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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()...

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