all lab

Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/data/small_movies_b.csv

@@ -0,0 +1 @@
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Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/public_tests.py

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+import numpy as np
+
+def test_cofi_cost_func(target):
+    num_users_r = 4
+    num_movies_r = 5 
+    num_features_r = 3
+
+    X_r = np.ones((num_movies_r, num_features_r))
+    W_r = np.ones((num_users_r, num_features_r))
+    b_r = np.zeros((1, num_users_r))
+    Y_r = np.zeros((num_movies_r, num_users_r))
+    R_r = np.zeros((num_movies_r, num_users_r))
+
+    J = target(X_r, W_r, b_r, Y_r, R_r, 2);
+    assert not np.isclose(J, 13.5), f"Wrong value. Got {J}. Did you multiplied the regulartization term by lambda_?"
+    assert np.isclose(J, 27), f"Wrong value. Expected {27}, got {J}. Check the regularization term"
+
+
+    X_r = np.ones((num_movies_r, num_features_r))
+    W_r = np.ones((num_users_r, num_features_r))
+    b_r = np.ones((1, num_users_r))
+    Y_r = np.ones((num_movies_r, num_users_r))
+    R_r = np.ones((num_movies_r, num_users_r))
+
+    # Evaluate cost function
+    J = target(X_r, W_r, b_r, Y_r, R_r, 0);
+
+    assert np.isclose(J, 90), f"Wrong value. Expected {90}, got {J}. Check the term without the regularization"
+
+
+    X_r = np.ones((num_movies_r, num_features_r))
+    W_r = np.ones((num_users_r, num_features_r))
+    b_r = np.ones((1, num_users_r))
+    Y_r = np.zeros((num_movies_r, num_users_r))
+    R_r = np.ones((num_movies_r, num_users_r))
+
+    # Evaluate cost function
+    J = target(X_r, W_r, b_r, Y_r, R_r, 0);
+
+    assert np.isclose(J, 160), f"Wrong value. Expected {160}, got {J}. Check the term without the regularization"
+
+    X_r = np.ones((num_movies_r, num_features_r))
+    W_r = np.ones((num_users_r, num_features_r))
+    b_r = np.ones((1, num_users_r))
+    Y_r = np.ones((num_movies_r, num_users_r))
+    R_r = np.ones((num_movies_r, num_users_r))
+
+    # Evaluate cost function
+    J = target(X_r, W_r, b_r, Y_r, R_r, 1);
+
+    assert np.isclose(J, 103.5), f"Wrong value. Expected {103.5}, got {J}. Check the term without the regularization"
+
+    num_users_r = 3
+    num_movies_r = 4 
+    num_features_r = 4
+
+    #np.random.seed(247)
+    X_r = np.array([[0.36618032, 0.9075415,  0.8310605,  0.08590986],
+                     [0.62634721, 0.38234325, 0.85624346, 0.55183039],
+                     [0.77458727, 0.35704147, 0.31003294, 0.20100006],
+                     [0.34420469, 0.46103436, 0.88638208, 0.36175401]])#np.random.rand(num_movies_r, num_features_r)
+    W_r = np.array([[0.04786854, 0.61504665, 0.06633146, 0.38298908], 
+                    [0.16515965, 0.22320207, 0.89826005, 0.14373251], 
+                    [0.1274051 , 0.22757303, 0.96865613, 0.70741111]])#np.random.rand(num_users_r, num_features_r)
+    b_r = np.array([[0.14246472, 0.30110933, 0.56141144]])#np.random.rand(1, num_users_r)
+    Y_r = np.array([[0.20651685, 0.60767914, 0.86344527], 
+                    [0.82665019, 0.00944765, 0.4376798 ], 
+                    [0.81623732, 0.26776794, 0.03757507], 
+                    [0.37232161, 0.19890823, 0.13026598]])#np.random.rand(num_movies_r, num_users_r)
+    R_r = np.array([[1, 0, 1], [1, 0, 0], [1, 0, 0], [0, 1, 0]])#(np.random.rand(num_movies_r, num_users_r) > 0.4) * 1
+
+    # Evaluate cost function
+    J = target(X_r, W_r, b_r, Y_r, R_r, 3);
+
+    assert np.isclose(J, 13.621929978531858, atol=1e-8), f"Wrong value. Expected {13.621929978531858}, got {J}."
+
+    print('\033[92mAll tests passed!')
+

Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/recsys_utils.py

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+import numpy as np
+import pandas as pd
+from numpy import loadtxt
+
+def normalizeRatings(Y, R):
+    """
+    Preprocess data by subtracting mean rating for every movie (every row).
+    Only include real ratings R(i,j)=1.
+    [Ynorm, Ymean] = normalizeRatings(Y, R) normalized Y so that each movie
+    has a rating of 0 on average. Unrated moves then have a mean rating (0)
+    Returns the mean rating in Ymean.
+    """
+    Ymean = (np.sum(Y*R,axis=1)/(np.sum(R, axis=1)+1e-12)).reshape(-1,1)
+    Ynorm = Y - np.multiply(Ymean, R) 
+    return(Ynorm, Ymean)
+
+def load_precalc_params_small():
+
+    file = open('./data/small_movies_X.csv', 'rb')
+    X = loadtxt(file, delimiter = ",")
+
+    file = open('./data/small_movies_W.csv', 'rb')
+    W = loadtxt(file,delimiter = ",")
+
+    file = open('./data/small_movies_b.csv', 'rb')
+    b = loadtxt(file,delimiter = ",")
+    b = b.reshape(1,-1)
+    num_movies, num_features = X.shape
+    num_users,_ = W.shape
+    return(X, W, b, num_movies, num_features, num_users)
+
+def load_ratings_small():
+    file = open('./data/small_movies_Y.csv', 'rb')
+    Y = loadtxt(file,delimiter = ",")
+
+    file = open('./data/small_movies_R.csv', 'rb')
+    R = loadtxt(file,delimiter = ",")
+    return(Y,R)
+
+def load_Movie_List_pd():
+    """ returns df with and index of movies in the order they are in in the Y matrix """
+    df = pd.read_csv('./data/small_movie_list.csv', header=0, index_col=0,  delimiter=',', quotechar='"')
+    mlist = df["title"].to_list()
+    return(mlist, df)
+
+
+
+
+