ - "## Setup Snowflake connection\n", + "## Setup Snowflake connection and database\n", "For detailed session connection config, please follow this [tutorial](https://medium.com/snowflake/snowflakeloginoptions-an-easier-way-to-connect-using-python-2f0e726da936).\n" ] }, @@ -57,6 +68,38 @@ "session = Session.builder.configs(SnowflakeLoginOptions()).create()" ] }, + { + "cell_type": "markdown", + "id": "494e1503", + "metadata": {}, + "source": [ + "Following sections will use below database and schema name to store test data and feature store objects. You can rename with your own name if needed. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9622928", + "metadata": {}, + "outputs": [], + "source": [ + "# database name where test data and feature store lives.\n", + "FS_DEMO_DB = f\"FEATURE_STORE_BASIC_FEATURE_NOTEBOOK_DEMO\"\n", + "# schema where test data lives.\n", + "TEST_DATASET_SCHEMA = 'TEST_DATASET'\n", + "# feature store name.\n", + "FS_DEMO_SCHEMA = \"AWESOME_FS_BASIC_FEATURES\"\n", + "# Model registry database name.\n", + "MR_DEMO_DB = f\"FEATURE_STORE_BASIC_FEATURE_NOTEBOOK_MR_DEMO\"\n", + "\n", + "session.sql(f\"DROP DATABASE IF EXISTS {FS_DEMO_DB}\").collect()\n", + "session.sql(f\"DROP DATABASE IF EXISTS {MR_DEMO_DB}\").collect()\n", + "session.sql(f\"CREATE DATABASE IF NOT EXISTS {FS_DEMO_DB}\").collect()\n", + "session.sql(f\"\"\"\n", + " CREATE SCHEMA IF NOT EXISTS \n", + " {FS_DEMO_DB}.{TEST_DATASET_SCHEMA}\"\"\").collect()" + ] + }, { "cell_type": "markdown", "id": "b79ba9be", @@ -78,9 +121,6 @@ "session.file.put(\n", " f\"file://{TEST_CSV_FILE_PATH}\",session.get_session_stage())\n", "\n", - "SOURCE_DB = session.get_current_database()\n", - "SOURCE_SCHEMA = session.get_current_schema()\n", - "\n", "from snowflake.snowpark.types import (\n", " StructType, \n", " StructField, \n", @@ -106,7 +146,7 @@ "df = session.read.options({\"field_delimiter\": \";\", \"skip_header\": 1}) \\\n", " .schema(input_schema) \\\n", " .csv(f\"{session.get_session_stage()}/winequality-red.csv\")\n", - "df.write.mode(\"overwrite\").save_as_table(\"wine_data\")" + "df.write.mode(\"overwrite\").save_as_table(\"WINE_DATA\")" ] }, { @@ -128,19 +168,14 @@ "metadata": {}, "outputs": [], "source": [ - "DEMO_DB = \"FS_DEMO_DB\"\n", - "DEMO_SCHEMA = \"AWESOME_FS\"\n", - "\n", - "session.sql(f\"DROP DATABASE IF EXISTS {DEMO_DB}\").collect()\n", - "session.sql(f\"CREATE DATABASE IF NOT EXISTS {DEMO_DB}\").collect()\n", "session.sql(f\"\"\"CREATE OR REPLACE WAREHOUSE PUBLIC WITH \n", " WAREHOUSE_SIZE='XSMALL'\n", " \"\"\").collect()\n", "\n", "fs = FeatureStore(\n", " session=session, \n", - " database=DEMO_DB, \n", - " name=DEMO_SCHEMA, \n", + " database=FS_DEMO_DB, \n", + " name=FS_DEMO_SCHEMA, \n", " default_warehouse=\"PUBLIC\",\n", " creation_mode=CreationMode.CREATE_IF_NOT_EXIST,\n", ")" @@ -165,7 +200,7 @@ "metadata": {}, "outputs": [], "source": [ - "entity = Entity(name=\"wine\", join_keys=[\"wine_id\"])\n", + "entity = Entity(name=\"WINE\", join_keys=[\"WINE_ID\"])\n", "fs.register_entity(entity)\n", "fs.list_entities().to_pandas()" ] @@ -180,7 +215,7 @@ "Then we will load from the source table and conduct some simple feature engineerings.\n", "\n", "Here we are just doing two simple data manipulation (but more complex ones are carried out the same way):\n", - "1. Assign a wine_id column to the source\n", + "1. Assign a WINE_ID column to the source\n", "2. Derive a new column by multipying two existing feature columns" ] }, @@ -191,7 +226,7 @@ "metadata": {}, "outputs": [], "source": [ - "source_df = session.table(f\"{SOURCE_DB}.{SOURCE_SCHEMA}.wine_data\")\n", + "source_df = session.table(f\"{FS_DEMO_DB}.