experiment-tracking

name	experiment-tracking
description	This skill contains some example code snippets for running experiment tracking

Experiment Tracking

When to Use

You should load in this skill when the user is requesting to do some form of experiment tracking for a machine learning or data science project. This contains a bunch of code snippets and docs for the API for doing anything the user may request.

Code Snippets

Intializing the Experiment

from snowflake.ml.experiment import ExperimentTracking
session.use_database("MY_DATABASE")
session.use_schema("MY_SCHEMA")
exp = ExperimentTracking(session=session)
exp.set_experiment("My_Experiment")

Auto Logging Metrics XGBoost

from xgboost import XGBClassifier
from snowflake.ml.experiment.callback.xgboost import SnowflakeXgboostCallback
from snowflake.ml.model.model_signature import infer_signature

sig = infer_signature(X, y)
callback = SnowflakeXgboostCallback(
    exp, model_name="name", model_signature=sig
)
model = XGBClassifier(callbacks=[callback])
with exp.start_run("my_run"):
    model.fit(X, y, eval_set=[(X, y)])

Auto Logging Metrics Keras

import keras
from snowflake.ml.experiment.callback.keras import SnowflakeKerasCallback
from snowflake.ml.model.model_signature import infer_signature

sig = infer_signature(X, y)
callback = SnowflakeKerasCallback(
    exp, model_name="name", model_signature=sig
)
model = keras.Sequential()
model.add(keras.layers.Dense(1))
model.compile(
    optimizer=keras.optimizers.RMSprop(learning_rate=0.1),
    loss="mean_squared_error",
    metrics=["mean_absolute_error"],
)
with exp.start_run("my_run"):
    model.fit(X, y, validation_split=0.5, callbacks=[callback])

Auto logging Metrics LightGBM

from lightgbm import LGBMClassifier
from snowflake.ml.experiment.callback.lightgbm import SnowflakeLightgbmCallback
from snowflake.ml.model.model_signature import infer_signature

sig = infer_signature(X, y)
callback = SnowflakeLightgbmCallback(
    exp, model_name="name", model_signature=sig
)
model = LGBMClassifier()
with exp.start_run("my_run"):
    model.fit(X, y, eval_set=[(X, y)], callbacks=[callback])

Manual Metric Logging

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score
from snowflake.ml.model.model_signature import infer_signature

model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42)
model.fit(X, Y)

y_pred = model.predict(X)
accuracy = accuracy_score(Y, y_pred)
f1 = f1_score(Y, y_pred, average='weighted')

with exp.start_run("sklearn_random_forest"):
    exp.log_params({"max_depth": 5, "random_state": 42}) # Log Params
    exp.log_metrics({"accuracy": accuracy, "f1_score": f1}) # Log Metrics

Ending a Run

exp.end_run("my_run")

Deleting Information

exp.delete_experiment("my_experiment") # Delete a whole experiement
exp.set_experiment("my_experiment") # Or
exp.delete_run("my_run") # Delete a single run from an experiement