From ad7920cbb8779222c5e9fe5e056fb48ed798ff58 Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Thu, 14 Nov 2024 23:11:15 -0600 Subject: [PATCH 01/14] initial draft complete --- .gitignore | 1 + README.md | 13 --- data/__init__.py | 0 lib.py | 248 ++++++++++++++++++++++++++++++++++++++++++++++ modelSelection.py | 87 ++++++++++++++++ params.py | 51 ++++++++++ test.py | 34 +++++++ 7 files changed, 421 insertions(+), 13 deletions(-) create mode 100644 data/__init__.py create mode 100644 lib.py create mode 100644 modelSelection.py create mode 100644 params.py create mode 100644 test.py diff --git a/.gitignore b/.gitignore index 82f9275..ef9d69b 100644 --- a/.gitignore +++ b/.gitignore @@ -21,6 +21,7 @@ sdist/ var/ wheels/ share/python-wheels/ +reference/ *.egg-info/ .installed.cfg *.egg diff --git a/README.md b/README.md index f746e56..6e52d2a 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,5 @@ # Project 2 -Select one of the following two options: - -## Boosting Trees - -Implement the gradient-boosting tree algorithm (with the usual fit-predict interface) as described in Sections 10.9-10.10 of Elements of Statistical Learning (2nd Edition). Answer the questions below as you did for Project 1. - -Put your README below. Answer the following questions. - -* What does the model you have implemented do and when should it be used? -* How did you test your model to determine if it is working reasonably correctly? -* What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.) -* Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental? - ## Model Selection Implement generic k-fold cross-validation and bootstrapping model selection methods. diff --git a/data/__init__.py b/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lib.py b/lib.py new file mode 100644 index 0000000..87c814e --- /dev/null +++ b/lib.py @@ -0,0 +1,248 @@ +""" +References: + 1. AIC definition: Wikipedia - Akaike information criterion + [LINK] https://en.wikipedia.org/wiki/Akaike_information_criterion + 2. Log likelihood formula - StatLect "Log-likelihood" + [LINK] https://www.statlect.com/glossary/log-likelihood +""" +import numpy as np +import csv +from params import params +from sklearn.linear_model import * +from sklearn import datasets + +# Collection of auxiliary functions + +""" + Model selections +""" + + +def get_model(model_type: str): + """ + get_model() + This function returns the model object for the given model_type defined in 'params,py'. + If no model_type is defined, it returns default model object as LinearRegression. + :param model_type: the type of model to use [options] "LinearRegression(default)", "LogisticRegression" + :return: a model object to train. + """ + print(f"Model Type: {model_type}") + + if model_type == "LinearRegression": + return LinearRegression() + elif model_type == "LogisticRegression": + return LogisticRegression(max_iter=1000) + else: # Invalid type described + print(f"[Warning] Invalid model type has given. Uses default model type as 'LinearRegression'") + return LinearRegression() + + +def get_metric(metric_type: str): + """ + get_metric() + This function returns the metric function for the given metric_type defined in 'params.py'. + If no metric_type is defined, it returns default setting as MSE. + :param metric_type: the type of metric to use [options] "MSE(default)", "Accuracy score" + :return: a metric function to apply. + """ + print(f"Metric Type: {metric_type}") + + if metric_type == "MSE": + return MSE + elif metric_type == "Accuracy score": + return accuracy_score + else: # Invalid type described + print(f"[Warning] Invalid metric type has given. Uses default metric type as 'MSE'") + return MSE + + +""" + Data import/create functions +""" + + +def read_csv(file_name: str) -> tuple: + """ + read_data() + This function reads the data from the given csv file + :param file_name: name of the file to read + :return: X, y + """ + data = [] + with open(file_name, "r") as file: + reader = csv.DictReader(file) + for row in reader: + data.append(row) + + X = np.array([[float(v) for k, v in datum.items() if k.startswith('x')] for datum in data]) + y = np.array([[float(v) for k, v in datum.items() if k == 'y'] for datum in data]) + + return X, y + + +def generate_synthetic_data(size: int, dimension: int, noise_std: float, random_state: int, test_ratio: float) -> tuple: + """ + generate_synthetic_data() + This function generates data based on randomly assigned weights, bias, and noise deviations. + If the model tracks the assigned weights and deviations similarly from that function, + the model is assumed to have been trained correctly. + + :param size: Number of samples to generate + :param dimension: Number of features to generate + :param noise_std: Set noise scale to test durability of the trained model + :param test_ratio: test ratio from the created dataset + :param random_state: random seed + :returns + train_x, train_y, test_x, test_y: train and test set + """ + # Setup random seed + np.random.seed(random_state) + + # random sample + X = np.random.rand(size, dimension) + + # create weights, bias, and noise + weights = np.random.randn(dimension) + bias = np.random.rand() + noise_std = np.random.normal(0, noise_std, size=size) + + # create label + y = np.dot(X, weights) + (bias + noise_std) + + # Split data into train and test + test_size = int(test_ratio * size) + train_X, test_X = X[:test_size], X[test_size:] + train_y, test_y = y[:test_size], y[test_size:] + return train_X, train_y, test_X, test_y + + +def generate_multi_collinear_data(size: int, dimension: int, correlation: float, noise_std: float, random_state: int, + test_ratio: float) -> tuple: + """ + generate_multi_collinear_data() + This is a function that generates data with multi_collinearity + The data produced from this function is intended to measure the ElasticNet model's ability + to handle multi_collinearity, which is a critical feature of the model + + :param size: Number of samples to generate + :param dimension: Number of features to generate + :param correlation: Correlation coefficient between features (1 is the worst) + :param noise_std: Set noise scale to test durability of the trained model + :param random_state: random seed + :param test_ratio: test ratio from the created dataset + :returns: + train_x, train_y, test_x, test_y: train and test set + """ + if random_state: + np.random.seed(random_state) + + # random base sample (1st feature) + X_base = np.random.rand(size, 1) + + # Create another feature based on the data for the first feature + X = X_base + correlation * np.random.randn(size, dimension) * noise_std + + # Increase the correlation between each feature (e.g. through linear combination) + for i in range(1, dimension): + X[:, i] = correlation * X_base[:, 0] + (1 - correlation) * np.random.randn(size) + + # create weights, bias, and noise + weights = np.random.randn(dimension) + bias = np.random.rand() + noise_std = np.random.normal(0, noise_std, size=size) + + # create y (Add noise to multi-collinearity) + y = X.dot(weights) + bias + (bias + noise_std) + + # Split data into train and test + test_size = int(test_ratio * size) + train_X, test_X = X[:test_size], X[test_size:] + train_y, test_y = y[:test_size], y[test_size:] + return train_X, train_y, test_X, test_y + + +def get_data(data_type: str) -> tuple: + """ + get_data() + This function loads training and test from chosen data type(high order function) + :param data_type: the type of data to use [data options] 'iris'(default), 'file' ,'multi_collinear', 'synthetic' + :return: tuples of data from executed functions + """ + + print(f"Data Type: {data_type}") + + if data_type == "kaggle": + args = params["data"]["kaggle"] + housing = datasets.fetch_california_housing() + X, y = housing.data, housing.target + + # Split data into train and test + test_size = int(args["test_ratio"] * X.shape[0]) + train_X, test_X = X[:test_size], X[test_size:] + train_y, test_y = y[:test_size], y[test_size:] + return train_X, train_y, test_X, test_y + elif data_type == 'file': + args = params["data"]["file"] + print(f"Data Parameters:\n{args}") + return read_csv(**args) + elif data_type == 'multi-collinear': + args = params["data"]["multi-collinear"] + print(f"Data Parameters:\n{args}") + return generate_multi_collinear_data(**args) + else: + args = params["data"]["synthetic"] + print(f"Data Parameters:\n{args}") + return generate_synthetic_data(**args) # default is normal file generation + + +""" + Metric functions +""" + + +def MSE(y: np.ndarray, y_pred: np.ndarray) -> float: + """ + MSE() + This function calculates a value based on mean squared error (MSE), + one of the evaluation indicators for regression models. + :param y: desired target values + :param y_pred: predicted values + :return: mean squared error of the given data + """ + return float(np.mean((y - y_pred) ** 2)) + + +def accuracy_score(y: np.ndarray, y_pred: np.ndarray) -> float: + """ + accuracy_rate() + This function calculates a value based on accuracy rate, + which is one of the evaluation metrics for a classification model. + :param y: desired target values + :param y_pred: predicted values + :return: accuracy rate of the model + """ + return float(np.sum(y == y_pred) / len(y)) + + +def AIC(y: np.ndarray, X: np.ndarray, y_pred: np.ndarray): + """ + aic() + This function computes the AIC evaluation for the given model + :param y: Actual values + :param X: Feature vectors + :param y_pred: Predicted values + :return: AIC value + """ + # Number of samples + n = X.shape[0] + + # Number of feature including a residual + k = X.shape[1] + 1 + + # Mean squared error + MSE = np.sum((y - y_pred) ** 2) / n + + # Log likelihood formula: log_likelihood = - n/2*log(2*π) - n/2*log(MSE) - n/2 + log_likelihood = - n / 2 * np.log(2 * np.pi) - n / 2 - n * np.log(MSE) / 2 + + return 2 * k - 2 * log_likelihood # AIC = 2*k - 2*log_likelihood diff --git a/modelSelection.py b/modelSelection.py new file mode 100644 index 0000000..a4fd211 --- /dev/null +++ b/modelSelection.py @@ -0,0 +1,87 @@ +from lib import * + + +def k_fold_cross_validation(model, metric, X: np.ndarray, y: np.ndarray, k: int, shuffle: bool): + """ + k_fold_cross_validation() + This function is used to validate the model using k-fold cross validation method + + :param model: The statistic model to test + :param metric: The metric to measure the model performance + :param X: The training feature vectors + :param y: The training labels + :param k: Number of folds + :param shuffle: Whether to shuffle the data before the operation + :return: metric scores of folds and their average score + """ + scores = [] + n = X.shape[0] # Size of sample + fold_size = n // k # Size of each fold + + if shuffle: # shuffle the data + indices = np.arange(n) + np.random.shuffle(indices) + + X = X[indices] + y = y[indices] + + for i in range(k): + # Get indices(start, end) of the current validation fold + start, end = i * fold_size, (i + 1) * fold_size + + # Get validation set + X_val, y_val = X[start:end], y[start:end] + + # Get train set + X_train = np.concatenate([X[:start], X[end:]], axis=0) + y_train = np.concatenate([y[:start], y[end:]], axis=0) + + # Train the model + model.fit(X_train, y_train) + + # Get predicted data + y_predicted = model.predict(X_val) + + # Compute accuracy rate + score = metric(y_val, y_predicted) + + # Add to score list + scores.append(score) + + return scores, np.average(scores) + + +def bootstrapping(model, metric, X: np.ndarray, y: np.ndarray, s: int, epochs: int): + """ + bootstrapping() + This function performs bootstrapping + + :param model: The statistic model to test + :param metric: The metric to measure the model performance + :param X: The training feature vectors + :param y: The training labels + :param s: The size of a train dataset + :param epochs: The number of trials to perform train/verify its performance + :return: metric scores of trials and their average score + """ + scores = [] + n = X.shape[0] # Size of dataset + + for _ in range(epochs): + # Pick train set data by sampling of size s + indices = np.random.choice(range(n), size=s, replace=True) + X_train, y_train = X[indices], y[indices] + + # The others will be assigned as validation set + out_of_sample = [i for i in range(n) if i not in indices] + X_val, y_val = X[out_of_sample], y[out_of_sample] + + model.fit(X_train, y_train) + y_pred = model.predict(X_val) + + if len(out_of_sample) > 0: # Avoid cases with no out-of-sample data + score = metric(y_val, y_pred) + scores.append(score) + + average_score = np.mean(scores) + return scores, average_score diff --git a/params.py b/params.py new file mode 100644 index 0000000..e9ac43c --- /dev/null +++ b/params.py @@ -0,0 +1,51 @@ +# All User parameter can be adjusted from here. + +""" + Test configuration +""" + +params = { + "test": { + "general": { + "model": "LinearRegression", # [options] "LinearRegression(default)", "LogisticRegression" + "metric": "MSE", # [options] "MSE(default)", "Accuracy score" + "data": "synthetic", # [options] 'kaggle'(default), 'file', 'synthetic', 'multi-collinear' + }, + "k_fold_cross_validation": { + "k": 5, # Number of folds + "shuffle": True # shuffle: Whether to shuffle the data before the operation + }, + "bootstrapping": { + "size": 50, # The size of a train dataset + "epochs": 100 # The number of trials to perform train/verify its performance + } + }, + "data": { + # User defined params for kaggle data import setting + "kaggle": { + "test_ratio": 0.3 # Portion of the test set from total sample. + }, + # User defined params for data import setting + "file": { + "file_name": "small_test.csv", # File path to read + "test_ratio": 0.3 # Portion of the test set from total sample. + }, + # User defined params for synthetic data creation setting + "synthetic": { + "size": 1000, # Number of samples to create + "dimension": 9, # Number of features of the data + "noise_std": 0.00, # Noise scale to check durability of the model + "random_state": 42, # Random seed + "test_ratio": 0.3 # Portion of the test set from total sample. + }, + # User defined params for multi-collinear data creation setting + "multi-collinear": { + "size": 100, # Number of samples to create + "dimension": 3, # Number of features of the data + "correlation": 1, # Correlation coefficient between features + "noise_std": 0.01, # Noise scale to check durability of the model + "random_state": 42, # Random seed + "test_ratio": 0.3 # Portion of the test set from total sample. + } + } +} diff --git a/test.py b/test.py new file mode 100644 index 0000000..9dd168f --- /dev/null +++ b/test.py @@ -0,0 +1,34 @@ +from modelSelection import * +from lib import * + + +def main(): + # Init settings + args = params["test"]["general"] + model = get_model(args["model"]) + metric = get_metric(args["metric"]) + train_X, train_y, test_X, test_y = get_data(args["data"]) + X = np.concatenate([train_X, test_X], axis=0) + y = np.concatenate([train_y, test_y], axis=0) + + # k-Fold Cross-Validation + args = params["test"]["k_fold_cross_validation"] + k_fold_scores, k_fold_avg = k_fold_cross_validation(model, metric, X=X, y=y, k=args["k"], shuffle=args["shuffle"]) + print("k-Fold Cross-Validation Scores:", k_fold_scores) + print("k-Fold Average Score:", k_fold_avg) + + # Compare with simple AIC + model.fit(train_X, train_y) + y_pred = model.predict(test_X) + y_pred = model.predict(test_X) + print("AIC Scores: ", AIC(X=test_X, y=test_y, y_pred=y_pred)) + + # bootstrapping + args = params["test"]["bootstrapping"] + bootstrap_scores, bootstrap_avg = bootstrapping(model, metric, X=X, y=y, s=args["size"], epochs=args["epochs"]) + print("Bootstrap Scores:", bootstrap_scores) + print("Bootstrap Average Score:", bootstrap_avg) + + +if __name__ == "__main__": + main() From aca591ae46cf4b368dfdecd700cc4e727b5a3859 Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Sat, 16 Nov 2024 22:58:43 -0600 Subject: [PATCH 02/14] Add test case - Multi dataset test --- .gitignore | 4 +- lib.py | 133 +++++++++++++++++---------------------- params.py | 51 --------------- params/__init__.py | 0 params/param.py | 100 +++++++++++++++++++++++++++++ params/param_list.csv | 3 + params/param_multi.json | 93 +++++++++++++++++++++++++++ params/param_single.json | 36 +++++++++++ results/__init__.py | 0 test.py | 89 +++++++++++++++++++------- 10 files changed, 355 insertions(+), 154 deletions(-) delete mode 100644 params.py create mode 100644 params/__init__.py create mode 100644 params/param.py create mode 100644 params/param_list.csv create mode 100644 params/param_multi.json create mode 100644 params/param_single.json create mode 100644 results/__init__.py diff --git a/.gitignore b/.gitignore index ef9d69b..b4887b2 100644 --- a/.gitignore +++ b/.gitignore @@ -131,11 +131,11 @@ ENV/ env.bak/ venv.bak/ -# Spyder project settings +# Spyder project params .spyderproject .spyproject -# Rope project settings +# Rope project params .ropeproject # mkdocs documentation diff --git a/lib.py b/lib.py index 87c814e..7e66997 100644 --- a/lib.py +++ b/lib.py @@ -7,12 +7,22 @@ """ import numpy as np import csv -from params import params +import json from sklearn.linear_model import * -from sklearn import datasets + # Collection of auxiliary functions +""" + Parameter setting read +""" + + +def get_param(file_path: str) -> dict: + with open(file_path, "r", encoding="utf-8") as file: + return json.load(file) + + """ Model selections """ @@ -40,7 +50,7 @@ def get_model(model_type: str): def get_metric(metric_type: str): """ get_metric() - This function returns the metric function for the given metric_type defined in 'params.py'. + This function returns the metric function for the given metric_type defined in 'param.py'. If no metric_type is defined, it returns default setting as MSE. :param metric_type: the type of metric to use [options] "MSE(default)", "Accuracy score" :return: a metric function to apply. @@ -80,44 +90,8 @@ def