# Snowflake connector
from snowflake import connector
#from snowflake.ml.utils import connection_params
# Snowpark for Python
from snowflake.snowpark.session import Session
from snowflake.snowpark.types import Variant
from snowflake.snowpark.version import VERSION
from snowflake.snowpark import functions as F
from snowflake.snowpark.types import *
# Snowpark ML
from snowflake.ml.modeling.compose import ColumnTransformer
from snowflake.ml.modeling.pipeline import Pipeline
from snowflake.ml.modeling.preprocessing import StandardScaler, OrdinalEncoder
from snowflake.ml.modeling.impute import SimpleImputer
from snowflake.ml.modeling.model_selection import GridSearchCV
from snowflake.ml.modeling.xgboost import XGBRegressor
from snowflake.ml import version
mlversion = version.VERSION
# Misc
import pandas as pd
import json
import logging
logger = logging.getLogger("snowflake.snowpark.session")
logger.setLevel(logging.ERROR)This notebook introduces several key features of Snowflake ML in the process of training a machine learning model for forecasting Chicago bus ridership.
- Establish secure connection to Snowflake
- Load features and target from Snowflake table into Snowpark DataFrame
- Prepare features for model training
- Train ML model using Snowpark ML distributed processing
- Save the model to the Snowflake Model Registry
- Run model predictions inside Snowflake
This notebook is intended to highlight Snowflake functionality and should not be taken as a best practice for time series forecasting.
See more snowflake notebooks from raj
1. Setup Environment
Establish Secure Connection to Snowflake
Using the Snowflake ML Python API, it’s quick and easy to establish a secure connection between Snowflake and Notebook. I prefer using a toml configuration file as documented here. Note: Other connection options include Username/Password, MFA, OAuth, Okta, SSO
The creds.json should look like this:
{
"account": "awb99999",
"user": "your_user_name",
"password": "your_password",
"warehouse": "your_warehouse"
}
::: {#953f6906 .cell execution_count=2}
``` {.python .cell-code}
with open('../../creds.json') as f:
data = json.load(f)
USERNAME = data['user']
PASSWORD = data['password']
SF_ACCOUNT = data['account']
SF_WH = data['warehouse']
CONNECTION_PARAMETERS = {
"account": SF_ACCOUNT,
"user": USERNAME,
"password": PASSWORD,
}
session = Session.builder.configs(CONNECTION_PARAMETERS).create():::
Verify everything is connected. I like to do this to remind people to make sure they are using the latest versions.
snowflake_environment = session.sql('select current_user(), current_version()').collect()
snowpark_version = VERSION
# Current Environment Details
print('User : {}'.format(snowflake_environment[0][0]))
print('Role : {}'.format(session.get_current_role()))
print('Database : {}'.format(session.get_current_database()))
print('Schema : {}'.format(session.get_current_schema()))
print('Warehouse : {}'.format(session.get_current_warehouse()))
print('Snowflake version : {}'.format(snowflake_environment[0][1]))
print('Snowpark for Python version : {}.{}.{}'.format(snowpark_version[0],snowpark_version[1],snowpark_version[2]))
print('Snowflake ML version : {}.{}.{}'.format(mlversion[0],mlversion[2],mlversion[4]))User : RSHAH
Role : "RAJIV"
Database : "RAJIV"
Schema : "DOCAI"
Warehouse : "RAJIV"
Snowflake version : 8.19.2
Snowpark for Python version : 1.15.0a1
Snowflake ML version : 1.5.0Throughout this notebook, I will change warehouse sizes. For this notebook warehouse size really doesn’t matter much, but I want people to understand how easily and quickly you can change the warehouse size. This is one of my favorite features of Snowflake, just how its always ready for me.
session.sql("create or replace warehouse snowpark_opt_wh with warehouse_size = 'SMALL'").collect()
session.sql("USE SCHEMA PUBLIC").collect()[Row(status='Statement executed successfully.')]2. Load Data in Snowflake
Let’s get the data (900k rows) and also make the column names all upper cases. It’s easier to work with columns names that aren’t case sensitive.
