import numpy as np import pandas as pd from regression_model.processing.data_management import load_pipeline from regression_model.config import config from regression_model.processing.validation import validate_inputs from regression_model import __version__ as _version import logging import typing as t _logger = logging.getLogger(__name__) pipeline_file_name = f"{config.PIPELINE_SAVE_FILE}{_version}.pkl" _price_pipe = load_pipeline(file_name=pipeline_file_name) def make_prediction(*, input_data: t.Union[pd.DataFrame, dict]) -> dict: """Make a prediction using a saved model pipeline. Args: input_data: Array of model prediction inputs. Returns: Predictions for each input row, as well as the model version. """ data = pd.DataFrame(input_data) validated_data = validate_inputs(input_data=data) prediction = _price_pipe.predict(validated_data[config.FEATURES])
import logging import typing as t def convert_input(jsonData) -> dict: #res = pd.read_json(jsonData, orient='records') res = pd.DataFrame(jsonData) print(res.shape) return res _logger = logging.getLogger(__name__) pipeline_file_name = f'{config.PIPELINE_SAVE_FILE}{_version}.pkl' _basket_pipe = load_pipeline(file_name=pipeline_file_name) BASKET_FEATURES = [ 100010, 100015, 100016, 100017, 100018, 300057, 300058, 300060, 300061, 300062, 300064, 300065, 300570, 300640, 500811, 500812, 500813, 500814, 500815, 500816, 500818, 500819, 500821, 500822, 500823, 500825, 500827 ] def make_predict2(input_data: t.Union[pd.DataFrame, dict], ) -> dict: x_raw = pd.DataFrame(input_data) xx = x_raw.pivot_table('Quantity', ['TransactionId', 'StoreId'], 'MerchandiseId') xx_index = xx.index
import numpy as np import pandas as pd from regression_model.processing.data_management import load_pipeline from regression_model.config import config from regression_model.processing.validation import validate_inputs from regression_model import __version__ as _version import logging _logger = logging.getLogger(__name__) pipeline_file_name = f"{config.PIPELINE_SAVE_FILE}{_version}.pkl" _energy_pipe = load_pipeline(filename = pipeline_file_name) def make_prediction(*, input_data) -> dict: """Make predictions using the saved model pipeline.""" data = pd.DataFrame(input_data) validated_data = validate_inputs(input_data=data) prediction = _energy_pipe.predict(validated_data[config.FEATURES]) results = {"predictions": prediction, "version": _version} _logger.info( f"Making predictions with model version: {_version} " f"Inputs: {validated_data} " f"Predictions: {results}" ) return results
import pandas as pd from regression_model.processing.data_management import load_pipeline from regression_model.config import config from regression_model.processing.validation import validate_inputs from regression_model import __version__ as _version import logging _logger = logging.getLogger(__name__) pipeline_file_name = f"{config.PIPELINE_SAVE_FILE}{_version}.pkl" _titanic_pipe = load_pipeline(file_name=pipeline_file_name) def make_prediction(*, input_data) -> dict: """Make a prediction using the saved model pipeline.""" data = pd.DataFrame(input_data) validated_data = validate_inputs(input_data=data) prediction = _titanic_pipe.predict(validated_data[config.FEATURES]) results = {"predictions": prediction, "version": _version} _logger.info(f"Making predictions with model version: {_version} " f"Inputs: {validated_data} " f"Predictions: {results}") return results
""" import numpy as np import pandas as pd from regression_model.processing.data_management import load_pipeline from regression_model.config import config from regression_model import __version__ as _version import logging import typing as t _logger = logging.getLogger(__name__) pipeline_file_name = f"{config.PIPELINE_SAVE_FILE}{_version}.pkl" pipeline = load_pipeline(file_name=pipeline_file_name) def make_prediction( *, input_data: t.Union[pd.DataFrame, dict], form_input=False, ) -> dict: """Make a prediction using a saved model pipeline. Args: input_data: Array of model prediction inputs. Returns: Predictions for each input row, as well as the model version. """
import numpy as np import pandas as pd from regression_model.processing.data_management import load_pipeline from regression_model.config import config from regression_model.processing.validation import validate_inputs from sklearn.pipeline import Pipeline pipeline_filename = "regression_model.pkl" _price_pipeline: Pipeline = load_pipeline(pipeline_filename) def make_prediction(data: dict) -> dict: data = pd.read_json(data) validated_data = validate_inputs(data[config.FEATURES]) prediction = _price_pipeline.predict(validated_data) output = np.exp(prediction) response = {"predictions": output} return response