Add Network Modeling Assignment 1 notes

IML Projects/Task 1b /task1b_ql4jfi6af0/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 numpy as np
+import pandas as pd
+
+# Add any additional imports here (however, the task is solvable without using 
+# any additional imports)
+# import ...
+
+def transform_features(X):
+    """
+    This function transforms the 5 input features of matrix X (x_i denoting the i-th component in a given row of X)
+    into 21 new features phi(X) in the following manner:
+    5 linear features: phi_1(X) = x_1, phi_2(X) = x_2, phi_3(X) = x_3, phi_4(X) = x_4, phi_5(X) = x_5
+    5 quadratic features: phi_6(X) = x_1^2, phi_7(X) = x_2^2, phi_8(X) = x_3^2, phi_9(X) = x_4^2, phi_10(X) = x_5^2
+    5 exponential features: phi_11(X) = exp(x_1), phi_12(X) = exp(x_2), phi_13(X) = exp(x_3), phi_14(X) = exp(x_4), phi_15(X) = exp(x_5)
+    5 cosine features: phi_16(X) = cos(x_1), phi_17(X) = cos(x_2), phi_18(X) = cos(x_3), phi_19(X) = cos(x_4), phi_20(X) = cos(x_5)
+    1 constant feature: phi_21(X)=1
+
+    Parameters
+    ----------
+    X: matrix of floats, dim = (700,5), inputs with 5 features
+
+    Returns
+    ----------
+    X_transformed: matrix of floats: dim = (700,21), transformed input with 21 features
+    """
+    X_transformed = np.zeros((700, 21))
+    # TODO: Enter your code here
+    assert X_transformed.shape == (700, 21)
+    return X_transformed
+
+
+def fit_logistic_regression(X, y):
+    """
+    This function receives training data points, transforms them, and then fits the logistic regression on this 
+    transformed data. Finally, it outputs the weights of the fitted logistic regression. 
+
+    Parameters
+    ----------
+    X: matrix of floats, dim = (700,5), inputs with 5 features
+    y: array of integers \in {0,1}, dim = (700,), input labels
+
+    Returns
+    ----------
+    weights: array of floats: dim = (21,), optimal parameters of logistic regression
+    """
+    weights = np.zeros((21,))
+    X_transformed = transform_features(X)
+    # TODO: Enter your code here
+    assert weights.shape == (21,)
+    return weights
+
+
+# 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=["Id", "y"])
+    # print a few data samples
+    print(data.head())
+
+    X = data.to_numpy()
+    # The function retrieving optimal LR parameters
+    w = fit_logistic_regression(X, y)
+    # Save results in the required format
+    np.savetxt("./results.csv", w, fmt="%.12f")