{TEST_DATASET_SCHEMA}.WINE_DATA\")\n", "source_df.to_pandas()" ] }, @@ -211,9 +246,9 @@ "def generate_new_feature(df):\n", " # Derive a new feature column\n", " return df.withColumn(\n", - " \"my_new_feature\", df[\"FIXED_ACIDITY\"] * df[\"CITRIC_ACID\"])\n", + " \"MY_NEW_FEATURE\", df[\"FIXED_ACIDITY\"] * df[\"CITRIC_ACID\"])\n", "\n", - "df = addIdColumn(source_df, \"wine_id\")\n", + "df = addIdColumn(source_df, \"WINE_ID\")\n", "feature_df = generate_new_feature(df)\n", "feature_df = feature_df.select(\n", " [\n", @@ -227,7 +262,7 @@ " 'TOTAL_SULFUR_DIOXIDE',\n", " 'DENSITY',\n", " 'PH',\n", - " 'my_new_feature',\n", + " 'MY_NEW_FEATURE',\n", " ]\n", ")\n", "feature_df.to_pandas()" @@ -269,14 +304,14 @@ "outputs": [], "source": [ "fv = FeatureView(\n", - " name=\"wine_features\", \n", + " name=\"WINE_FEATURES\", \n", " entities=[entity], \n", " feature_df=feature_df, \n", " desc=\"wine features\"\n", ")\n", "fs.register_feature_view(\n", " feature_view=fv, \n", - " version=\"v1\", \n", + " version=\"V1\", \n", " refresh_freq=\"1 minute\", \n", " block=True\n", ")" @@ -321,14 +356,14 @@ ")\n", "\n", "new_fv = FeatureView(\n", - " name=\"extra_wine_features\", \n", + " name=\"EXTRA_WINE_FEATURES\", \n", " entities=[entity], \n", " feature_df=extra_feature_df, \n", " desc=\"extra wine features\"\n", ")\n", "fs.register_feature_view(\n", " feature_view=new_fv, \n", - " version=\"v1\", \n", + " version=\"V1\", \n", " refresh_freq=\"1 minute\", \n", " block=True\n", ")" @@ -342,7 +377,8 @@ "outputs": [], "source": [ "# We can easily retrieve all FeatureViews for a given Entity.\n", - "fs.list_feature_views(entity_name=\"wine\").select([\"NAME\", \"ENTITIES\", \"FEATURE_DESC\"]).show()" + "fs.list_feature_views(entity_name=\"WINE\"). \\\n", + " select([\"NAME\", \"ENTITIES\", \"FEATURE_DESC\"]).show()" ] }, { @@ -365,8 +401,8 @@ "outputs": [], "source": [ "full_fv = fs.merge_features(\n", - " features=[fv, new_fv], name=\"full_wine_features\")\n", - "fs.register_feature_view(feature_view=full_fv, version=\"v1\")" + " features=[fv, new_fv], name=\"FULL_WINE_FEATURES\")\n", + "fs.register_feature_view(feature_view=full_fv, version=\"V2\")" ] }, { @@ -376,9 +412,7 @@ "source": [ "## Generate Training Data\n", "\n", - "After our feature pipelines are fully setup, we can start using them to generate training data and later do model prediction.\n", - "\n", - "Generate training data is easy since materialized FeatureViews already carry most of the metadata like join keys, timestamp for point-in-time lookup, etc. We just need to provide the spine data (it's called spine because we are essentially enriching the data by joining features with it)." + "After our feature pipelines are fully setup, we can start using them to generate training data and later do model prediction." ] }, { @@ -388,12 +422,20 @@ "metadata": {}, "outputs": [], "source": [ - "spine_df = session.table(f\"{SOURCE_DB}.{SOURCE_SCHEMA}.wine_data\")\n", - "spine_df = addIdColumn(source_df, \"wine_id\")\n", - "spine_df = spine_df.select(\"wine_id\", \"quality\")\n", + "spine_df = session.table(f\"{FS_DEMO_DB}.{TEST_DATASET_SCHEMA}.WINE_DATA\")\n", + "spine_df = addIdColumn(source_df, \"WINE_ID\")\n", + "spine_df = spine_df.select(\"WINE_ID\", \"QUALITY\")\n", "spine_df.to_pandas()" ] }, + { + "cell_type": "markdown", + "id": "9ad126af", + "metadata": {}, + "source": [ + "Generate training data is easy since materialized FeatureViews already carry most of the metadata like join keys, timestamp for point-in-time lookup, etc. We just need to provide the spine data (it's called spine because we are essentially enriching the data by joining features with it). We can also generate dataset with a subset of features in the feature view by `slice`." + ] + }, { "cell_type": "code", "execution_count": null, @@ -401,16 +443,24 @@ "metadata": {}, "outputs": [], "source": [ - "training_dataset_full_path = f\"{DEMO_DB}.