def load_data():
header_types = OrderedDict()
data = []
with open(train_filename) as f:
for line in f:
if "@ATTRIBUTE" in line:
_, header, arff_type = line.split()
header_types[header] = float if arff_type=="NUMERIC" else str
else:
row = line[:-1].split(",") #TODO: This is a naive way of splitting, captain.
row = [header_types[h](v) for h,v in zip(header_types, row)]
data.append(row)
with open(test_filename) as f:
for line in f:
if "@ATTRIBUTE" not in line:
row = line[:-1].split(",") #TODO: This is a naive way of splitting, captain.
row = [header_types[h](v) for h,v in zip(header_types, row)]
data.append(row)
headers = header_types.keys()
# Translate the response into a binary.
index = headers.index(response)
translate = lambda row: row[:index] + ["1" if row[index] in "34" else "0"] + row[index+1:]
data = map(translate, data)
train, test = split_by_percent(data, train_percentage)
return headers, train, test
def load_DRP(data):
train_filename = preloaded[data]["filepath"]
test_filename = preloaded[data]["testdata"]
train_percentage = preloaded[data]["train_percentage"]
header = preloaded[data]["response_header"]
features_to_ignore = preloaded[data]["features_to_ignore"]
header_types = OrderedDict()
data = []
with open(train_filename) as f:
for line in f:
if "@attribute" in line:
_, header, arff_type = line.split()
header_types[header] = float if arff_type == "numeric" else str
elif "@relation" in line or "@data" in line or line == "\n":
pass
else:
row = line[:-1].split(
",") #TODO: This is a naive way of splitting, captain.
row = [header_types[h](v) for h, v in zip(header_types, row)]
data.append(row)
with open(test_filename) as f:
for line in f:
if "@attribute" not in line and "@relation" not in line and "@data" not in line and line != "\n":
row = line[:-1].split(
",") #TODO: This is a naive way of splitting, captain.
row = [header_types[h](v) for h, v in zip(header_types, row)]
data.append(row)
headers = header_types.keys()
train, test = split_by_percent(data, train_percentage)
return headers, train, test, header, features_to_ignore
def load_data(data):
if data not in preloaded:
raise KeyError("{} is not an available dataset".format(data))
if data == "DRP":
return load_DRP(data)
filename = preloaded[data]["filepath"]
testdata = preloaded[data]["testdata"]
correct_types = preloaded[data]["correct_types"]
train_percentage = preloaded[data]["train_percentage"]
response_header = preloaded[data]["response_header"]
features_to_ignore = preloaded[data]["features_to_ignore"]
with open(filename) as f:
reader = csv.reader(f)
data = [row for row in reader]
headers = data.pop(0)
for i, row in enumerate(data):
for j, correct_type in enumerate(correct_types):
data[i][j] = correct_type(row[j])
if testdata is None:
train, test = split_by_percent(data, train_percentage)
else:
train = data
with open(testdata) as f:
reader = csv.reader(f)
test = [row for row in reader][1:] # Ignore headers.
for i, row in enumerate(test):
for j, correct_type in enumerate(correct_types):
test[i][j] = correct_type(row[j])
return headers, train, test, response_header, features_to_ignore
def load_data():
header_types = OrderedDict()
data = []
with open(train_filename) as f:
for line in f:
if "@attribute" in line:
_, header, arff_type = line.split()
header_types[header] = float if arff_type == "numeric" else str
elif "@relation" in line or "@data" in line or line == "\n":
pass
else:
row = line[:-1].split(
",") #TODO: This is a naive way of splitting, captain.
row = [header_types[h](v) for h, v in zip(header_types, row)]
data.append(row)
with open(test_filename) as f:
for line in f:
if "@attribute" not in line and "@relation" not in line and "@data" not in line and line != "\n":
row = line[:-1].split(
",") #TODO: This is a naive way of splitting, captain.
row = [header_types[h](v) for h, v in zip(header_types, row)]
data.append(row)
headers = list(header_types.keys())
# Translate the response into a binary.
# index = headers.index(response)
# translate = lambda row: row[:index] + ["1" if row[index] in "34" else "0"] + row[index+1:]
# data = map(translate, data)
train, test = split_by_percent(data, train_percentage)
return headers, train, test
def load_data():
N = 6000
headers = [
"Feature A (i)", "Feature B (2i)", "Feature C (-i)",
"Constant Feature", "Random Feature", "Outcome"
]
data = [[i, 2*i, -i, 1, random.random(), "A"] for i in range(0,N/2)] + \
[[i, 2*i, -i, 1, random.random(), "B"] for i in range(N/2,N)]
train, test = split_by_percent(data, train_percentage)
return headers, train, test
def load_data():
with open(filename) as f:
reader = csv.reader(f)
data = [row for row in reader]
headers = data.pop(0)
if max_entries:
data = random.sample(data, max_entries)
for i, row in enumerate(data):
for j, correct_type in enumerate(correct_types):
data[i][j] = correct_type(row[j])
train, test = split_by_percent(data, train_percentage)
return headers, train, test
def load_data():
filename = "" # file not publicly available
with open(filename) as f:
reader = csv.reader(f)
data = [row for row in reader]
headers = data.pop(0)
correct_types = [str] * len(headers) # All categorical.
for i, row in enumerate(data):
for j, correct_type in enumerate(correct_types):
data[i][j] = correct_type(row[j])
train, test = split_by_percent(data, train_percentage)
return headers, train, test
def load_data():
with open(filename) as f:
reader = csv.reader(f)
data = [row for row in reader]
headers = data.pop(0)
if max_entries:
data = random.sample(data, max_entries)
for i, row in enumerate(data):
for j, correct_type in enumerate(correct_types):
data[i][j] = correct_type(row[j])
# Replace the numeric age with "young" and "old" categories.
# Threshold based on: F. Kamiran and T. Calders. Classifying without discriminating.
data[i][AGE_COL] = "old" if row[AGE_COL] > 25 else "young"
train, test = split_by_percent(data, train_percentage)
return headers, train, test
def load_from_file(datafile,
testdata=None,
correct_types=None,
train_percentage=2.0 / 3.0,
response_header=None,
features_to_ignore=None,
missing_data_symbol=""):
with open(datafile) as f:
reader = csv.reader(f)
data = [row for row in reader]
headers = data.pop(0)
# Set defaults in case they are not handed in as arguments
if response_header is None:
response_header = headers[-1]
if features_to_ignore is None:
features_to_ignore = []
if correct_types is None:
correct_types = get_types(data, [None] * len(headers),
missing_data_symbol)
for i, row in enumerate(data):
for j, correct_type in enumerate(correct_types):
data[i][j] = correct_type(row[j])
if testdata is None:
train, test = split_by_percent(data, train_percentage)
else:
train = data
with open(testdata) as f:
reader = csv.reader(f)
test = [row for row in reader][1:] # Ignore headers.
for i, row in enumerate(test):
for j, correct_type in enumerate(correct_types):
test[i][j] = correct_type(row[j])
return headers, train, test, response_header, features_to_ignore
