From c7fc74c8b41045918893038343fc6e8affa06fcd Mon Sep 17 00:00:00 2001 From: rishabh-jain14 Date: Tue, 31 Aug 2021 15:53:19 +0530 Subject: [PATCH] Naive Bayes Classifier added along with a unit testing file --- numpy_ml/linear_models/naive_bayes.py | 222 ++++++++++++------------ numpy_ml/linear_models/test_use_case.py | 116 +++++++++++++ numpy_ml/linear_models/wine.data | 178 +++++++++++++++++++ numpy_ml/tests/test_naive_bayes.py | 2 +- 4 files changed, 409 insertions(+), 109 deletions(-) create mode 100644 numpy_ml/linear_models/test_use_case.py create mode 100644 numpy_ml/linear_models/wine.data diff --git a/numpy_ml/linear_models/naive_bayes.py b/numpy_ml/linear_models/naive_bayes.py index d174fd1..a2b7dc6 100644 --- a/numpy_ml/linear_models/naive_bayes.py +++ b/numpy_ml/linear_models/naive_bayes.py @@ -2,8 +2,8 @@ class GaussianNBClassifier: - def __init__(self, eps=1e-6): - r""" + def __init__(self): + """ A naive Bayes classifier for real-valued data. Notes @@ -46,33 +46,46 @@ def __init__(self, eps=1e-6): associated with class :math:`c`, :math:`\mu_c` and :math:`\Sigma_c` (where :math:`1 \leq c \leq K`), are estimated via MLE from the set of training examples with label :math:`c`. + """ + pass - Parameters - ---------- - eps : float - A value added to the variance to prevent numerical error. Default - is 1e-6. + # Separate the dataset into a subset of data for each class - Attributes - ---------- - parameters : dict - Dictionary of model parameters: "mean", the `(K, M)` array of - feature means under each class, "sigma", the `(K, M)` array of - feature variances under each class, and "prior", the `(K,)` array of - empirical prior probabilities for each class label. - hyperparameters : dict - Dictionary of model hyperparameters - labels : :py:class:`ndarray ` of shape `(K,)` - An array containing the unique class labels for the training - examples. + def separate_classes(self, X, y): """ - self.labels = None - self.hyperparameters = {"eps": eps} - self.parameters = { - "mean": None, # shape: (K, M) - "sigma": None, # shape: (K, M) - "prior": None, # shape: (K,) - } + Separates the dataset in to a subset of data for each class. + Parameters: + ------------ + X- array, list of features + y- list, target + Returns: + A dictionary with y as keys and assigned X as values. + """ + separated_classes = {} + for i in range(len(X)): + feature_values = X[i] + class_name = y[i] + if class_name not in separated_classes: + separated_classes[class_name] = [] + separated_classes[class_name].append(feature_values) + return separated_classes + + # Standard deviation and mean are required for the (Gaussian) distribution function + + def stat_info(self, X): + """ + Calculates standard deviation and mean of features. + Parameters: + ------------ + X- array , list of features + Returns: + A dictionary with STD and Mean as keys and assigned features STD and Mean as values. + """ + for feature in zip(*X): + yield { + 'std' : np.std(feature), + 'mean' : np.mean(feature) + } def fit(self, X, y): """ @@ -80,68 +93,66 @@ def fit(self, X, y): Notes ----- - The model parameters are stored in the :py:attr:`parameters` attribute. + The model parameters are stored in the :py:attr:`class_summary` attribute. The following keys are present: - mean: :py:class:`ndarray ` of shape `(K, M)` - Feature means for each of the `K` label classes - sigma: :py:class:`ndarray ` of shape `(K, M)` - Feature variances for each of the `K` label classes - prior : :py:class:`ndarray ` of shape `(K,)` + prior_proba :py:class:`ndarray ` of shape `(K,)` Prior probability of each of the `K` label classes, estimated empirically from the training data + summary : Dictionary having both the keys and the values of + py:class:`ndarray ` of shape `(K, M)` + Feature means for each of the `K` label classes along with the + Feature STDs for each of the `K` label classes Parameters ---------- - X : :py:class:`ndarray ` of shape `(N, M)` + X :py:class:`ndarray ` of shape `(N, M)` A dataset consisting of `N` examples, each of dimension `M` - y: :py:class:`ndarray ` of shape `(N,)` + y :py:class:`ndarray ` of shape `(N,)` The class label for each of the `N` examples in `X` Returns ------- - self: object + Dictionary with the prior probability, mean, and standard deviation of each class """ - P = self.parameters - H = self.hyperparameters - - self.labels = np.unique(y) - K = len(self.labels) - N, M = X.shape + separated_classes = self.separate_classes(X, y) + self.class_summary = {} - P["mean"] = np.zeros((K, M)) - P["sigma"] = np.zeros((K, M)) - P["prior"] = np.zeros((K,)) + for class_name, feature_values in separated_classes.items(): + self.class_summary[class_name] = { + 'prior_proba': len(feature_values)/len(X), + 'summary': [i for i in self.stat_info(feature_values)], + } + return self.class_summary - for i, c in enumerate(self.labels): - X_c = X[y == c, :] + # Gaussian distribution function - P["mean"][i, :] = np.mean(X_c, axis=0) - P["sigma"][i, :] = np.var(X_c, axis=0) + H["eps"] - P["prior"][i] = X_c.shape[0] / N - return self - - def predict(self, X): + def distribution(self, x, mean, std): """ - Use the trained classifier to predict the class label for each example - in **X**. + Holds the computation for the Gaussian Distribution Function Parameters ---------- - X: :py:class:`ndarray ` of shape `(N, M)` - A dataset of `N` examples, each of dimension `M` + x: type: float, value of feature 'x' + mean: type: float, the mean value of feature 'x' + stdev: type: float, the standard deviation of feature 'x' Returns - ------- - labels : :py:class:`ndarray ` of shape `(N)` - The predicted class labels for each example in `X` + -------- + f: A float value of Normal Probability """ - return self.labels[self._log_posterior(X).argmax(axis=1)] - def _log_posterior(self, X): - r""" - Compute the (unnormalized) log posterior for each class. + exponent = np.exp(-((x-mean)**2 / (2*std**2))) + f = exponent / (np.sqrt(2*np.pi)*std) + return f + + # Required predict method, to predict the class + + def predict(self, X): + """ + Use the trained classifier to predict the class label for each example + in **X**. Parameters ---------- @@ -150,62 +161,57 @@ def _log_posterior(self, X): Returns ------- - log_posterior : :py:class:`ndarray ` of shape `(N, K)` - Unnormalized log posterior probability of each class for each - example in `X` + MAPs : :py:class:`ndarray ` of shape `(N)` + The predicted class labels for each example in `X` """ - K = len(self.labels) - log_posterior = np.zeros((X.shape[0], K)) - for i in range(K): - log_posterior[:, i] = self._log_class_posterior(X, i) - return log_posterior - def _log_class_posterior(self, X, class_idx): - r""" - Compute the (unnormalized) log posterior for the label at index - `class_idx` in :py:attr:`labels`. + # Using Maximum a posteriori (MAP) probability - Notes - ----- - Unnormalized log posterior for example :math:`\mathbf{x}_i` and class - :math:`c` is:: + MAPs = [] - .. math:: + for row in X: + joint_proba = {} + + for class_name, features in self.class_summary.items(): + total_features = len(features['summary']) + likelihood = 1 - \log P(y_i = c \mid \mathbf{x}_i, \theta) - &\propto \log P(y=c \mid \theta) + - \log P(\mathbf{x}_i \mid y_i = c, \theta) \\ - &\propto \log P(y=c \mid \theta) - \sum{j=1}^M \log P(x_j \mid y_i = c, \theta) + for idx in range(total_features): + feature = row[idx] + mean = features['summary'][idx]['mean'] + stdev = features['summary'][idx]['std'] + normal_proba = self.distribution(feature, mean, stdev) + likelihood *= normal_proba + prior_proba = features['prior_proba'] + joint_proba[class_name] = prior_proba * likelihood - In the Gaussian naive Bayes model, the feature likelihood for class - :math:`c`, :math:`P(\mathbf{x}_i \mid y_i = c, \theta)` is assumed to - be normally distributed + MAP = max(joint_proba, key= joint_proba.get) + MAPs.append(MAP) - .. math:: + return MAPs - \mathbf{x}_i \mid y_i = c, \theta \sim \mathcal{N}(\mu_c, \Sigma_c) + # Calculate the model's accuracy + def accuracy(self, y_test, y_pred): + """ + Calculates model's accuracy using label comparison