read_csv(file_name: str) -> tuple: return X, y -def generate_synthetic_data(size: int, dimension: int, noise_std: float, random_state: int, test_ratio: float) -> tuple: - """ - generate_synthetic_data() - This function generates data based on randomly assigned weights, bias, and noise deviations. - If the model tracks the assigned weights and deviations similarly from that function, - the model is assumed to have been trained correctly. - - :param size: Number of samples to generate - :param dimension: Number of features to generate - :param noise_std: Set noise scale to test durability of the trained model - :param test_ratio: test ratio from the created dataset - :param random_state: random seed - :returns - train_x, train_y, test_x, test_y: train and test set - """ - # Setup random seed - np.random.seed(random_state) - - # random sample - X = np.random.rand(size, dimension) - - # create weights, bias, and noise - weights = np.random.randn(dimension) - bias = np.random.rand() - noise_std = np.random.normal(0, noise_std, size=size) - - # create label - y = np.dot(X, weights) + (bias + noise_std) - - # Split data into train and test - test_size = int(test_ratio * size) - train_X, test_X = X[:test_size], X[test_size:] - train_y, test_y = y[:test_size], y[test_size:] - return train_X, train_y, test_X, test_y - - -def generate_multi_collinear_data(size: int, dimension: int, correlation: float, noise_std: float, random_state: int, - test_ratio: float) -> tuple: +def generate_data(size: int, dimension: int, correlation: float, noise_std: float, random_state: int, + test_ratio: float) -> tuple: """ generate_multi_collinear_data() This is a function that generates data with multi_collinearity @@ -131,7 +105,9 @@ def generate_multi_collinear_data(size: int, dimension: int, correlation: float, :param random_state: random seed :param test_ratio: test ratio from the created dataset :returns: - train_x, train_y, test_x, test_y: train and test set + X: Dataset with features + y: Dataset with labels + train_X, train_y, test_X, test_y: Split dataset by split_dataset() function """ if random_state: np.random.seed(random_state) @@ -154,45 +130,51 @@ def generate_multi_collinear_data(size: int, dimension: int, correlation: float, # create y (Add noise to multi-collinearity) y = X.dot(weights) + bias + (bias + noise_std) + # Split dataset into train and test + train_X, train_y, test_X, test_y = split_dataset(X, y, test_ratio) + return X, y, train_X, train_y, test_X, test_y + + +def split_dataset(X: np.ndarray, y: np.ndarray, test_ratio: float) -> tuple: + """ + split_dataset() + This function splits the dataset into training and test sets + :param X: Dataset with features + :param y: Dataset labels + :param test_ratio: size of the test set + :return: + train_X: train dataset + train_y: train dataset label + test_X: test dataset + test_y: test dataset label + """ # Split data into train and test - test_size = int(test_ratio * size) + test_size = int(test_ratio * X.shape[0]) train_X, test_X = X[:test_size], X[test_size:] train_y, test_y = y[:test_size], y[test_size:] return train_X, train_y, test_X, test_y -def get_data(data_type: str) -> tuple: +def get_data(data_type: str, args: dict) -> tuple: """ get_data() - This function loads training and test from chosen data type(high order function) - :param data_type: the type of data to use [data options] 'iris'(default), 'file' ,'multi_collinear', 'synthetic' - :return: tuples of data from executed functions - """ - - print(f"Data Type: {data_type}") - - if data_type == "kaggle": - args = params["data"]["kaggle"] - housing = datasets.fetch_california_housing() - X, y = housing.data, housing.target - - # Split data into train and test - test_size = int(args["test_ratio"] * X.shape[0]) - train_X, test_X = X[:test_size], X[test_size:] - train_y, test_y = y[:test_size], y[test_size:] - return train_X, train_y, test_X, test_y - elif data_type == 'file': - args = params["data"]["file"] - print(f"Data Parameters:\n{args}") - return read_csv(**args) - elif data_type == 'multi-collinear': - args = params["data"]["multi-collinear"] - print(f"Data Parameters:\n{args}") - return generate_multi_collinear_data(**args) - else: - args = params["data"]["synthetic"] - print(f"Data Parameters:\n{args}") - return generate_synthetic_data(**args) # default is normal file generation + This function loads dataset chosen data type + :param data_type: the type of data to use [data options] 'iris'(default), 'file' ,'generate' + :param args: a parameter setting of the dataset + :returns: tuples produced by chosen function. Specific details as below: + X: Dataset with features + y: Dataset with labels + train_X: train dataset + train_y: train dataset label + test_X: test dataset + test_y: test dataset label + """ + if data_type == 'file': + X, y = read_csv(**args) + train_X, train_y, test_X, test_y = split_dataset(X, y, args["test_ratio"]) + return X, y, train_X, train_y, test_X, test_y + else: # (Default) Generate data + return generate_data(**args) """ @@ -239,10 +221,7 @@ def AIC(y: np.ndarray, X: np.ndarray, y_pred: np.ndarray): # Number of feature including a residual k = X.shape[1] + 1 - # Mean squared error - MSE = np.sum((y - y_pred) ** 2) / n - # Log likelihood formula: log_likelihood = - n/2*log(2*π) - n/2*log(MSE) - n/2 - log_likelihood = - n / 2 * np.log(2 * np.pi) - n / 2 - n * np.log(MSE) / 2 + log_likelihood = - n / 2 * np.log(2 * np.pi) - n / 2 - n * np.log(MSE(y, y_pred)) / 2 return 2 * k - 2 * log_likelihood # AIC = 2*k - 2*log_likelihood diff --git a/params.py b/params.py deleted file mode 100644 index e9ac43c..0000000 --- a/params.py +++ /dev/null @@ -1,51 +0,0 @@ -# All User parameter can be adjusted from here. - -""" - Test configuration -""" - -params = { - "test": { - "general": { - "model": "LinearRegression", # [options] "LinearRegression(default)", "LogisticRegression" - "metric": "MSE", # [options] "MSE(default)", "Accuracy score" - "data": "synthetic", # [options] 'kaggle'(default), 'file', 'synthetic', 'multi-collinear' - }, - "k_fold_cross_validation": { - "k": 5, # Number of folds - "shuffle": True # shuffle: Whether to shuffle the data before the operation - }, - "bootstrapping": { - "size": 50, # The size of a train dataset - "epochs": 100 # The number of trials to perform train/verify its performance - } - }, - "data": { - # User defined params for kaggle data import setting - "kaggle": { - "test_ratio": 0.3 # Portion of the test set from total sample. - }, - # User defined params for data import setting - "file": { - "file_name": "small_test.csv", # File path to read - "test_ratio": 0.3 # Portion of the test set from total sample. - }, - # User defined params for synthetic data creation setting - "synthetic": { - "size": 1000, # Number of samples to create - "dimension": 9, # Number of features of the data - "noise_std": 0.00, # Noise scale to check durability of the model - "random_state": 42, # Random seed - "test_ratio": 0.3 # Portion of the test set from total sample. - }, - # User defined params for multi-collinear data creation setting - "multi-collinear": { - "size": 100, # Number of samples to create - "dimension": 3, # Number of features of the data - "correlation": 1, # Correlation coefficient between features - "noise_std": 0.01, # Noise scale to check durability of the model - "random_state": 42, # Random seed - "test_ratio": 0.3 # Portion of the test set from total sample. - } - } -} diff --git a/params/__init__.py b/params/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/params/param.py b/params/param.py new file mode 100644 index 0000000..51a306d --- /dev/null +++ b/params/param.py @@ -0,0 +1,100 @@ +# All User parameter can be adjusted from here. + +""" + Test configuration +""" + +params = { + "test": { + "general": { + "activate": {"k_fold_CV": True, "bootstrapping": True}, # True to execute the target test + "model": "LinearRegression", # [options] "LinearRegression(default)", "LogisticRegression" + "metric": "MSE", # [options] "MSE(default)", "Accuracy score" + "data": "generate", # [options] 'kaggle'(default), 'file', 'generate' + }, + "k_fold_cross_validation": { + "k": [5], # Number of folds + "shuffle": True # shuffle: Whether to shuffle the data before the operation + }, + "bootstrapping": { + "size": [50], # The size of a train dataset + "epochs": [100] # The number of trials to perform train/verify its performance + } + }, + "data": { + # User defined params for data import setting + "file": [ + { + "file_name": "small_test.csv", # File path to read + "test_ratio": 0.25 # Portion of the test set from total sample. + }, + ], + # User defined params data creation setting + "generate": [ + { # Default dataset with strict linear relationship + "size": 100, # Number of samples to create + "dimension": 10, # Number of features of the data + "correlation": 0, # Correlation coefficient between features + "noise_std": 0.0, # Noise scale to check durability of the model + "random_state": 42, # Random seed + "test_ratio": 0.25 # Portion of the test set from total sample. + }, + { # Larger dataset with strict linear relationship + "size": 500, # Number of samples to create + "dimension": 10, # Number of features of the data + "correlation": 0, # Correlation coefficient between features + "noise_std": 0.00, # Noise scale to check durability of the model + "random_state": 42, # Random seed + "test_ratio": 0.25 # Portion of the test set from total sample. + }, + { # Default dataset with some noise + "size": 100, # Number of samples to create + "dimension": 10, # Number of features of the data + "correlation": 0, # Correlation coefficient between features + "noise_std": 0.01, # Noise scale to check durability of the model + "random_state": 42, # Random seed + "test_ratio": 0.25 # Portion of the test set from total sample. + }, + { # Larger dataset with some noise + "size": 500, # Number of samples to create + "dimension": 10, # Number of features of the data + "correlation": 0, # Correlation coefficient between features + "noise_std": 0.01, # Noise scale to check durability of the model + "random_state": 42, # Random seed + "test_ratio": 0.25 # Portion of the test set from total sample. + }, + { # Default dataset with high correlation + "size": 100, # Number of samples to create + "dimension": 10, # Number of features of the data + "correlation": 0.9, # Correlation coefficient between features + "noise_std": 0.0, # Noise scale to check durability of the model + "random_state": 42, # Random seed + "test_ratio": 0.25 # Portion of the test set from total sample. + }, + { # Larger dataset with high correlation + "size": 100, # Number of samples to create + "dimension": 10, # Number of features of the data + "correlation": 0.9, # Correlation coefficient between features + "noise_std": 0.0, # Noise scale to check durability of the model + "random_state": 42, # Random seed + "test_ratio": 0.25 # Portion of the test set from total sample. + }, + { # Default dataset with some noise and high correlation + "size": 100, # Number of samples to create + "dimension": 10, # Number of features of the data + "correlation": 0.9, # Correlation coefficient between features + "noise_std": 0.01, # Noise scale to check durability of the model + "random_state": 42, # Random seed + "test_ratio": 0.25 # Portion of the test set from total sample. + }, + { # Larger dataset with some noise and high correlation + "size": 100, # Number of samples to create + "dimension": 10, # Number of features of the data + "correlation": 0.9, # Correlation coefficient between features + "noise_std": 0.01, # Noise scale to check durability of the model + "random_state": 42, # Random seed + "test_ratio": 0.25 # Portion of the test set from total sample. + } + ] + } +} diff --git a/params/param_list.csv b/params/param_list.csv new file mode 100644 index 0000000..178c10d --- /dev/null +++ b/params/param_list.csv @@ -0,0 +1,3 @@ +Json file name, Description +"param_single.json", "Singular test" +"param_multi.json", "Multi-data test" \ No newline at end of file diff --git a/params/param_multi.json b/params/param_multi.json new file mode 100644 index 0000000..33e9ba0 --- /dev/null +++ b/params/param_multi.json @@ -0,0 +1,93 @@ +{ + "description": "A parameter setting for data settings to batch test multiple generated datasets.\n The primary goal is to observe the differences among datasets with varying sizes and complexities\n (multi-collinearity, noise) using a simple linear regression model.", + "test": { + "general": { + "activate": {"k_fold_CV": true, "bootstrapping": true}, + "model": "LinearRegression", + "metric": "MSE", + "data": "generate" + }, + "k_fold_cross_validation": { + "k": [5], + "shuffle": false + }, + "bootstrapping": { + "size": [50], + "epochs": [100] + } + }, + "data": { + "file": [ + { + "file_name": "small_test.csv", + "test_ratio": 0.25 + } + ], + "generate": [ + { + "size": 100, + "dimension": 10, + "correlation": 0, + "noise_std": 0.0, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 10, + "correlation": 0, + "noise_std": 0.00, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 100, + "dimension": 10, + "correlation": 0, + "noise_std": 0.01, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 10, + "correlation": 0, + "noise_std": 0.01, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 100, + "dimension": 10, + "correlation": 0.9, + "noise_std": 0.0, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 100, + "dimension": 10, + "correlation": 0.9, + "noise_std": 0.0, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 100, + "dimension": 10, + "correlation": 0.9, + "noise_std": 0.01, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 100, + "dimension": 10, + "correlation": 0.9, + "noise_std": 0.01, + "random_state": 42, + "test_ratio": 0.25 + } + ] + } +} diff --git a/params/param_single.json b/params/param_single.json new file mode 100644 index 0000000..97fa95c --- /dev/null +++ b/params/param_single.json @@ -0,0 +1,36 @@ +{ + "test": { + "general": { + "activate": {"k_fold_CV": true, "bootstrapping": true}, + "model": "LinearRegression", + "metric": "MSE", + "data": "generate" + }, + "k_fold_cross_validation": { + "k": [5], + "shuffle": true + }, + "bootstrapping": { + "size": [50], + "epochs": [100] + } + }, + "data": { + "file": [ + { + "file_name": "small_test.csv", + "test_ratio": 0.25 + } + ], + "generate": [ + { + "size": 100, + "dimension": 10, + "correlation": 0, + "noise_std": 0.0, + "random_state": 42, + "test_ratio": 0.25 + } + ] + } +} diff --git a/results/__init__.py b/results/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test.py b/test.py index 9dd168f..74d438b 100644 --- a/test.py +++ b/test.py @@ -1,33 +1,74 @@ from modelSelection import * -from lib import * -def main(): - # Init settings - args = params["test"]["general"] - model = get_model(args["model"]) - metric = get_metric(args["metric"]) - train_X, train_y, test_X, test_y = get_data(args["data"]) - X = np.concatenate([train_X, test_X], axis=0) - y = np.concatenate([train_y, test_y], axis=0) - - # k-Fold Cross-Validation - args = params["test"]["k_fold_cross_validation"] - k_fold_scores, k_fold_avg = k_fold_cross_validation(model, metric, X=X, y=y, k=args["k"], shuffle=args["shuffle"]) - print("k-Fold Cross-Validation Scores:", k_fold_scores) - print("k-Fold Average Score:", k_fold_avg) - - # Compare with simple AIC +def test_k_Fold_CV(model, metric, X: np.ndarray, y: np.ndarray, ks: list[int], shuffle: bool): + for k in ks: + k_fold_scores, k_fold_avg = k_fold_cross_validation(model, metric, X=X, y=y, k=k, shuffle=shuffle) + print(f"k-Fold Cross-Validation Scores:\n\t{k_fold_scores}") + print(f"k-Fold Average Score: {k_fold_avg}") + + +def test_bootstrapping(model, metric, X: np.ndarray, y: np.ndarray, ss: list[int], epochs_list: list[int]): + for s in ss: + for epochs in epochs_list: + bootstrap_scores, bootstrap_avg = bootstrapping(model, metric, X=X, y=y, s=s, epochs=epochs) + print(f"Bootstrap Scores (Pick first 5 out of {len(bootstrap_scores)}):\n\t{bootstrap_scores[:5]}") + print(f"Bootstrap Score range: [{min(bootstrap_scores)}, {max(bootstrap_scores)}]") + print(f"Bootstrap Median Score: {np.median(bootstrap_scores)}") + print(f"Bootstrap Average Score: {bootstrap_avg}") + + +def test_AIC(model, train_X: np.ndarray, train_y: np.ndarray, test_X: np.ndarray, test_y: np.ndarray): model.fit(train_X, train_y) y_pred = model.predict(test_X) - y_pred = model.predict(test_X) - print("AIC Scores: ", AIC(X=test_X, y=test_y, y_pred=y_pred)) + print(f"\t(Comparison) AIC Score: {AIC(X=test_X, y=test_y, y_pred=y_pred)}") + + +def main(): + param = get_param('./params/param_multi.json') + # Init params + print(f"{'*' * 52} Global Setting {'*' * 52}") + args_g = param["test"]["general"] + print(f"Description:\n\t{param['description']}") + model = get_model(args_g["model"]) + metric = get_metric(args_g["metric"]) + print(f"Data Type: {args_g['data']}") + print("*" * 121, "\n") + + i = 0 + while i < len(param["data"][args_g["data"]]): + print(f"{'=' * 52} [Test {i:2d}] Start {'=' * 52}") + args = param["data"][args_g["data"]][i] + print(f"Data Parameters:\n\t{args}") + + # Get dataset + X, y, train_X, train_y, test_X, test_y = get_data(args_g["data"], args) + + print("-" * 121) + + if args_g["activate"]["k_fold_CV"]: + # k-Fold Cross-Validation + print("[Test] K-Fold Cross-Validation") + args_k = param["test"]["k_fold_cross_validation"] + + # Test K-Fold CV + test_k_Fold_CV(model, metric, X, y, ks=args_k["k"], shuffle=args_k["shuffle"]) + + # Compare with simple AIC + test_AIC(model, train_X, train_y, test_X, test_y) + + print("-" * 121) + + if args_g["activate"]["bootstrapping"]: + # bootstrapping + print("[Test] Bootstrapping") + args_b = param["test"]["bootstrapping"] + test_bootstrapping(model, metric, X, y, ss=args_b["size"], epochs_list=args_b["epochs"]) + print(f"{'=' * 52} [Test {i:2d}] End {'=' * 52}") - # bootstrapping - args = params["test"]["bootstrapping"] - bootstrap_scores, bootstrap_avg = bootstrapping(model, metric, X=X, y=y, s=args["size"], epochs=args["epochs"]) - print("Bootstrap Scores:", bootstrap_scores) - print("Bootstrap Average Score:", bootstrap_avg) + # Increase index number + i += 1 + print("") if __name__ == "__main__": From 6eadcc238ca919f8920bc54155429ac8c9c2dadd Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Sun, 17 Nov 2024 23:21:06 -0600 Subject: [PATCH 03/14] Add and validated file-based test. --- .gitignore | 2 +- data/IRIS.csv | 151 +++ data/dataSource.txt | 15 + data/housing.csv | 490 ++++++++++ data/winequality-red.csv | 1600 ++++++++++++++++++++++++++++++++ lib.py | 62 +- params/{param.py => param.txt} | 5 +- params/param_multi.json | 2 +- params/param_single.json | 7 +- test.py | 15 +- 10 files changed, 2321 insertions(+), 28 deletions(-) create mode 100644 data/IRIS.csv create mode 100644 data/dataSource.txt create mode 100644 data/housing.csv create mode 100644 data/winequality-red.csv rename params/{param.py => param.txt} (96%) diff --git a/.gitignore b/.gitignore index b4887b2..fa5eb4e 100644 --- a/.gitignore +++ b/.gitignore @@ -21,7 +21,6 @@ sdist/ var/ wheels/ share/python-wheels/ -reference/ *.egg-info/ .installed.cfg *.egg @@ -161,3 +160,4 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +refer1.py \ No newline at end of file diff --git a/data/IRIS.csv b/data/IRIS.csv new file mode 100644 index 0000000..2328736 --- /dev/null +++ b/data/IRIS.csv @@ -0,0 +1,151 @@ +sepal_length,sepal_width,petal_length,petal_width,species +5.1,3.5,1.4,0.2,0 +4.9,3,1.4,0.2,0 +4.7,3.2,1.3,0.2,0 +4.6,3.1,1.5,0.2,0 +5,3.6,1.4,0.2,0 +5.4,3.9,1.7,0.4,0 +4.6,3.4,1.4,0.3,0 +5,3.4,1.5,0.2,0 +4.4,2.9,1.4,0.2,0 +4.9,3.1,1.5,0.1,0 +5.4,3.7,1.5,0.2,0 +4.8,3.4,1.6,0.2,0 +4.8,3,1.4,0.1,0 +4.3,3,1.1,0.1,0 +5.8,4,1.2,0.2,0 +5.7,4.4,1.5,0.4,0 +5.4,3.9,1.3,0.4,0 +5.1,3.5,1.4,0.3,0 +5.7,3.8,1.7,0.3,0 +5.1,3.8,1.5,0.3,0 +5.4,3.4,1.7,0.2,0 +5.1,3.7,1.5,0.4,0 +4.6,3.6,1,0.2,0 +5.1,3.3,1.7,0.5,0 +4.8,3.4,1.9,0.2,0 +5,3,1.6,0.2,0 +5,3.4,1.6,0.4,0 +5.2,3.5,1.5,0.2,0 +5.2,3.4,1.4,0.2,0 +4.7,3.2,1.6,0.2,0 +4.8,3.1,1.6,0.2,0 +5.4,3.4,1.5,0.4,0 +5.2,4.1,1.5,0.1,0 +5.5,4.2,1.4,0.2,0 +4.9,3.1,1.5,0.1,0 +5,3.2,1.2,0.2,0 +5.5,3.5,1.3,0.2,0 +4.9,3.1,1.5,0.1,0 +4.4,3,1.3,0.2,0 +5.1,3.4,1.5,0.2,0 +5,3.5,1.3,0.3,0 +4.5,2.3,1.3,0.3,0 +4.4,3.2,1.3,0.2,0 +5,3.5,1.6,0.6,0 +5.1,3.8,1.9,0.4,0 +4.8,3,1.4,0.3,0 +5.1,3.8,1.6,0.2,0 +4.6,3.2,1.4,0.2,0 +5.3,3.7,1.5,0.2,0 +5,3.3,1.4,0.2,0 +7,3.2,4.7,1.4,1 +6.4,3.2,4.5,1.5,1 +6.9,3.1,4.9,1.5,1 +5.5,2.3,4,1.3,1 +6.5,2.8,4.6,1.5,1 +5.7,2.8,4.5,1.3,1 +6.3,3.3,4.7,1.6,1 +4.9,2.4,3.3,1,1 +6.6,2.9,4.6,1.3,1 +5.2,2.7,3.9,1.4,1 +5,2,3.5,1,1 +5.9,3,4.2,1.5,1 +6,2.2,4,1,1 +6.1,2.9,4.7,1.4,1 +5.6,2.9,3.6,1.3,1 +6.7,3.1,4.4,1.4,1 +5.6,3,4.5,1.5,1 +5.8,2.7,4.1,1,1 +6.2,2.2,4.5,1.5,1 +5.6,2.5,3.9,1.1,1 +5.9,3.2,4.8,1.8,1 +6.1,2.8,4,1.3,1 +6.3,2.5,4.9,1.5,1 +6.1,2.8,4.7,1.2,1 +6.4,2.9,4.3,1.3,1 +6.6,3,4.4,1.4,1 +6.8,2.8,4.8,1.4,1 +6.7,3,5,1.7,1 +6,2.9,4.5,1.5,1 +5.7,2.6,3.5,1,1 +5.5,2.4,3.8,1.1,1 +5.5,2.4,3.7,1,1 +5.8,2.7,3.9,1.2,1 +6,2.7,5.1,1.6,1 +5.4,3,4.5,1.5,1 +6,3.4,4.5,1.6,1 +6.7,3.1,4.7,1.5,1 +6.3,2.3,4.4,1.3,1 +5.6,3,4.1,1.3,1 +5.5,2.5,4,1.3,1 +5.5,2.6,4.4,1.2,1 +6.1,3,4.6,1.4,1 +5.8,2.6,4,1.2,1 +5,2.3,3.3,1,1 +5.6,2.7,4.2,1.3,1 +5.7,3,4.2,1.2,1 +5.7,2.9,4.2,1.3,1 +6.2,2.9,4.3,1.3,1 +5.1,2.5,3,1.1,1 +5.7,2.8,4.1,1.3,1 +6.3,3.3,6,2.5,2 +5.8,2.7,5.1,1.9,2 +7.1,3,5.9,2.1,2 +6.3,2.9,5.6,1.8,2 +6.5,3,5.8,2.2,2 +7.6,3,6.6,2.1,2 +4.9,2.5,4.5,1.7,2 +7.3,2.9,6.3,1.8,2 +6.7,2.5,5.8,1.8,2 +7.2,3.6,6.1,2.5,2 +6.5,3.2,5.1,2,2 +6.4,2.7,5.3,1.9,2 +6.8,3,5.5,2.1,2 +5.7,2.5,5,2,2 +5.8,2.8,5.1,2.4,2 +6.4,3.2,5.3,2.3,2 +6.5,3,5.5,1.8,2 +7.7,3.8,6.7,2.2,2 +7.7,2.6,6.9,2.3,2 +6,2.2,5,1.5,2 +6.9,3.2,5.7,2.3,2 +5.6,2.8,4.9,2,2 +7.7,2.8,6.7,2,2 +6.3,2.7,4.9,1.8,2 +6.7,3.3,5.7,2.1,2 +7.2,3.2,6,1.8,2 +6.2,2.8,4.8,1.8,2 +6.1,3,4.9,1.8,2 +6.4,2.8,5.6,2.1,2 +7.2,3,5.8,1.6,2 +7.4,2.8,6.1,1.9,2 +7.9,3.8,6.4,2,2 +6.4,2.8,5.6,2.2,2 +6.3,2.8,5.1,1.5,2 +6.1,2.6,5.6,1.4,2 +7.7,3,6.1,2.3,2 +6.3,3.4,5.6,2.4,2 +6.4,3.1,5.5,1.8,2 +6,3,4.8,1.8,2 +6.9,3.1,5.4,2.1,2 +6.7,3.1,5.6,2.4,2 +6.9,3.1,5.1,2.3,2 +5.8,2.7,5.1,1.9,2 +6.8,3.2,5.9,2.3,2 +6.7,3.3,5.7,2.5,2 +6.7,3,5.2,2.3,2 +6.3,2.5,5,1.9,2 +6.5,3,5.2,2,2 +6.2,3.4,5.4,2.3,2 +5.9,3,5.1,1.8,2 diff --git a/data/dataSource.txt b/data/dataSource.txt new file mode 100644 index 0000000..a821e90 --- /dev/null +++ b/data/dataSource.txt @@ -0,0 +1,15 @@ + +[File #1] Boston Housing +[description] Concerns housing values in suburbs of Boston +[LINK] https://www.kaggle.com/datasets/schirmerchad/bostonhoustingmlnd + +[File #2] Iris Flower Dataset +[description] Iris flower data set used for multi-class classification. + +* Modified species as numerical values: + 0: Iris-setosa 1: Iris-versicolor 2: Iris-virginica +[LINK] https://www.kaggle.com/datasets/arshid/iris-flower-dataset + +[File #3] Red Wine Quality +[description] Simple and clean practice dataset for regression or classification modelling +[LINK] https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009 diff --git a/data/housing.csv b/data/housing.csv new file mode 100644 index 0000000..9b9116f --- /dev/null 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b/lib.py @@ -5,11 +5,10 @@ 2. Log likelihood formula - StatLect "Log-likelihood" [LINK] https://www.statlect.com/glossary/log-likelihood """ -import numpy as np -import csv import json -from sklearn.linear_model import * +import numpy as np +from sklearn.linear_model import * # Collection of auxiliary functions @@ -50,7 +49,7 @@ def get_model(model_type: str): def get_metric(metric_type: str): """ get_metric() - This function returns the metric function for the given metric_type defined in 'param.py'. + This function returns the metric function for the given metric_type defined in 'param.txt'. If no metric_type is defined, it returns default setting as MSE. :param metric_type: the type of metric to use [options] "MSE(default)", "Accuracy score" :return: a metric function to apply. @@ -61,6 +60,8 @@ def get_metric(metric_type: str): return MSE elif metric_type == "Accuracy score": return accuracy_score + elif metric_type == "R2": + return R2 else: # Invalid type described print(f"[Warning] Invalid metric type has given. Uses default metric type as 'MSE'") return MSE @@ -71,23 +72,28 @@ def get_metric(metric_type: str): """ -def read_csv(file_name: str) -> tuple: +def read_csv(file_name: str, test_ratio: float) -> tuple: """ - read_data() + read_csv() This function reads the data from the given csv file + * This function accepts the following valid file format: + a dataset with either a single or multiple features and a single label, + where all values excluding the header are numeric. :param file_name: name of the file to read - :return: X, y + :param test_ratio: test ratio from the created dataset + :return: + X: Dataset with features + y: Dataset with labels + train_X, train_y, test_X, test_y: Split dataset by split_dataset() function """ - data = [] - with open(file_name, "r") as file: - reader = csv.DictReader(file) - for row in reader: - data.append(row) + data = np.loadtxt(file_name, delimiter=',', skiprows=1) # Skip header - X = np.array([[float(v) for k, v in datum.items() if k.startswith('x')] for datum in data]) - y = np.array([[float(v) for k, v in datum.items() if k == 'y'] for datum in data]) + X = data[:, :-1] + y = data[:, -1] - return X, y + # Split dataset into train and test + train_X, train_y, test_X, test_y = split_dataset(X, y, test_ratio) + return X, y, train_X, train_y, test_X, test_y def generate_data(size: int, dimension: int, correlation: float, noise_std: float, random_state: int, @@ -170,9 +176,7 @@ def get_data(data_type: str, args: dict) -> tuple: test_y: test dataset label """ if data_type == 'file': - X, y = read_csv(**args) - train_X, train_y, test_X, test_y = split_dataset(X, y, args["test_ratio"]) - return X, y, train_X, train_y, test_X, test_y + return read_csv(**args) else: # (Default) Generate data return generate_data(**args) @@ -225,3 +229,25 @@ def AIC(y: np.ndarray, X: np.ndarray, y_pred: np.ndarray): log_likelihood = - n / 2 * np.log(2 * np.pi) - n / 2 - n * np.log(MSE(y, y_pred)) / 2 return 2 * k - 2 * log_likelihood # AIC = 2*k - 2*log_likelihood + + +def R2(y: np.ndarray, y_pred: np.ndarray) -> float: + """ + R2() + This function calculates the coefficient of determination (R2) between given data (actual y and predicted y). + The higher the value of R², the higher the explanatory power of the model. + Formula used: + R² = 1 - (∑((y_i - y_pred_i)²) / ∑((y_i - y_mean)²)) + where: + y_i : actual weight of ith feature + y_pred_i : predicted weight of ith feature + y_mean : mean of the actual weight features + + :param y: Actual weights + :param y_pred: Predicted weights + :return: R2 score between two weights + """ + # Average of actual weights + y_mean = np.mean(y) + r_2 = 1 - np.sum((y - y_pred) ** 2) / np.sum((y - y_mean) ** 2) # R2 = 1 - SSR/SST + return float(r_2) diff --git a/params/param.py b/params/param.txt similarity index 96% rename from params/param.py rename to params/param.txt index 51a306d..ecb08b5 100644 --- a/params/param.py +++ b/params/param.txt @@ -5,12 +5,13 @@ """ params = { + "description": "test": { "general": { "activate": {"k_fold_CV": True, "bootstrapping": True}, # True to execute the target test "model": "LinearRegression", # [options] "LinearRegression(default)", "LogisticRegression" - "metric": "MSE", # [options] "MSE(default)", "Accuracy score" - "data": "generate", # [options] 'kaggle'(default), 'file', 'generate' + "metric": "MSE", # [options] "MSE", "Accuracy score" + "data": "generate", # [options] file', 'generate' }, "k_fold_cross_validation": { "k": [5], # Number of folds diff --git a/params/param_multi.json b/params/param_multi.json index 33e9ba0..e88c4e0 100644 --- a/params/param_multi.json +++ b/params/param_multi.json @@ -1,5 +1,5 @@ { - "description": "A parameter setting for data settings to batch test multiple generated datasets.\n The primary goal is to observe the differences among datasets with varying sizes and complexities\n (multi-collinearity, noise) using a simple linear regression model.", + "description": "A parameter setting for data settings to test multiple generated datasets as batch.\n The primary goal is to observe the differences among datasets with varying sizes and complexities\n (multi-collinearity, noise) using a simple linear regression model.", "test": { "general": { "activate": {"k_fold_CV": true, "bootstrapping": true}, diff --git a/params/param_single.json b/params/param_single.json index 97fa95c..347fce0 100644 --- a/params/param_single.json +++ b/params/param_single.json @@ -1,10 +1,11 @@ { + "description": "Test designed for program execution verification and optimal settings for unit data sets.", "test": { "general": { "activate": {"k_fold_CV": true, "bootstrapping": true}, "model": "LinearRegression", - "metric": "MSE", - "data": "generate" + "metric": "R2", + "data": "file" }, "k_fold_cross_validation": { "k": [5], @@ -18,7 +19,7 @@ "data": { "file": [ { - "file_name": "small_test.csv", + "file_name": "./data/housing.csv", "test_ratio": 0.25 } ], diff --git a/test.py b/test.py index 74d438b..72bff21 100644 --- a/test.py +++ b/test.py @@ -1,3 +1,5 @@ +# import sys + from modelSelection import * @@ -24,8 +26,9 @@ def test_AIC(model, train_X: np.ndarray, train_y: np.ndarray, test_X: np.ndarray print(f"\t(Comparison) AIC Score: {AIC(X=test_X, y=test_y, y_pred=y_pred)}") -def main(): - param = get_param('./params/param_multi.json') +def main(file_path: str): + + param = get_param(file_path) # Init params print(f"{'*' * 52} Global Setting {'*' * 52}") args_g = param["test"]["general"] @@ -72,4 +75,10 @@ def main(): if __name__ == "__main__": - main() + # args = sys.argv + args = ["QuickStart", "./params/param_single.json"] + if len(args) > 1: + main(args[1]) + else: + print("[Warning] No parameter configuration(file Path with param_*.json) has provided!") + print("[Info] Termination program...") From 3519efbf3cddbf9145e7e874abb63b174abfb613 Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Wed, 20 Nov 2024 10:47:02 -0600 Subject: [PATCH 04/14] Comment update. --- lib.py | 174 +++++++++++++++++++++------------------------- modelSelection.py | 70 ++++++++++--------- test.py | 54 +++++++++++--- 3 files changed, 164 insertions(+), 134 deletions(-) diff --git a/lib.py b/lib.py index 72e7beb..cf26f25 100644 --- a/lib.py +++ b/lib.py @@ -4,9 +4,10 @@ [LINK] https://en.wikipedia.org/wiki/Akaike_information_criterion 2. Log likelihood formula - StatLect "Log-likelihood" [LINK] https://www.statlect.com/glossary/log-likelihood + 3. R2 score formula: Wikipedia - Coefficient of determination + [LINK] https://en.wikipedia.org/wiki/Coefficient_of_determination """ import json - import numpy as np from sklearn.linear_model import * @@ -18,22 +19,28 @@ def get_param(file_path: str) -> dict: + """ + get_param() + This function reads parameter settings from a JSON file. + :param file_path: Path to the JSON file containing parameter settings. + :return: A dictionary of parameters. + """ with open(file_path, "r", encoding="utf-8") as file: return json.load(file) """ - Model selections + Model selection functions """ def get_model(model_type: str): """ get_model() - This function returns the model object for the given model_type defined in 'params,py'. - If no model_type is defined, it returns default model object as LinearRegression. - :param model_type: the type of model to use [options] "LinearRegression(default)", "LogisticRegression" - :return: a model object to train. + This function returns a model object based on the given model_type. + If an invalid model_type is provided, it defaults to LinearRegression. + :param model_type: The type of model to use. Options: "LinearRegression" (default), "LogisticRegression". + :return: A model object. """ print(f"Model Type: {model_type}") @@ -42,17 +49,17 @@ def get_model(model_type: str): elif model_type == "LogisticRegression": return LogisticRegression(max_iter=1000) else: # Invalid type described - print(f"[Warning] Invalid model type has given. Uses default model type as 'LinearRegression'") + print(f"[Warning] Invalid model type given. Defaulting to 'LinearRegression'.") return LinearRegression() def get_metric(metric_type: str): """ get_metric() - This function returns the metric function for the given metric_type defined in 'param.txt'. - If no metric_type is defined, it returns default setting as MSE. - :param metric_type: the type of metric to use [options] "MSE(default)", "Accuracy score" - :return: a metric function to apply. + This function returns the metric function corresponding to the given metric_type. + Defaults to MSE if an invalid type is provided. + :param metric_type: The type of metric to use. Options: "MSE" (default), "Accuracy score", "R2". + :return: A metric function. """ print(f"Metric Type: {metric_type}") @@ -63,30 +70,24 @@ def get_metric(metric_type: str): elif metric_type == "R2": return R2 else: # Invalid type described - print(f"[Warning] Invalid metric type has given. Uses default metric type as 'MSE'") + print(f"[Warning] Invalid metric type given. Defaulting to 'MSE'.") return MSE """ - Data import/create functions + Data import and generation functions """ def read_csv(file_name: str, test_ratio: float) -> tuple: """ read_csv() - This function reads the data from the given csv file - * This function accepts the following valid file format: - a dataset with either a single or multiple features and a single label, - where all values excluding the header are numeric. - :param file_name: name of the file to read - :param test_ratio: test ratio from the created dataset - :return: - X: Dataset with features - y: Dataset with labels - train_X, train_y, test_X, test_y: Split dataset by split_dataset() function - """ - data = np.loadtxt(file_name, delimiter=',', skiprows=1) # Skip header + This function reads data from a CSV file and splits it into training and test sets. + :param file_name: The name of the CSV file to read. + :param test_ratio: The proportion of data to use for the test set. + :return: A tuple containing the full dataset (X, y) and the split datasets (train_X, train_y, test_X, test_y). + """ + data = np.loadtxt(file_name, delimiter=',', skiprows=1) # Skip the header row X = data[:, :-1] y = data[:, -1] @@ -99,41 +100,36 @@ def read_csv(file_name: str, test_ratio: float) -> tuple: def generate_data(size: int, dimension: int, correlation: float, noise_std: float, random_state: int, test_ratio: float) -> tuple: """ - generate_multi_collinear_data() - This is a function that generates data with multi_collinearity - The data produced from this function is intended to measure the ElasticNet model's ability - to handle multi_collinearity, which is a critical feature of the model - - :param size: Number of samples to generate - :param dimension: Number of features to generate - :param correlation: Correlation coefficient between features (1 is the worst) - :param noise_std: Set noise scale to test durability of the trained model - :param random_state: random seed - :param test_ratio: test ratio from the created dataset - :returns: - X: Dataset with features - y: Dataset with labels - train_X, train_y, test_X, test_y: Split dataset by split_dataset() function + generate_data() + This function generates synthetic data with multi-collinearity, designed for testing models like ElasticNet. + :param size: Number of samples to generate. + :param dimension: Number of features to generate. + :param correlation: Correlation coefficient between features (1 indicates perfect correlation). + :param noise_std: Standard deviation of noise added to the data. + :param random_state: Random seed for reproducibility. + :param test_ratio: The proportion of data to use for the test set. + :return: A tuple containing the full dataset (X, y) and