diff --git a/how-to-use-azureml/data-drift/score.py b/how-to-use-azureml/data-drift/score.py deleted file mode 100644 index cda62f8b..00000000 --- a/how-to-use-azureml/data-drift/score.py +++ /dev/null @@ -1,58 +0,0 @@ -import pickle -import json -import numpy -import azureml.train.automl -from sklearn.externals import joblib -from sklearn.linear_model import Ridge -from azureml.core.model import Model -from azureml.core.run import Run -from azureml.monitoring import ModelDataCollector -import time -import pandas as pd - - -def init(): - global model, inputs_dc, prediction_dc, feature_names, categorical_features - - print("Model is initialized" + time.strftime("%H:%M:%S")) - model_path = Model.get_model_path(model_name="driftmodel") - model = joblib.load(model_path) - - feature_names = ["usaf", "wban", "latitude", "longitude", "station_name", "p_k", - "sine_weekofyear", "cosine_weekofyear", "sine_hourofday", "cosine_hourofday", - "temperature-7"] - - categorical_features = ["usaf", "wban", "p_k", "station_name"] - - inputs_dc = ModelDataCollector(model_name="driftmodel", - identifier="inputs", - feature_names=feature_names) - - prediction_dc = ModelDataCollector("driftmodel", - identifier="predictions", - feature_names=["temperature"]) - - -def run(raw_data): - global inputs_dc, prediction_dc - - try: - data = json.loads(raw_data)["data"] - data = pd.DataFrame(data) - - # Remove the categorical features as the model expects OHE values - input_data = data.drop(categorical_features, axis=1) - - result = model.predict(input_data) - - # Collect the non-OHE dataframe - collected_df = data[feature_names] - - inputs_dc.collect(collected_df.values) - prediction_dc.collect(result) - return result.tolist() - except Exception as e: - error = str(e) - - print(error + time.strftime("%H:%M:%S")) - return error