Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Simplify TimeSeriesCloudPredictor API #105

Merged
merged 2 commits into from
Jan 18, 2024
Merged

Simplify TimeSeriesCloudPredictor API #105

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
8334f00
Simplify TimeSeriesCloudPredictor API
shchur Jan 17, 2024
20309c0
Use bigger dataset in demo
shchur Jan 18, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
28 changes: 8 additions & 20 deletions docs/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -110,40 +110,28 @@ result = cloud_predictor.predict(test_data)
import pandas as pd
from autogluon.cloud import TimeSeriesCloudPredictor

data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/cloud/timeseries_train.csv")
id_column="item_id"
timestamp_column="timestamp"
target="target"
data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly_tiny/train.csv")

predictor_init_args = {
"target": target
"target": "target",
"prediction_length" : 24,
} # args used when creating TimeSeriesPredictor()
predictor_fit_args = {
"train_data": data,
"time_limit": 120
"time_limit": 120,
} # args passed to TimeSeriesPredictor.fit()
cloud_predictor = TimeSeriesCloudPredictor(cloud_output_path="YOUR_S3_BUCKET_PATH")
cloud_predictor.fit(
predictor_init_args=predictor_init_args,
predictor_fit_args=predictor_fit_args,
id_column=id_column,
timestamp_column=timestamp_column
id_column="item_id",
timestamp_column="timestamp",
)
cloud_predictor.deploy()
result = cloud_predictor.predict_real_time(
test_data=data,
id_column=id_column,
timestamp_column=timestamp_column,
target=target
)
result = cloud_predictor.predict_real_time(data)
cloud_predictor.cleanup_deployment()
# Batch inference
result = cloud_predictor.predict(
test_data=data,
id_column=id_column,
timestamp_column=timestamp_column,
target=target
)
result = cloud_predictor.predict(data)
```
:::

Expand Down
62 changes: 34 additions & 28 deletions src/autogluon/cloud/predictor/timeseries_cloud_predictor.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,23 @@ class TimeSeriesCloudPredictor(CloudPredictor):
predictor_file_name = "TimeSeriesCloudPredictor.pkl"
backend_map = {SAGEMAKER: TIMESERIES_SAGEMAKER}

def __init__(
self,
local_output_path: Optional[str] = None,
cloud_output_path: Optional[str] = None,
backend: str = SAGEMAKER,
verbosity: int = 2,
) -> None:
super().__init__(
local_output_path=local_output_path,
cloud_output_path=cloud_output_path,
backend=backend,
verbosity=verbosity,
)
self.target_column: Optional[str] = None
self.id_column: Optional[str] = None
self.timestamp_column: Optional[str] = None

@property
def predictor_type(self):
"""
Expand All @@ -33,8 +50,8 @@ def fit(
*,
predictor_init_args: Dict[str, Any],
predictor_fit_args: Dict[str, Any],
id_column: str,
timestamp_column: str,
id_column: str = "item_id",
timestamp_column: str = "timestamp",
static_features: Optional[Union[str, pd.DataFrame]] = None,
framework_version: str = "latest",
job_name: Optional[str] = None,
Expand All @@ -56,10 +73,10 @@ def fit(
Init args for the predictor
predictor_fit_args: dict
Fit args for the predictor
id_column: str
Name of the 'item_id' column
timestamp_column: str
Name of the 'timestamp' column
id_column: str, default = "item_id"
Name of the item ID column
timestamp_column: str, default = "timestamp"
Name of the timestamp column
static_features: Optional[pd.DataFrame]
An optional data frame describing the metadata attributes of individual items in the item index.
For more detail, please refer to `TimeSeriesDataFrame` documentation:
Expand Down Expand Up @@ -102,6 +119,11 @@ def fit(
), "Predictor is already fit! To fit additional models, create a new `CloudPredictor`"
if backend_kwargs is None:
backend_kwargs = {}

self.target_column = predictor_init_args.get("target", "target")
self.id_column = id_column
self.timestamp_column = timestamp_column

backend_kwargs = self.backend.parse_backend_fit_kwargs(backend_kwargs)
self.backend.fit(
predictor_init_args=predictor_init_args,
Expand All @@ -124,9 +146,6 @@ def fit(
def predict_real_time(
self,