the split datasets (train_X, train_y, test_X, test_y). """ if random_state: np.random.seed(random_state) - # random base sample (1st feature) + # TODO: Update Multi-collinearity function part + # Generate the base feature X_base = np.random.rand(size, 1) - # Create another feature based on the data for the first feature + # Create correlated features X = X_base + correlation * np.random.randn(size, dimension) * noise_std # Increase the correlation between each feature (e.g. through linear combination) for i in range(1, dimension): X[:, i] = correlation * X_base[:, 0] + (1 - correlation) * np.random.randn(size) - # create weights, bias, and noise + # Generate weights, bias, and noise weights = np.random.randn(dimension) bias = np.random.rand() noise_std = np.random.normal(0, noise_std, size=size) - # create y (Add noise to multi-collinearity) + # Create the target variable y y = X.dot(weights) + bias + (bias + noise_std) # Split dataset into train and test @@ -144,15 +140,11 @@ def generate_data(size: int, dimension: int, correlation: float, noise_std: floa def split_dataset(X: np.ndarray, y: np.ndarray, test_ratio: float) -> tuple: """ split_dataset() - This function splits the dataset into training and test sets - :param X: Dataset with features - :param y: Dataset labels - :param test_ratio: size of the test set - :return: - train_X: train dataset - train_y: train dataset label - test_X: test dataset - test_y: test dataset label + This function splits the dataset into training and test sets based on the given test_ratio. + :param X: Features. + :param y: Labels. + :param test_ratio: Proportion of the dataset to use as the test set. + :return: Training and test sets (train_X, train_y, test_X, test_y). """ # Split data into train and test test_size = int(test_ratio * X.shape[0]) @@ -164,20 +156,14 @@ def split_dataset(X: np.ndarray, y: np.ndarray, test_ratio: float) -> tuple: def get_data(data_type: str, args: dict) -> tuple: """ get_data() - This function loads dataset chosen data type - :param data_type: the type of data to use [data options] 'iris'(default), 'file' ,'generate' - :param args: a parameter setting of the dataset - :returns: tuples produced by chosen function. Specific details as below: - X: Dataset with features - y: Dataset with labels - train_X: train dataset - train_y: train dataset label - test_X: test dataset - test_y: test dataset label + This function loads or generates a dataset based on the data_type. + :param data_type: The type of dataset to use. Options: "file" (from CSV) or "generate" (synthetic data). + :param args: Arguments specific to the chosen data type. + :return: Dataset tuples (X, y, train_X, train_y, test_X, test_y). """ if data_type == 'file': return read_csv(**args) - else: # (Default) Generate data + else: # Default to generated data return generate_data(**args) @@ -189,63 +175,63 @@ def get_data(data_type: str, args: dict) -> tuple: def MSE(y: np.ndarray, y_pred: np.ndarray) -> float: """ MSE() - This function calculates a value based on mean squared error (MSE), - one of the evaluation indicators for regression models. - :param y: desired target values - :param y_pred: predicted values - :return: mean squared error of the given data + This function calculates the Mean Squared Error (MSE) between actual and predicted values. + :param y: Actual values. + :param y_pred: Predicted values. + :return: Mean Squared Error. """ return float(np.mean((y - y_pred) ** 2)) def accuracy_score(y: np.ndarray, y_pred: np.ndarray) -> float: """ - accuracy_rate() - This function calculates a value based on accuracy rate, - which is one of the evaluation metrics for a classification model. - :param y: desired target values - :param y_pred: predicted values - :return: accuracy rate of the model + accuracy_score() + This function calculates the accuracy score for classification tasks. + :param y: Actual labels. + :param y_pred: Predicted labels. + :return: Accuracy score. """ return float(np.sum(y == y_pred) / len(y)) def AIC(y: np.ndarray, X: np.ndarray, y_pred: np.ndarray): """ - aic() - This function computes the AIC evaluation for the given model - :param y: Actual values - :param X: Feature vectors - :param y_pred: Predicted values - :return: AIC value - """ - # Number of samples + AIC() + This function computes the Akaike Information Criterion (AIC) for the given model. + Formula used: + * Refer to above references + log-likelihood = - n/2*log(2*π) - n/2*log(MSE) - n/2 + AIC = 2*k - 2*(log-likelihood) + = - (∑((y_i - y_pred_i)²) / ∑((y_i - y_mean)²)) + :param y: Actual values. + :param X: Feature matrix. + :param y_pred: Predicted values. + :return: AIC value. + """ + # Number of samples and features n = X.shape[0] + k = X.shape[1] + 1 # Include residual as a parameter - # Number of feature including a residual - k = X.shape[1] + 1 - - # Log likelihood formula: log_likelihood = - n/2*log(2*π) - n/2*log(MSE) - n/2 + # compute Log-likelihood log_likelihood = - n / 2 * np.log(2 * np.pi) - n / 2 - n * np.log(MSE(y, y_pred)) / 2 - return 2 * k - 2 * log_likelihood # AIC = 2*k - 2*log_likelihood + # Return AIC value + return 2 * k - 2 * log_likelihood def R2(y: np.ndarray, y_pred: np.ndarray) -> float: """ R2() - This function calculates the coefficient of determination (R2) between given data (actual y and predicted y). - The higher the value of R², the higher the explanatory power of the model. + This function calculates the R² score (coefficient of determination) for regression models. Formula used: R² = 1 - (∑((y_i - y_pred_i)²) / ∑((y_i - y_mean)²)) where: y_i : actual weight of ith feature y_pred_i : predicted weight of ith feature y_mean : mean of the actual weight features - - :param y: Actual weights - :param y_pred: Predicted weights - :return: R2 score between two weights + :param y: Actual values. + :param y_pred: Predicted values. + :return: R² score. """ # Average of actual weights y_mean = np.mean(y) diff --git a/modelSelection.py b/modelSelection.py index a4fd211..3187cf7 100644 --- a/modelSelection.py +++ b/modelSelection.py @@ -4,21 +4,21 @@ def k_fold_cross_validation(model, metric, X: np.ndarray, y: np.ndarray, k: int, shuffle: bool): """ k_fold_cross_validation() - This function is used to validate the model using k-fold cross validation method - - :param model: The statistic model to test - :param metric: The metric to measure the model performance - :param X: The training feature vectors - :param y: The training labels - :param k: Number of folds - :param shuffle: Whether to shuffle the data before the operation - :return: metric scores of folds and their average score + This function validates the model using the k-fold cross-validation method. + + :param model: The statistical model to test. + :param metric: The metric function to measure the model's performance. + :param X: The feature matrix for training. + :param y: The target labels for training. + :param k: The number of folds to divide the data into. + :param shuffle: Whether to shuffle the data before splitting into folds. + :return: A tuple containing the list of metric scores for each fold and their average score. """ scores = [] - n = X.shape[0] # Size of sample - fold_size = n // k # Size of each fold + n = X.shape[0] # Total number of samples + fold_size = n // k # Number of samples per fold - if shuffle: # shuffle the data + if shuffle: # Shuffle the data indices = np.arange(n) np.random.shuffle(indices) @@ -26,62 +26,68 @@ def k_fold_cross_validation(model, metric, X: np.ndarray, y: np.ndarray, k: int, y = y[indices] for i in range(k): - # Get indices(start, end) of the current validation fold + # Define the start and end indices of the current validation fold start, end = i * fold_size, (i + 1) * fold_size - # Get validation set + # Extract the validation set X_val, y_val = X[start:end], y[start:end] - # Get train set + # Extract the training set by excluding the current fold X_train = np.concatenate([X[:start], X[end:]], axis=0) y_train = np.concatenate([y[:start], y[end:]], axis=0) - # Train the model + # Train the model on the training set model.fit(X_train, y_train) - # Get predicted data + # Predict on the validation set y_predicted = model.predict(X_val) - # Compute accuracy rate + # Calculate the score using the metric function score = metric(y_val, y_predicted) - # Add to score list + # Append the score to the list scores.append(score) + # Return the list of scores and their average return scores, np.average(scores) def bootstrapping(model, metric, X: np.ndarray, y: np.ndarray, s: int, epochs: int): """ bootstrapping() - This function performs bootstrapping - - :param model: The statistic model to test - :param metric: The metric to measure the model performance - :param X: The training feature vectors - :param y: The training labels - :param s: The size of a train dataset - :param epochs: The number of trials to perform train/verify its performance - :return: metric scores of trials and their average score + This function performs bootstrapping to evaluate the model's performance. + + :param model: The statistical model to test. + :param metric: The metric function to measure the model's performance. + :param X: The feature matrix for training. + :param y: The target labels for training. + :param s: The size of the training dataset for each bootstrap sample. + :param epochs: The number of bootstrap iterations to perform. + :return: A tuple containing the list of metric scores for each iteration and their average score. """ scores = [] - n = X.shape[0] # Size of dataset + n = X.shape[0] # Total number of samples for _ in range(epochs): - # Pick train set data by sampling of size s + # Randomly sample 's(=size of sample)' indices with replacement to create the training set indices = np.random.choice(range(n), size=s, replace=True) X_train, y_train = X[indices], y[indices] - # The others will be assigned as validation set + # Use the remaining data as the validation set out_of_sample = [i for i in range(n) if i not in indices] X_val, y_val = X[out_of_sample], y[out_of_sample] + # Train the model on the bootstrap sample model.fit(X_train, y_train) + + # Predict on the validation set y_pred = model.predict(X_val) - if len(out_of_sample) > 0: # Avoid cases with no out-of-sample data + # Calculate the score if there is any validation data + if len(out_of_sample) > 0: score = metric(y_val, y_pred) scores.append(score) + # Calculate the average score across all iterations average_score = np.mean(scores) return scores, average_score diff --git a/test.py b/test.py index 72bff21..b053492 100644 --- a/test.py +++ b/test.py @@ -1,9 +1,19 @@ # import sys - from modelSelection import * def test_k_Fold_CV(model, metric, X: np.ndarray, y: np.ndarray, ks: list[int], shuffle: bool): + """ + test_k_Fold_CV() + This function tests the k-fold cross-validation implementation with different values of k. + + :param model: The statistical model to be validated. + :param metric: The metric function used to evaluate model performance. + :param X: The feature matrix. + :param y: The target labels. + :param ks: A list of k values to test (number of folds). + :param shuffle: Whether to shuffle the data before splitting into folds. + """ for k in ks: k_fold_scores, k_fold_avg = k_fold_cross_validation(model, metric, X=X, y=y, k=k, shuffle=shuffle) print(f"k-Fold Cross-Validation Scores:\n\t{k_fold_scores}") @@ -11,6 +21,17 @@ def test_k_Fold_CV(model, metric, X: np.ndarray, y: np.ndarray, ks: list[int], s def test_bootstrapping(model, metric, X: np.ndarray, y: np.ndarray, ss: list[int], epochs_list: list[int]): + """ + test_bootstrapping() + This function tests the bootstrapping implementation with different sample sizes and epochs. + + :param model: The statistical model to be tested. + :param metric: The metric function used to evaluate model performance. + :param X: The feature matrix. + :param y: The target labels. + :param ss: A list of sample sizes for the bootstrapping training set. + :param epochs_list: A list of epoch values to determine the number of iterations for bootstrapping. + """ for s in ss: for epochs in epochs_list: bootstrap_scores, bootstrap_avg = bootstrapping(model, metric, X=X, y=y, s=s, epochs=epochs) @@ -21,15 +42,32 @@ def test_bootstrapping(model, metric, X: np.ndarray, y: np.ndarray, ss: list[int def test_AIC(model, train_X: np.ndarray, train_y: np.ndarray, test_X: np.ndarray, test_y: np.ndarray): + """ + test_AIC() + This function tests the AIC (Akaike Information Criterion) computation for the given model. + + :param model: The trained statistical model. + :param train_X: Training feature matrix. + :param train_y: Training labels. + :param test_X: Test feature matrix. + :param test_y: Test labels. + """ model.fit(train_X, train_y) y_pred = model.predict(test_X) print(f"\t(Comparison) AIC Score: {AIC(X=test_X, y=test_y, y_pred=y_pred)}") def main(file_path: str): + """ + main() + This function is an entry point for the test suite. Loads parameters, initializes models, + and tests k-fold cross-validation with AIC performance and bootstrapping. + :param file_path: Path to the JSON configuration file containing test parameters. + """ param = get_param(file_path) - # Init params + + # Initialize global parameters print(f"{'*' * 52} Global Setting {'*' * 52}") args_g = param["test"]["general"] print(f"Description:\n\t{param['description']}") @@ -44,7 +82,7 @@ def main(file_path: str): args = param["data"][args_g["data"]][i] print(f"Data Parameters:\n\t{args}") - # Get dataset + # Load dataset X, y, train_X, train_y, test_X, test_y = get_data(args_g["data"], args) print("-" * 121) @@ -54,16 +92,16 @@ def main(file_path: str): print("[Test] K-Fold Cross-Validation") args_k = param["test"]["k_fold_cross_validation"] - # Test K-Fold CV + # Perform K-Fold CV testing test_k_Fold_CV(model, metric, X, y, ks=args_k["k"], shuffle=args_k["shuffle"]) - # Compare with simple AIC + # Compare results with AIC test_AIC(model, train_X, train_y, test_X, test_y) print("-" * 121) if args_g["activate"]["bootstrapping"]: - # bootstrapping + # Bootstrapping Testing print("[Test] Bootstrapping") args_b = param["test"]["bootstrapping"] test_bootstrapping(model, metric, X, y, ss=args_b["size"], epochs_list=args_b["epochs"]) @@ -80,5 +118,5 @@ def main(file_path: str): if len(args) > 1: main(args[1]) else: - print("[Warning] No parameter configuration(file Path with param_*.json) has provided!") - print("[Info] Termination program...") + print("[Warning] No parameter configuration (file path with param_*.json) provided!") + print("[Info] Program terminated.") From 0df971cea79f9eee639a8d00b3dc3097e7056d93 Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Wed, 20 Nov 2024 11:03:27 -0600 Subject: [PATCH 05/14] Comment update. --- .gitignore | 3 +- README.md | 4 -- params/param.txt | 148 ++++++++++++++++++++------------------- params/param_multi.json | 8 ++- params/param_single.json | 2 +- 5 files changed, 84 insertions(+), 81 deletions(-) diff --git a/.gitignore b/.gitignore index fa5eb4e..e259a2a 100644 --- a/.gitignore +++ b/.gitignore @@ -159,5 +159,4 @@ cython_debug/ # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ -refer1.py \ No newline at end of file +#.idea/ \ No newline at end of file diff --git a/README.md b/README.md index 6e52d2a..134aea2 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,3 @@ In your README, answer the following questions: * In what cases might the methods you've written fail or give incorrect or undesirable results? * What could you implement given more time to mitigate these cases or help users of your methods? * What parameters have you exposed to your users in order to use your model selectors. - -See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2. - -As usual, above-and-beyond efforts will be considered for bonus points. diff --git a/params/param.txt b/params/param.txt index ecb08b5..3d0971e 100644 --- a/params/param.txt +++ b/params/param.txt @@ -1,101 +1,107 @@ -# All User parameter can be adjusted from here. +# All user parameters can be adjusted from this file. """ Test configuration """ params = { - "description": + "description": "Configuration settings for testing various model selection methods.", # Description of the test configuration "test": { "general": { - "activate": {"k_fold_CV": True, "bootstrapping": True}, # True to execute the target test - "model": "LinearRegression", # [options] "LinearRegression(default)", "LogisticRegression" - "metric": "MSE", # [options] "MSE", "Accuracy score" - "data": "generate", # [options] file', 'generate' + "activate": { + "k_fold_CV": True, # Set to True to perform k-fold cross-validation. + "bootstrapping": True # Set to True to perform bootstrapping. + }, + "model": "LinearRegression", # [Options] "LinearRegression" (default), "LogisticRegression". + "metric": "MSE", # [Options] "MSE" (default), "Accuracy score". + "data": "generate", # [Options] "file", "generate". }, "k_fold_cross_validation": { - "k": [5], # Number of folds - "shuffle": True # shuffle: Whether to shuffle the data before the operation + "k": [5], # Number of folds for k-fold cross-validation. + "shuffle": True, # Whether to shuffle the data before splitting into folds. + "visualize": False # Set to True to visualize k-fold cross-validation results (currently not implemented). }, "bootstrapping": { - "size": [50], # The size of a train dataset - "epochs": [100] # The number of trials to perform train/verify its performance + "size": [50], # The size of the training dataset for each bootstrap sample. + "epochs": [100], # The number of bootstrapping iterations to perform. + "visualize": False # Set to True to visualize bootstrapping results (currently not implemented). } }, + # For each data parameter "file", "generate": Single mode -> set 1 element Multiple mode -> set N elements "data": { - # User defined params for data import setting + # User-defined parameters