wehub-resource-sync
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
import numpy as np
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import pandas as pd
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from prophet import Prophet, serialize
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from prophet.diagnostics import cross_validation, performance_metrics
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import mlflow
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SOURCE_DATA = (
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"https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv"
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)
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np.random.seed(12345)
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def extract_params(pr_model):
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params = {attr: getattr(pr_model, attr) for attr in serialize.SIMPLE_ATTRIBUTES}
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return {k: v for k, v in params.items() if isinstance(v, (int, float, str, bool))}
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sales_data = pd.read_csv(SOURCE_DATA)
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with mlflow.start_run():
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model = Prophet().fit(sales_data)
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params = extract_params(model)
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metrics_raw = cross_validation(
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model=model,
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horizon="365 days",
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period="180 days",
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initial="710 days",
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parallel="threads",
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disable_tqdm=True,
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)
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cv_metrics = performance_metrics(metrics_raw)
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metrics = cv_metrics.drop(columns=["horizon"]).mean().to_dict()
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# The training data can be retrieved from the fit model for convenience
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train = model.history
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model_info = mlflow.prophet.log_model(
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model, name="prophet_model", input_example=train[["ds"]].head(10)
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)
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mlflow.log_params(params)
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mlflow.log_metrics(metrics)
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loaded_model = mlflow.prophet.load_model(model_info.model_uri)
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forecast = loaded_model.predict(loaded_model.make_future_dataframe(60))
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forecast = forecast[["ds", "yhat"]].tail(90)
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print(f"forecast:\n${forecast.head(30)}")
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