class TestLassoRegression(unittest.TestCase): """Tests for TestLassoRegression. Uses housing data to test LassoRegression. Statics: _multiprocess_can_split_ (bool): Flag for nose tests to run tests in parallel. """ _multiprocess_can_split_ = True def setUp(self): """Constructor for TestLassoRegression. Loads housing data, and creates training and testing data. """ self.convert_numpy = ConvertNumpy() self.normalize_features = NormalizeFeatures() self.lasso = LassoRegression() self.predict_output = PredictOutput() self.residual_sum_squares = ResidualSumSquares() self.k_fold_cross_validation = KFoldCrossValidation() # Create a dictionary type to store relevant data types so that our pandas # will read the correct information dtype_dict = {'bathrooms': float, 'waterfront': int, 'sqft_above': int, 'sqft_living15': float, 'grade': int, 'yr_renovated': int, 'price': float, 'bedrooms': float, 'zipcode': str, 'long': float, 'sqft_lot15': float, 'sqft_living': float, 'floors': str, 'condition': int, 'lat': float, 'date': str, 'sqft_basement': int, 'yr_built': int, 'id': str, 'sqft_lot': int, 'view': int} # Create a kc_house that encompasses all test and train data self.kc_house = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_data.csv', dtype=dtype_dict) # Create a kc_house_test_frame that encompasses only train data self.kc_house_train = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_train_data.csv', dtype=dtype_dict) # Create a kc_house_frames that encompasses only test data self.kc_house_test = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_test_data.csv', dtype=dtype_dict) # Convert all the frames with the floors to float type self.kc_house['floors'] = self.kc_house['floors'].astype(float) self.kc_house_train['floors'] = self.kc_house['floors'].astype(float) self.kc_house_test['floors'] = self.kc_house['floors'].astype(float) # Then back to int type self.kc_house['floors'] = self.kc_house['floors'].astype(int) self.kc_house_train['floors'] = self.kc_house['floors'].astype(int) self.kc_house_test['floors'] = self.kc_house['floors'].astype(int) def test_01_normalize_features(self): """Tests normalizing features. Test normalization features, and compare it with known values. """ # Normalize the features, and also return the norms features, norms = self.normalize_features.l2_norm(np.array([[3., 6., 9.], [4., 8., 12.]])) # Assert that the np array is equal to features self.assertTrue(np.array_equal(np.array([[0.6, 0.6, 0.6], [0.8, 0.8, 0.8]]), features), True) # Assert that the np array is equal to norms self.assertTrue(np.array_equal(np.array([5., 10., 15.]), norms), True) def test_02_compute_ro(self): """Test compute ro Test compute one round of ro. """ # We will use sqft_iving, and sqft_living15 features = ['sqft_living', 'bedrooms'] # Output will use price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house, features, output, 1) # Create our initial weights normalized_feature_matrix, _ = self.normalize_features.l2_norm(feature_matrix) # Set initial weights weights = np.array([1., 4., 1.]) # Compute ro_j ro_j = self.lasso.compute_ro_j(normalized_feature_matrix, output, weights) # Assert the output of ro_j self.assertTrue(np.allclose(ro_j, np.array([79400300.03492916, 87939470.77299108, 80966698.67596565]))) def test_03_compute_coordinate_descent_step(self): """Test one coordinate descent step. Test one coordinate descent step and compare it with known values. """ # Assert that both are equal self.assertEquals(round(self.lasso.lasso_coordinate_descent_step({"i": 1, "weights": np.array([1., 4.])