{DEMO_SCHEMA}.wine_training_data_table\"\n", - "session.sql(f\"DROP TABLE IF EXISTS {training_dataset_full_path}\").collect()\n", + "training_dataset_full_path = \\\n", + " f\"{FS_DEMO_DB}.{FS_DEMO_SCHEMA}.WINE_TRAINING_DATA_TABLE\"\n", + "session.sql(f\"DROP TABLE IF EXISTS {training_dataset_full_path}\") \\\n", + " .collect()\n", "training_data = fs.generate_dataset(\n", " spine_df=spine_df, \n", - " features=[full_fv], \n", - " materialized_table=\"wine_training_data_table\", \n", + " features=[\n", + " full_fv.slice([\n", + " \"FIXED_ACIDITY\", \n", + " \"VOLATILE_ACIDITY\", \n", + " \"CITRIC_ACID\"\n", + " ])\n", + " ], \n", + " materialized_table=\"WINE_TRAINING_DATA_TABLE\", \n", " spine_timestamp_col=None, \n", - " spine_label_cols=[\"quality\"],\n", + " spine_label_cols=[\"QUALITY\"],\n", " save_mode=\"merge\",\n", - " exclude_columns=['wine_id']\n", + " exclude_columns=['WINE_ID']\n", ")\n", "\n", "training_data.df.show()" @@ -468,14 +518,49 @@ "print(rf)" ] }, + { + "cell_type": "markdown", + "id": "1ad8031f", + "metadata": {}, + "source": [ + "## [Optional 1] Predict with local model\n", + "Now let's predict with the model and the feature values retrieved from feature store. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9452d138", + "metadata": {}, + "outputs": [], + "source": [ + "test_df = spine_df.limit(3).select(\"WINE_ID\")\n", + "\n", + "enriched_df = fs.retrieve_feature_values(\n", + " test_df, training_data.load_features())\n", + "enriched_df = enriched_df.drop('WINE_ID')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46c2546e", + "metadata": {}, + "outputs": [], + "source": [ + "pred = rf.predict(enriched_df.to_pandas())\n", + "\n", + "print(pred)" + ] + }, { "cell_type": "markdown", "id": "21b81639", "metadata": {}, "source": [ - "## Log model with Model Registry\n", + "## [Option 2.1] Log model with Model Registry\n", "\n", - "We can log the model along with its training dataset metadata with model registry." + "We can also log the model along with its training dataset metadata into Model Registry." ] }, { @@ -490,7 +575,7 @@ "\n", "registry = model_registry.ModelRegistry(\n", " session=session, \n", - " database_name=\"my_cool_registry\", \n", + " database_name=MR_DEMO_DB, \n", " create_if_not_exists=True\n", ")" ] @@ -504,9 +589,9 @@ "source": [ "artifact_ref = registry.log_artifact(\n", " artifact_type=artifact.ArtifactType.DATASET,\n", - " artifact_name=\"my_cool_dataset\",\n", + " artifact_name=\"MY_COOL_DATASET\",\n", " artifact_spec=training_data.to_json(),\n", - " artifact_version=\"v1\",\n", + " artifact_version=\"v3\",\n", ")" ] }, @@ -517,11 +602,11 @@ "metadata": {}, "outputs": [], "source": [ - "model_name = f\"my_random_forest_regressor{time.time()}\"\n", + "model_name = f\"MY_RANDOM_FOREST_REGRESSOR_{time.time()}\"\n", "\n", "model_ref = registry.log_model(\n", " model_name=model_name,\n", - " model_version=\"v2\",\n", + " model_version=\"V2\",\n", " model=rf,\n", " tags={\"author\": \"my_rf_with_training_data\"},\n", " artifacts=[artifact_ref],\n", @@ -534,7 +619,7 @@ "id": "3ccf2743", "metadata": {}, "source": [ - "## Restore model and predict with latest features\n", + "## [Optional 2.2] Restore model and predict with features\n", "\n", "We retrieve the training dataset from registry then construct dataframe of latest feature values. Then we restore the model from registry. At last, we can predict with