df_clean = pd.read_csv('CTA_Daily_Totals_by_Route.csv')
df_clean.columns = df_clean.columns.str.upper()
print (df_clean.shape)
print (df_clean.dtypes)
df_clean.head()(893603, 4)
ROUTE object
DATE object
DAYTYPE object
RIDES int64
dtype: object| ROUTE | DATE | DAYTYPE | RIDES | |
|---|---|---|---|---|
| 0 | 3 | 01/01/2001 | U | 7354 |
| 1 | 4 | 01/01/2001 | U | 9288 |
| 2 | 6 | 01/01/2001 | U | 6048 |
| 3 | 8 | 01/01/2001 | U | 6309 |
| 4 | 9 | 01/01/2001 | U | 11207 |
Let’s create a Snowpark dataframe and split the data for test/train. This operation is done inside Snowflake and not in your local environment. We will also save this as a table so we don’t ever have to manually upload this dataset again.
PRO TIP – Snowpark will inherit the schema of a pandas dataframe into Snowflake. Either change your schema before importing or after it has landed in snowflake. People that put models into production are very careful about data types.
input_df = session.create_dataframe(df_clean)
schema = input_df.schema
print(schema)StructType([StructField('ROUTE', StringType(16777216), nullable=True), StructField('DATE', StringType(16777216), nullable=True), StructField('DAYTYPE', StringType(16777216), nullable=True), StructField('RIDES', LongType(), nullable=True)])input_df.write.mode('overwrite').save_as_table('CHICAGO_BUS_RIDES')Let’s read from the table, since that is generally what you will be doing in production. We have 893,000 rows of ridership data.
df = session.read.table('CHICAGO_BUS_RIDES')
print (df.count())
df.show()893603
----------------------------------------------
|"ROUTE" |"DATE" |"DAYTYPE" |"RIDES" |
----------------------------------------------
|3 |01/01/2001 |U |7354 |
|4 |01/01/2001 |U |9288 |
|6 |01/01/2001 |U |6048 |
|8 |01/01/2001 |U |6309 |
|9 |01/01/2001 |U |11207 |
|10 |01/01/2001 |U |385 |
|11 |01/01/2001 |U |610 |
|12 |01/01/2001 |U |3678 |
|18 |01/01/2001 |U |375 |
|20 |01/01/2001 |U |7096 |
----------------------------------------------
3. Distributed Feature Engineering
Let’s add the Day of the week and then Aggregate the data by day. Let’s join in weather data
These operations are done inside the Snowpark warehouse which provides improved performance and scalability with distributed execution for these scikit-learn preprocessing functions. This dataset uses SMALL, but you can always move up to larger ones including Snowpark Optimized warehouses (16x memory per node than a standard warehouse), e.g., session.sql("create or replace warehouse snowpark_opt_wh with warehouse_size = 'MEDIUM' warehouse_type = 'SNOWPARK-OPTIMIZED'").collect()
session.sql("create or replace warehouse snowpark_opt_wh with warehouse_size = 'MEDIUM' warehouse_type = 'SNOWPARK-OPTIMIZED'").collect()[Row(status='Warehouse SNOWPARK_OPT_WH successfully created.')]Simple feature engineering
from snowflake.snowpark.functions import col, to_timestamp, dayofweek, month,sum, listagg, lag
from snowflake.snowpark import Window
df = df.with_column('DATE', to_timestamp(col('DATE'), 'MM/DD/YYYY'))
# Add a new column for the day of the week
# The day of week is represented as an integer, with 0 = Sunday, 1 = Monday, ..., 6 = Saturday
df = df.with_column('DAY_OF_WEEK', dayofweek(col('DATE')))
# Show the resulting dataframe
df.show()-----------------------------------------------------------------------
|"ROUTE" |"DAYTYPE" |"RIDES" |"DATE" |"DAY_OF_WEEK" |
-----------------------------------------------------------------------
|3 |U |7354 |2001-01-01 00:00:00 |1 |
|4 |U |9288 |2001-01-01 00:00:00 |1 |
|6 |U |6048 |2001-01-01 00:00:00 |1 |
|8 |U |6309 |2001-01-01 00:00:00 |1 |
|9 |U |11207 |2001-01-01 00:00:00 |1 |
|10 |U |385 |2001-01-01 00:00:00 |1 |
|11 |U |610 |2001-01-01 00:00:00 |1 |
|12 |U |3678 |2001-01-01 00:00:00 |1 |
|18 |U |375 |2001-01-01 00:00:00 |1 |
|20 |U |7096 |2001-01-01 00:00:00 |1 |
-----------------------------------------------------------------------
A bit more feature engineering, but again, this is very familiar syntax.