}, np.array([[3./math.sqrt(13), 1./math.sqrt(10)], [2./math.sqrt(13), 3./math.sqrt(10)]]), np.array([1., 1.]), {"l1_penalty": 0.1}), 8), round(0.425558846691, 8)) def test_04_coordinate_descent(self): """Test coordinate descent. Test coordinate descent and compare with known values. """ # We will use sqft_iving, and sqft_living15 features = ['sqft_living', 'bedrooms'] # Output will use price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house, features, output, 1) # Create our initial weights normalized_feature_matrix, _ = self.normalize_features.l2_norm(feature_matrix) # Set initial weights initial_weights = np.zeros(3) # Set l1 penalty l1_penalty = 1e7 # Set tolerance tolerance = 1.0 # Compute the weights using coordinate descent weights = self.lasso.lasso_cyclical_coordinate_descent(normalized_feature_matrix, output, {"initial_weights": initial_weights, "l1_penalty": l1_penalty, "tolerance": tolerance}) # Assert that these two numpy arrays are the same self.assertTrue(np.allclose(weights, np.array([21624998.3663629, 63157246.78545423, 0.]), True)) # Predict the output predicted_output = self.predict_output.regression(normalized_feature_matrix, weights) # Assert that the RSS is what we wanted self.assertEquals(round(self.residual_sum_squares.residual_sum_squares_regression(output, predicted_output), -10), round(1.63049248148e+15, -10)) def test_05_coordinate_descent_with_normalization(self): """Test coordinate descent with normalization. Test coordinate descent and then normalize the result, so that we can use the weights on a test set. """ # We will use multiple features features = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated'] # Output will use price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1) # Create our initial weights normalized_feature_matrix, norms = self.normalize_features.l2_norm(feature_matrix) # Compute Multiple Weights weights1e7 = self.lasso.lasso_cyclical_coordinate_descent(normalized_feature_matrix, output, {"initial_weights": np.zeros(len(features)+1), "l1_penalty": 1e7, "tolerance": 1}) weights1e8 = self.lasso.lasso_cyclical_coordinate_descent(normalized_feature_matrix, output, {"initial_weights": np.zeros(len(features)+1), "l1_penalty": 1e8, "tolerance": 1}) weights1e4 = self.lasso.lasso_cyclical_coordinate_descent(normalized_feature_matrix, output, {"initial_weights": np.zeros(len(features)+1), "l1_penalty": 1e4, "tolerance": 5e5}) # Compute multiple normalized normalized_weights1e4 = weights1e4 / norms normalized_weights1e7 = weights1e7 / norms normalized_weights1e8 = weights1e8 / norms # We will use multiple features features = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated'] # Output will use price output = ['price'] # Convert our test pandas frame to numpy test_feature_matrix, test_output = self.convert_numpy.convert_to_numpy(self.kc_house_test, features, output, 1) # Predict the output predicted_output = self.predict_output.regression(test_feature_matrix, normalized_weights1e4) # Assert that the RSS is what we wanted self.assertEquals(round(self.residual_sum_squares.residual_sum_squares_regression(test_output, predicted_output), -12), round(2.2778100476e+14, -12)) # Predict the output predicted_output = self.predict_output.regression(test_feature_matrix, normalized_weights1e7) # Assert that the RSS is what we wanted self.assertEquals(round(self.residual_sum_squares.residual_sum_squares_regression(test_output, predicted_output), -12), round(2.75962079909e+14, -12)) # Predict the output predicted_output = self.predict_output.regression(test_feature_matrix, normalized_weights1e8) # Assert that the RSS is what we wanted self.assertEquals(round(self.residual_sum_squares.residual_sum_squares_regression(test_output, predicted_output), -12), round(5.37049248148e+14, -12))
class LassoRegression: """Class to compute Lasso Regression. Lasso Regression is essentially L1 Norm with Linear Regression. We cannot use gradient descent since the absolute value for L1 Norm is not differentiable. Hence we use coordinate descent. Attributes: predict_output (PredictOutput): A PredictOutput class that can predict output given features and weights. """ def __init__(self): """Constructor for LassoRegression to setup PredictOutput. Constructor for the LassoRegression class, mainly used to setup PredictOutput. """ self.predict_output = PredictOutput() def lasso_cyclical_coordinate_descent(self, feature_matrix, output, model_parameters): """Coordinate descent algorithm for Lasso regression. Performs a Lasso Cyclical Coordinate Descent, which will loop over each features and then perform coordinate descent, and if all of the weight changes are less than the tolerance, then we will stop. Lasso Regression is based on: w_j = ro_j + delta/2 if ro_j < -delta/2 0 if ro_j between [-delta/2,delta/2] ro_j - delta/2 if ro_j > delta/2 Where, ro_j = Sigma(N, i=1, h_j(x_i)(y_i-y^_i(w_-j). h_j(x_i): Normalized features of x_i (input features, but without j feature). y_i: Real output. y^_i(w_-j): Predicted output without feature j. Args: feature_matrix (numpy.ndarray): Feature matrix. output (numpy.array): Real output for the feature matrix. model_parameters (dict): A dictionary of model parameters, { initial_weights (numpy.array): The starting initial weights, step_size (float): Step size, tolerance (float or None): Tolerance (or epsilon), l1_penalty (float): L1 penalty value, max_iteration (int): Maximum iteration to compute. } Returns: numpy.array: final weights after coordinate descent has been completed """ # Flag to indicate that the change is too low low_change = False # Set Weights to initial_weights weights = model_parameters["initial_weights"] # While the change is not too low (meaning lower than tolerance) while not low_change: # An array of boolean to detect if all the changes are less than tolerance change = [] # Need to incorporate all the new changes to the weights for i in range(len(weights)): # Remember the old weights old_weights_i = weights[i] # Compute the current weight weights[i] = self.lasso_coordinate_descent_step({"i": i, "weights": weights}, feature_matrix, output, model_parameters) # Returns true if any weight changes greater than tolerance change.append(abs(old_weights_i-weights[i]) > model_parameters["tolerance"]) # Returns true if all the changes are less than tolerance low_change = not any(change) return weights def lasso_coordinate_descent_step(self, step_parameters, feature_matrix, output, model_parameters): """Computes the Lasso coordinate descent step. Computes the Lasso coordinate descent step, which is essentially computing a new ro_i, and based on the index and ro_i, compute new w_i weight. Args: step_parameters (dict): A dictionary for step data, { i (int): Feature i, weights (numpy.array): Current weights. } feature_matrix (numpy.ndarray): Feature matrix. output (numpy.array): Real output for feature_matrix. model_parameters (dict): A dictionary of model parameters, { step_size (float): Step size, tolerance (float or None): Tolerance (or epsilon), l1_penalty (float): L1 penalty value, max_iteration (int): Maximum iteration to compute. } Returns: new_weight_i (float): New weight for the feature i. """ # compute ro[i] = SUM[ [feature_i]*(output - prediction + weight[i]*[feature_i]) ] ro_i = self.compute_ro_j(feature_matrix, output, step_parameters["weights"])[step_parameters["i"]] # when i == 0, then it's a intercept -- do not regularize # else # w_i = ro_i + delta/2 if ro_i < -delta/2 # 0 if ro_i between [-delta/2,delta/2] # ro_i - delta/2 if ro_i > delta/2 if step_parameters["i"] == 0: new_weight_i = ro_i elif ro_i < -model_parameters["l1_penalty"]/2.: new_weight_i = ro_i + model_parameters["l1_penalty"]/2 elif ro_i > model_parameters["l1_penalty"]/2.: new_weight_i = ro_i - model_parameters["l1_penalty"]/2 else: new_weight_i = 0. # Return the new weight for feature i return new_weight_i def compute_ro_j(self, feature_matrix, real_output, weights): """Computes ro_j. Computes ro_j using ro_j = Sigma(N, i=1, h_j(x_i)(y_i-y^_i(w_-j). Args: feature_matrix (numpy.ndarray): Feature matrix. real_output (numpy.array): Real output (not predicted) for feature_matrix. weights (numpy.array): The current weights. Returns: ro (numpy.array): ro (or new weights for each feature). """ # Number of features (columns) feature_num = feature_matrix.shape[1] # Set ro to be an array that is feature_num size ro = np.zeros(feature_num) # Loop through feature for j in range(feature_num): # prediction = y_i(w_-j), prediction without feature j prediction = self.predict_output.regression(np.delete(feature_matrix, j, axis=1), np.delete(weights, j)) # residual = output - prediction residual = real_output-prediction # ro[j] = Sigma(N, i=1, feature_i) * residual ro[j] = np.sum([feature_matrix[:, j]*residual]) return ro