latest feature values." ] @@ -548,17 +633,15 @@ "source": [ "from snowflake.ml.dataset.dataset import Dataset\n", "\n", - "registered_artifact = registry.get_artifact(artifact_ref.id)\n", - "\n", + "registered_artifact = registry.get_artifact(\n", + " artifact_ref.name, \n", + " artifact_ref.version)\n", "registered_dataset = Dataset.from_json(registered_artifact.spec, session)\n", - "\n", - "# test_pdf = training_pd.sample(3, random_state=996)[['WINE_ID']]\n", "test_df = spine_df.limit(3).select(\"WINE_ID\")\n", - "# test_df = session.create_dataframe(test_pdf)\n", "\n", "enriched_df = fs.retrieve_feature_values(\n", " test_df, registered_dataset.load_features())\n", - "enriched_df = enriched_df.drop('wine_id')" + "enriched_df = enriched_df.drop('WINE_ID')" ] }, { @@ -571,13 +654,33 @@ "model_ref = model_registry.ModelReference(\n", " registry=registry, \n", " model_name=model_name, \n", - " model_version=\"v2\"\n", + " model_version=\"V2\"\n", ")\n", "restored_model = model_ref.load_model()\n", "restored_prediction = restored_model.predict(enriched_df.to_pandas())\n", "\n", "print(restored_prediction)" ] + }, + { + "cell_type": "markdown", + "id": "8173da73", + "metadata": {}, + "source": [ + "## Cleanup notebook\n", + "Cleanup resources created in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea4e1ad9", + "metadata": {}, + "outputs": [], + "source": [ + "session.sql(f\"DROP DATABASE IF EXISTS {FS_DEMO_DB}\").collect()\n", + "session.sql(f\"DROP DATABASE IF EXISTS {MR_DEMO_DB}\").collect()" + ] } ], "metadata": { diff --git a/snowflake/ml/feature_store/notebooks/customer_demo/Basic_Feature_Demo.pdf b/snowflake/ml/feature_store/notebooks/customer_demo/Basic_Feature_Demo.pdf index 25e97e8a587253fce9414cff2969fc16a83d3bf7..00a95424576a723849615eaf7e469f02a8e1209e 100644 GIT binary patch delta 110126 zcmZ6RLy#>DjHuhTZQHhO+qQk$zHM8#ZQHhO+q|v!zpB~HRArfLl2q^|LF+Jgqi_iZ 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b/snowflake/ml/feature_store/notebooks/customer_demo/Time_Series_Feature_Demo.ipynb index 6e304b1d..138c202c 100644 --- a/snowflake/ml/feature_store/notebooks/customer_demo/Time_Series_Feature_Demo.ipynb +++ b/snowflake/ml/feature_store/notebooks/customer_demo/Time_Series_Feature_Demo.ipynb @@ -5,9 +5,20 @@ "id": "4f029c96", "metadata": {}, "source": [ - "Notebook version: 0.1.0\n", + "Notebook version: 0.1.1\n", "\n", - "Updated date: 10/10/2023" + "Updated date: 10/11/2023" + ] + }, + { + "cell_type": "markdown", + "id": "5ba51119", + "metadata": {}, + "source": [ + "## Before getting started\n", + "\n", + "### Watch out object name case sensitivity\n", + "The Model Registry and Feature Store are not consistent with each other in the way they case names for databases, schemas, and other SQL objects. (Keep in mind that the objects in both APIs are Snowflake objects on the back end.) The model registry preserves the case of names for these objects, while the feature store converts names to uppercase unless you enclose them in double quotes. The way the feature store handles names is consistent with Snowflake’s identifier requirements. We are working to make this more consistent. In the meantime, we suggest using uppercase names in both APIs to ensure correct interoperation between the feature store and the model registry." ] }, { @@ -47,7 +58,7 @@ "id": "2b09ef0d", "metadata": {}, "source": [ - "## Setup Snowflake connection\n", + "## Setup Snowflake connection and database\n", "For detailed session connection config, please follow this [tutorial](https://medium.com/snowflake/snowflakeloginoptions-an-easier-way-to-connect-using-python-2f0e726da936)." ] }, @@ -61,6 +72,42 @@ "session = Session.builder.configs(SnowflakeLoginOptions()).create()" ] }, + { + "cell_type": "markdown", + "id": "9affb013", + "metadata": {}, + "source": [ + "Following sections will use below database and schema name to store test data and feature store objects. You can rename with your own name if needed. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96ea8af3", + "metadata": {}, + "outputs": [], + "source": [ + "# database name where test data and feature store lives.\n", + "FS_DEMO_DB = f\"FEATURE_STORE_TIME_SERIES_FEATURE_NOTEBOOK_DEMO\"\n", + "# schema where test data lives.\n", + "TEST_DATASET_SCHEMA = 'TEST_DATASET'\n", + "# feature store name.\n", + "FS_DEMO_SCHEMA = \"AWESOME_FS_TIME_SERIES_FEATURES\"\n", + "# model registry database name.\n", + "MR_DEMO_DB = f\"FEATURE_STORE_TIME_SERIES_FEATURE_NOTEBOOK_MR_DEMO\"\n", + "# stages for UDF.\n", + "FS_DEMO_STAGE = \"FEATURE_STORE_TIME_SERIES_FEATURE_NOTEBOOK_STAGE_DEMO\"\n", + "FS_DEMO_STAGE_FULL_PATH = \\\n", + " f\"{FS_DEMO_DB}.{TEST_DATASET_SCHEMA}.{FS_DEMO_STAGE}\"\n", + "\n", + "session.sql(f\"DROP DATABASE IF EXISTS {FS_DEMO_DB}\").collect()\n", + "session.sql(f\"DROP DATABASE IF EXISTS {MR_DEMO_DB}\").collect()\n", + "session.sql(f\"CREATE DATABASE IF NOT EXISTS {FS_DEMO_DB}\").collect()\n", + "session.sql(f\"\"\"CREATE SCHEMA IF NOT EXISTS \n", + " {FS_DEMO_DB}.{TEST_DATASET_SCHEMA}\"\"\").collect()\n", + "session.sql(f\"CREATE OR REPLACE STAGE {FS_DEMO_STAGE_FULL_PATH}\").collect()" + ] + }, { "cell_type": "markdown", "id": "1a67136a", @@ -74,9 +121,7 @@ "id": "d0e75f08", "metadata": {}, "source": [ - "Download Yellow Taxi Trip Records data (Jan. 2016) from https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page if you don't have it already. Rename `PARQUET_FILE_LOCAL_PATH` with your local file path. Rename `TEST_DATASET_DB` and `TEST_DATASET_SCHEMA` with your location.\n", - "\n", - "Below code create a table with the test dataset." + "Download Yellow Taxi Trip Records data (Jan. 2016) from https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page if you don't have it already. Rename `PARQUET_FILE_LOCAL_PATH` with your local file path. Below code create a table with the test dataset." ] }, { @@ -88,8 +133,6 @@ "source": [ "PARQUET_FILE_NAME = f\"yellow_tripdata_2016-01.parquet\"\n", "PARQUET_FILE_LOCAL_PATH = f\"file://~/Downloads/{PARQUET_FILE_NAME}\"\n", - "TEST_DATASET_DB = \"SNOWML_FEATURE_STORE_TEST_DB\"\n", - "TEST_DATASET_SCHEMA = 'TEST_DATASET'\n", "\n", "def get_destination_table_name(original_file_name: str) -> str:\n", " return original_file_name.split(\".\")[0].replace(\"-\", \"_\").upper()\n", @@ -105,13 +148,12 @@ " identifier.get_unescaped_names(old_col_name)\n", " )\n", "\n", - "full_table_name = f\"{TEST_DATASET_DB}.{TEST_DATASET_SCHEMA}.{table_name}\"\n", - "df.write.mode(\"ignore\").save_as_table(full_table_name)\n", + "full_table_name = f\"{FS_DEMO_DB}.{TEST_DATASET_SCHEMA}.{table_name}\"\n", + "df.write.mode(\"overwrite\").save_as_table(full_table_name)\n", "rows_count = session.sql(\n", " f\"SELECT COUNT(*) FROM {full_table_name}\").collect()[0][0]\n", "\n", - "print(f\"{full_table_name} has total {rows_count} rows.\")\n", - "print(\"Script completes successfully.\")" + "print(f\"{full_table_name} has total {rows_count} rows.\")" ] }, { @@ -122,8 +164,10 @@ "outputs": [], "source": [ "source_df = session.table(\n", - " f\"{TEST_DATASET_DB}.{TEST_DATASET_SCHEMA}.{table_name}\")\n", + " f\"{FS_DEMO_DB}.{TEST_DATASET_SCHEMA}.