"""
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THE SOFTWARE CODE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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"""
from azureml.core.run import Run
import os
import argparse
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import joblib
import json


def train_model(run, data, alpha):
    run.log("alpha", alpha)
    run.parent.log("alpha", alpha)
    reg = Ridge(alpha=alpha)
    reg.fit(data["train"]["X"], data["train"]["y"])
    preds = reg.predict(data["test"]["X"])
    run.log("mse", mean_squared_error(
        preds, data["test"]["y"]), description="Mean squared error metric")
    run.parent.log("mse", mean_squared_error(
        preds, data["test"]["y"]), description="Mean squared error metric")
    return reg


def main():
    print("Running train.py")

    parser = argparse.ArgumentParser("train")
    parser.add_argument(
        "--build_id",
        type=str,
        help="The build ID of the build triggering this pipeline run",
    )
    parser.add_argument(
        "--model_name",
        type=str,
        help="Name of the Model",
        default="sklearn_regression_model.pkl",
    )

    parser.add_argument(
        "--step_output",
        type=str,
        help=("output for passing data to next step")
    )

    args = parser.parse_args()

    print("Argument [build_id]: %s" % args.build_id)
    print("Argument [model_name]: %s" % args.model_name)
    print("Argument [step_output]: %s" % args.step_output)

    model_name = args.model_name
    build_id = args.build_id
    step_output_path = args.step_output

    print("Getting training parameters")

    alpha = 0.5

    print("Parameter alpha: %s" % alpha)

    run = Run.get_context()

    # Get the dataset
    dataset = run.input_datasets['training_data']
    if (dataset):
        df = dataset.to_pandas_dataframe()
        X = df.values
        y = df.Y
    else:
        e = ("No dataset provided")
        print(e)
        raise Exception(e)

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=0)
    data = {"train": {"X": X_train, "y": y_train},
            "test": {"X": X_test, "y": y_test}}

    reg = train_model(run, data, alpha)

    # Pass model file to next step
    os.makedirs(step_output_path, exist_ok=True)
    model_output_path = os.path.join(step_output_path, model_name)
    joblib.dump(value=reg, filename=model_output_path)

    # Also upload model file to run outputs for history
    os.makedirs('outputs', exist_ok=True)
    output_path = os.path.join('outputs', model_name)
    joblib.dump(value=reg, filename=output_path)

    # Add properties to identify this specific training run
    run.parent.tag("BuildId", value=build_id)
    run.tag("BuildId", value=build_id)
    run.tag("run_type", value="train")
    builduri_base = os.environ.get("BUILDURI_BASE")
    if (builduri_base is not None):
        build_uri = builduri_base + build_id
        run.tag("BuildUri", value=build_uri)
        run.parent.tag("BuildUri", value=build_uri)
    print(f"tags now present for run: {run.tags}")

    run.complete()


if __name__ == '__main__':
    main()