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Merge pull request #1 from gieljnssns/develop
Add a csv prediction function
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,135 @@ | ||
| #!/usr/bin/env python3 | ||
| # -*- coding: utf-8 -*- | ||
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| import logging | ||
| import pathlib | ||
| import time | ||
| from typing import Tuple | ||
| import pandas as pd | ||
| import numpy as np | ||
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| from sklearn.linear_model import LinearRegression | ||
| from sklearn.linear_model import ElasticNet | ||
| from sklearn.model_selection import train_test_split | ||
| from sklearn.neighbors import KNeighborsRegressor | ||
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| import warnings | ||
| warnings.filterwarnings("ignore", category=DeprecationWarning) | ||
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| class CsvPredictor: | ||
| r""" | ||
| A forecaster class using machine learning models. | ||
| This class uses the `skforecast` module and the machine learning models are from `scikit-learn`. | ||
| It exposes one main method: | ||
| - `predict`: to obtain a forecast from a csv file. | ||
| """ | ||
| def __init__(self, csv_file: str, independent_variables: list, dependent_variable: str, sklearn_model: str, new_values:list, root: str, | ||
| logger: logging.Logger) -> None: | ||
| r"""Define constructor for the forecast class. | ||
| :param csv_file: The name of the csv file to retrieve data from. \ | ||
| Example: `prediction.csv`. | ||
| :type csv_file: str | ||
| :param independent_variables: A list of independent variables. \ | ||
| Example: [`solar`, `degree_days`]. | ||
| :type independent_variables: list | ||
| :param dependent_variable: The dependent variable(to be predicted). \ | ||
| Example: `hours`. | ||
| :type dependent_variable: str | ||
| :param sklearn_model: The `scikit-learn` model that will be used. For now only \ | ||
| this options are possible: `LinearRegression`, `ElasticNet` and `KNeighborsRegressor`. | ||
| :type sklearn_model: str | ||
| :param new_values: The new values for the independent variables(in the same order as the independent variables list). \ | ||
| Example: [2.24, 5.68]. | ||
| :type new_values: list | ||
| :param root: The parent folder of the path where the config.yaml file is located | ||
| :type root: str | ||
| :param logger: The passed logger object | ||
| :type logger: logging.Logger | ||
| """ | ||
| self.csv_file = csv_file | ||
| self.independent_variables = independent_variables | ||
| self.dependent_variable = dependent_variable | ||
| self.sklearn_model = sklearn_model | ||
| self.new_values = new_values | ||
| self.root = root | ||
| self.logger = logger | ||
| self.is_tuned = False | ||
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| def load_data(self) -> pd.DataFrame: | ||
| """Load the data.""" | ||
| filename_path = pathlib.Path(self.root) / self.csv_file | ||
| if filename_path.is_file(): | ||
| with open(filename_path, 'rb') as inp: | ||
| data = pd.read_csv(filename_path) | ||
| else: | ||
| self.logger.error("The cvs file was not found.") | ||
| raise ValueError( | ||
| f"The CSV file "+ self.csv_file +" was not found." | ||
| ) | ||
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| required_columns = self.independent_variables | ||
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| if not set(required_columns).issubset(data.columns): | ||
| raise ValueError( | ||
| f"CSV file should contain the following columns: {', '.join(required_columns)}" | ||
| ) | ||
| return data | ||
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| def prepare_data(self, data) -> Tuple[np.ndarray, np.ndarray]: | ||
| """ | ||
| Prepare the data. | ||
| :param data: Input Data | ||
| :type data: pd.DataFrame | ||
| :return: A tuple containing the train data. | ||
| :rtype: Tuple[np.ndarray, np.ndarray] | ||
| """ | ||
| X = data[self.independent_variables].values | ||
| y = data[self.dependent_variable].values | ||
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | ||
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| return X_train, y_train | ||
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| def predict(self) -> np.ndarray: | ||
| r"""The predict method to generate a forecast from a csv file. | ||
| :return: The np.ndarray containing the predicted value. | ||
| :rtype: np.ndarray | ||
| """ | ||
| self.logger.info("Performing a prediction for "+self.csv_file) | ||
| # Preparing the data: adding exogenous features | ||
| data = self.load_data() | ||
| if data is not None: | ||
| X, y = self.prepare_data(data) | ||
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| if self.sklearn_model == 'LinearRegression': | ||
| base_model = LinearRegression() | ||
| elif self.sklearn_model == 'ElasticNet': | ||
| base_model = ElasticNet() | ||
| elif self.sklearn_model == 'KNeighborsRegressor': | ||
| base_model = KNeighborsRegressor() | ||
| else: | ||
| self.logger.error("Passed sklearn model "+self.sklearn_model+" is not valid") | ||
| # Define the forecaster object | ||
| self.forecaster = base_model | ||
| # Fit and time it | ||
| self.logger.info("Predict through a "+self.sklearn_model+" model") | ||
| start_time = time.time() | ||
| self.forecaster.fit(X, y) | ||
| self.logger.info(f"Elapsed time for model fit: {time.time() - start_time}") | ||
| new_values = np.array([self.new_values]) | ||
| prediction = self.forecaster.predict(new_values) | ||
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| return prediction | ||
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