# Add a new column for the month
df = df.with_column('MONTH', month(col('DATE')))
# Group by DATE, DAY_OF_WEEK, and MONTH, then aggregate
total_riders = df.group_by('DATE','DAY_OF_WEEK','MONTH').agg(
F.listagg('DAYTYPE', is_distinct=True).alias('DAYTYPE'),
F.sum('RIDES').alias('TOTAL_RIDERS')
).order_by('DATE')
#Define a window specification
window_spec = Window.order_by('DATE')
# Add a lagged column for total ridership of the previous day
total_riders = total_riders.with_column('PREV_DAY_RIDERS', lag(col('TOTAL_RIDERS'), 1).over(window_spec))
# Show the resulting dataframe
print (total_riders.count())
print (total_riders.show())7364
--------------------------------------------------------------------------------------------------
|"DATE" |"DAY_OF_WEEK" |"MONTH" |"DAYTYPE" |"TOTAL_RIDERS" |"PREV_DAY_RIDERS" |
--------------------------------------------------------------------------------------------------
|2001-01-01 00:00:00 |1 |1 |U |295439 |NULL |
|2001-01-02 00:00:00 |2 |1 |W |776862 |295439 |
|2001-01-03 00:00:00 |3 |1 |W |820048 |776862 |
|2001-01-04 00:00:00 |4 |1 |W |867675 |820048 |
|2001-01-05 00:00:00 |5 |1 |W |887519 |867675 |
|2001-01-06 00:00:00 |6 |1 |A |575407 |887519 |
|2001-01-07 00:00:00 |0 |1 |U |374435 |575407 |
|2001-01-08 00:00:00 |1 |1 |W |980660 |374435 |
|2001-01-09 00:00:00 |2 |1 |W |974858 |980660 |
|2001-01-10 00:00:00 |3 |1 |W |980656 |974858 |
--------------------------------------------------------------------------------------------------
NoneAlso, you can use ChatGPT to generate the code for you.
Join in the Weather Data from the Snowflake Marketplace
Instead of downloading data and building pipelines, Snowflake has a lot of useful data, including weather data in it’s Marketplace. This means the data is only a SQL query away.
SQL QUERY:
SELECT
ts.noaa_weather_station_id,
ts.DATE,
COALESCE(MAX(CASE WHEN ts.variable = 'minimum_temperature' THEN ts.Value ELSE NULL END), 0) AS minimum_temperature,
COALESCE(MAX(CASE WHEN ts.variable = 'precipitation' THEN ts.Value ELSE NULL END), 0) AS precipitation,
COALESCE(MAX(CASE WHEN ts.variable = 'maximum_temperature' THEN ts.Value ELSE NULL END), 0) AS maximum_temperature
FROM
cybersyn.noaa_weather_metrics_timeseries AS ts
JOIN
cybersyn.noaa_weather_station_index AS idx
ON
(ts.noaa_weather_station_id = idx.noaa_weather_station_id)
WHERE
idx.NOAA_WEATHER_STATION_ID = 'USW00014819'
AND (ts.VARIABLE = 'minimum_temperature' OR ts.VARIABLE = 'precipitation' OR ts.VARIABLE = 'maximum_temperature')
GROUP BY
ts.noaa_weather_station_id,
ts.DATE
LIMIT 1000;weather = session.read.table('CHICAGO_WEATHER')
from snowflake.snowpark.types import DoubleType
weather = weather.withColumn('MINIMUM_TEMPERATURE', weather['MINIMUM_TEMPERATURE'].cast(DoubleType()))
weather = weather.withColumn('MAXIMUM_TEMPERATURE', weather['MAXIMUM_TEMPERATURE'].cast(DoubleType()))
weather = weather.withColumn('PRECIPITATION', weather['PRECIPITATION'].cast(DoubleType()))
weather.show()------------------------------------------------------------------------------------------------------------
|"NOAA_WEATHER_STATION_ID" |"DATE" |"MINIMUM_TEMPERATURE" |"MAXIMUM_TEMPERATURE" |"PRECIPITATION" |
------------------------------------------------------------------------------------------------------------