for data import settings. "file": [ { - "file_name": "small_test.csv", # File path to read - "test_ratio": 0.25 # Portion of the test set from total sample. + "file_name": "small_test.csv", # File path to load data. + "test_ratio": 0.25 # Proportion of the dataset used for the test set. }, ], - # User defined params data creation setting + # User-defined parameters for synthetic data generation settings. "generate": [ - { # Default dataset with strict linear relationship - "size": 100, # Number of samples to create - "dimension": 10, # Number of features of the data - "correlation": 0, # Correlation coefficient between features - "noise_std": 0.0, # Noise scale to check durability of the model - "random_state": 42, # Random seed - "test_ratio": 0.25 # Portion of the test set from total sample. + { # Default dataset with a strict linear relationship (no noise or correlation). + "size": 100, # Number of samples to generate. + "dimension": 10, # Number of features in the dataset. + "correlation": 0, # Correlation coefficient between features (0 means no correlation). + "noise_std": 0.0, # Noise level to assess model robustness. + "random_state": 42, # Random seed for reproducibility. + "test_ratio": 0.25 # Proportion of the dataset used for the test set. }, - { # Larger dataset with strict linear relationship - "size": 500, # Number of samples to create - "dimension": 10, # Number of features of the data - "correlation": 0, # Correlation coefficient between features - "noise_std": 0.00, # Noise scale to check durability of the model - "random_state": 42, # Random seed - "test_ratio": 0.25 # Portion of the test set from total sample. + { # Larger dataset with a strict linear relationship (no noise or correlation). + "size": 500, # Number of samples to generate. + "dimension": 10, # Number of features in the dataset. + "correlation": 0, # Correlation coefficient between features. + "noise_std": 0.0, # Noise level to assess model robustness. + "random_state": 42, # Random seed for reproducibility. + "test_ratio": 0.25 # Proportion of the dataset used for the test set. }, - { # Default dataset with some noise - "size": 100, # Number of samples to create - "dimension": 10, # Number of features of the data - "correlation": 0, # Correlation coefficient between features - "noise_std": 0.01, # Noise scale to check durability of the model - "random_state": 42, # Random seed - "test_ratio": 0.25 # Portion of the test set from total sample. + { # Default dataset with added noise (no correlation between features). + "size": 100, # Number of samples to generate. + "dimension": 10, # Number of features in the dataset. + "correlation": 0, # Correlation coefficient between features. + "noise_std": 0.01, # Noise level to assess model robustness. + "random_state": 42, # Random seed for reproducibility. + "test_ratio": 0.25 # Proportion of the dataset used for the test set. }, - { # Larger dataset with some noise - "size": 500, # Number of samples to create - "dimension": 10, # Number of features of the data - "correlation": 0, # Correlation coefficient between features - "noise_std": 0.01, # Noise scale to check durability of the model - "random_state": 42, # Random seed - "test_ratio": 0.25 # Portion of the test set from total sample. + { # Larger dataset with added noise (no correlation between features). + "size": 500, # Number of samples to generate. + "dimension": 10, # Number of features in the dataset. + "correlation": 0, # Correlation coefficient between features. + "noise_std": 0.01, # Noise level to assess model robustness. + "random_state": 42, # Random seed for reproducibility. + "test_ratio": 0.25 # Proportion of the dataset used for the test set. }, - { # Default dataset with high correlation - "size": 100, # Number of samples to create - "dimension": 10, # Number of features of the data - "correlation": 0.9, # Correlation coefficient between features - "noise_std": 0.0, # Noise scale to check durability of the model - "random_state": 42, # Random seed - "test_ratio": 0.25 # Portion of the test set from total sample. + { # Default dataset with a high correlation between features (no noise). + "size": 100, # Number of samples to generate. + "dimension": 10, # Number of features in the dataset. + "correlation": 0.9, # High correlation coefficient between features. + "noise_std": 0.0, # Noise level to assess model robustness. + "random_state": 42, # Random seed for reproducibility. + "test_ratio": 0.25 # Proportion of the dataset used for the test set. }, - { # Larger dataset with high correlation - "size": 100, # Number of samples to create - "dimension": 10, # Number of features of the data - "correlation": 0.9, # Correlation coefficient between features - "noise_std": 0.0, # Noise scale to check durability of the model - "random_state": 42, # Random seed - "test_ratio": 0.25 # Portion of the test set from total sample. + { # Larger dataset with a high correlation between features (no noise). + "size": 500, # Number of samples to generate. + "dimension": 10, # Number of features in the dataset. + "correlation": 0.9, # High correlation coefficient between features. + "noise_std": 0.0, # Noise level to assess model robustness. + "random_state": 42, # Random seed for reproducibility. + "test_ratio": 0.25 # Proportion of the dataset used for the test set. }, - { # Default dataset with some noise and high correlation - "size": 100, # Number of samples to create - "dimension": 10, # Number of features of the data - "correlation": 0.9, # Correlation coefficient between features - "noise_std": 0.01, # Noise scale to check durability of the model - "random_state": 42, # Random seed - "test_ratio": 0.25 # Portion of the test set from total sample. + { # Default dataset with high correlation and added noise. + "size": 100, # Number of samples to generate. + "dimension": 10, # Number of features in the dataset. + "correlation": 0.9, # High correlation coefficient between features. + "noise_std": 0.01, # Noise level to assess model robustness. + "random_state": 42, # Random seed for reproducibility. + "test_ratio": 0.25 # Proportion of the dataset used for the test set. }, - { # Larger dataset with some noise and high correlation - "size": 100, # Number of samples to create - "dimension": 10, # Number of features of the data - "correlation": 0.9, # Correlation coefficient between features - "noise_std": 0.01, # Noise scale to check durability of the model - "random_state": 42, # Random seed - "test_ratio": 0.25 # Portion of the test set from total sample. + { # Larger dataset with high correlation and added noise. + "size": 500, # Number of samples to generate. + "dimension": 10, # Number of features in the dataset. + "correlation": 0.9, # High correlation coefficient between features. + "noise_std": 0.01, # Noise level to assess model robustness. + "random_state": 42, # Random seed for reproducibility. + "test_ratio": 0.25 # Proportion of the dataset used for the test set. } ] } -} +} \ No newline at end of file diff --git a/params/param_multi.json b/params/param_multi.json index e88c4e0..8fc27e4 100644 --- a/params/param_multi.json +++ b/params/param_multi.json @@ -1,5 +1,5 @@ { - "description": "A parameter setting for data settings to test multiple generated datasets as batch.\n The primary goal is to observe the differences among datasets with varying sizes and complexities\n (multi-collinearity, noise) using a simple linear regression model.", + "description": "Parameter settings for testing multiple generated datasets in batch.\nThe primary goal is to observe differences among datasets with varying sizes and complexities (e.g., multi-collinearity, noise) using a simple linear regression model.", "test": { "general": { "activate": {"k_fold_CV": true, "bootstrapping": true}, @@ -9,11 +9,13 @@ }, "k_fold_cross_validation": { "k": [5], - "shuffle": false + "shuffle": false, + "visualize": false }, "bootstrapping": { "size": [50], - "epochs": [100] + "epochs": [100], + "visualize": false } }, "data": { diff --git a/params/param_single.json b/params/param_single.json index 347fce0..b9fec78 100644 --- a/params/param_single.json +++ b/params/param_single.json @@ -1,5 +1,5 @@ { - "description": "Test designed for program execution verification and optimal settings for unit data sets.", + "description": "Configuration settings for testing various model selection methods.", "test": { "general": { "activate": {"k_fold_CV": true, "bootstrapping": true}, From b81a4d63e5ee242694158a00b1bec5201c651f2a Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Wed, 20 Nov 2024 11:14:15 -0600 Subject: [PATCH 06/14] Comment update & add some todos. --- lib.py | 15 +++++++++++++++ params/param.txt | 12 +++++++----- params/param_multi.json | 6 ++++-- params/param_single.json | 8 ++++++-- 4 files changed, 32 insertions(+), 9 deletions(-) diff --git a/lib.py b/lib.py index cf26f25..2d7c03e 100644 --- a/lib.py +++ b/lib.py @@ -237,3 +237,18 @@ def R2(y: np.ndarray, y_pred: np.ndarray) -> float: y_mean = np.mean(y) r_2 = 1 - np.sum((y - y_pred) ** 2) / np.sum((y - y_mean) ** 2) # R2 = 1 - SSR/SST return float(r_2) + + +""" + Data write & Visualization +""" + + +def visualize(data: np.ndarray): + """TODO Create this function""" + pass + + +def write_result(file_path: str, data: np.ndarray): + """TODO Create this function""" + pass diff --git a/params/param.txt b/params/param.txt index 3d0971e..c2428c7 100644 --- a/params/param.txt +++ b/params/param.txt @@ -9,8 +9,8 @@ params = { "test": { "general": { "activate": { - "k_fold_CV": True, # Set to True to perform k-fold cross-validation. - "bootstrapping": True # Set to True to perform bootstrapping. + "k_fold_CV": true, # Set to True to perform k-fold cross-validation. + "bootstrapping": true # Set to True to perform bootstrapping. }, "model": "LinearRegression", # [Options] "LinearRegression" (default), "LogisticRegression". "metric": "MSE", # [Options] "MSE" (default), "Accuracy score". @@ -18,13 +18,15 @@ params = { }, "k_fold_cross_validation": { "k": [5], # Number of folds for k-fold cross-validation. - "shuffle": True, # Whether to shuffle the data before splitting into folds. - "visualize": False # Set to True to visualize k-fold cross-validation results (currently not implemented). + "shuffle": true, # Whether to shuffle the data before splitting into folds. + "visualize": false, # Set to True to visualize k-fold cross-validation results (currently not implemented). + "write": false # Set to True to write k-fold cross-validation results (currently not implemented). }, "bootstrapping": { "size": [50], # The size of the training dataset for each bootstrap sample. "epochs": [100], # The number of bootstrapping iterations to perform. - "visualize": False # Set to True to visualize bootstrapping results (currently not implemented). + "visualize": false, # Set to True to visualize bootstrapping results (currently not implemented). + "write": false # Set to True to write k-fold cross-validation results (currently not implemented). } }, # For each data parameter "file", "generate": Single mode -> set 1 element Multiple mode -> set N elements diff --git a/params/param_multi.json b/params/param_multi.json index 8fc27e4..56fc822 100644 --- a/params/param_multi.json +++ b/params/param_multi.json @@ -10,12 +10,14 @@ "k_fold_cross_validation": { "k": [5], "shuffle": false, - "visualize": false + "visualize": false, + "write": false }, "bootstrapping": { "size": [50], "epochs": [100], - "visualize": false + "visualize": false, + "write": false } }, "data": { diff --git a/params/param_single.json b/params/param_single.json index b9fec78..a7e65ff 100644 --- a/params/param_single.json +++ b/params/param_single.json @@ -9,11 +9,15 @@ }, "k_fold_cross_validation": { "k": [5], - "shuffle": true + "shuffle": false, + "visualize": false, + "write": false }, "bootstrapping": { "size": [50], - "epochs": [100] + "epochs": [100], + "visualize": false, + "write": false } }, "data": { From 518973587ae4aac590ebd9e7a4dfc9d4ae3164bc Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Wed, 20 Nov 2024 11:21:30 -0600 Subject: [PATCH 07/14] Comment update. --- lib.py | 6 +++--- params/param.txt | 13 ++++++------- params/param_multi.json | 15 +++++++-------- params/param_single.json | 15 +++++++-------- 4 files changed, 23 insertions(+), 26 deletions(-) diff --git a/lib.py b/lib.py index 2d7c03e..fa3fc11 100644 --- a/lib.py +++ b/lib.py @@ -79,15 +79,15 @@ def get_metric(metric_type: str): """ -def read_csv(file_name: str, test_ratio: float) -> tuple: +def read_csv(file_path: str, test_ratio: float) -> tuple: """ read_csv() This function reads data from a CSV file and splits it into training and test sets. - :param file_name: The name of the CSV file to read. + :param file_path: The path of the CSV file to read. :param test_ratio: The proportion of data to use for the test set. :return: A tuple containing the full dataset (X, y) and the split datasets (train_X, train_y, test_X, test_y). """ - data = np.loadtxt(file_name, delimiter=',', skiprows=1) # Skip the header row + data = np.loadtxt(file_path, delimiter=',', skiprows=1) # Skip the header row X = data[:, :-1] y = data[:, -1] diff --git a/params/param.txt b/params/param.txt index c2428c7..df71a67 100644 --- a/params/param.txt +++ b/params/param.txt @@ -15,18 +15,17 @@ params = { "model": "LinearRegression", # [Options] "LinearRegression" (default), "LogisticRegression". "metric": "MSE", # [Options] "MSE" (default), "Accuracy score". "data": "generate", # [Options] "file", "generate". + "visualize": false, # Set to True to visualize k-fold cross-validation results (currently not implemented). + "write": false, # Set to True to write k-fold cross-validation results (currently not implemented). + "file_path": "./data/result(k_fold_CV).csv" # File path to write data. }, "k_fold_cross_validation": { "k": [5], # Number of folds for k-fold cross-validation. - "shuffle": true, # Whether to shuffle the data before splitting into folds. - "visualize": false, # Set to True to visualize k-fold cross-validation results (currently not implemented). - "write": false # Set to True to write k-fold cross-validation results (currently not implemented). + "shuffle": true # Whether to shuffle the data before splitting into folds. }, "bootstrapping": { "size": [50], # The size of the training dataset for each bootstrap sample. - "epochs": [100], # The number of bootstrapping iterations to perform. - "visualize": false, # Set to True to visualize bootstrapping results (currently not implemented). - "write": false # Set to True to write k-fold cross-validation results (currently not implemented). + "epochs": [100] # The number of bootstrapping iterations to perform. } }, # For each data parameter "file", "generate": Single mode -> set 1 element Multiple mode -> set N elements @@ -34,7 +33,7 @@ params = { # User-defined parameters for data import settings. "file": [ { - "file_name": "small_test.csv", # File path to load data. + "file_path": "small_test.csv", # File path to load data. "test_ratio": 0.25 # Proportion of the dataset used for the test set. }, ], diff --git a/params/param_multi.json b/params/param_multi.json index 56fc822..9c3df0b 100644 --- a/params/param_multi.json +++ b/params/param_multi.json @@ -5,25 +5,24 @@ "activate": {"k_fold_CV": true, "bootstrapping": true}, "model": "LinearRegression", "metric": "MSE", - "data": "generate" + "data": "generate", + "visualize": false, + "write": false, + "file_path": "./data/result(k_fold_CV).csv" }, "k_fold_cross_validation": { "k": [5], - "shuffle": false, - "visualize": false, - "write": false + "shuffle": false }, "bootstrapping": { "size": [50], - "epochs": [100], - "visualize": false, - "write": false + "epochs": [100] } }, "data": { "file": [ { - "file_name": "small_test.csv", + "file_path": "small_test.csv", "test_ratio": 0.25 } ], diff --git a/params/param_single.json b/params/param_single.json index a7e65ff..af3366b 100644 --- a/params/param_single.json +++ b/params/param_single.json @@ -5,25 +5,24 @@ "activate": {"k_fold_CV": true, "bootstrapping": true}, "model": "LinearRegression", "metric": "R2", - "data": "file" + "data": "file", + "visualize": false, + "write": false, + "file_path": "./data/result(k_fold_CV).csv" }, "k_fold_cross_validation": { "k": [5], - "shuffle": false, - "visualize": false, - "write": false + "shuffle": false }, "bootstrapping": { "size": [50], - "epochs": [100], - "visualize": false, - "write": false + "epochs": [100] } }, "data": { "file": [ { - "file_name": "./data/housing.csv", + "file_path": "./data/housing.csv", "test_ratio": 0.25 } ], From 2250fab66dddb745136cf8a83904f300f9b3d2ea Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Thu, 21 Nov 2024 22:48:42 -0600 Subject: [PATCH 08/14] Test completed --- README.md | 3 + lib.py | 81 ++++++++++++-- modelSelection.py | 4 +- params/param_bootstrap.json | 77 +++++++++++++ params/{param.txt => param_example.txt} | 28 ++++- params/param_k_fold.json | 77 +++++++++++++ params/param_multi.json | 115 +++++++++++++++---- params/param_single.json | 37 +++++-- params/test_correlation.json | 109 ++++++++++++++++++ params/test_size.json | 141 ++++++++++++++++++++++++ results/size_test_bootstrapping.png | Bin 0 -> 59694 bytes results/size_test_k_fold.png | Bin 0 -> 65928 bytes test.py | 126 +++++++++++++++++---- 13 files changed, 738 insertions(+), 60 deletions(-) create mode 100644 params/param_bootstrap.json rename params/{param.txt => param_example.txt} (80%) create mode 100644 params/param_k_fold.json create mode 100644 params/test_correlation.json create mode 100644 params/test_size.json create mode 100644 results/size_test_bootstrapping.png create mode 100644 results/size_test_k_fold.png diff --git a/README.md b/README.md index 134aea2..1e4abe8 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,9 @@ Implement generic k-fold cross-validation and bootstrapping model selection meth In