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utils.py
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utils.py
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"""bla"""
# from __future__ import print_function
import copy
import sklearn
import numpy as np
import lime
import lime.lime_tabular
# import string
import os
import sys
class Bunch(object):
"""bla"""
def __init__(self, adict):
self.__dict__.update(adict)
def map_array_values(array, value_map):
# value map must be { src : target }
ret = array.copy()
for src, target in value_map.items():
ret[ret == src] = target
return ret
def replace_binary_values(array, values):
return map_array_values(array, {'0': values[0], '1': values[1]})
def load_dataset(dataset_name, balance=False, discretize=True, dataset_folder='./'):
if dataset_name == 'adult':
feature_names = ["Age", "Workclass", "fnlwgt", "Education",
"Education-Num", "Marital Status", "Occupation",
"Relationship", "Race", "Sex", "Capital Gain",
"Capital Loss", "Hours per week", "Country", 'Income']
features_to_use = [0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13]
categorical_features = [1, 3, 5, 6, 7, 8, 9, 10, 11, 13]
education_map = {
'10th': 'Dropout', '11th': 'Dropout', '12th': 'Dropout', '1st-4th':
'Dropout', '5th-6th': 'Dropout', '7th-8th': 'Dropout', '9th':
'Dropout', 'Preschool': 'Dropout', 'HS-grad': 'High School grad',
'Some-college': 'High School grad', 'Masters': 'Masters',
'Prof-school': 'Prof-School', 'Assoc-acdm': 'Associates',
'Assoc-voc': 'Associates',
}
occupation_map = {
"Adm-clerical": "Admin", "Armed-Forces": "Military",
"Craft-repair": "Blue-Collar", "Exec-managerial": "White-Collar",
"Farming-fishing": "Blue-Collar", "Handlers-cleaners":
"Blue-Collar", "Machine-op-inspct": "Blue-Collar", "Other-service":
"Service", "Priv-house-serv": "Service", "Prof-specialty":
"Professional", "Protective-serv": "Other", "Sales":
"Sales", "Tech-support": "Other", "Transport-moving":
"Blue-Collar",
}
country_map = {
'Cambodia': 'SE-Asia', 'Canada': 'British-Commonwealth', 'China':
'China', 'Columbia': 'South-America', 'Cuba': 'Other',
'Dominican-Republic': 'Latin-America', 'Ecuador': 'South-America',
'El-Salvador': 'South-America', 'England': 'British-Commonwealth',
'France': 'Euro_1', 'Germany': 'Euro_1', 'Greece': 'Euro_2',
'Guatemala': 'Latin-America', 'Haiti': 'Latin-America',
'Holand-Netherlands': 'Euro_1', 'Honduras': 'Latin-America',
'Hong': 'China', 'Hungary': 'Euro_2', 'India':
'British-Commonwealth', 'Iran': 'Other', 'Ireland':
'British-Commonwealth', 'Italy': 'Euro_1', 'Jamaica':
'Latin-America', 'Japan': 'Other', 'Laos': 'SE-Asia', 'Mexico':
'Latin-America', 'Nicaragua': 'Latin-America',
'Outlying-US(Guam-USVI-etc)': 'Latin-America', 'Peru':
'South-America', 'Philippines': 'SE-Asia', 'Poland': 'Euro_2',
'Portugal': 'Euro_2', 'Puerto-Rico': 'Latin-America', 'Scotland':
'British-Commonwealth', 'South': 'Euro_2', 'Taiwan': 'China',
'Thailand': 'SE-Asia', 'Trinadad&Tobago': 'Latin-America',
'United-States': 'United-States', 'Vietnam': 'SE-Asia'
}
married_map = {
'Never-married': 'Never-Married', 'Married-AF-spouse': 'Married',
'Married-civ-spouse': 'Married', 'Married-spouse-absent':
'Separated', 'Separated': 'Separated', 'Divorced':
'Separated', 'Widowed': 'Widowed'
}
label_map = {'<=50K': 'Less than $50,000', '>50K': 'More than $50,000'}
def cap_gains_fn(x):
x = x.astype(float)
d = np.digitize(x, [0, np.median(x[x > 0]), float('inf')],
right=True).astype('|S128')
return map_array_values(d, {'0': 'None', '1': 'Low', '2': 'High'})
transformations = {
3: lambda x: map_array_values(x, education_map),
5: lambda x: map_array_values(x, married_map),
6: lambda x: map_array_values(x, occupation_map),
10: cap_gains_fn,
11: cap_gains_fn,
13: lambda x: map_array_values(x, country_map),
14: lambda x: map_array_values(x, label_map),
}
dataset = load_csv_dataset(
os.path.join(dataset_folder, 'adult/adult.data'), -1, ', ',