|USW00014819 |2019-07-16 |22.2 |28.9 |3.8 |
|USW00014819 |2002-01-06 |-3.9 |3.3 |0.0 |
|USW00014819 |2008-03-17 |-0.5 |4.4 |2.0 |
|USW00014819 |2000-01-29 |-6.7 |-2.2 |0.0 |
|USW00014819 |2004-06-12 |16.7 |26.7 |6.6 |
|USW00014819 |2017-07-15 |16.1 |28.3 |0.0 |
|USW00014819 |2001-10-22 |12.2 |18.9 |2.3 |
|USW00014819 |2021-05-01 |6.1 |28.3 |0.0 |
|USW00014819 |2016-11-29 |7.2 |14.4 |0.0 |
|USW00014819 |2020-08-01 |18.3 |26.1 |5.1 |
------------------------------------------------------------------------------------------------------------
# Perform the join operation
joined_df = weather.join(
total_riders,
weather["DATE"] == total_riders["DATE"],
"inner", # This is the type of join: inner, outer, left, right,
lsuffix="w"
)
# Show the result of the join
joined_df.show()-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"NOAA_WEATHER_STATION_ID" |"DATEW" |"MINIMUM_TEMPERATURE" |"MAXIMUM_TEMPERATURE" |"PRECIPITATION" |"DATE" |"DAY_OF_WEEK" |"MONTH" |"DAYTYPE" |"TOTAL_RIDERS" |"PREV_DAY_RIDERS" |
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|USW00014819 |2005-10-15 |8.9 |20.0 |0.0 |2005-10-15 00:00:00 |6 |10 |A |666129 |1087863 |
|USW00014819 |2019-04-29 |6.1 |11.7 |29.0 |2019-04-29 00:00:00 |1 |4 |W |724030 |332461 |
|USW00014819 |2019-09-26 |13.9 |22.8 |0.0 |2019-09-26 00:00:00 |4 |9 |W |847678 |852326 |
|USW00014819 |2006-12-09 |-4.9 |3.3 |0.0 |2006-12-09 00:00:00 |6 |12 |A |586623 |948538 |
|USW00014819 |2015-05-05 |10.6 |15.6 |20.1 |2015-05-05 00:00:00 |2 |5 |W |913079 |926775 |
|USW00014819 |2006-05-05 |8.9 |15.6 |0.0 |2006-05-05 00:00:00 |5 |5 |W |1018785 |1042392 |
|USW00014819 |2019-11-04 |3.9 |12.2 |0.0 |2019-11-04 00:00:00 |1 |11 |W |842258 |354020 |
|USW00014819 |2013-02-07 |0.0 |2.2 |14.5 |2013-02-07 00:00:00 |4 |2 |W |963866 |1026678 |
|USW00014819 |2013-08-30 |20.6 |35.6 |11.9 |2013-08-30 00:00:00 |5 |8 |W |1004986 |1029901 |
|USW00014819 |2007-04-28 |4.4 |22.2 |0.0 |2007-04-28 00:00:00 |6 |4 |A |662079 |1018455 |
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## Dropping any null values
from snowflake.snowpark.functions import col, is_null
# Create a filter condition for non-finite values across all columns
non_finite_filter = None
# Iterate over all columns and update the filter condition
for column in joined_df.columns:
current_filter = is_null(col(column))
non_finite_filter = current_filter if non_finite_filter is None else (non_finite_filter | current_filter)
# Apply the filter to the DataFrame to exclude rows with any non-finite values
df_filtered = joined_df.filter(~non_finite_filter)#Split the data into training and test sets
train = df_filtered.filter(col('DATE') < '2019-01-01')
test = df_filtered.filter(col('DATE') >= '2019-01-01')print (train.count())
print (test.count())6570
7904. Distributed Feature Engineering in a Pipeline
Feature engineering + XGBoost
session.sql("create or replace warehouse snowpark_opt_wh with warehouse_size = 'MEDIUM' warehouse_type = 'SNOWPARK-OPTIMIZED'").collect()
session.sql("alter warehouse snowpark_opt_wh set max_concurrency_level = 1").collect()[Row(status='Statement executed successfully.')] ## Distributed Preprocessing - 25X to 50X faster
numeric_features = ['DAY_OF_WEEK','MONTH','PREV_DAY_RIDERS','MINIMUM_TEMPERATURE','MAXIMUM_TEMPERATURE','PRECIPITATION']
numeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])
categorical_cols = ['DAYTYPE']
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='most_frequent')),
('onehot', OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-99999))
])
preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, numeric_features),
('cat', categorical_transformer, categorical_cols)
])
pipeline = Pipeline(steps=[('preprocessor', preprocessor),('model', XGBRegressor())])5. Distributed Training
These operations are done inside the Snowpark warehouse which provides improved performance and scalability with distributed execution for these scikit-learn preprocessing functions and XGBoost training (and many other types of models).
## Distributed HyperParameter Optimization
hyper_param = dict(
model__max_depth=[2,4],
model__learning_rate=[0.1,0.3],
)
xg_model = GridSearchCV(
estimator=pipeline,
param_grid=hyper_param,
#cv=5,
input_cols=numeric_features + categorical_cols,
label_cols=['TOTAL_RIDERS'],
output_cols=["TOTAL_RIDERS_FORECAST"],
)
# Fit and Score
xg_model.fit(train)
##Takes 25 seconds<snowflake.ml.modeling.model_selection.grid_search_cv.GridSearchCV at 0x173418df0>6. Model Evaluation
Look at the results of the mode. cv_results is a dictionary, where each key is a string describing one of the metrics or parameters, and the corresponding value is an array with one entry per combination of parameters
session.sql("create or replace warehouse snowpark_opt_wh with warehouse_size = 'SMALL'").collect()[Row(status='Warehouse SNOWPARK_OPT_WH successfully created.')]cv_results = xg_model.to_sklearn().cv_results_
for i in range(len(cv_results['params'])):
print(f"Parameters: {cv_results['params'][i]}")
print(f"Mean Test Score: {cv_results['mean_test_score'][i]}")
print()Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 2}
Mean Test Score: 0.927693653032996
Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 4}
Mean Test Score: 0.9440192568004221
Parameters: {'model__learning_rate': 0.3, 'model__max_depth': 2}
Mean Test Score: 0.9367972284370352
Parameters: {'model__learning_rate': 0.3, 'model__max_depth': 4}
Mean Test Score: 0.9425057277525181
Look at the accuracy of the model
from snowflake.ml.modeling.metrics import mean_absolute_error
testpreds = xg_model.predict(test)
print('MSE:', mean_absolute_error(df=testpreds, y_true_col_names='TOTAL_RIDERS', y_pred_col_names='"TOTAL_RIDERS_FORECAST"'))
testpreds.select("DATEW", "TOTAL_RIDERS", "TOTAL_RIDERS_FORECAST").show(10) MSE: 183320.1351068038
---------------------------------------------------------
|"DATEW" |"TOTAL_RIDERS" |"TOTAL_RIDERS_FORECAST" |
---------------------------------------------------------
|2019-11-09 |476467 |489406.65625 |
|2019-05-31 |810422 |836847.0625 |
|2020-12-15 |270178 |633812.25 |
|2020-08-05 |315741 |710399.9375 |
|2020-05-17 |118681 |347373.59375 |
|2019-02-27 |793731 |792628.8125 |
|2019-01-27 |257918 |286517.4375 |
|2020-02-07 |771641 |789460.875 |
|2020-12-06 |143231 |333279.25 |
|2020-04-02 |213131 |656467.625 |
---------------------------------------------------------
Materialize the results to a table
testpreds.write.save_as_table(table_name='CHICAGO_BUS_RIDES_FORECAST', mode='overwrite')Using metrics from snowpark so calculation is done inside snowflake
7. Save to the Model Registry and use for Predictions (Python & SQL)