Parameters - ---------- - X: :py:class:`ndarray ` of shape `(N, M)` - A dataset of `N` examples, each of dimension `M` - class_idx : int - The index of the current class in :py:attr:`labels` + ------------ + y_test: :py:class:`ndarray ` of shape `(N,)` + The true class label for each of the `N` examples in `X` + y_pred: :py:class:`ndarray ` of shape `(N,)` + The predicted class label for each of the `N` examples in `X` Returns - ------- - log_class_posterior : :py:class:`ndarray ` of shape `(N,)` - Unnormalized log probability of the label at index `class_idx` - in :py:attr:`labels` for each example in `X` + -------- + acc: A number between 0-1, representing the percentage of correct predictions. + The accuracy of the GaussianNB model using numpy-ml environment """ - P = self.parameters - mu = P["mean"][class_idx] - prior = P["prior"][class_idx] - sigsq = P["sigma"][class_idx] - - # log likelihood = log X | N(mu, sigsq) - log_likelihood = -0.5 * np.sum(np.log(2 * np.pi * sigsq)) - log_likelihood -= 0.5 * np.sum(((X - mu) ** 2) / sigsq, axis=1) - return log_likelihood + np.log(prior) + + true_true = 0 + for y_t, y_p in zip(y_test, y_pred): + if y_t == y_p: + true_true += 1 + acc = true_true / len(y_test) + return acc \ No newline at end of file diff --git a/numpy_ml/linear_models/test_use_case.py b/numpy_ml/linear_models/test_use_case.py new file mode 100644 index 0000000..b3e3a76 --- /dev/null +++ b/numpy_ml/linear_models/test_use_case.py @@ -0,0 +1,116 @@ +from csv import reader +import random + +from naive_bayes import GaussianNBClassifier + +from sklearn.naive_bayes import GaussianNB +from sklearn.metrics import accuracy_score + + +# Load csv function to load the data + +def load_csv(filename): + dataset = list() + with open(filename, 'r') as file: + csv_reader = reader(file) + for row in csv_reader: + if not row: + continue + dataset.append(row) + return dataset + +# Load the UCI wine data + +df = load_csv('wine.data') + +# Print the data in exploration + +#print(df[0]) + +# Convert strings to floats + +for i in range(len(df)): + for j in range(len(df[i])): + df[i][j]=float(df[i][j]) + +# Check if converted correctly +#print(df[0]) +#print(type(df[0][0])) + + +# Split data function + +def split_data(data, weight): + """ + Random split of a data set into training and test data sets + + Parameters: + ----------- + data: array-like, dataset + weight: float, percentage of data to be used as training + + Returns: + List of two datasets + """ + train_length = int(len(data) * weight) + train = [] + for i in range(train_length): + idx = random.randrange(len(data)) + train.append(data[idx]) + data.pop(idx) + return [train, data] + +# Split data into train and test + +train, test = split_data(df, 0.8) + + +# Define target and features +# Target is the first column in the wine dataset + +X_train = [] +y_train = [] +X_test = [] +y_test = [] + +for i in range(len(train)): + y_train.append(train[i][0]) + X_train.append(train[i][1:]) + +for i in range(len(test)): + y_test.append(test[i][0]) + X_test.append(test[i][1:]) + + +# Instantiate the Naive Bayes classifier model made with numpy + +model = GaussianNBClassifier() + +# Fit the model + +model.fit(X_train, y_train) + +# Make predictions + +y_pred = model.predict(X_test) + +# Print the accuracy of the model + +print("NaiveBayesClassifier accuracy using given numpy environment: {0:.3f}".format(model.accuracy(y_test, y_pred))) + + +# Instantiate the Gaussian Naive Bayes classifier model from Scikit-learn + +model = GaussianNB() + +# Fit the model + +model.fit(X_train, y_train) + +# Make predictions + +y_pred = model.predict(X_test) + +# Print the accuracy of the model + +print("Gaussian NaiveBayesClassifier accuracy using Scikit-learn: {0:.3f}".format(accuracy_score(y_test, y_pred))) \ No newline at end of file diff --git a/numpy_ml/linear_models/wine.data b/numpy_ml/linear_models/wine.data new file mode 100644 index 0000000..a0b3962 --- /dev/null +++ b/numpy_ml/linear_models/wine.data @@ -0,0 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