feature_names=feature_names, features_to_use=features_to_use,
categorical_features=categorical_features, discretize=discretize,
balance=balance, feature_transformations=transformations)
elif dataset_name == 'diabetes':
categorical_features = [2, 3, 4, 5, 6, 7, 8, 10, 11, 18, 19, 20, 22,
23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
47, 48]
label_map = {'<30': 'YES', '>30': 'YES'}
transformations = {
49: lambda x: map_array_values(x, label_map),
}
dataset = load_csv_dataset(
os.path.join(dataset_folder, 'diabetes/diabetic_data.csv'), -1, ',',
features_to_use=range(2, 49),
categorical_features=categorical_features, discretize=discretize,
balance=balance, feature_transformations=transformations)
elif dataset_name == 'default':
categorical_features = [2, 3, 4, 6, 7, 8, 9, 10, 11]
dataset = load_csv_dataset(
os.path.join(dataset_folder, 'default/default.csv'), -1, ',',
features_to_use=range(1, 24),
categorical_features=categorical_features, discretize=discretize,
balance=balance)
elif dataset_name == 'recidivism':
features_to_use = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14]
feature_names = ['Race', 'Alcohol', 'Junky', 'Supervised Release',
'Married', 'Felony', 'WorkRelease',
'Crime against Property', 'Crime against Person',
'Gender', 'Priors', 'YearsSchool', 'PrisonViolations',
'Age', 'MonthsServed', '', 'Recidivism']
def violations_fn(x):
x = x.astype(float)
d = np.digitize(x, [0, 5, float('inf')],
right=True).astype('|S128')
return map_array_values(d, {'0': 'NO', '1': '1 to 5', '2': 'More than 5'})
def priors_fn(x):
x = x.astype(float)
d = np.digitize(x, [-1, 0, 5, float('inf')],
right=True).astype('|S128')
return map_array_values(d, {'0': 'UNKNOWN', '1': 'NO', '2': '1 to 5', '3': 'More than 5'})
transformations = {
0: lambda x: replace_binary_values(x, ['Black', 'White']),
1: lambda x: replace_binary_values(x, ['No', 'Yes']),
2: lambda x: replace_binary_values(x, ['No', 'Yes']),
3: lambda x: replace_binary_values(x, ['No', 'Yes']),
4: lambda x: replace_binary_values(x, ['No', 'Married']),
5: lambda x: replace_binary_values(x, ['No', 'Yes']),
6: lambda x: replace_binary_values(x, ['No', 'Yes']),
7: lambda x: replace_binary_values(x, ['No', 'Yes']),
8: lambda x: replace_binary_values(x, ['No', 'Yes']),
9: lambda x: replace_binary_values(x, ['Female', 'Male']),
10: lambda x: priors_fn(x),
12: lambda x: violations_fn(x),
13: lambda x: (x.astype(float) / 12).astype(int),
16: lambda x: replace_binary_values(x, ['No more crimes',
'Re-arrested'])
}
dataset = load_csv_dataset(
os.path.join(dataset_folder, 'recidivism/Data_1980.csv'), 16,
feature_names=feature_names, discretize=discretize,
features_to_use=features_to_use, balance=balance,
feature_transformations=transformations, skip_first=True)
elif dataset_name == 'lending':
def filter_fn(data):
to_remove = ['Does not meet the credit policy. Status:Charged Off',
'Does not meet the credit policy. Status:Fully Paid',
'In Grace Period', '-999', 'Current']
for x in to_remove:
data = data[data[:, 16] != x]
return data
bad_statuses = set(["Late (16-30 days)", "Late (31-120 days)", "Default", "Charged Off"])
transformations = {
16: lambda x: np.array([y in bad_statuses for y in x]).astype(int),
19: lambda x: np.array([len(y) for y in x]).astype(int),
6: lambda x: np.array([y.strip('%') if y else -1 for y in x]).astype(float),
35: lambda x: np.array([y.strip('%') if y else -1 for y in x]).astype(float),
}
features_to_use = [2, 12, 13, 19, 29, 35, 51, 52, 109]
categorical_features = [12, 109]
dataset = load_csv_dataset(
os.path.join(dataset_folder, 'lendingclub/LoanStats3a_securev1.csv'),
16, ',', features_to_use=features_to_use,
feature_transformations=transformations, fill_na='-999',
categorical_features=categorical_features, discretize=discretize,
filter_fn=filter_fn, balance=True)
dataset.class_names = ['Good Loan', 'Bad Loan']
return dataset
def load_csv_dataset(data, target_idx, delimiter=',',
feature_names=None, categorical_features=None,
