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| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import pandas as pd | |
| from joblib import load | |
| from sklearn.preprocessing import MinMaxScaler | |
| from sklearn.ensemble import VotingClassifier | |
| from xgboost import XGBClassifier | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.svm import SVC | |
| class SmokerModel: | |
| """ | |
| Smoker Model Class that can predict new instances | |
| INPUTS | |
| --- | |
| model_path: the path to the model file | |
| scaler_path: the path to the min max scaler file | |
| """ | |
| def __init__(self, model_path, scaler_path): | |
| self.model = load(model_path) | |
| self.scaler = load(scaler_path) | |
| self.labels = ["non-smoker", "smoker"] | |
| def scale(self, X): | |
| """ | |
| Apply the scaler used to train the model to the new data | |
| INPUT | |
| ----- | |
| X: the data to be scaled | |
| OUTPUT | |
| ------ | |
| returns the scaled data | |
| """ | |
| new_data_scaled = self.scaler.transform(X) | |
| return new_data_scaled | |
| def predict(self, X: pd.DataFrame) -> str: | |
| """ | |
| Make a prediction on one sample using the loaded model. | |
| INPUT | |
| ----- | |
| X: the data to predict a label for | |
| OUTPUT | |
| ------ | |
| predicted label | |
| """ | |
| # scale the data | |
| X_scaled = self.scale(X) | |
| #check array only has one sample | |
| if X.shape[0] != 1: | |
| raise ValueError("Input array must contain only one sample, but {} samples were found".format(X.shape[0])) | |
| return | |
| # Now, use the scaled data to make predictions using the loaded model | |
| array = self.model.predict(X_scaled) | |
| #predict | |
| predicted_label = array[0] | |
| str_label = self.labels[predicted_label] | |
| return str_label |