class KFoldCrossValidation: """Class for K Fold Cross Validation. Class for K Fold Cross Validation for selecting best parameters. Attributes: convert_numpy (ConvertNumpy): Pandas to Numpy conversion class. predict_output (PredictOutput): Output prediction. residual_sum_squares (ResidualSumSquares): Computes residual sum of squares. """ def __init__(self): """Constructor for KFoldCrossValidation. Constructor for KFoldCrossValidation, used to setup numpy conversion, output prediction, and residual sum of squares. """ self.convert_numpy = ConvertNumpy() self.predict_output = PredictOutput() self.residual_sum_squares = ResidualSumSquares() def k_fold_cross_validation(self, k, model, model_parameters, data_parameters): """Performs K Fold Cross Validation. Takes in our data, and splits the data to smaller subsets, and these smaller subsets are used as validation sets, and everything else not included in the validation set is used as training sets. The model will be trained using the training set, and the performance assessment such as RSS would be used on the validation set against the model. Args: k (int): Number of folds.= model (obj): Model used for k folds cross validation. model_parameters (dict): Model parameters to train the specified model. data_parameters (dict): A dictionary of data information: { data (pandas.DataFrame): Data used for k folds cross validation, output (str): Output name, features (list of str): A list of feature names. } Returns: float: Average validation error. """ # Sum of the validation error, will divide by k (fold) later validation_error_sum = 0 # Loop through each fold for i in range(k): # Computes validation, and training set validation_set, training_set = self.create_validation_training_set(data_parameters["data"], k, i) # Convert our pandas frame to numpy to create validation set validation_set_matrix, validation_output = self.convert_numpy.convert_to_numpy(validation_set, data_parameters["features"], data_parameters["output"], 1) # Create a model with Train Set 1 + Train Set 2 final_weights = self.create_weights(model, model_parameters, training_set, data_parameters) # Predict the output of test features predicted_output = self.predict_output.regression(validation_set_matrix, final_weights) # compute squared error (in other words, rss) validation_error_sum += self.residual_sum_squares.residual_sum_squares_regression(validation_output, predicted_output) # Return the validation_error_sum divided by fold return validation_error_sum/k @staticmethod def create_validation_training_set(data, k, iteration): """Slice data according to k, iteration, and size of data. Computes the validation, and training set according to the k number of folds, and the current iteration. Args: data (pandas.DataFrame): Data used for k folds cross validation. k (int): Number of folds. iteration (int): Current K fold validation iteration. Returns: A tuple that contains training set, and validation set: ( validation_set (pandas.DataFrame): Validation set. training_set (pandas.DataFrame): Training set. ) """ length_data = len(data) # Compute the start section of the current fold start = int((length_data * iteration) / k) # Compute the end section of the current fold end = int((length_data * (iteration + 1)) / k - 1) # Get our validation set from the start to the end+1 (+1 since we need to include the end) # <Start : end + 1> Validation Set validation_set = data[start:end + 1] # The Training set the left and the right parts of the validation set # < 0 : Start > Train Set 1 # < Start : End + 1 > Validation Set # < End + 1 : n > Train Set 2 # Train Set 1 + Train Set 2 = All data excluding validation set training_set = data[0:start].append(data[end + 1:length_data]) return validation_set, training_set def create_weights(self, model, model_parameters, training_set, data_parameters): """Use model to create weights. Use model, model parameters, and training set, create a set of coefficients. Args: model (obj): Model that can be run. model_parameters (dict): A dictionary of model parameters. training_set (pandas.DataFrame): Train set used for k folds cross validation. data_parameters (dict): A dictionary of data information: { data (pandas.DataFrame): Data used for k folds cross validation, output (str): Output name, features (list of str): A list of feature names. } Returns: numpy.array: numpy array of weights created by running model. """ # Convert our pandas frame to numpy to create training set training_feature_matrix, training_output = self.convert_numpy.convert_to_numpy(training_set, data_parameters["features"], data_parameters["output"], 1) # Create a model with Train Set 1 + Train Set 2 return model(model_parameters=model_parameters, feature_matrix=training_feature_matrix, output=training_output)
