Solve IML task 1a

IML Projects/Task 1a/data/template_solution.py

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+# This serves as a template which will guide you through the implementation of this task. It is advised
+# to first read the whole template and get a sense of the overall structure of the code before trying to fill in any of the TODO gaps.
+# First, we import necessary libraries:
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import KFold
+
+# Add any additional imports here (however, the task is solvable without using 
+# any additional imports)
+# import ...
+
+def fit(X, y, lam):
+    """
+    This function receives training data points, then fits the ridge regression on this data
+    with regularization hyperparameter lambda. The weights w of the fitted ridge regression
+    are returned. 
+
+    Parameters
+    ----------
+    X: matrix of floats, dim = (135,13), inputs with 13 features
+    y: array of floats, dim = (135,), input labels
+    lam: float. lambda parameter, used in regularization term
+
+    Returns
+    ----------
+    w: array of floats: dim = (13,), optimal parameters of ridge regression
+    """
+    weights = np.zeros((13,))
+    # TODO: Enter your code here
+    assert weights.shape == (13,)
+    return weights
+
+
+def calculate_RMSE(w, X, y):
+    """This function takes test data points (X and y), and computes the empirical RMSE of 
+    predicting y from X using a linear model with weights w. 
+
+    Parameters
+    ----------
+    w: array of floats: dim = (13,), optimal parameters of ridge regression 
+    X: matrix of floats, dim = (15,13), inputs with 13 features
+    y: array of floats, dim = (15,), input labels
+
+    Returns
+    ----------
+    rmse: float: dim = 1, RMSE value
+    """
+    rmse = 0
+    # TODO: Enter your code here
+    assert np.isscalar(rmse)
+    return rmse
+
+
+def average_LR_RMSE(X, y, lambdas, n_folds):
+    """
+    Main cross-validation loop, implementing 10-fold CV. In every iteration (for every train-test split), the RMSE for every lambda is calculated, 
+    and then averaged over iterations.
+    
+    Parameters
+    ---------- 
+    X: matrix of floats, dim = (150, 13), inputs with 13 features
+    y: array of floats, dim = (150, ), input labels
+    lambdas: list of floats, len = 5, values of lambda for which ridge regression is fitted and RMSE estimated
+    n_folds: int, number of folds (pieces in which we split the dataset), parameter K in KFold CV
+    
+    Returns
+    ----------
+    avg_RMSE: array of floats: dim = (5,), average RMSE value for every lambda
+    """
+    RMSE_mat = np.zeros((n_folds, len(lambdas)))
+
+    # TODO: Enter your code here. Hint: Use functions 'fit' and 'calculate_RMSE' with training and test data
+    # and fill all entries in the matrix 'RMSE_mat'
+
+    avg_RMSE = np.mean(RMSE_mat, axis=0)
+    assert avg_RMSE.shape == (5,)
+    return avg_RMSE
+
+
+# Main function. You don't have to change this
+if __name__ == "__main__":
+    # Data loading
+    data = pd.read_csv("train.csv")
+    y = data["y"].to_numpy()
+    data = data.drop(columns="y")
+    # print a few data samples
+    print(data.head())
+
+    X = data.to_numpy()
+    # The function calculating the average RMSE
+    lambdas = [0.1, 1, 10, 100, 200]
+    n_folds = 10
+    avg_RMSE = average_LR_RMSE(X, y, lambdas, n_folds)
+    # Save results in the required format
+    np.savetxt("./results.csv", avg_RMSE, fmt="%.12f")