your README, answer the following questions: * Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)? +- Regarding simple data sets, it can be confirmed that the previous two models and simpler models such as AIC reach the same conclusion. However, when testing complex datasets that are difficult to estimate (e.g., with many random elements) or when testing with multi-collinearity in mind, the results are somewhat inconsistent. +- * In what cases might the methods you've written fail or give incorrect or undesirable results? +- K겹 교차검증의 경우, 다중공선성이 포함되면 * What could you implement given more time to mitigate these cases or help users of your methods? * What parameters have you exposed to your users in order to use your model selectors. diff --git a/lib.py b/lib.py index fa3fc11..08a64b9 100644 --- a/lib.py +++ b/lib.py @@ -7,9 +7,13 @@ 3. R2 score formula: Wikipedia - Coefficient of determination [LINK] https://en.wikipedia.org/wiki/Coefficient_of_determination """ +import csv import json +import re import numpy as np +import matplotlib.pyplot as plt from sklearn.linear_model import * +from datetime import datetime # Collection of auxiliary functions @@ -113,7 +117,6 @@ def generate_data(size: int, dimension: int, correlation: float, noise_std: floa if random_state: np.random.seed(random_state) - # TODO: Update Multi-collinearity function part # Generate the base feature X_base = np.random.rand(size, 1) @@ -244,11 +247,75 @@ def R2(y: np.ndarray, y_pred: np.ndarray) -> float: """ -def visualize(data: np.ndarray): - """TODO Create this function""" - pass +def visualize(data: list, target: str, feature: list): + # data: ["size", "dimension", "correlation","noise_std", "k-value","epochs","Average", "AIC"] + # Transform into numpy array + data = np.array(data) -def write_result(file_path: str, data: np.ndarray): - """TODO Create this function""" - pass + # Split feature label + X = data[:, :-2] # Excluding Average, AIC + y_average = data[:, -2] # Label Average + y_AIC = data[:, -1] # Label Average + + # Pick specific feature parameter + i = feature[1] + X_i = X[:, i] + + fig, axes = plt.subplots(1, 2, figsize=(12, 6)) # 1 row, 2 columns + + # First plot: Average Score + axes[0].scatter(X_i, y_average, color='blue', label=f'Feature {feature[0]} vs Average Score') + + # Fit trend line for Average + coeffs_avg = np.polyfit(X_i, y_average, 1) # slope parameter + trend_avg = np.polyval(coeffs_avg, X_i) # trend line + + # plot graph + axes[0].plot(X_i, trend_avg, color='cyan', linestyle='--', label='Trend Line (Average)') + axes[0].set_title(f'{target} Average Score: {feature[0]}') + axes[0].set_xlabel(f'Feature {feature[0]}') + axes[0].set_ylabel('Average Score') + axes[0].legend() + axes[0].grid(True) + + # Second plot: AIC Score + axes[1].scatter(X_i, y_AIC, color='red', label=f'Feature {feature[0]} vs AIC Score') + + # Fit trend line for AIC + coeffs_aic = np.polyfit(X_i, y_AIC, 1) # slope parameter + trend_aic = np.polyval(coeffs_aic, X_i) # trend line + + # plot graph + axes[1].plot(X_i, trend_aic, color='cyan', linestyle='--', label='Trend Line (AIC)') + axes[1].set_title(f'{target} AIC Score: {feature[0]}') + axes[1].set_xlabel(f'Feature {feature[0]}') + axes[1].set_ylabel('AIC Score') + axes[1].legend() + axes[1].grid(True) + + # Adjust layout and display + plt.tight_layout() + plt.show() + + +def write(file_path: str, data: list, header: list): + """ + write() + This function writes data to a given file with a timestamped file name. + :param file_path: The original file path (string). + :param data: A list of rows to write into the CSV file. Each row is a list of values. + :param header: A list representing the header row of the given file. + """ + # Update file name - Append current time to discriminate + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + new_path = re.sub( + r'([^/]+)\.([a-zA-Z0-9]+)$', + rf'\1_{timestamp}.\2', + file_path + ) + # Write a file to designated path + with open(new_path, "wt", newline="", encoding="utf-8") as f: + csv_writer = csv.writer(f) + csv_writer.writerow(header) + csv_writer.writerows(data) diff --git a/modelSelection.py b/modelSelection.py index 3187cf7..77ee7c0 100644 --- a/modelSelection.py +++ b/modelSelection.py @@ -49,7 +49,7 @@ def k_fold_cross_validation(model, metric, X: np.ndarray, y: np.ndarray, k: int, scores.append(score) # Return the list of scores and their average - return scores, np.average(scores) + return scores, float(np.average(scores)) def bootstrapping(model, metric, X: np.ndarray, y: np.ndarray, s: int, epochs: int): @@ -90,4 +90,4 @@ def bootstrapping(model, metric, X: np.ndarray, y: np.ndarray, s: int, epochs: i # Calculate the average score across all iterations average_score = np.mean(scores) - return scores, average_score + return scores, float(average_score) diff --git a/params/param_bootstrap.json b/params/param_bootstrap.json new file mode 100644 index 0000000..969d74b --- /dev/null +++ b/params/param_bootstrap.json @@ -0,0 +1,77 @@ +{ + "description": "Batch test setting: bootstrapping exclusive test", + "test": { + "general": { + "activate": {"k_fold_CV": false, "bootstrapping": true}, + "model": "LinearRegression", + "metric": "R2", + "data": "generate" + }, + "k_fold_cross_validation": { + "k": [5], + "shuffle": true + }, + "bootstrapping": { + "size": [10, 50, 100], + "epochs": [10, 100, 500, 1000] + }, + "analysis": { + "visualize": { + "k_fold_CV":{ + "label_X": [], + "activate": false + }, + "bootstrapping": { + "label_X": [], + "activate": false + } + }, + "write": { + "k_fold_CV": { + "activate": false, + "header": [], + "file_path": "" + }, + "bootstrapping": { + "activate": true, + "header": ["size", "dimension", "correlation","noise_std","sample_size","epochs","Average", "AIC"], + "file_path": "./results/bootstrap_only.csv" + } + } + } +}, + "data": { + "file": [ + { + "file_path": "./data/housing.csv", + "test_ratio": 0.25 + } + ], + "generate": [ + { + "size": 500, + "dimension": 10, + "correlation": 0.0, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 20, + "correlation": 0, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 20, + "correlation": 0.9, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + } + ] + } +} diff --git a/params/param.txt b/params/param_example.txt similarity index 80% rename from params/param.txt rename to params/param_example.txt index df71a67..e11147d 100644 --- a/params/param.txt +++ b/params/param_example.txt @@ -13,7 +13,7 @@ params = { "bootstrapping": true # Set to True to perform bootstrapping. }, "model": "LinearRegression", # [Options] "LinearRegression" (default), "LogisticRegression". - "metric": "MSE", # [Options] "MSE" (default), "Accuracy score". + "metric": "MSE", # [Options] "MSE" (default), "Accuracy score", "R2". "data": "generate", # [Options] "file", "generate". "visualize": false, # Set to True to visualize k-fold cross-validation results (currently not implemented). "write": false, # Set to True to write k-fold cross-validation results (currently not implemented). @@ -26,6 +26,32 @@ params = { "bootstrapping": { "size": [50], # The size of the training dataset for each bootstrap sample. "epochs": [100] # The number of bootstrapping iterations to perform. + }, + "analysis": { + "visualize": { # visualization configuration + "k_fold_CV": { + # [Options] "size": 0, "dimension": 1, "correlation": 2, "noise_std": 3, K-value": 4 + "label_X": ["size", 0], # Selected feature to visualize + "activate": true # Set to True to visualize k-fold cross-validation. + }, + "bootstrapping": { + # [Options] "size": 0, "dimension": 1, "correlation": 2, "noise_std": 3, "sample_size": 4, "epoch": 5 + "label_X": ["size", 0], # Selected feature to visualize + "activate": false # Set to True to visualize bootstrapping. + } + }, + "write": { # file write configuration + "k_fold_CV": { + "activate": true, # Set to True to write a result file for k-fold cross-validation. + "header": ["size", "dimension", "correlation","noise_std",",K-value","shuffled", "Average", "AIC"], # Header for file. + "file_path": "./results/k_fold.csv" # Original file path + }, + "bootstrapping": { + "activate": false, # Set to True to write a result file for bootstrapping. + "header": ["size", "dimension", "correlation","noise_std", "sample_size","epochs","Average", "AIC"], # Header for file. + "file_path": "./results/bootstrap.csv" # Original file path. + } + } } }, # For each data parameter "file", "generate": Single mode -> set 1 element Multiple mode -> set N elements diff --git a/params/param_k_fold.json b/params/param_k_fold.json new file mode 100644 index 0000000..36c6361 --- /dev/null +++ b/params/param_k_fold.json @@ -0,0 +1,77 @@ +{ + "description": "Batch test setting: bootstrapping exclusive test", + "test": { + "general": { + "activate": {"k_fold_CV": true, "bootstrapping": false}, + "model": "LinearRegression", + "metric": "R2", + "data": "generate" + }, + "k_fold_cross_validation": { + "k": [2,5,10,20], + "shuffle": true + }, + "bootstrapping": { + "size": [50], + "epochs": [100] + }, + "analysis": { + "visualize": { + "k_fold_CV":{ + "label_X": [], + "activate": false + }, + "bootstrapping": { + "label_X": [], + "activate": false + } + }, + "write": { + "k_fold_CV": { + "activate": true, + "header": ["size", "dimension", "correlation","noise_std","K-value","shuffled", "Average", "AIC"], + "file_path": "./results/k_fold_only.csv" + }, + "bootstrapping": { + "activate": false, + "header": [], + "file_path": "" + } + } + } + }, + "data": { + "file": [ + { + "file_path": "", + "test_ratio": 0.25 + } + ], + "generate": [ + { + "size": 500, + "dimension": 10, + "correlation": 0.0, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 20, + "correlation": 0, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 20, + "correlation": 0.9, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + } + ] + } +} diff --git a/params/param_multi.json b/params/param_multi.json index 9c3df0b..9cf0ee8 100644 --- a/params/param_multi.json +++ b/params/param_multi.json @@ -1,22 +1,43 @@ { - "description": "Parameter settings for testing multiple generated datasets in batch.\nThe primary goal is to observe differences among datasets with varying sizes and complexities (e.g., multi-collinearity, noise) using a simple linear regression model.", + "description": "Batch test setting: mix-up test", "test": { "general": { "activate": {"k_fold_CV": true, "bootstrapping": true}, "model": "LinearRegression", - "metric": "MSE", - "data": "generate", - "visualize": false, - "write": false, - "file_path": "./data/result(k_fold_CV).csv" + "metric": "R2", + "data": "generate" }, "k_fold_cross_validation": { "k": [5], - "shuffle": false + "shuffle": true }, "bootstrapping": { - "size": [50], + "size": [100], "epochs": [100] + }, + "analysis": { + "visualize": { + "k_fold_CV":{ + "label_X": [], + "activate": false + }, + "bootstrapping": { + "label_X": [], + "activate": false + } + }, + "write": { + "k_fold_CV": { + "activate": true, + "header": ["size", "dimension", "correlation","noise_std",",K-value","shuffled", "Average", "AIC"], + "file_path": "./results/k_fold_multi.csv" + }, + "bootstrapping": { + "activate": true, + "header": ["size", "dimension", "correlation","noise_std", "sample_size","epochs","Average", "AIC"], + "file_path": "./results/bootstrap_multi.csv" + } + } } }, "data": { @@ -31,7 +52,15 @@ "size": 100, "dimension": 10, "correlation": 0, - "noise_std": 0.0, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 300, + "dimension": 10, + "correlation": 0, + "noise_std": 0.2, "random_state": 42, "test_ratio": 0.25 }, @@ -39,7 +68,23 @@ "size": 500, "dimension": 10, "correlation": 0, - "noise_std": 0.00, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 1000, + "dimension": 10, + "correlation": 0, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 1500, + "dimension": 10, + "correlation": 0, + "noise_std": 0.2, "random_state": 42, "test_ratio": 0.25 }, @@ -47,7 +92,15 @@ "size": 100, "dimension": 10, "correlation": 0, - "noise_std": 0.01, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 300, + "dimension": 10, + "correlation": 0, + "noise_std": 0.5, "random_state": 42, "test_ratio": 0.25 }, @@ -55,39 +108,63 @@ "size": 500, "dimension": 10, "correlation": 0, - "noise_std": 0.01, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 1000, + "dimension": 10, + "correlation": 0, + "noise_std": 0.5, "random_state": 42, "test_ratio": 0.25 }, { + "size": 1500, + "dimension": 10, + "correlation": 0, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { "size": 100, "dimension": 10, "correlation": 0.9, - "noise_std": 0.0, + "noise_std": 0.5, "random_state": 42, "test_ratio": 0.25 }, { - "size": 100, + "size": 300, "dimension": 10, "correlation": 0.9, - "noise_std": 0.0, + "noise_std": 0.5, "random_state": 42, "test_ratio": 0.25 }, { - "size": 100, + "size": 500, "dimension": 10, "correlation": 0.9, - "noise_std": 0.01, + "noise_std": 0.5, "random_state": 42, "test_ratio": 0.25 }, { - "size": 100, + "size": 1000, + "dimension": 10, + "correlation": 0.9, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 1500, "dimension": 10, "correlation": 0.9, - "noise_std": 0.01, + "noise_std": 0.5, "random_state": 42, "test_ratio": 0.25 } diff --git a/params/param_single.json b/params/param_single.json index af3366b..4ae7c6e 100644 --- a/params/param_single.json +++ b/params/param_single.json @@ -1,22 +1,43 @@ { - "description": "Configuration settings for testing various model selection methods.", + "description": "Single test setting: verification test", "test": { "general": { "activate": {"k_fold_CV": true, "bootstrapping": true}, "model": "LinearRegression", "metric": "R2", - "data": "file", - "visualize": false, - "write": false, - "file_path": "./data/result(k_fold_CV).csv" + "data": "file" }, "k_fold_cross_validation": { "k": [5], - "shuffle": false + "shuffle": true }, "bootstrapping": { "size": [50], "epochs": [100] + }, + "analysis": { + "visualize": { + "k_fold_CV":{ + "label_X": [], + "activate": false + }, + "bootstrapping": { + "label_X": [], + "activate": false + } + }, + "write": { + "k_fold_CV": { + "activate": false, + "header": [], + "file_path": "./results/k_fold.csv" + }, + "bootstrapping": { + "activate": false, + "header": [], + "file_path": "./results/bootstrap.csv" + } + } } }, "data": { @@ -28,10 +49,10 @@ ], "generate": [ { - "size": 100, + "size": 200, "dimension": 10, "correlation": 0, - "noise_std": 0.0, + "noise_std": 0.01, "random_state": 42, "test_ratio": 0.25 } diff --git a/params/test_correlation.json b/params/test_correlation.json new file mode 100644 index 0000000..ac3381b --- /dev/null +++ b/params/test_correlation.json @@ -0,0 +1,109 @@ +{ + "description": "Batch test setting: correlation test", + "test": { + "general": { + "activate": {"k_fold_CV": true, "bootstrapping": true}, + "model": "LinearRegression", + "metric": "R2", + "data": "generate" + }, + "k_fold_cross_validation": { + "k": [5], + "shuffle": true + }, + "bootstrapping": { + "size": [100], + "epochs": [100] + }, + "analysis": { + "visualize": { + "k_fold_CV":{ + "label_X": ["correlation", 2], + "activate": true + }, + "bootstrapping": { + "label_X": ["correlation", 2], + "activate": true + } + }, + "write": { + "k_fold_CV": { + "activate": true, + "header": ["size", "dimension", "correlation","noise_std",",K-value","shuffled", "Average", "AIC"], + "file_path": "./results/k_fold_correlation.csv" + }, + "bootstrapping": { + "activate": true, + "header": ["size", "dimension", "correlation","noise_std", "sample_size","epochs","Average", "AIC"], + "file_path": "./results/bootstrap_correlation.csv" + } + } + } + }, + "data": { + "file": [ + { + "file_path": "small_test.csv", + "test_ratio": 0.25 + } + ], + "generate": [ + { + "size": 500, + "dimension": 20, + "correlation": -1, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 20, + "correlation": -0.5, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 20, + "correlation": -0.2, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 20, + "correlation": 0, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 20, + "correlation": 0.2, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 20, + "correlation": 0.5, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 500, + "dimension": 20, + "correlation": 1, + "noise_std": 0.2, + "random_state": 42, + "test_ratio": 0.25 + } + ] + } +} diff --git a/params/test_size.json b/params/test_size.json new file mode 100644 index 0000000..1d1e48b --- /dev/null +++ b/params/test_size.json @@ -0,0 +1,141 @@ +{ + "description": "Batch test setting: size test", + "test": { + "general": { + "activate": {"k_fold_CV": true, "bootstrapping": true}, + "model": "LinearRegression", + "metric": "R2", + "data": "generate" + }, + "k_fold_cross_validation": { + "k": [5], + "shuffle": true + }, + "bootstrapping": { + "size": [100], + "epochs": [100] + }, + "analysis": { + "visualize": { + "k_fold_CV":{ + "label_X": ["size", 0], + "activate": true + }, + "bootstrapping": { + "label_X": ["size", 0], + "activate": true + } + }, + "write": { + "k_fold_CV": { + "activate": true, + "header": ["size", "dimension", "correlation","noise_std",",K-value","shuffled", "Average", "AIC"], + "file_path": "./results/k_fold_size.csv" + }, + "bootstrapping": { + "activate": true, + "header": ["size", "dimension", "correlation","noise_std", "sample_size","epochs","Average", "AIC"], + "file_path": "./results/bootstrap_size.csv" + } + } + } + }, + "data": { + "file": [ + { + "file_path": "small_test.csv", + "test_ratio": 0.25 + } + ], + "generate": [ + { + "size": 100, + "dimension": 20, + "correlation": 0, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 200, + "dimension": 20, + "correlation": 0, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 400, + "dimension": 20, + "correlation": 0, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 600, + "dimension": 20, + "correlation": 0, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 800, + "dimension": 20, + "correlation": 0, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 1000, + "dimension": 20, + "correlation": 0, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 1200, + "dimension": 20, + "correlation": 0, + "noise_std": 0.5, + "random_state": 42, + "test_ratio": 0.25 + }, + { + "size": 1400, + "dimension": 20, 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a/test.py +++ b/test.py @@ -1,4 +1,4 @@ -# import sys +import sys from modelSelection import * @@ -13,14 +13,22 @@ def test_k_Fold_CV(model, metric, X: np.ndarray, y: np.ndarray, ks: list[int], s :param y: The target labels. :param ks: A list of k values to test (number of folds). :param shuffle: Whether to shuffle the data before splitting into folds. + :return: A list of results, where each result is a list containing: + [k (fold count), shuffle (bool), scores (list of fold scores), average (mean score)]. """ + results = [] for k in ks: - k_fold_scores, k_fold_avg = k_fold_cross_validation(model, metric, X=X, y=y, k=k, shuffle=shuffle) - print(f"k-Fold Cross-Validation Scores:\n\t{k_fold_scores}") - print(f"k-Fold Average Score: {k_fold_avg}") + print(f"\tk-Fold Cross-Validation K-value: {k}") + print(f"\tk-Fold Cross-Validation shuffling: {shuffle}") + scores, average = k_fold_cross_validation(model, metric, X=X, y=y, k=k, shuffle=shuffle) + print(f"\tk-Fold Cross-Validation Scores:\n\t\t{scores}") + print(f"\tk-Fold Average Score: {round(average, 5)}") + results.append([k, shuffle, round(average, 5)]) + print("") + return results -def test_bootstrapping(model, metric, X: np.ndarray, y: np.ndarray, ss: list[int], epochs_list: list[int]): +def test_bootstrapping(model, metric, X: np.ndarray, y: np.ndarray, ss: list[int], epochs_list: list[int]) -> list: """ test_bootstrapping() This function tests the bootstrapping implementation with different sample sizes and epochs. @@ -31,14 +39,24 @@ def test_bootstrapping(model, metric, X: np.ndarray, y: np.ndarray, ss: list[int :param y: The target labels. :param ss: A list of sample sizes for the bootstrapping training set. :param epochs_list: A list of epoch values to determine the number of iterations for bootstrapping. + :return: A list of results, where each result is a list containing: + [s (sample size), epochs, scores (list of metric scores for each epoch), average (mean score)]. + """ + results = [] for s in ss: for epochs in epochs_list: - bootstrap_scores, bootstrap_avg = bootstrapping(model, metric, X=X, y=y, s=s, epochs=epochs) - print(f"Bootstrap Scores (Pick first 5 out of {len(bootstrap_scores)}):\n\t{bootstrap_scores[:5]}") - print(f"Bootstrap Score range: [{min(bootstrap_scores)}, {max(bootstrap_scores)}]") - print(f"Bootstrap Median Score: {np.median(bootstrap_scores)}") - print(f"Bootstrap Average Score: {bootstrap_avg}") + print(f"\tBootstrap sample size: {s}") + print(f"\tBootstrap epochs: {epochs}") + scores, average = bootstrapping(model, metric, X=X, y=y, s=s, epochs=epochs) + print(f"\tBootstrap Scores (Pick first 5 out of {len(scores)}):\n\t\t{scores[:5]}") + print(f"\tBootstrap Score range: [{round(min(scores), 5)}, {round(max(scores), 5)}]") + print(f"\tBootstrap Median Score: {round(np.median(scores), 5)}") + print(f"\tBootstrap Average Score: {round(average, 5)}") + results.append([s, epochs, round(average, 5)]) + print("") + + return results def test_AIC(model, train_X: np.ndarray, train_y: np.ndarray, test_X: np.ndarray, test_y: np.ndarray): @@ -51,10 +69,12 @@ def test_AIC(model, train_X: np.ndarray, train_y: np.ndarray, test_X: np.ndarray :param train_y: Training labels. :param test_X: Test feature matrix. :param test_y: Test labels. + :return: AIC value. """ model.fit(train_X, train_y) y_pred = model.predict(test_X) - print(f"\t(Comparison) AIC Score: {AIC(X=test_X, y=test_y, y_pred=y_pred)}") + aic = AIC(X=test_X, y=test_y, y_pred=y_pred) + return round(aic, 5) def main(file_path: str): @@ -76,27 +96,41 @@ def main(file_path: str): print(f"Data Type: {args_g['data']}") print("*" * 121, "\n") + # Store global results + results_K_fold = [] + result_boostrap = [] + i = 0 while i < len(param["data"][args_g["data"]]): print(f"{'=' * 52} [Test {i:2d}] Start {'=' * 52}") - args = param["data"][args_g["data"]][i] - print(f"Data Parameters:\n\t{args}") + args_d = param["data"][args_g["data"]][i] + print(f"Data Parameters:\n\t{args_d}") # Load dataset - X, y, train_X, train_y, test_X, test_y = get_data(args_g["data"], args) + X, y, train_X, train_y, test_X, test_y = get_data(args_g["data"], args_d) print("-" * 121) + # Compute AIC in advance + aic = float(test_AIC(model, train_X, train_y, test_X, test_y)) + if args_g["activate"]["k_fold_CV"]: # k-Fold Cross-Validation print("[Test] K-Fold Cross-Validation") args_k = param["test"]["k_fold_cross_validation"] # Perform K-Fold CV testing - test_k_Fold_CV(model, metric, X, y, ks=args_k["k"], shuffle=args_k["shuffle"]) - - # Compare results with AIC - test_AIC(model, train_X, train_y, test_X, test_y) + results = test_k_Fold_CV(model, metric, X, y, ks=args_k["k"], shuffle=args_k["shuffle"]) + + # Append test results to global list results_K_fold + if args_g["data"] == 'generate': + for result in results: + result.insert(0, args_d["noise_std"]) + result.insert(0, args_d["correlation"]) + result.insert(0, args_d["dimension"]) + result.insert(0, args_d["size"]) + result.append(aic) + results_K_fold.append(result) print("-" * 121) @@ -104,19 +138,65 @@ def main(file_path: str): # Bootstrapping Testing print("[Test] Bootstrapping") args_b = param["test"]["bootstrapping"] - test_bootstrapping(model, metric, X, y, ss=args_b["size"], epochs_list=args_b["epochs"]) + print(f"Bootstrapping Parameters:\n\t{args_b}") + + # Perform Bootstrapping testing + results = test_bootstrapping(model, metric, X, y, ss=args_b["size"], epochs_list=args_b["epochs"]) + # Append test results to global list result_boostrap + if args_g["data"] == 'generate': + for result in results: + result.insert(0, args_d["noise_std"]) + result.insert(0, args_d["correlation"]) + result.insert(0, args_d["dimension"]) + result.insert(0, args_d["size"]) + result.append(aic) + result_boostrap.append(result) + + print("-" * 121) + + # Show the comparative score from AIC + print(f"[Test] AIC Score: {round(aic, 5)}") + print(f"{'=' * 52} [Test {i:2d}] End {'=' * 52}") # Increase index number i += 1 print("") + # Visualization and file write only support for the dataset type 'generate'! + if args_g["data"] == 'generate': + args_a = param["test"]['analysis'] + # Visualize if activated & only if results are many then just 1 + if args_a["visualize"]["k_fold_CV"]["activate"] and len(results_K_fold) > 1: + visualize(results_K_fold, "k-fold Cross Validation", args_a["visualize"]["k_fold_CV"]["label_X"]) + + if args_a["visualize"]["bootstrapping"]["activate"] and len(result_boostrap) > 1: + visualize(result_boostrap, "Bootstrapping", args_a["visualize"]["bootstrapping"]["label_X"]) + + # Write data if activated & only if results are many then just 1 + if args_a["write"]["k_fold_CV"]["activate"] and len(results_K_fold) > 1: + file_path = args_a["write"]["k_fold_CV"]["file_path"] + header = args_a["write"]["k_fold_CV"]["header"] + write(file_path, results_K_fold, header) + + if args_a["write"]["bootstrapping"]["activate"] and len(result_boostrap) > 1: + file_path = args_a["write"]["bootstrapping"]["file_path"] + header = args_a["write"]["bootstrapping"]["header"] + write(file_path, result_boostrap, header) + if __name__ == "__main__": - # args = sys.argv - args = ["QuickStart", "./params/param_single.json"] - if len(args) > 1: - main(args[1]) + arguments = sys.argv + # Test code below + # arguments = ["QuickStart", "./params/param_single.json"] + # arguments = ["QuickStart", "./params/param_multi.json"] + # arguments = ["QuickStart", "./params/param_k_fold.json"] + # arguments = ["QuickStart", "./params/param_bootstrap.json"] + # arguments = ["QuickStart", "./params/test_size.json"] + # arguments = ["QuickStart", "./params/test_correlation.json"] + + if len(arguments) > 1: + main(arguments[1]) else: print("[Warning] No parameter configuration (file path with param_*.json) provided!") print("[Info] Program terminated.") From b752f3eca5688a17ca8a29c0d08375c850ff42cf Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Fri, 22 Nov 2024 00:55:26 -0600 Subject: [PATCH 09/14] README.md Completed --- README.md | 150 +++++++++++++++++++++++++++++++++++++++--- params/param_list.csv | 8 ++- 2 files changed, 146 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 1e4abe8..5391098 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,145 @@ # Project 2 -## Model Selection +### Implemented model +* generic k-fold cross-validation and bootstrapping model selection methods. -Implement generic k-fold cross-validation and bootstrapping model selection methods. +### Creator Description +- Name: Haeun Suh +- HawkID: A20542585 +- Class: CS584-04 Machine Learning(Instructor: Steve Avsec) +- Email: hsuh7@hawk.iit.edu +- +#### [Question 1] Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)? +- For simple datasets, it was observed that cross-validation and bootstrapping models generally reach the same conclusions as simpler methods like AIC. +- However, on more complex datasets, particularly those with random elements or multi-collinearity, the results were somewhat inconsistent. +- Below is a comparison of the average metric scores from cross-validation and bootstrapping with AIC scores for the same tests. -In your README, answer the following questions: +##### Size test: +- Model: Simple Linear Regression +- Metrics: R^2 +- [K-fold] k = 5, shuffling = Yes +- [bootstrapping] sampling size: 100, epochs: 100 +- [Configuration file] /params/test_size.json +
-* Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)? -- Regarding simple data sets, it can be confirmed that the previous two models and simpler models such as AIC reach the same conclusion. However, when testing complex datasets that are difficult to estimate (e.g., with many random elements) or when testing with multi-collinearity in mind, the results are somewhat inconsistent. -- -* In what cases might the methods you've written fail or give incorrect or undesirable results? -- K겹 교차검증의 경우, 다중공선성이 포함되면 -* What could you implement given more time to mitigate these cases or help users of your methods? -* What parameters have you exposed to your users in order to use your model selectors. + + +
+ +- AIC tends to increase proportionally with the dataset size. However, the average metric scores for cross-validation and bootstrapping are relatively irregular, with gentler slopes in their trend lines, regardless of the dataset size. +- The k-value applied to the model may have contributed to an underestimation of the dataset size. As a result, these methods do not reach the same conclusions as AIC. + +- Below is another test to evaluate the impact of correlation. + +##### Correlation test: +- Model: Simple Linear Regression +- Metrics: R^2 +- [K-fold] k = 5, shuffling = Yes +- [bootstrapping] sampling size: 100, epochs: 100 +- [Configuration file] params/test_correlation.json +
+ + +
+- Under multi-collinearity, the trends for cross-validation and bootstrapping models were strong. However, AIC did not exhibit a similarly strong trend and showed artificially high performance under the assumption of a perfect correlation coefficient of 1. +- The two models developed do not yield the same conclusions as AIC. In fact, the conclusions were often contradictory (e.g., lower scores being better for AIC). +
+
+- In datasets with multi-collinearity or heavily biased structures, cross-validation and bootstrapping model selectors may not align with simpler model selectors like AIC. + +#### [Question 2] In what cases might the methods you've written fail or give incorrect or undesirable results? + +- According to the test results mentioned above, there is a high possibility of incorrect conclusions if the test data is too large, has multi-collinearity, or has a biased structure. +- In particular, according to the test results of testing multiple factors together as shown below, performance fluctuations were most severe when multi-collinearity existed. + +##### Multi-factor test: +- Model: Simple Linear Regression +- Metrics: R^2 +- [K-fold] k = 5, shuffling = Yes +- [bootstrapping] sampling size: 100, epochs: 100 +- [Configuration file] /params/test_multi.json +
+ + +
+- When the data was predictable but noisy, the cross-validation and bootstrapping models performed better than AIC. However, in more complex scenarios, such as with multi-collinearity or data bias, both models exhibited unstable metric scores. +- Since both methods aim to equalize performance across folds or samples, they may not be appropriate indicators for model selection if the data distribution is highly unstable. +
+
+- In summary, if the data distribution is overly complex and biased, the two developed models may not be suitable for model selection. +
+ +#### [Question 3] What could you implement given more time to mitigate these cases or help users of your methods? +- To address these limitations, additional improvements could include: +> - Adding regularization techniques (e.g., Ridge, Lasso, ElasticNet) to handle multi-collinearity. +> - Implementing preprocessing methods, such as Principal Component Analysis (PCA), for datasets with high correlation coefficients. +> - Providing automated warnings or recommendations for datasets with bias, multi-collinearity, or extreme size imbalances. +
+ +#### [Question 4] What parameters have you exposed to your users in order to use your model selectors. +- For user convenience, all parameter settings, including data generation conditions, are included in: +> params/ +- When running the mail script test.py, the user can specify the desired settings by selecting the json format settings file that exists in the location. +- Regarding parameter settings, specifications are provided in 'params/param_example.txt' and sample images are as follows. + +
+- However, the parameters related to the actual model are limited and the specifications are as follows. +>> "test": { + "general": { + "model": "LinearRegression", # [Options] "LinearRegression" (default), "LogisticRegression". + "metric": "MSE" # [Options] "MSE" (default), "Accuracy score", "R2". + }, + "k_fold_cross_validation": { + "k": [5], # Number of folds for k-fold cross-validation. + "shuffle": true # Whether to shuffle the data before splitting into folds. + }, + "bootstrapping": { + "size": [50], # The size of the training dataset for each bootstrap sample. + "epochs": [100] # The number of bootstrapping iterations to perform. + } +} +- Regarding k-fold cross validation, the direct variables are as follows. +>> - model: The statistical model to test. +>> - metric: The metric function to measure the model's performance. +>> - X: The feature matrix for training. +>> - y: The target labels for training. +>> - k: The number of folds to divide the data into. +>> - shuffle: Whether to shuffle the data before splitting into folds. +
+- Regarding Bootstrapping model, the direct variables are as follows. +>> - model: The statistical model to test. +>> - metric: The metric function to measure the model's performance. +>> - X: The feature matrix for training. +>> - y: The target labels for training. +>> - s: The size of the training dataset for each bootstrap sample. +>> - epochs: The number of bootstrap iterations to perform. + +#### Additional Notes +Since the description of each function and execution is written in comments, only points to keep in mind when executing are explained in detail: +- You can refer to the guidelines in 'params/param_example.txt' or run one of several pre-written configuration files. + - The simplest and easiest to modify file is 'param_single.json'. Use this file to test and verify execution for the program. +- Visualization is only enabled for some items and is only supported for 'generate' type datasets, including file creation. +- Since the purpose of this task is to implement a model related to model selection, the learning model to be evaluated is a linear model from scikit-learn. +- The data folder contains files that you can simply experiment with running. The name, description, and source of each file are included in dataSource.txt. + +#### Sample execution +* To directly execute only the implemented model, you can call the relevant function in modelSelection.py. +>> from modelSelection import k_fold_cross_validation, bootstrapping +1. Adjust configuration file. Refer to 'params/param_example.txt' to set up + * Alternatively, you can choose one of the settings in the params folder. See param_list.csv for a brief introduction to those settings. +2. Execute 'test.py' at the prompt(at that script location) and run it by entering the path to the file. (The example used param_single.json.) + * Sample code as below: + >> python test.py ./params/param_single.json + * Sample execution image as below: +
+ simple test +3. (Optional) Another execution for size testing as batch job + * Sample code as below: + >> python test.py ./params/test_size.json + * Sample execution image as below: +
+ size test +
+ * Sample execution image as below: +