Connect to the registry
from snowflake.ml.registry import Registry
reg = Registry(session=session, database_name="RAJIV", schema_name="PUBLIC")model_ref = reg.log_model(
model_name="Forecasting_Bus_Ridership",
version_name="v37",
model=xg_model,
conda_dependencies=["scikit-learn","xgboost"],
sample_input_data=train,
comment="XGBoost model, run 36, May13"
)/Users/rajishah/anaconda3/envs/working38/lib/python3.8/contextlib.py:113: UserWarning: `relax_version` is not set and therefore defaulted to True. Dependency version constraints relaxed from ==x.y.z to >=x.y, <(x+1). To use specific dependency versions for compatibility, reproducibility, etc., set `options={'relax_version': False}` when logging the model.
return next(self.gen)
/Users/rajishah/anaconda3/envs/working38/lib/python3.8/site-packages/snowflake/ml/model/_packager/model_packager.py:92: UserWarning: Inferring model signature from sample input or providing model signature for Snowpark ML Modeling model is not required. Model signature will automatically be inferred during fitting.
handler.save_model(reg.show_models()| created_on | name | database_name | schema_name | comment | owner | default_version_name | versions | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2024-01-23 18:58:41.929000-08:00 | DIABETES_XGBOOSTER | RAJIV | PUBLIC | None | RAJIV | V2 | ["V2","V3","V4","V5","V7"] |
| 1 | 2024-02-19 17:12:27.005000-08:00 | E5_BASE_V2 | RAJIV | PUBLIC | None | RAJIV | V1 | ["V1"] |
| 2 | 2024-02-07 13:00:56.292000-08:00 | FINBERT | RAJIV | PUBLIC | None | RAJIV | V1 | ["V1"] |
| 3 | 2024-02-26 18:55:00.548000-08:00 | FORECASTING_BUS_RIDERSHIP | RAJIV | PUBLIC | None | RAJIV | V7 | ["V10","V11","V12","V13","V14","V15","V16","V1... |
| 4 | 2024-02-19 17:19:12.122000-08:00 | MINILMV2 | RAJIV | PUBLIC | None | RAJIV | V1 | ["V1","V2","V4","V5"] |
| 5 | 2024-02-07 13:14:44.823000-08:00 | MPNET_BASE | RAJIV | PUBLIC | None | RAJIV | V1 | ["V1","V2","V3"] |
| 6 | 2024-01-25 14:54:04.655000-08:00 | TPCDS_XGBOOST_DEMO | RAJIV | PUBLIC | None | RAJIV | V5 | ["V5","V6","V7","V8","V9"] |
| 7 | 2024-01-23 18:49:09.294000-08:00 | XGBOOSTER | RAJIV | PUBLIC | None | RAJIV | V1 | ["V1","V2"] |
Let’s retrieve the model from the registry
reg_model = reg.get_model("Forecasting_Bus_Ridership").version("v37")Let’s do predictions inside the warehouse
remote_prediction = reg_model.run(test, function_name='predict')
remote_prediction.sort("DATEW").select("DATEW","TOTAL_RIDERS","TOTAL_RIDERS_FORECAST").show(10)---------------------------------------------------------
|"DATEW" |"TOTAL_RIDERS" |"TOTAL_RIDERS_FORECAST" |
---------------------------------------------------------
|2019-01-01 |247279 |290942.375 |
|2019-01-02 |585996 |668251.3125 |
|2019-01-03 |660631 |767229.875 |
|2019-01-04 |662011 |759055.3125 |
|2019-01-05 |440848 |491881.78125 |
|2019-01-06 |316844 |351156.84375 |
|2019-01-07 |717818 |762515.625 |
|2019-01-08 |779946 |879376.0625 |
|2019-01-09 |743021 |790567.625 |
|2019-01-10 |743075 |764690.8125 |
---------------------------------------------------------
If you look in the activity view, you can find the SQL which will run a bit faster. This SQL command is showing the result in a snowflake dataframe. You could use collect to pull the info out into your local session.