{table_name}\")\n", "\n", + "# source_df.TPEP_PICKUP_DATETIME.alias(\"PICKUP_TS\"),\n", + "# source_df.TPEP_DROPOFF_DATETIME.alias(\"DROPOFF_TS\"),\n", "source_df = source_df.select(\n", " [\n", " \"TRIP_DISTANCE\", \n", @@ -131,9 +175,9 @@ " \"PASSENGER_COUNT\",\n", " \"PULOCATIONID\",\n", " \"DOLOCATIONID\",\n", - " F.cast(source_df.TPEP_PICKUP_DATETIME / 1000000, TimestampType())\n", + " F.cast(F.col(\"TPEP_PICKUP_DATETIME\") / 1000000, TimestampType())\n", " .alias(\"PICKUP_TS\"),\n", - " F.cast(source_df.TPEP_DROPOFF_DATETIME / 1000000, TimestampType())\n", + " F.cast(F.col(\"TPEP_DROPOFF_DATETIME\") / 1000000, TimestampType())\n", " .alias(\"DROPOFF_TS\"),\n", " ]).filter(\n", " \"\"\"DROPOFF_TS >= '2016-01-01 00:00:00' \n", @@ -151,7 +195,7 @@ "\n", "Let's first create a feature store client.\n", "\n", - "We can pass in an existing database name, or a new database will be created upon the feature store initialization. Replace `DEMO_DB` and `DEMO_SCHEMA` with your database and schema names." + "We can pass in an existing database name, or a new database will be created upon the feature store initialization." ] }, { @@ -161,19 +205,14 @@ "metadata": {}, "outputs": [], "source": [ - "DEMO_DB = \"FS_TIME_SERIES_EXAMPLE\"\n", - "DEMO_SCHEMA = \"AWESOME_FS\"\n", - "\n", - "session.sql(f\"DROP DATABASE IF EXISTS {DEMO_DB}\").collect()\n", - "session.sql(f\"CREATE DATABASE IF NOT EXISTS {DEMO_DB}\").collect()\n", "session.sql(f\"\"\"CREATE OR REPLACE WAREHOUSE PUBLIC WITH\n", " WAREHOUSE_SIZE='XSMALL'\n", " \"\"\").collect()\n", "\n", "fs = FeatureStore(\n", " session=session, \n", - " database=DEMO_DB, \n", - " name=DEMO_SCHEMA, \n", + " database=FS_DEMO_DB, \n", + " name=FS_DEMO_SCHEMA, \n", " default_warehouse=\"PUBLIC\",\n", " creation_mode=CreationMode.CREATE_IF_NOT_EXIST,\n", ")" @@ -195,8 +234,8 @@ "metadata": {}, "outputs": [], "source": [ - "trip_pickup = Entity(name=\"trip_pickup\", join_keys=[\"PULOCATIONID\"])\n", - "trip_dropoff = Entity(name=\"trip_dropoff\", join_keys=[\"DOLOCATIONID\"])\n", + "trip_pickup = Entity(name=\"TRIP_PICKUP\", join_keys=[\"PULOCATIONID\"])\n", + "trip_dropoff = Entity(name=\"TRIP_DROPOFF\", join_keys=[\"DOLOCATIONID\"])\n", "fs.register_entity(trip_pickup)\n", "fs.register_entity(trip_dropoff)\n", "fs.list_entities().to_pandas()" @@ -227,18 +266,6 @@ "We will later turn these into built in functions for feature store" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "13e403f7", - "metadata": {}, - "outputs": [], - "source": [ - "DEMO_STAGE = \"FS_STAGE\"\n", - "\n", - "session.sql(f\"CREATE OR REPLACE STAGE {DEMO_STAGE}\").collect()" - ] - }, { "cell_type": "code", "execution_count": null, @@ -249,7 +276,7 @@ "@F.pandas_udf(\n", " name=\"vec_window_end\",\n", " is_permanent=True,\n", - " stage_location=f'@{DEMO_STAGE}',\n", + " stage_location=f'@{FS_DEMO_STAGE_FULL_PATH}',\n", " packages=[\"numpy\", \"pandas\", \"pytimeparse\"],\n", " replace=True,\n", " session=session,\n", @@ -295,8 +322,8 @@ " df = df.with_column(\"WINDOW_END\", \n", " F.call_udf(\"vec_window_end\", F.col(ts_col), \"15m\"))\n", " df = df.group_by(group_by_cols + [\"WINDOW_END\"]).agg(\n", - " F.sum(\"FARE_AMOUNT\").alias(\"FARE_SUM_1_hr\"),\n", - " F.count(\"*\").alias(\"TRIP_COUNT_1_hr\")\n", + " F.sum(\"FARE_AMOUNT\").alias(\"FARE_SUM_1_HR\"),\n", + " F.count(\"*\").alias(\"TRIP_COUNT_1_HR\")\n", " )\n", " return df\n", "\n", @@ -312,25 +339,25 @@ "\n", " df = df.with_columns(\n", " [\n", - " \"SUM_FARE_2_hr\",\n", - " \"COUNT_TRIP_2hr\",\n", - " \"SUM_FARE_5_hr\",\n", - " \"COUNT_TRIP_5hr\",\n", + " \"SUM_FARE_2_HR\",\n", + " \"COUNT_TRIP_2HR\",\n", + " \"SUM_FARE_5_HR\",\n", + " \"COUNT_TRIP_5HR\",\n", " ],\n", " [\n", - " F.sum(\"FARE_SUM_1_hr\").over(window1),\n", - " F.sum(\"TRIP_COUNT_1_hr\").over(window1),\n", - " F.sum(\"FARE_SUM_1_hr\").over(window2),\n", - " F.sum(\"TRIP_COUNT_1_hr\").over(window2),\n", + " F.sum(\"FARE_SUM_1_HR\").over(window1),\n", + " F.sum(\"TRIP_COUNT_1_HR\").over(window1),\n", + " F.sum(\"FARE_SUM_1_HR\").over(window2),\n", + " F.sum(\"TRIP_COUNT_1_HR\").over(window2),\n", " ]\n", " ).select(\n", " [\n", " col(\"PULOCATIONID\"),\n", " col(\"WINDOW_END\").alias(\"TS\"),\n", - " (col(\"SUM_FARE_2_hr\") / col(\"COUNT_TRIP_2hr\"))\n", - " .alias(\"MEAN_FARE_2_hr\"),\n", - " (col(\"SUM_FARE_5_hr\") / col(\"COUNT_TRIP_5hr\"))\n", - " .alias(\"MEAN_FARE_5_hr\"),\n", + " (col(\"SUM_FARE_2_HR\") / col(\"COUNT_TRIP_2HR\"))\n", + " .alias(\"MEAN_FARE_2_HR\"),\n", + " (col(\"SUM_FARE_5_hr\") / col(\"COUNT_TRIP_5HR\"))\n", + " .alias(\"MEAN_FARE_5_HR\"),\n", " ]\n", " )\n", " return df\n", @@ -348,10 +375,10 @@ " [\n", " col(\"DOLOCATIONID\"),\n", " col(\"WINDOW_END\").alias(\"TS\"),\n", - " F.sum(\"TRIP_COUNT_1_hr\").over(window1) \\\n", - " .alias(\"COUNT_TRIP_2_hr\"),\n", - " F.sum(\"TRIP_COUNT_1_hr\").over(window2) \\\n", - " .alias(\"COUNT_TRIP_5_hr\"),\n", + " F.sum(\"TRIP_COUNT_1_HR\").over(window1) \\\n", + " .alias(\"COUNT_TRIP_2_HR\"),\n", + " F.sum(\"TRIP_COUNT_1_HR\").over(window2) \\\n", + " .alias(\"COUNT_TRIP_5_HR\"),\n", " ]\n", " )\n", " return df\n", @@ -399,14 +426,14 @@ "outputs": [], "source": [ "pickup_fv = FeatureView(\n", - " name=\"trip_pickup_time_series_features\", \n", + " name=\"TRIP_PICKUP_TIME_SERIES_FEATURES\", \n", " entities=[trip_pickup], \n", " feature_df=pickup_df, \n", - " timestamp_col=\"ts\"\n", + " timestamp_col=\"TS\"\n", ")\n", "pickup_fv = fs.register_feature_view(\n", " feature_view=pickup_fv, \n", - " version=\"v1\", \n", + " version=\"V1\", \n", " refresh_freq=\"1 minute\", \n", " block=True\n", ")" @@ -420,14 +447,14 @@ "outputs": [], "source": [ "dropoff_fv = FeatureView(\n", - " name=\"trip_dropoff_time_series_features\", \n", + " name=\"TRIP_DROPOFF_TIME_SERIES_FEATURES\", \n", " entities=[trip_dropoff], \n", " feature_df=dropoff_df, \n", - " timestamp_col=\"ts\"\n", + " timestamp_col=\"TS\"\n", ")\n", "fs.register_feature_view(\n", " feature_view=dropoff_fv, \n", - " version=\"v1\", \n", + " version=\"V1\", \n", " refresh_freq=\"1 minute\", \n", " block=True\n", ")" @@ -451,7 +478,7 @@ "metadata": {}, "outputs": [], "source": [ - "fs.list_feature_views(entity_name=\"trip_pickup\") \\\n", + "fs.list_feature_views(entity_name=\"TRIP_PICKUP\") \\\n", " .select([\"NAME\", \"VERSION\", \"ENTITIES\", \"FEATURE_DESC\"]).show()" ] }, @@ -528,12 +555,56 @@ "print(\"Mean squared error: %.2f\" % mean_squared_error(y_test, y_pred))" ] }, + { + "cell_type": "markdown", + "id": "aceef9e3", + "metadata": {}, + "source": [ + "## [Optional 1] Predict with local model\n", + "Now let's predict with the model and the feature values retrieved from feature store. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e41f0552", + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare some source prediction data\n", + "pred_df = training_pd.sample(3, random_state=996)[\n", + " ['PULOCATIONID', 'DOLOCATIONID', 'PICKUP_TS']]\n", + "pred_df = session.create_dataframe(pred_df)\n", + "pred_df = pred_df.select(\n", + " 'PULOCATIONID', \n", + " 'DOLOCATIONID', \n", + " F.cast(pred_df.PICKUP_TS / 1000000, TimestampType()) \\\n", + " .alias('PICKUP_TS'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdfb8857", + "metadata": {}, + "outputs": [], + "source": [ + "enriched_df = fs.retrieve_feature_values(\n", + " spine_df=pred_df, \n", + " features=training_data.load_features(), \n", + " spine_timestamp_col='PICKUP_TS'\n", + ").drop(['PICKUP_TS']).to_pandas()\n", + "\n", + "pred = estimator.predict(enriched_df)\n", + "print(pred)" + ] + }, { "cell_type": "markdown", "id": "0142c25c", "metadata": {}, "source": [ - "## Log model with Model Registry\n", + "## [Optional 2.1] Log model with Model Registry\n", "We can log the model along with its training dataset metadata with model registry." ] }, @@ -549,7 +620,7 @@ "\n", "registry = model_registry.ModelRegistry(\n", " session=session, \n", - " database_name=\"my_cool_registry\", \n", + " database_name=MR_DEMO_DB, \n", " create_if_not_exists=True\n", ")" ] @@ -563,9 +634,9 @@ "source": [ "artifact_ref = registry.log_artifact(\n", " artifact_type=artifact.ArtifactType.DATASET,\n", - " artifact_name=\"my_cool_dataset\",\n", + " artifact_name=\"MY_COOL_DATASET\",\n", " artifact_spec=training_data.to_json(),\n", - " artifact_version=\"v1\",\n", + " artifact_version=\"v5\",\n", ")" ] }, @@ -576,11 +647,11 @@ "metadata": {}, "outputs": [], "source": [ - "model_name = f\"my_model_{time.time()}\"\n", + "model_name = f\"MY_MODEL_{time.time()}\"\n", "\n", "model_ref = registry.log_model(\n", " model_name=model_name,\n", - " model_version=\"v1\",\n", + " model_version=\"V1\",\n", " model=estimator,\n", " artifacts=[artifact_ref],\n", ")" @@ -591,28 +662,10 @@ "id": "e6e0581d", "metadata": {}, "source": [ - "## Restore model and predict with latest features\n", + "## [Option 2.2] Restore model and predict with features\n", "Retrieve the training dataset from registry and construct dataframe of latest feature values. Then we restore the model from registry. Finally, we can predict with latest feature values." ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "999a633d", - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare some source prediction data\n", - "pred_df = training_pd.sample(3, random_state=996)[\n", - " ['PULOCATIONID', 'DOLOCATIONID', 'PICKUP_TS']]\n", - "pred_df = session.create_dataframe(pred_df)\n", - "pred_df = pred_df.select(\n", - " 'PULOCATIONID', \n", - " 'DOLOCATIONID', \n", - " F.cast(pred_df.PICKUP_TS / 1000000, TimestampType()) \\\n", - " .alias('PICKUP_TS'))" - ] - }, { "cell_type": "code", "execution_count": null, @@ -623,8 +676,9 @@ "# Enrich source prediction data with features\n", "from snowflake.ml.dataset.dataset import Dataset\n", "\n", - "registered_artifact = registry.get_artifact(artifact_ref.id)\n", - "\n", + "registered_artifact = registry.get_artifact(\n", + " artifact_ref.name, \n", + " artifact_ref.version)\n", "registered_dataset = Dataset.from_json(registered_artifact.spec, session)\n", "\n", "enriched_df = fs.retrieve_feature_values(\n", @@ -644,13 +698,33 @@ "model_ref = model_registry.ModelReference(\n", " registry=registry, \n", " model_name=model_name, \n", - " model_version=\"v1\"\n", + " model_version=\"V1\"\n", ").load_model()\n", "\n", "pred = model_ref.predict(enriched_df)\n", "\n", "print(pred)" ] + }, + { + "cell_type": "markdown", + "id": "3d338c76", + "metadata": {}, + "source": [ + "## Cleanup notebook\n", + "Cleanup resources created in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acceeecc", + "metadata": {}, + "outputs": [], + "source": [ + "session.sql(f\"DROP DATABASE IF EXISTS {FS_DEMO_DB}\").collect()\n", + "session.sql(f\"DROP DATABASE IF EXISTS {MR_DEMO_DB}\").collect()" + ] } ], "metadata": { diff --git a/snowflake/ml/feature_store/notebooks/customer_demo/Time_Series_Feature_Demo.pdf 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end: str) -> str: - return f"""SELECT DISTINCT TPEP_DROPOFF_DATETIME AS DROPOFF_TIME, + return f"""SELECT DISTINCT TO_TIMESTAMP(TPEP_DROPOFF_DATETIME / 1000000) AS DROPOFF_TIME, PULOCATIONID, TIP_AMOUNT, TOTAL_AMOUNT FROM {raw_dataset} WHERE DROPOFF_TIME >= '{start}' AND DROPOFF_TIME < '{end}'