features_to_use=None, feature_transformations=None,
discretize=False, balance=False, fill_na='-1', filter_fn=None, skip_first=False):
"""if not feature names, takes 1st line as feature names
if not features_to_use, use all except for target
if not categorical_features, consider everything < 20 as categorical"""
if feature_transformations is None:
feature_transformations = {}
try:
data = np.genfromtxt(data, delimiter=delimiter, dtype='|S128')
except:
import pandas
data = pandas.read_csv(data,
header=None,
delimiter=delimiter,
na_filter=True,
dtype=str).fillna(fill_na).values
if target_idx < 0:
target_idx = data.shape[1] + target_idx
ret = Bunch({})
if feature_names is None:
feature_names = list(data[0])
data = data[1:]
else:
feature_names = copy.deepcopy(feature_names)
if skip_first:
data = data[1:]
if filter_fn is not None:
data = filter_fn(data)
for feature, fun in feature_transformations.items():
data[:, feature] = fun(data[:, feature])
labels = data[:, target_idx]
le = sklearn.preprocessing.LabelEncoder()
le.fit(labels)
ret.labels = le.transform(labels)
labels = ret.labels
ret.class_names = list(le.classes_)
ret.class_target = feature_names[target_idx]
if features_to_use is not None:
data = data[:, features_to_use]
feature_names = ([x for i, x in enumerate(feature_names)
if i in features_to_use])
if categorical_features is not None:
categorical_features = ([features_to_use.index(x)
for x in categorical_features])
else:
data = np.delete(data, target_idx, 1)
feature_names.pop(target_idx)
if categorical_features:
categorical_features = ([x if x < target_idx else x - 1
for x in categorical_features])
if categorical_features is None:
categorical_features = []
for f in range(data.shape[1]):
if len(np.unique(data[:, f])) < 20:
categorical_features.append(f)
categorical_names = {}
for feature in categorical_features:
le = sklearn.preprocessing.LabelEncoder()
le.fit(data[:, feature])
data[:, feature] = le.transform(data[:, feature])
categorical_names[feature] = le.classes_
data = data.astype(float)
ordinal_features = []
if discretize:
disc = lime.lime_tabular.QuartileDiscretizer(data,
categorical_features,
feature_names)
data = disc.discretize(data)
ordinal_features = [x for x in range(data.shape[1])
if x not in categorical_features]
categorical_features = list(range(data.shape[1]))
categorical_names.update(disc.names)
for x in categorical_names:
categorical_names[x] = [y.decode() if type(y) == np.bytes_ else y for y in categorical_names[x]]
ret.ordinal_features = ordinal_features
ret.categorical_features = categorical_features
ret.categorical_names = categorical_names
ret.feature_names = feature_names
np.random.seed(1)
if balance:
idxs = np.array([], dtype='int')
min_labels = np.min(np.bincount(labels))
for label in np.unique(labels):
idx = np.random.choice(np.where(labels == label)[0], min_labels)
idxs = np.hstack((idxs, idx))
data = data[idxs]
labels = labels[idxs]
ret.data = data
ret.labels = labels
splits = sklearn.model_selection.ShuffleSplit(n_splits=1,
test_size=.2,
random_state=1)
train_idx, test_idx = [x for x in splits.split(data)][0]
ret.train = data[train_idx]
ret.labels_train = ret.labels[train_idx]
cv_splits = sklearn.model_selection.ShuffleSplit(n_splits=1,
test_size=.5,
random_state=1)
cv_idx, ntest_idx = [x for x in cv_splits.split(test_idx)][0]
cv_idx = test_idx[cv_idx]
test_idx = test_idx[ntest_idx]
ret.validation = data[cv_idx]
ret.labels_validation = ret.labels[cv_idx]
ret.test = data[test_idx]
ret.labels_test = ret.labels[test_idx]
ret.test_idx = test_idx
ret.validation_idx = cv_idx
ret.train_idx = train_idx
# ret.train, ret.test, ret.labels_train, ret.labels_test = (
# sklearn.cross_validation.train_test_split(data, ret.labels,
# train_size=0.80))
# ret.validation, ret.test, ret.labels_validation, ret.labels_test = (
# sklearn.cross_validation.train_test_split(ret.test, ret.labels_test,
# train_size=.5))
ret.data = data
return ret