class TestRidgeRegression(unittest.TestCase): """Test for RidgeRegression. Uses housing data to test RidgeRegression. Statics: _multiprocess_can_split_ (bool): Flag for nose tests to run tests in parallel. """ _multiprocess_can_split_ = True def setUp(self): """Constructor for TestRidgeRegression. Loads housing data, and creates training and testing data. """ self.convert_numpy = ConvertNumpy() self.ridge_regression = RidgeRegression() self.predict_output = PredictOutput() self.residual_sum_squares = ResidualSumSquares() self.k_fold_cross_validation = KFoldCrossValidation() # Create a dictionary type to store relevant data types so that our pandas # will read the correct information dtype_dict = {'bathrooms': float, 'waterfront': int, 'sqft_above': int, 'sqft_living15': float, 'grade': int, 'yr_renovated': int, 'price': float, 'bedrooms': float, 'zipcode': str, 'long': float, 'sqft_lot15': float, 'sqft_living': float, 'floors': str, 'condition': int, 'lat': float, 'date': str, 'sqft_basement': int, 'yr_built': int, 'id': str, 'sqft_lot': int, 'view': int} # Create a kc_house that encompasses all test and train data self.kc_house = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_data.csv', dtype=dtype_dict) # Create a kc_house_test_frame that encompasses only train data self.kc_house_train = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_train_data.csv', dtype=dtype_dict) # Create a kc_house_frames that encompasses only test data self.kc_house_test = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_test_data.csv', dtype=dtype_dict) # Create a kc_house_train_valid_shuffled that encompasses both train and valid data and shuffled self.kc_house_train_valid_shuffled = pd.read_csv('./unit_tests/test_data/regression/' 'kc_house_with_validation_k_fold/' 'wk3_kc_house_train_valid_shuffled.csv', dtype=dtype_dict) def test_01_gradient_descent_no_penalty(self): """Tests gradient descent algorithm. Tests the result on gradient descent with low penalty. """ # We will use sqft_living for our features features = ['sqft_living'] # Output will use price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1) # Create our initial weights initial_weights = np.array([0., 0.]) # Step size step_size = 1e-12 # Max Iterations to Run max_iterations = 1000 # Tolerance tolerance = None # L2 Penalty l2_penalty = 0.0 # Compute our gradient descent value final_weights = self.ridge_regression.gradient_descent(feature_matrix, output, {"initial_weights": initial_weights, "step_size": step_size, "tolerance": tolerance, "l2_penalty": l2_penalty, "max_iteration": max_iterations}) # We will use sqft_iving, and sqft_living15 test_features = ['sqft_living'] # Output will be price test_output = ['price'] # Convert our test pandas frame to numpy test_feature_matrix, test_output = self.convert_numpy.convert_to_numpy(self.kc_house_test, test_features, test_output, 1) # Predict the output of test features predicted_output = self.predict_output.regression(test_feature_matrix, final_weights) # Compute RSS rss = self.residual_sum_squares.residual_sum_squares_regression(test_output, predicted_output) # Assert that the weights is correct self.assertEquals(round(-0.16311351478746433, 5), round(final_weights[0], 5)) self.assertEquals(round(263.02436896538489, 3), round(final_weights[1], 3)) # Assert that rss is correct self.assertEquals(round(275723632153607.72, -5), round(rss, -5)) def test_02_gradient_descent_high_penalty(self): """Tests gradient descent. Tests the result on gradient descent with high penalty. """ # We will use sqft_living for our features features = ['sqft_living'] # Output will use price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1) # Create