Create AutoSKLearn Class

Here is the starter code:

import autosklearn.classification
import autosklearn.regression
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_squared_error
from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
import joblib

class AutoMLModel:
def __init__(self, task='classification', time_left_for_this_task=3600, per_run_time_limit=300):
"""
Initialize the AutoMLModel.

:param task: Type of task, either 'classification', 'regression', or 'clustering'.
:param time_left_for_this_task: Time limit for the AutoML task in seconds.
:param per_run_time_limit: Time limit for each model run in seconds.
"""
self.task = task
self.time_left_for_this_task = time_left_for_this_task
self.per_run_time_limit = per_run_time_limit
self.automl = None
self.model = None

def fit(self, X, y=None, cluster_algorithm='kmeans', **kwargs):
"""
Fit the AutoML model to the data.

:param X: Features as a DataFrame or array.
:param y: Target variable as a Series or array (for classification and regression tasks).
:param cluster_algorithm: Clustering algorithm to use ('kmeans', 'dbscan', or 'agglomerative').
:param kwargs: Additional keyword arguments for the clustering algorithm.
"""
if self.task == 'classification':
self.automl = autosklearn.classification.AutoSklearnClassifier(
time_left_for_this_task=self.time_left_for_this_task,
per_run_time_limit=self.per_run_time_limit
)
self.automl.fit(X, y)
self.model = self.automl.show_models()
elif self.task == 'regression':
self.automl = autosklearn.regression.AutoSklearnRegressor(
time_left_for_this_task=self.time_left_for_this_task,
per_run_time_limit=self.per_run_time_limit
)
self.automl.fit(X, y)
self.model = self.automl.show_models()
elif self.task == 'clustering':
if cluster_algorithm == 'kmeans':
self.model = KMeans(**kwargs)
elif cluster_algorithm == 'dbscan':
self.model = DBSCAN(**kwargs)
elif cluster_algorithm == 'agglomerative':
self.model = AgglomerativeClustering(**kwargs)
else:
raise ValueError("Clustering algorithm must be 'kmeans', 'dbscan', or 'agglomerative'.")
self.model.fit(X)
else:
raise ValueError("Task must be 'classification', 'regression', or 'clustering'.")

def predict(self, X):
"""
Predict using the trained AutoML model.

:param X: Features as a DataFrame or array.
:return: Predictions as an array.
"""
if self.automl:
return self.automl.predict(X)
elif self.model:
return self.model.predict(X)
else:
print("Model has not been fitted yet.")
return None

def evaluate(self, X, y):
"""
Evaluate the model on the given data.

:param X: Features as a DataFrame or array.
:param y: True target variable values as a Series or array.
:return: Evaluation metric.
"""
predictions = self.predict(X)
if self.task == 'classification':
return accuracy_score(y, predictions)
elif self.task == 'regression':
return mean_squared_error(y, predictions, squared=False)
elif self.task == 'clustering':
# For clustering, evaluation metrics are different (e.g., silhouette score, etc.)
# Implement specific metrics as needed
return None

def save_model(self, file_path):
"""
Save the trained model to a file.

:param file_path: Path to the file where the model will be saved.
"""
if self.automl or self.model:
joblib.dump(self.automl if self.automl else self.model, file_path)
else:
print("Model has not been fitted yet.")

def load_model(self, file_path):
"""
Load a trained model from a file.

:param file_path: Path to the file where the model is saved.
"""
self.model = joblib.load(file_path)

def get_model(self):
"""
Get the underlying AutoML or clustering model.

:return: AutoML or clustering model object.
"""
return self.automl if self.task in ['classification', 'regression'] else self.model

def show_models(self):
"""
Show the models found during the AutoML process.

:return: List of models.
"""
if self.task in ['classification', 'regression'] and self.automl:
return self.model
else:
print("Model has not been fitted yet.")
return None