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
