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from sklearn.ensemble import AdaBoostClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import joblib
import pandas as pd
import os
import numpy as np
class Classifier:
def __init__(self):
pass
def train_and_save(self):
print("\nIRIS model training...")
iris = load_iris()
ada = AdaBoostClassifier(n_estimators=5)
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.1, random_state=42)
model = ada.fit(X_train, y_train)
print(f"Model score: {ada.score(X_train, y_train):.3f}")
print(f"Test Accuracy: {ada.score(X_test, y_test):.3f}")
current_dir = os.path.dirname(os.path.abspath(__file__))
parent_dir = os.path.dirname(current_dir)
test_data_csv_path = os.path.join(parent_dir, "data", "test_data.csv")
pd.concat([pd.DataFrame(X_test), pd.DataFrame(y_test, columns=['4'])], axis=1).to_csv(test_data_csv_path,
index=False)
model_path = os.path.join(parent_dir, "models", "model.pkl")
joblib.dump(model, model_path)
print(f"Model saved to {model_path}")
def load_and_test(self, data):
print("\nIRIS model prediction...")
current_dir = os.path.dirname(os.path.abspath(__file__))
parent_dir = os.path.dirname(current_dir)
model_path = os.path.join(parent_dir, "models", "model.pkl")
model = joblib.load(model_path)
features = np.array(data)
if features.shape[-1] != 4:
raise ValueError("Expected 4 features per input.")
# Predict the class
predictions = model.predict(features).tolist()
probabilities = model.predict_proba(features).tolist()
# Map predictions to class labels
iris_types = {0: "setosa", 1: "versicolor", 2: "virginica"}
prediction_labels = [iris_types[pred] for pred in predictions]
return {"predictions": prediction_labels, "probabilities": probabilities}
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