Modify the SQL with by adding in your specific model with this line: WITH MODEL_VERSION_ALIAS AS MODEL RAJIV.PUBLIC.DIABETES_XGBOOSTER VERSION V7 and updating the location of your target predictions which is located here: SNOWPARK_ML_MODEL_INFERENCE_INPUT
sqlquery = """SELECT "DATEW", "TOTAL_RIDERS", CAST ("TMP_RESULT"['TOTAL_RIDERS_FORECAST'] AS DOUBLE) AS "TOTAL_RIDERS_FORECAST" FROM (WITH SNOWPARK_ML_MODEL_INFERENCE_INPUT AS (SELECT * FROM ( SELECT * FROM (( SELECT "NOAA_WEATHER_STATION_ID" AS "NOAA_WEATHER_STATION_ID", "DATE" AS "DATEW", "MINIMUM_TEMPERATURE" AS "MINIMUM_TEMPERATURE", "MAXIMUM_TEMPERATURE" AS "MAXIMUM_TEMPERATURE", "PRECIPITATION" AS "PRECIPITATION" FROM ( SELECT "NOAA_WEATHER_STATION_ID", "DATE", CAST ("MINIMUM_TEMPERATURE" AS DOUBLE) AS "MINIMUM_TEMPERATURE", CAST ("MAXIMUM_TEMPERATURE" AS DOUBLE) AS "MAXIMUM_TEMPERATURE", CAST ("PRECIPITATION" AS DOUBLE) AS "PRECIPITATION" FROM CHICAGO_WEATHER)) AS SNOWPARK_LEFT INNER JOIN ( SELECT "DATE" AS "DATE", "DAY_OF_WEEK" AS "DAY_OF_WEEK", "MONTH" AS "MONTH", "DAYTYPE" AS "DAYTYPE", "TOTAL_RIDERS" AS "TOTAL_RIDERS", "PREV_DAY_RIDERS" AS "PREV_DAY_RIDERS" FROM ( SELECT "DATE", "DAY_OF_WEEK", "MONTH", "DAYTYPE", "TOTAL_RIDERS", LAG("TOTAL_RIDERS", 1, NULL) OVER ( ORDER BY "DATE" ASC NULLS FIRST ) AS "PREV_DAY_RIDERS" FROM ( SELECT "DATE", "DAY_OF_WEEK", "MONTH", LISTAGG ( DISTINCT "DAYTYPE", '') AS "DAYTYPE", sum("RIDES") AS "TOTAL_RIDERS" FROM ( SELECT "ROUTE", "DAYTYPE", "RIDES", "DATE", dayofweek("DATE") AS "DAY_OF_WEEK", month("DATE") AS "MONTH" FROM ( SELECT "ROUTE", "DAYTYPE", "RIDES", to_timestamp("DATE", 'MM/DD/YYYY') AS "DATE" FROM CHICAGO_BUS_RIDES)) GROUP BY "DATE", "DAY_OF_WEEK", "MONTH") ORDER BY "DATE" ASC NULLS FIRST)) AS SNOWPARK_RIGHT ON ("DATEW" = "DATE"))) WHERE (NOT (((((((((("NOAA_WEATHER_STATION_ID" IS NULL OR "DATEW" IS NULL) OR "MINIMUM_TEMPERATURE" IS NULL) OR "MAXIMUM_TEMPERATURE" IS NULL) OR "PRECIPITATION" IS NULL) OR "DATE" IS NULL) OR "DAY_OF_WEEK" IS NULL) OR "MONTH" IS NULL) OR "DAYTYPE" IS NULL) OR "TOTAL_RIDERS" IS NULL) OR "PREV_DAY_RIDERS" IS NULL) AND ("DATE" >= '2019-01-01'))),MODEL_VERSION_ALIAS AS MODEL RAJIV.PUBLIC.FORECASTING_BUS_RIDERSHIP VERSION V27
SELECT *,
MODEL_VERSION_ALIAS!PREDICT(DAY_OF_WEEK, MONTH, PREV_DAY_RIDERS, MINIMUM_TEMPERATURE, MAXIMUM_TEMPERATURE, PRECIPITATION, DAYTYPE) AS TMP_RESULT
