from sklearn import datasets, model_selection import sklearn.preprocessing import pandas as pd import ssl from os import path, mkdir from six.moves.urllib.request import urlretrieve def _load_iris(test_set_size: float = 0.3): iris = datasets.load_iris() data = iris.data labels = iris.target # Split training and test sets x_train, x_test, y_train, y_test = model_selection.train_test_split(data, labels, test_size=test_set_size, random_state=18, stratify=labels) return (x_train, y_train), (x_test, y_test) def get_iris_dataset(test_set: float = 0.3): """ Loads the Iris dataset from scikit-learn. :param test_set: Proportion of the data to use as validation split (value between 0 and 1). :return: Entire dataset and labels as numpy array. """ return _load_iris(test_set) def _load_diabetes(test_set_size: float = 0.3): diabetes = datasets.load_diabetes() data = diabetes.data labels = diabetes.target # Split training and test sets x_train, x_test, y_train, y_test = model_selection.train_test_split(data, labels, test_size=test_set_size, random_state=18) return (x_train, y_train), (x_test, y_test) def get_diabetes_dataset(): """ Loads the Iris dataset from scikit-learn. :param test_set: Proportion of the data to use as validation split (value between 0 and 1). :return: Entire dataset and labels as numpy array. """ return _load_diabetes() def get_german_credit_dataset(test_set: float = 0.3): """ Loads the UCI German_credit dataset from `tests/datasets/german` or downloads it if necessary. :param test_set: Proportion of the data to use as validation split (value between 0 and 1). :return: Dataset and labels as pandas dataframes. """ url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data' data_dir = '../datasets/german' data_file = '../datasets/german/data' if not path.exists(data_dir): mkdir(data_dir) ssl._create_default_https_context = ssl._create_unverified_context if not path.exists(data_file): urlretrieve(url, data_file) # load data features = ["Existing_checking_account", "Duration_in_month", "Credit_history", "Purpose", "Credit_amount", "Savings_account", "Present_employment_since", "Installment_rate", "Personal_status_sex", "debtors", "Present_residence", "Property", "Age", "Other_installment_plans", "Housing", "Number_of_existing_credits", "Job", "N_people_being_liable_provide_maintenance", "Telephone", "Foreign_worker", "label"] data = pd.read_csv(data_file, sep=" ", names=features, engine="python") # remove rows with missing label data = data.dropna(subset=["label"]) _modify_german_dataset(data) # Split training and test sets stratified = sklearn.model_selection.StratifiedShuffleSplit(n_splits=1, test_size=test_set, random_state=18) for train_set, test_set in stratified.split(data, data["label"]): train = data.iloc[train_set] test = data.iloc[test_set] x_train = train.drop(["label"], axis=1) y_train = train.loc[:, "label"] x_test = test.drop(["label"], axis=1) y_test = test.loc[:, "label"] categorical_features = ["Existing_checking_account", "Credit_history", "Purpose", "Savings_account", "Present_employment_since", "Personal_status_sex", "debtors", "Property", "Other_installment_plans", "Housing", "Job"] x_train.reset_index(drop=True, inplace=True) y_train.reset_index(drop=True, inplace=True) x_test.reset_index(drop=True, inplace=True) y_test.reset_index(drop=True, inplace=True) return (x_train, y_train), (x_test, y_test) def _modify_german_dataset(data): def modify_Foreign_worker(value): if value == 'A201': return 1 elif value == 'A202': return 0 else: raise Exception('Bad value') def modify_Telephone(value): if value == 'A191': return 0 elif value == 'A192': return 1 else: raise Exception('Bad value') data['Foreign_worker'] = data['Foreign_worker'].apply(modify_Foreign_worker) data['Telephone'] = data['Telephone'].apply(modify_Telephone) def get_adult_dataset(): """ Loads the UCI Adult dataset from `tests/datasets/adult` or downloads it if necessary. :return: Dataset and labels as pandas dataframes. """ features = ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'label'] train_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data' test_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test' data_dir = '../datasets/adult' train_file = '../datasets/adult/train' test_file = '../datasets/adult/test' if not path.exists(data_dir): mkdir(data_dir) ssl._create_default_https_context = ssl._create_unverified_context if not path.exists(train_file): urlretrieve(train_url, train_file) if not path.exists(test_file): urlretrieve(test_url, test_file) train = pd.read_csv(train_file, sep=', ', names=features, engine='python') test = pd.read_csv(test_file, sep=', ', names=features, engine='python') test = test.iloc[1:] train = _modify_adult_dataset(train) test = _modify_adult_dataset(test) x_train = train.drop(['label'], axis=1) y_train = train.loc[:, 'label'] x_test = test.drop(['label'], axis=1) y_test = test.loc[:, 'label'] return (x_train, y_train), (x_test, y_test) def _modify_adult_dataset(data): def modify_label(value): if value == '<=50K.' or value == '<=50K': return 0 elif value == '>50K.' or value == '>50K': return 1 else: raise Exception('Bad label value') def