our initial weights initial_weights = np.array([0., 0.]) # Step size step_size = 1e-12 # Max Iterations to Run max_iterations = 1000 # Tolerance tolerance = None # L2 Penalty l2_penalty = 1e11 # Compute our gradient descent value final_weights = self.ridge_regression.gradient_descent(feature_matrix, output, {"initial_weights": initial_weights, "step_size": step_size, "tolerance": tolerance, "l2_penalty": l2_penalty, "max_iteration": max_iterations}) # We will use sqft_iving test_features = ['sqft_living'] # Output will be price test_output = ['price'] # Convert our test pandas frame to numpy test_feature_matrix, test_output = self.convert_numpy.convert_to_numpy(self.kc_house_test, test_features, test_output, 1) # Predict the output of test features predicted_output = self.predict_output.regression(test_feature_matrix, final_weights) # Compute RSS rss = self.residual_sum_squares.residual_sum_squares_regression(test_output, predicted_output) # Assert that the weights is correct self.assertEquals(round(9.7673000000000005, 5), round(final_weights[0], 5)) self.assertEquals(round(124.572, 3), round(final_weights[1], 3)) # Assert that rss is correct self.assertEquals(round(694642101500000.0, -5), round(rss, -5)) def test_03_gradient_descent_multiple_high_penalty(self): """Tests gradient descent. Tests gradient descent with multiple features, and high penalty. """ # We will use sqft_iving, and sqft_living15 features = ['sqft_living', 'sqft_living15'] # Output will use price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1) # Create our initial weights initial_weights = np.array([0.0, 0.0, 0.0]) # Step size step_size = 1e-12 # Max Iterations to Run max_iterations = 1000 # Tolerance tolerance = None # L2 Penalty l2_penalty = 1e11 # Compute our gradient descent value final_weights = self.ridge_regression.gradient_descent(feature_matrix, output, {"initial_weights": initial_weights, "step_size": step_size, "tolerance": tolerance, "l2_penalty": l2_penalty, "max_iteration": max_iterations}) # We will use sqft_iving, and sqft_living15 test_features = ['sqft_living', 'sqft_living15'] # Output will be price test_output = ['price'] # Convert our test pandas frame to numpy test_feature_matrix, test_output = self.convert_numpy.convert_to_numpy(self.kc_house_test, test_features, test_output, 1) # Predict the output of test features predicted_output = self.predict_output.regression(test_feature_matrix, final_weights) # Compute RSS rss = self.residual_sum_squares.residual_sum_squares_regression(test_output, predicted_output) # Assert that the weights is correct self.assertEquals(round(6.7429699999999997, 5), round(final_weights[0], 5)) self.assertEquals(round(91.489000000000004, 3), round(final_weights[1], 3)) self.assertEquals(round(78.437490333967176, 3), round(final_weights[2], 3)) # Assert that rss is correct self.assertEquals(round(500404800500842.0, -5), round(rss, -5)) # Look at the first predicted output self.assertEquals(round(270453.53000000003, 3), round(predicted_output[0], 3)) # The first output should be 310000 in the test set self.assertEquals(310000.0, test_output[0]) def test_04_gradient_descent_k_fold(self): """Tests gradient descent with K fold cross validation. Tests best l2_penalty for ridge regression using gradient descent. """ # We will use sqft_living for our features features = ['sqft_living'] # Output will use price output = ['price'] # Create our initial weights initial_weights = np.array([0., 0.]) # Step size step_size = 1e-12 # Tolerance tolerance = None # Max Iterations to Run max_iterations = 1000 # Number of Folds folds = 10 # Store Cross Validation results cross_validation_results = [] # We want to test l2 penalty values in [10^1, 10^2, 10^3, 10^4, ..., 10^11] for l2_penalty in np.logspace(1, 11, num=11): # Create a dictionary of model_parameters model_parameters = {'step_size': step_size, 'max_iteration': max_iterations, 'initial_weights': initial_weights, 'tolerance': tolerance, 'l2_penalty': l2_penalty} # Compute the cross validation results cv = self.k_fold_cross_validation.k_fold_cross_validation(folds, self.ridge_regression.gradient_descent, model_parameters, {"data": self.kc_house_train, "output": output, "features": features}) # Append