FROM SNOWPARK_ML_MODEL_INFERENCE_INPUT) ORDER BY "DATEW" ASC NULLS FIRST LIMIT 10"""results = session.sql(sqlquery).show()---------------------------------------------------------
|"DATEW" |"TOTAL_RIDERS" |"TOTAL_RIDERS_FORECAST" |
---------------------------------------------------------
|2019-01-01 |247279 |290942.375 |
|2019-01-02 |585996 |668251.3125 |
|2019-01-03 |660631 |767229.875 |
|2019-01-04 |662011 |759055.3125 |
|2019-01-05 |440848 |491881.78125 |
|2019-01-06 |316844 |351156.84375 |
|2019-01-07 |717818 |762515.625 |
|2019-01-08 |779946 |879376.0625 |
|2019-01-09 |743021 |790567.625 |
|2019-01-10 |743075 |764690.8125 |
---------------------------------------------------------
test.show()-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|"NOAA_WEATHER_STATION_ID" |"DATEW" |"MINIMUM_TEMPERATURE" |"MAXIMUM_TEMPERATURE" |"PRECIPITATION" |"DATE" |"DAY_OF_WEEK" |"MONTH" |"DAYTYPE" |"TOTAL_RIDERS" |"PREV_DAY_RIDERS" |
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|USW00014819 |2019-07-23 |16.7 |27.2 |0.0 |2019-07-23 00:00:00 |2 |7 |W |751862 |729088 |
|USW00014819 |2020-12-25 |-12.7 |-5.5 |0.0 |2020-12-25 00:00:00 |5 |12 |U |80199 |199439 |
|USW00014819 |2020-07-23 |18.3 |27.2 |0.0 |2020-07-23 00:00:00 |4 |7 |W |312243 |303124 |
|USW00014819 |2021-01-15 |-2.1 |3.3 |0.0 |2021-01-15 00:00:00 |5 |1 |W |274858 |273087 |
|USW00014819 |2019-06-05 |15.6 |30.0 |9.7 |2019-06-05 00:00:00 |3 |6 |W |814691 |794543 |
|USW00014819 |2019-05-08 |8.3 |24.4 |9.7 |2019-05-08 00:00:00 |3 |5 |W |820018 |802783 |
|USW00014819 |2021-01-17 |-2.1 |1.1 |0.5 |2021-01-17 00:00:00 |0 |1 |U |141354 |190486 |
|USW00014819 |2019-12-09 |-3.8 |9.4 |0.5 |2019-12-09 00:00:00 |1 |12 |W |780897 |341326 |
|USW00014819 |2020-07-30 |22.2 |28.3 |0.0 |2020-07-30 00:00:00 |4 |7 |W |304656 |302637 |
|USW00014819 |2019-05-23 |17.8 |24.4 |0.5 |2019-05-23 00:00:00 |4 |5 |W |799367 |805534 |
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Let’s calculate and save the metrics to the registry
from snowflake.ml.modeling.metrics import mean_absolute_error
testpreds = reg_model.run(test, function_name='predict')
mae = mean_absolute_error(df=testpreds, y_true_col_names='TOTAL_RIDERS', y_pred_col_names='"TOTAL_RIDERS_FORECAST"')
reg_model.set_metric("MAE", value=mae)reg_model.show_metrics(){'MAE': 183320.1351068038}session.sql("create or replace warehouse snowpark_opt_wh with warehouse_size = 'SMALL'").collect()[Row(status='Warehouse SNOWPARK_OPT_WH successfully created.')]#session.close()