How to use parse_features method in Behave

Best Python code snippet using behave

hw1_regression.py

Source:hw1_regression.py

...12    # a. lambda for weight generation--non-negative integer:13    w_lambda = int(sys.argv[1])14    # b. sigma squared for observation--arbitrary positive number:15    y_sigma2 = int(sys.argv[2])16    def parse_features(filename):17        with open(filename) as f:18            lines = f.readlines()19        features = np.asarray(20            [[float(val) for val in line.split(",")] for line in lines]21        )22        return features23    # c. 'X_train.csv'24    X_train = parse_features(sys.argv[3])25    # d. 'y_train.csv'26    y_train = parse_features(sys.argv[4])27    # e. 'X_test.csv'28    X_test = parse_features(sys.argv[5])29    # ## Posterior Estimation30    #31    # Posterior covariance matrix:32    w_cov_posterior = np.linalg.pinv(w_lambda + np.matmul(X_train.T, X_train) / y_sigma2)33    # ## Ridge Regression34    #35    # Given samples and observations, solve the following ridge regression problem under the given generation & observation noises36    #37    # $$38    # w_{RR} = \arg\min_w \|y - Xw\|^2 + \lambda\|w\|^2.39    # $$40    #41    # Which is42    #...

features.py

Source:features.py

...43    def storage(self):44        return "enable_{0}".format(self.identifier)45def add_feature(group, feature):46    Feature(group, feature).add_options()47def parse_features(opt, group_name, features):48    def is_feature(dep):49        return dep['name'].find('--') >= 050    def strip_feature(dep):51        dep['name'] = dep['name'].lstrip('-')52        return dep53    features = [strip_feature(dep) for dep in features if is_feature(dep)]54    group = opt.get_option_group(group_name)55    if not group:56        group = opt.add_option_group(group_name)57    [add_feature(group, feature) for feature in features]...

hw2_classification.py

Source:hw2_classification.py

...9    if len(sys.argv) != 4:10        print "usage: python hw2_classification.py X_train.csv y_train.csv X_test.csv"11        exit(1)12    ## Parse parameters:13    def parse_features(filename):14        with open(filename) as f:15            lines = f.readlines()16        features = np.asarray(17            [[float(val) for val in line.split(",")] for line in lines]18        )19        return features20    # a. 'X_train.csv'21    X_train = parse_features(sys.argv[1])22    # b. 'y_train.csv'23    y_train = parse_features(sys.argv[2]).astype(np.int).ravel()24    # c. 'X_test.csv'25    X_test = parse_features(sys.argv[3])26    # ## Maximum-Likelihood Estimation for Priors & Generative Parameters27    # Class priors:28    prior_ml = np.bincount(y_train).astype(np.float) / y_train.shape[0]29    # Gaussian parameters:30    class_indices = np.unique(y_train)31    mu_ml = np.asarray(32        [np.mean(X_train[y_train == class_idx], axis = 0) for class_idx in class_indices]33    )34    cov_ml = np.asarray(35        [np.cov(X_train[y_train == class_idx].T, bias=True) for class_idx in class_indices]36    )37    # Posterior probabilities for test cases:38    pdfs = [multivariate_normal(mean=mu_ml[class_idx], cov=cov_ml[class_idx]) for class_idx in class_indices]39    posteriors = np.column_stack(...

MyPlotLib.py

Source:MyPlotLib.py

1import matplotlib.pyplot as plt2import numpy as np3import seaborn as sns4def parse_features(features, data):5    good_features = []6    for elem in features:7        if elem in data:8            try:9                t = data[elem] + 110            except TypeError:11                pass12            else:13                good_features.append(elem)14    if good_features == []:15        return False16    return good_features17    18class MyPlotLib():19    def __init__(self):20        pass21    def histogram(self, data, features):22        good_features = parse_features(features, data)23        l = len(good_features)24        f = plt.figure(1)25        data = data.fillna(data.median())26        for i, feature in enumerate(good_features):27            a = plt.subplot(1, l, i + 1)28            a.hist(data[feature], color='blue', edgecolor='black',29                   bins = 30)30            a.title.set_text(feature)31        plt.subplots_adjust(wspace=1)32        plt.show()33        return True34    def density(self, data, features):35        good_features = parse_features(features, data)36        f = plt.figure(1)37        data = data.fillna(data.median())38        for elem in good_features:39            subset = data[elem]40            sns.distplot(subset, hist=False, kde=True, kde_kws={'linewidth': 3},41                         label = elem)42        plt.show()43        return True44    def pair_plot(self, data, features):45        good_features = parse_features(features, data)46        data = data[features]47        data = data.fillna(data.median())48        sns.set(style="ticks", color_codes=True)49        sns.pairplot(data)50        plt.show()51        return True52    def box_plot(self, data, features):53        good_features = parse_features(features, data)54        l = len(good_features)55        data = data.fillna(data.median())56        f, plo = plt.subplots(1, l, sharey=True, squeeze=False)57        for i, feature in enumerate(good_features):58            plo[0, i].boxplot(data[feature])59            plo[0, i].title.set_text(feature)60        plt.subplots_adjust(wspace=1)61        plt.show()...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.