it into the results cross_validation_results.append((l2_penalty, cv)) # Lowest Result lowest = sorted(cross_validation_results, key=lambda x: x[1])[0] # Assert True that 10000000 is the l2_penalty that gives the lowest cross validation error self.assertEquals(10000000.0, lowest[0]) # Assert True that is the lowest l2_penalty self.assertEquals(round(120916225809145.0, 0), round(lowest[1], 0)) def test_05_gradient_ascent(self): """Tests gradient ascent. Tests gradient ascent and compare it with known values. """ # We will use sqft_living for our features features = ['sqft_living'] # Output will be price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1) # Create our initial weights initial_weights = np.array([0., 0.]) # Step size step_size = 1e-12 # Max Iterations to Run max_iterations = 1000 # Tolerance tolerance = None # L2 Penalty l2_penalty = 0.0 # Compute our hill climbing value final_weights = self.ridge_regression.gradient_ascent(feature_matrix, output, {"initial_weights": initial_weights, "step_size": step_size, "tolerance": tolerance, "l2_penalty": l2_penalty, "max_iteration": max_iterations}) # Assert that the weights is correct self.assertEquals(round(-7.7535764461428101e+70, -68), round(final_weights[0], -68)) self.assertEquals(round(-1.9293745396177612e+74, -70), round(final_weights[1], -70)) def test_07_gradient_ascent_high_tolerance(self): """Tests gradient ascent. Tests gradient ascent and compare it with known values. """ # We will use sqft_living for our features features = ['sqft_living'] # Output will be price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1) # Create our initial weights initial_weights = np.array([0., 0.]) # Step size step_size = 1e-12 # Max Iterations to Run max_iterations = 1000 # Tolerance tolerance = 1 # L2 Penalty l2_penalty = 0.0 # Compute our hill climbing value final_weights = self.ridge_regression.gradient_ascent(feature_matrix, output, {"initial_weights": initial_weights, "step_size": step_size, "tolerance": tolerance, "l2_penalty": l2_penalty, "max_iteration": max_iterations}) # Assert that the weights is correct self.assertEquals(0, round(final_weights[0], -68)) self.assertEquals(0, round(final_weights[1], -70)) def test_08_gradient_descent_no_penalty_high_tolerance(self): """Tests gradient descent algorithm. Tests the result on gradient descent with low penalty. """ # We will use sqft_living for our features features = ['sqft_living'] # Output will use price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1) # Create our initial weights initial_weights = np.array([0., 0.]) # Step size step_size = 1e-12 # Max Iterations to Run max_iterations = 100000 # Tolerance tolerance = 10000000000 # L2 Penalty l2_penalty = 0.0 # Compute our gradient descent value final_weights = self.ridge_regression.gradient_descent(feature_matrix, output, {"initial_weights": initial_weights, "step_size": step_size, "tolerance": tolerance, "l2_penalty": l2_penalty, "max_iteration": max_iterations}) # We will use sqft_iving, and sqft_living15 test_features = ['sqft_living'] # Output will be price test_output = ['price'] # Convert our test pandas frame to numpy test_feature_matrix, test_output = self.convert_numpy.convert_to_numpy(self.kc_house_test, test_features, test_output, 1) # Predict the output of test features predicted_output = self.predict_output.regression(test_feature_matrix, final_weights) # Compute RSS rss = self.residual_sum_squares.residual_sum_squares_regression(test_output, predicted_output) # Assert that the weights is correct self.assertEquals(round(0.093859999999999999, 5), round(final_weights[0], 5)) self.assertEquals(round(262.98200000000003, 3), round(final_weights[1], 3)) # Assert that rss is correct self.assertEquals(round(275724298300000.0, -5), round(rss, -5))
