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| """ | |
| This module provides the CancerPredictor class for training and predicting breast cancer diagnosis | |
| using logistic regression. It leverages scikit-learn for model training, evaluation, and prediction, | |
| and pandas for data manipulation. The predictor expects input features such as radius_mean, | |
| texture_mean, symmetry_mean, and fractal_dimension_mean, and outputs a diagnosis prediction. | |
| Classes: | |
| CancerPredictor: Handles training on a CSV dataset and making predictions | |
| based on input features. | |
| """ | |
| import pandas as pd | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.preprocessing import LabelEncoder | |
| from sklearn.metrics import accuracy_score | |
| class CancerPredictor: | |
| """ | |
| CancerPredictor is a class for training and making predictions on breast cancer diagnosis | |
| using logistic regression. | |
| """ | |
| def __init__(self): | |
| self.model = LogisticRegression() | |
| self.le_diagnosis = LabelEncoder() | |
| def train(self, csv_train, csv_test): | |
| """ | |
| Trains the logistic regression model using a CSV file containing breast cancer data. | |
| The CSV must include columns: 'radius_mean', 'texture_mean', 'symmetry_mean', | |
| 'fractal_dimension_mean', and 'diagnosis'. | |
| Prints the model accuracy after training. | |
| """ | |
| # Load the train data | |
| data_train = pd.read_csv(csv_train) | |
| # Encode categorical variables | |
| data_train['diagnosis'] = self.le_diagnosis.fit_transform(data_train['diagnosis']) | |
| # Split features and target | |
| X_train = data_train[ | |
| ['radius_mean', 'texture_mean', 'symmetry_mean', 'fractal_dimension_mean']] | |
| y_train = data_train['diagnosis'] | |
| # Train the model | |
| self.model.fit(X_train, y_train) | |
| # Load the test data | |
| data_test = pd.read_csv(csv_test) | |
| # Encode categorical variables | |
| data_test['diagnosis'] = self.le_diagnosis.fit_transform(data_test['diagnosis']) | |
| # Split features and target | |
| X_test = data_test[ | |
| ['radius_mean', 'texture_mean', 'symmetry_mean', 'fractal_dimension_mean']] | |
| y_test = data_test['diagnosis'] | |
| # Evaluate the model | |
| y_pred = self.model.predict(X_test) | |
| accuracy = accuracy_score(y_test, y_pred) | |
| print(f"Model accuracy: {accuracy:.2f}") | |
| def predict(self, radius_mean, texture_mean, symmetry_mean, fractal_dimension_mean): | |
| """ | |
| Predicts the diagnosis ('M' for malignant or 'B' for benign) based on the provided | |
| feature values. | |
| Returns the predicted diagnosis as a string. | |
| """ | |
| # Create a DataFrame with the same feature names as the training data | |
| input_data = pd.DataFrame( | |
| [[radius_mean, texture_mean, symmetry_mean, fractal_dimension_mean]], | |
| columns=['radius_mean', 'texture_mean', 'symmetry_mean', 'fractal_dimension_mean']) | |
| # Make prediction | |
| prediction = self.model.predict(input_data) | |
| # Decode prediction | |
| diagnosis = self.le_diagnosis.inverse_transform(prediction)[0] | |
| return diagnosis | |