test_data: Union[str, pd.DataFrame],
id_column: str,
timestamp_column: str,
target: str,
static_features: Optional[Union[str, pd.DataFrame]] = None,
accept: str = "application/x-parquet",
**kwargs,
Expand All @@ -141,16 +160,10 @@ def predict_real_time(
test_data: Union(str, pandas.DataFrame)
The test data to be inferenced.
Can be a pandas.DataFrame or a local path to a csv file.
id_column: str
Name of the 'item_id' column
timestamp_column: str
Name of the 'timestamp' column
static_features: Optional[pd.DataFrame]
An optional data frame describing the metadata attributes of individual items in the item index.
For more detail, please refer to `TimeSeriesDataFrame` documentation:
https://auto.gluon.ai/stable/api/autogluon.predictor.html#timeseriesdataframe
target: str
Name of column that contains the target values to forecast
accept: str, default = application/x-parquet
Type of accept output content.
Valid options are application/x-parquet, text/csv, application/json
Expand All @@ -164,9 +177,9 @@ def predict_real_time(
"""
return self.backend.predict_real_time(
test_data=test_data,
id_column=id_column,
timestamp_column=timestamp_column,
target=target,
id_column=self.id_column,
timestamp_column=self.timestamp_column,
target=self.target_column,
static_features=static_features,
accept=accept,
)
Expand All @@ -177,9 +190,6 @@ def predict_proba_real_time(self, **kwargs) -> pd.DataFrame:
def predict(
self,
test_data: Union[str, pd.DataFrame],
id_column: str,
timestamp_column: str,
target: str,
static_features: Optional[Union[str, pd.DataFrame]] = None,
predictor_path: Optional[str] = None,
framework_version: str = "latest",
Expand All @@ -203,10 +213,6 @@ def predict(
test_data: str
The test data to be inferenced.
Can be a pandas.DataFrame or a local path to a csv file.
id_column: str
Name of the 'item_id' column
timestamp_column: str
Name of the 'timestamp' column
static_features: Optional[Union[str, pd.DataFrame]]
An optional data frame describing the metadata attributes of individual items in the item index.
For more detail, please refer to `TimeSeriesDataFrame` documentation:
Expand Down Expand Up @@ -262,9 +268,9 @@ def predict(
backend_kwargs = self.backend.parse_backend_predict_kwargs(backend_kwargs)
return self.backend.predict(
test_data=test_data,
id_column=id_column,
timestamp_column=timestamp_column,
target=target,
id_column=self.id_column,
timestamp_column=self.timestamp_column,
target=self.target_column,
static_features=static_features,
predictor_path=predictor_path,
framework_version=framework_version,
Expand Down
17 changes: 2 additions & 15 deletions tests/unittests/timeseries/test_timeseries.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,16 +7,13 @@
def test_timeseries(test_helper, framework_version):
train_data = "timeseries_train.csv"
static_features = "timeseries_static_features.csv"
id_column = "item_id"
timestamp_column = "timestamp"
target = "target"
timestamp = test_helper.get_utc_timestamp_now()
with tempfile.TemporaryDirectory() as temp_dir:
os.chdir(temp_dir)
test_helper.prepare_data(train_data, static_features)
time_limit = 60

predictor_init_args = dict(target=target)
predictor_init_args = dict(target="target", prediction_length=3)

predictor_fit_args = dict(
train_data=train_data,
Expand All @@ -35,25 +32,15 @@ def test_timeseries(test_helper, framework_version):
predictor_fit_args,
train_data,
fit_kwargs=dict(
id_column=id_column,
timestamp_column=timestamp_column,
static_features=static_features,
framework_version=framework_version,
custom_image_uri=training_custom_image_uri,
),
deploy_kwargs=dict(framework_version=framework_version, custom_image_uri=inference_custom_image_uri),
predict_kwargs=dict(
id_column=id_column,
timestamp_column=timestamp_column,
target=target,
static_features=static_features,
framework_version=framework_version,
custom_image_uri=inference_custom_image_uri,
),
predict_real_time_kwargs=dict(
id_column=id_column,
timestamp_column=timestamp_column,
target=target,
static_features=static_features,
),
predict_real_time_kwargs=dict(static_features=static_features),
)
Loading