class TestLinearRegression(unittest.TestCase): """Test for LinearRegression. Uses housing data to test LinearRegression. Statics: _multiprocess_can_split_ (bool): Flag for nose tests to run tests in parallel. """ _multiprocess_can_split_ = True def setUp(self): """Constructor for TestLinearRegression. Loads housing data, and creates training and testing data. """ self.convert_numpy = ConvertNumpy() self.linear_regression = LinearRegression() self.predict_output = PredictOutput() self.residual_sum_squares = ResidualSumSquares() # Create a dictionary type to store relevant data types so that our pandas # will read the correct information dtype_dict = {'bathrooms': float, 'waterfront': int, 'sqft_above': int, 'sqft_living15': float, 'grade': int, 'yr_renovated': int, 'price': float, 'bedrooms': float, 'zipcode': str, 'long': float, 'sqft_lot15': float, 'sqft_living': float, 'floors': str, 'condition': int, 'lat': float, 'date': str, 'sqft_basement': int, 'yr_built': int, 'id': str, 'sqft_lot': int, 'view': int} # Create a kc_house that encompasses all test and train data self.kc_house = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_data.csv', dtype=dtype_dict) # Create a kc_house_test_frame that encompasses only train data self.kc_house_train = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_train_data.csv', dtype=dtype_dict) # Create a kc_house_frames that encompasses only test data self.kc_house_test = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_test_data.csv', dtype=dtype_dict) def test_01_gradient_descent(self): """Test gradient descent. Tests gradient descent and compare it to known values. """ # We will use sqft_living for our features features = ['sqft_living'] # Output will use price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1) # Create our initial weights initial_weights = np.array([-47000., 1.]) # Step size step_size = 7e-12 # Tolerance tolerance = 2.5e7 # Compute our gradient descent value final_weights = self.linear_regression.gradient_descent(feature_matrix, output, {"initial_weights": initial_weights, "step_size": step_size, "tolerance": tolerance}) # Assert that the weights is correct self.assertEquals(round(-46999.887165546708, 3), round(final_weights[0], 3)) self.assertEquals(round(281.91211917520917, 3), round(final_weights[1], 3)) def test_02_gradient_descent_multiple(self): """Tests gradient descent on multiple features. Computes gradient descent on multiple input, and computes predicted model and RSS. """ # We will use sqft_iving, and sqft_living15 features = ['sqft_living', 'sqft_living15'] # Output will be price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1) # Create our initial weights initial_weights = np.array([-100000., 1., 1.]) # Step size step_size = 4e-12 # Tolerance tolerance = 1e9 # Compute our gradient descent value final_weights = self.linear_regression.gradient_descent(feature_matrix, output, {"initial_weights": initial_weights, "step_size": step_size, "tolerance": tolerance}) # We will use sqft_iving, and sqft_living15 test_features = ['sqft_living', 'sqft_living15'] # Output will be price test_output = ['price'] # Convert our test pandas frame to numpy test_feature_matrix, test_output = self.convert_numpy.convert_to_numpy(self.kc_house_test, test_features, test_output, 1) # Predict the output of test features predicted_output = self.predict_output.regression(test_feature_matrix, final_weights) # Compute RSS rss = self.residual_sum_squares.residual_sum_squares_regression(test_output, predicted_output) # Assert that rss is correct self.assertEquals(round(270263443629803.41, -3), round(rss, -3)) def test_03_gradient_ascent(self): """Test gradient ascent. Test gradient ascent and compare it to known values. """ # We will use sqft_living for our features features = ['sqft_living'] # Output will be price output = ['price'] # Convert our pandas frame to numpy feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1) # Create our initial weights initial_weights = np.array([-47000., 1.]) # Step size step_size = 7e-12 # Tolerance tolerance = 2.5e7 # Compute our hill climbing value final_weights = self.linear_regression.gradient_ascent(feature_matrix, output, {"initial_weights": initial_weights, "step_size": step_size, "tolerance": tolerance}) # Assert that the weights is correct self.assertEquals(round(-47000.142201335177, 3), round(final_weights[0], 3)) self.assertEquals(round(-352.86068692252599, 3), round(final_weights[1], 3))