wehub-resource-sync
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
import numpy as np
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import pandas as pd
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from matplotlib.figure import Figure
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from sklearn.datasets import load_diabetes
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from sklearn.linear_model import LinearRegression
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from sklearn.model_selection import train_test_split
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import mlflow
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from mlflow.models import infer_signature, make_metric
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# loading the diabetes dataset
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diabetes_dataset = load_diabetes(as_frame=True)
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# split the dataset into train and test partitions
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X_train, X_test, y_train, y_test = train_test_split(
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diabetes_dataset.data, diabetes_dataset.target, test_size=0.2, random_state=123
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)
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# train the model
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lin_reg = LinearRegression().fit(X_train, y_train)
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# Infer model signature
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predictions = lin_reg.predict(X_train)
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signature = infer_signature(X_train, predictions)
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# creating the evaluation dataframe
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eval_data = X_test.copy()
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eval_data["target"] = y_test
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def custom_metric(eval_df, _builtin_metrics):
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return np.sum(np.abs(eval_df["prediction"] - eval_df["target"] + 1) ** 2)
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class ExampleClass:
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def __init__(self, x):
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self.x = x
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def custom_artifact(eval_df, builtin_metrics, _artifacts_dir):
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example_np_arr = np.array([1, 2, 3])
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example_df = pd.DataFrame({"test": [2.2, 3.1], "test2": [3, 2]})
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example_dict = {"hello": "there", "test_list": [0.1, 0.3, 4]}
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example_dict.update(builtin_metrics)
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example_dict_2 = '{"a": 3, "b": [1, 2, 3]}'
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example_image = Figure()
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ax = example_image.subplots()
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ax.scatter(eval_df["prediction"], eval_df["target"])
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ax.set_xlabel("Targets")
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ax.set_ylabel("Predictions")
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ax.set_title("Targets vs. Predictions")
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example_custom_class = ExampleClass(10)
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return {
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"example_np_arr_from_obj_saved_as_npy": example_np_arr,
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"example_df_from_obj_saved_as_csv": example_df,
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"example_dict_from_obj_saved_as_json": example_dict,
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"example_image_from_obj_saved_as_png": example_image,
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"example_dict_from_json_str_saved_as_json": example_dict_2,
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"example_class_from_obj_saved_as_pickle": example_custom_class,
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}
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with mlflow.start_run() as run:
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model_info = mlflow.sklearn.log_model(lin_reg, name="model", signature=signature)
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result = mlflow.evaluate(
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model=model_info.model_uri,
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data=eval_data,
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targets="target",
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model_type="regressor",
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evaluators=["default"],
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extra_metrics=[
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make_metric(
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eval_fn=custom_metric,
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greater_is_better=False,
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)
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],
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custom_artifacts=[
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custom_artifact,
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],
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)
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print(f"metrics:\n{result.metrics}")
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print(f"artifacts:\n{result.artifacts}")
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