modify_native_country(value): Euro_1 = ['Italy', 'Holand-Netherlands', 'Germany', 'France'] Euro_2 = ['Yugoslavia', 'South', 'Portugal', 'Poland', 'Hungary', 'Greece'] SE_Asia = ['Vietnam', 'Thailand', 'Philippines', 'Laos', 'Cambodia'] UnitedStates = ['United-States'] LatinAmerica = ['Trinadad&Tobago', 'Puerto-Rico', 'Outlying-US(Guam-USVI-etc)', 'Nicaragua', 'Mexico', 'Jamaica', 'Honduras', 'Haiti', 'Guatemala', 'Dominican-Republic'] China = ['Taiwan', 'Hong', 'China'] BritishCommonwealth = ['Scotland', 'Ireland', 'India', 'England', 'Canada'] SouthAmerica = ['Peru', 'El-Salvador', 'Ecuador', 'Columbia'] Other = ['Japan', 'Iran', 'Cuba'] if value in Euro_1: return 'Euro_1' elif value in Euro_2: return 'Euro_2' elif value in SE_Asia: return 'SE_Asia' elif value in UnitedStates: return 'UnitedStates' elif value in LatinAmerica: return 'LatinAmerica' elif value in China: return 'China' elif value in BritishCommonwealth: return 'BritishCommonwealth' elif value in SouthAmerica: return 'SouthAmerica' elif value in Other: return 'Other' elif value == '?': return 'Unknown' else: raise Exception('Bad native country value') data['label'] = data['label'].apply(modify_label) data['native-country'] = data['native-country'].apply(modify_native_country) for col in ('age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week'): try: data[col] = data[col].fillna(0) except KeyError: print('missing column ' + col) for col in ('workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'): try: data[col] = data[col].fillna('NA') except KeyError: print('missing column ' + col) return data.drop(['fnlwgt', 'education'], axis=1) def get_nursery_dataset(raw: bool = True, test_set: float = 0.2, transform_social: bool = False): """ Loads the UCI Nursery dataset from `tests/datasets/nursery` or downloads it if necessary. :param raw: `True` if no preprocessing should be applied to the data. Otherwise, categorical data is one-hot encoded and data is scaled using sklearn's StandardScaler. :param test_set: Proportion of the data to use as validation split. The value should be between 0 and 1. :param transform_social: If `True`, transforms the social feature to be binary for the purpose of attribute inference. This is done by assigning the original value 'problematic' the new value 1, and the other original values are assigned the new value 0. :return: Dataset and labels as pandas dataframes. """ url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/nursery/nursery.data' data_dir = '../datasets/nursery' data_file = '../datasets/nursery/data' if not path.exists(data_dir): mkdir(data_dir) ssl._create_default_https_context = ssl._create_unverified_context if not path.exists(data_file): urlretrieve(url, data_file) # load data features = ["parents", "has_nurs", "form", "children", "housing", "finance", "social", "health", "label"] categorical_features = ["parents", "has_nurs", "form", "housing", "finance", "social", "health"] data = pd.read_csv(data_file, sep=",", names=features, engine="python") # remove rows with missing label or too sparse label data = data.dropna(subset=["label"]) data.drop(data.loc[data["label"] == "recommend"].index, axis=0, inplace=True) # fill missing values data["children"] = data["children"].fillna(0) for col in ["parents", "has_nurs", "form", "housing", "finance", "social", "health"]: data[col] = data[col].fillna("other") # make categorical label def modify_label(value): # 5 classes if value == "not_recom": return 0 elif value == "very_recom": return 1 elif value == "priority": return 2 elif value == "spec_prior": return 3 else: raise Exception("Bad label value: %s" % value) data["label"] = data["label"].apply(modify_label) data["children"] = data["children"].apply(lambda x: "4" if x == "more" else x) if transform_social: def modify_social(value): if value == "problematic": return 1 else: return 0 data["social"] = data["social"].apply(modify_social) categorical_features.remove("social") if not raw: # one-hot-encode categorical features features_to_remove = [] for feature in categorical_features: all_values = data.loc[:, feature] values = list(all_values.unique()) data[feature] = pd.Categorical(data.loc[:, feature], categories=values, ordered=False) one_hot_vector = pd.get_dummies(data[feature], prefix=feature) data = pd.concat([data, one_hot_vector], axis=1) features_to_remove.append(feature) data = data.drop(features_to_remove, axis=1) # normalize data label = data.loc[:, "label"] features = data.drop(["label"], axis=1) scaler = sklearn.preprocessing.StandardScaler() scaler.fit(features) scaled_features = pd.DataFrame(scaler.transform(features), columns=features.columns) data = pd.concat([label, scaled_features], axis=1, join="inner") # Split training and test sets stratified = sklearn.model_selection.StratifiedShuffleSplit(n_splits=1, test_size=test_set, random_state=18) for train_set, test_set in stratified.split(data, data["label"]): train = data.iloc[train_set] test = data.iloc[test_set] x_train = train.drop(["label"], axis=1) y_train = train.loc[:, "label"] x_test = test.drop(["label"], axis=1) y_test = test.loc[:, "label"] return (x_train, y_train), (x_test, y_test)