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prophet_example.py
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prophet_example.py
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# Copyright 2023 Neal Lathia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
from datetime import datetime, timedelta
import pandas as pd
from prophet import Prophet
from modelstore.model_store import ModelStore
_DOMAIN_NAME = "example-prophet-forecast"
def _train_example_model() -> Prophet:
print("🤖 Creating fake time series data...")
now = datetime.now()
rows = []
for i in range(100):
rows.append({"ds": now + timedelta(days=i), "y": random.gauss(0, 1)})
df = pd.DataFrame(rows)
model = Prophet()
model.fit(df)
# Show some predictions
future = model.make_future_dataframe(periods=5)
print(f"🔍 Predictions = {future.tail().to_dict(orient='records')}.")
return model
def train_and_upload(modelstore: ModelStore) -> dict:
# Train an Annoy index
model = _train_example_model()
# Upload the model to the model store
print(f'⤴️ Uploading the Prophet model to the "{_DOMAIN_NAME}" domain.')
meta_data = modelstore.upload(
_DOMAIN_NAME,
model=model,
)
return meta_data
def load_and_test(modelstore: ModelStore, model_domain: str, model_id: str):
# Load the model back into memory!
print(f'⤵️ Loading the Prophet "{model_domain}" domain model={model_id}')
model = modelstore.load(model_domain, model_id)
# Show some predictions
future = model.make_future_dataframe(periods=5)
print(f"🔍 Predictions = {future.tail().to_dict(orient='records')}.")