+ file created \ No newline at end of file diff --git a/params/param_list.csv b/params/param_list.csv index 178c10d..56518de 100644 --- a/params/param_list.csv +++ b/params/param_list.csv @@ -1,3 +1,7 @@ Json file name, Description -"param_single.json", "Singular test" -"param_multi.json", "Multi-data test" \ No newline at end of file +"param_single.json", "one-time test, using file to execute, no visualization, no file writing" +"param_multi.json", "batch test, create data to execute, no visualization, file writing for both model" +"param_k_fold.json", "batch test, create data to execute, no visualization, file writing for k-fold CV model" +"param_bootstrap.json", "batch test, create data to execute, no visualization, file writing for bootstrap model" +"test_size.json", "batch test, create data to execute, visualization for all, file writing for both model" +"test_correlation.json", "batch test, create data to execute, visualization for all, file writing for both model" \ No newline at end of file From d0cb51d1042311b52e0912784cf6136fbfef7b52 Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Fri, 22 Nov 2024 00:57:14 -0600 Subject: [PATCH 10/14] Add result files --- .idea/.gitignore | 8 ++++ params/param_sample.jpg | Bin 0 -> 186229 bytes .../bootstrap_correlation_20241121_224255.csv | 8 ++++ results/bootstrap_multi_20241121_223206.csv | 16 ++++++++ results/bootstrap_only_20241121_223549.csv | 37 ++++++++++++++++++ results/bootstrap_size_20241121_223627.csv | 12 ++++++ results/bootstrap_size_20241122_004501.csv | 12 ++++++ results/correlation_test_bootstrapping.png | Bin 0 -> 72037 bytes results/correlation_test_k_fold.png | Bin 0 -> 72257 bytes results/execution_sample.jpg | Bin 0 -> 187669 bytes .../k_fold_correlation_20241121_224255.csv | 8 ++++ results/k_fold_multi_20241121_223206.csv | 16 ++++++++ results/k_fold_only_20241121_223216.csv | 13 ++++++ results/k_fold_size_20241121_223627.csv | 12 ++++++ results/k_fold_size_20241122_004501.csv | 12 ++++++ results/multi-test_bootstrapping.jpg | Bin 0 -> 105601 bytes results/multi-test_k_fold.jpg | Bin 0 -> 102946 bytes results/size-test-file_created.jpg | Bin 0 -> 155097 bytes results/size-test.jpg | Bin 0 -> 236767 bytes 19 files changed, 154 insertions(+) create mode 100644 .idea/.gitignore create mode 100644 params/param_sample.jpg create mode 100644 results/bootstrap_correlation_20241121_224255.csv create mode 100644 results/bootstrap_multi_20241121_223206.csv create mode 100644 results/bootstrap_only_20241121_223549.csv create mode 100644 results/bootstrap_size_20241121_223627.csv create mode 100644 results/bootstrap_size_20241122_004501.csv create mode 100644 results/correlation_test_bootstrapping.png create mode 100644 results/correlation_test_k_fold.png create mode 100644 results/execution_sample.jpg create mode 100644 results/k_fold_correlation_20241121_224255.csv create mode 100644 results/k_fold_multi_20241121_223206.csv create mode 100644 results/k_fold_only_20241121_223216.csv create mode 100644 results/k_fold_size_20241121_223627.csv create mode 100644 results/k_fold_size_20241122_004501.csv create mode 100644 results/multi-test_bootstrapping.jpg create mode 100644 results/multi-test_k_fold.jpg create mode 100644 results/size-test-file_created.jpg create mode 100644 results/size-test.jpg diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..c3f502a --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# 디폴트 무시된 파일 +/shelf/ +/workspace.xml +# 에디터 기반 HTTP 클라이언트 요청 +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/params/param_sample.jpg b/params/param_sample.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ac444025df3d96c6ca6f81f900cda4ffe3c95d39 GIT binary patch literal 186229 zcmeFZbzGF))-XH>2+|>)3JQ{fG)RcFNJ*E{FoZ}AISK;OC9QNw3)0<4N!Jj=(9H}v 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      - - + +
      - Under multi-collinearity, the trends for cross-validation and bootstrapping models were strong. However, AIC did not exhibit a similarly strong trend and showed artificially high performance under the assumption of a perfect correlation coefficient of 1. - The two models developed do not yield the same conclusions as AIC. In fact, the conclusions were often contradictory (e.g., lower scores being better for AIC). @@ -59,10 +59,10 @@ - [bootstrapping] sampling size: 100, epochs: 100 - [Configuration file] /params/test_multi.json
      - - + +
      -- When the data was predictable but noisy, the cross-validation and bootstrapping models performed better than AIC. However, in more complex scenarios, such as with multi-collinearity or data bias, both models exhibited unstable metric scores. +- When the data was predictable but noisy, the cross-validation and bootstrapping models performed better than AIC. However, in more complex scenarios, such as with multi-collinearity or data bias, both models exhibited unstable metric scores. - Since both methods aim to equalize performance across folds or samples, they may not be appropriate indicators for model selection if the data distribution is highly unstable.

      @@ -81,7 +81,7 @@ > params/ - When running the mail script test.py, the user can specify the desired settings by selecting the json format settings file that exists in the location. - Regarding parameter settings, specifications are provided in 'params/param_example.txt' and sample images are as follows. - +
      - However, the parameters related to the actual model are limited and the specifications are as follows. >> "test": { @@ -142,4 +142,4 @@ Since the description of each function and execution is written in comments, onl
      * Sample execution image as below:
      - file created \ No newline at end of file + file created From 63ff78fc5bcce449dfe765aef23abacee1c703b7 Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Fri, 22 Nov 2024 01:22:03 -0600 Subject: [PATCH 12/14] Update READ.md --- README.md | 24 ++++++++++-------------- 1 file changed, 10 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 5391098..a35f455 100644 --- a/README.md +++ b/README.md @@ -84,20 +84,16 @@
      - However, the parameters related to the actual model are limited and the specifications are as follows. ->> "test": { - "general": { - "model": "LinearRegression", # [Options] "LinearRegression" (default), "LogisticRegression". - "metric": "MSE" # [Options] "MSE" (default), "Accuracy score", "R2". - }, - "k_fold_cross_validation": { - "k": [5], # Number of folds for k-fold cross-validation. - "shuffle": true # Whether to shuffle the data before splitting into folds. - }, - "bootstrapping": { - "size": [50], # The size of the training dataset for each bootstrap sample. - "epochs": [100] # The number of bootstrapping iterations to perform. - } -} +>> - "test": +>> - "general": +>> - "model": "LinearRegression", # [Options] "LinearRegression" (default), "LogisticRegression". +>> - "metric": "MSE" # [Options] "MSE" (default), "Accuracy score", "R2". +>> - "k_fold_cross_validation": +>> - "k": [5], # Number of folds for k-fold cross-validation. +>> - "shuffle": true # Whether to shuffle the data before splitting into folds. +>> - "bootstrapping": +>> - "size": [50], # The size of the training dataset for each bootstrap sample. +>> - "epochs": [100] # The number of bootstrapping iterations to perform. - Regarding k-fold cross validation, the direct variables are as follows. >> - model: The statistical model to test. >> - metric: The metric function to measure the model's performance. From 2ebf9bce462470beb3f271e99931fa56a1743655 Mon Sep 17 00:00:00 2001 From: Haeun Suh Date: Fri, 22 Nov 2024 01:27:41 -0600 Subject: [PATCH 13/14] Update READ.md --- README.md | 32 +++----------------------------- 1 file changed, 3 insertions(+), 29 deletions(-) diff --git a/README.md b/README.md index 4ef80d8..3e77099 100644 --- a/README.md +++ b/README.md @@ -1,88 +1,62 @@ # Project 2 - ### Implemented model * generic k-fold cross-validation and bootstrapping model selection methods. - ### Creator Description - Name: Haeun Suh - HawkID: A20542585 - Class: CS584-04 Machine Learning(Instructor: Steve Avsec) - Email: hsuh7@hawk.iit.edu -- #### [Question 1] Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)? - For simple datasets, it was observed that cross-validation and bootstrapping models generally reach the same conclusions as simpler methods like AIC. - However, on more complex datasets, particularly those with random elements or multi-collinearity, the results were somewhat inconsistent. - Below is a comparison of the average metric scores from cross-validation and bootstrapping with AIC scores for the same tests. - ##### Size test: - Model: Simple Linear Regression - Metrics: R^2 - [K-fold] k = 5, shuffling = Yes - [bootstrapping] sampling size: 100, epochs: 100 - [Configuration file] /params/test_size.json -
      - -
      - - AIC tends to increase proportionally with the dataset size. However, the average metric scores for cross-validation and bootstrapping are relatively irregular, with gentler slopes in their trend lines, regardless of the dataset size. - The k-value applied to the model may have contributed to an underestimation of the dataset size. As a result, these methods do not reach the same conclusions as AIC. - - Below is another test to evaluate the impact of correlation. - ##### Correlation test: - Model: Simple Linear Regression - Metrics: R^2 - [K-fold] k = 5, shuffling = Yes - [bootstrapping] sampling size: 100, epochs: 100 - [Configuration file] params/test_correlation.json -
      -
      - Under multi-collinearity, the trends for cross-validation and bootstrapping models were strong. However, AIC did not exhibit a similarly strong trend and showed artificially high performance under the assumption of a perfect correlation coefficient of 1. - The two models developed do not yield the same conclusions as AIC. In fact, the conclusions were often contradictory (e.g., lower scores being better for AIC). -
      -
      - In datasets with multi-collinearity or heavily biased structures, cross-validation and bootstrapping model selectors may not align with simpler model selectors like AIC. - #### [Question 2] In what cases might the methods you've written fail or give incorrect or undesirable results? - - According to the test results mentioned above, there is a high possibility of incorrect conclusions if the test data is too large, has multi-collinearity, or has a biased structure. - In particular, according to the test results of testing multiple factors together as shown below, performance fluctuations were most severe when multi-collinearity existed. - ##### Multi-factor test: - Model: Simple Linear Regression - Metrics: R^2 - [K-fold] k = 5, shuffling = Yes - [bootstrapping] sampling size: 100, epochs: 100 - [Configuration file] /params/test_multi.json -
      - - -
      +multi-test_k_fold +multi-test_bootstrapping - When the data was predictable but noisy, the cross-validation and bootstrapping models performed better than AIC. However, in more complex scenarios, such as with multi-collinearity or data bias, both models exhibited unstable metric scores. - Since both methods aim to equalize performance across folds or samples, they may not be appropriate indicators for model selection if the data distribution is highly unstable. -
      -
      - In summary, if the data distribution is overly complex and biased, the two developed models may not be suitable for model selection. -
      - #### [Question 3] What could you implement given more time to mitigate these cases or help users of your methods? - To address these limitations, additional improvements could include: > - Adding regularization techniques (e.g., Ridge, Lasso, ElasticNet) to handle multi-collinearity. > - Implementing preprocessing methods, such as Principal Component Analysis (PCA), for datasets with high correlation coefficients. > - Providing automated warnings or recommendations for datasets with bias, multi-collinearity, or extreme size imbalances. -
      - #### [Question 4] What parameters have you exposed to your users in order to use your model selectors. - For user convenience, all parameter settings, including data generation conditions, are included in: > params/ - When running the mail script test.py, the user can specify the desired settings by selecting the json format settings file that exists in the location. - Regarding parameter settings, specifications are provided in 'params/param_example.txt' and sample images are as follows. - -
      +param_sample - However, the parameters related to the actual model are limited and the specifications are as follows. >> - "test": >> - "general": From 0df6e84c49783a60e7064ad56ccdd9d777b68e97 Mon Sep 17 00:00:00 2001 From: Haeun Suh <46240409+hsuh9416@users.noreply.github.com> Date: Fri, 22 Nov 2024 01:34:54 -0600 Subject: [PATCH 14/14] Fixed README.md --- README.md | 24 +++++++++++++++++++++--- 1 file changed, 21 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 3e77099..8c3c636 100644 --- a/README.md +++ b/README.md @@ -1,62 +1,79 @@ # Project 2 + ### Implemented model * generic k-fold cross-validation and bootstrapping model selection methods. + ### Creator Description - Name: Haeun Suh - HawkID: A20542585 - Class: CS584-04 Machine Learning(Instructor: Steve Avsec) - Email: hsuh7@hawk.iit.edu + #### [Question 1] Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)? - For simple datasets, it was observed that cross-validation and bootstrapping models generally reach the same conclusions as simpler methods like AIC. - However, on more complex datasets, particularly those with random elements or multi-collinearity, the results were somewhat inconsistent. - Below is a comparison of the average metric scores from cross-validation and bootstrapping with AIC scores for the same tests. + ##### Size test: - Model: Simple Linear Regression - Metrics: R^2 - [K-fold] k = 5, shuffling = Yes - [bootstrapping] sampling size: 100, epochs: 100 - [Configuration file] /params/test_size.json - - +` ` + + + - AIC tends to increase proportionally with the dataset size. However, the average metric scores for cross-validation and bootstrapping are relatively irregular, with gentler slopes in their trend lines, regardless of the dataset size. - The k-value applied to the model may have contributed to an underestimation of the dataset size. As a result, these methods do not reach the same conclusions as AIC. - Below is another test to evaluate the impact of correlation. + ##### Correlation test: - Model: Simple Linear Regression - Metrics: R^2 - [K-fold] k = 5, shuffling = Yes - [bootstrapping] sampling size: 100, epochs: 100 - [Configuration file] params/test_correlation.json +` ` + - Under multi-collinearity, the trends for cross-validation and bootstrapping models were strong. However, AIC did not exhibit a similarly strong trend and showed artificially high performance under the assumption of a perfect correlation coefficient of 1. - The two models developed do not yield the same conclusions as AIC. In fact, the conclusions were often contradictory (e.g., lower scores being better for AIC). - In datasets with multi-collinearity or heavily biased structures, cross-validation and bootstrapping model selectors may not align with simpler model selectors like AIC. + #### [Question 2] In what cases might the methods you've written fail or give incorrect or undesirable results? - According to the test results mentioned above, there is a high possibility of incorrect conclusions if the test data is too large, has multi-collinearity, or has a biased structure. - In particular, according to the test results of testing multiple factors together as shown below, performance fluctuations were most severe when multi-collinearity existed. + ##### Multi-factor test: - Model: Simple Linear Regression - Metrics: R^2 - [K-fold] k = 5, shuffling = Yes - [bootstrapping] sampling size: 100, epochs: 100 - [Configuration file] /params/test_multi.json +` ` multi-test_k_fold multi-test_bootstrapping + - When the data was predictable but noisy, the cross-validation and bootstrapping models performed better than AIC. However, in more complex scenarios, such as with multi-collinearity or data bias, both models exhibited unstable metric scores. - Since both methods aim to equalize performance across folds or samples, they may not be appropriate indicators for model selection if the data distribution is highly unstable. - In summary, if the data distribution is overly complex and biased, the two developed models may not be suitable for model selection. + #### [Question 3] What could you implement given more time to mitigate these cases or help users of your methods? - To address these limitations, additional improvements could include: > - Adding regularization techniques (e.g., Ridge, Lasso, ElasticNet) to handle multi-collinearity. > - Implementing preprocessing methods, such as Principal Component Analysis (PCA), for datasets with high correlation coefficients. > - Providing automated warnings or recommendations for datasets with bias, multi-collinearity, or extreme size imbalances. + #### [Question 4] What parameters have you exposed to your users in order to use your model selectors. - For user convenience, all parameter settings, including data generation conditions, are included in: > params/ - When running the mail script test.py, the user can specify the desired settings by selecting the json format settings file that exists in the location. - Regarding parameter settings, specifications are provided in 'params/param_example.txt' and sample images are as follows. +` ` param_sample + - However, the parameters related to the actual model are limited and the specifications are as follows. >> - "test": >> - "general": @@ -68,6 +85,7 @@ >> - "bootstrapping": >> - "size": [50], # The size of the training dataset for each bootstrap sample. >> - "epochs": [100] # The number of bootstrapping iterations to perform. + - Regarding k-fold cross validation, the direct variables are as follows. >> - model: The statistical model to test. >> - metric: The metric function to measure the model's performance. @@ -75,7 +93,7 @@ >> - y: The target labels for training. >> - k: The number of folds to divide the data into. >> - shuffle: Whether to shuffle the data before splitting into folds. -
      + - Regarding Bootstrapping model, the direct variables are as follows. >> - model: The statistical model to test. >> - metric: The metric function to measure the model's performance.