def process_data(self, data_file):
""" Use the inbuilt handle_missing_values since missing values can't be sent in
valid JSON without raising an error
"""
data_df = handle_missing_values(read_data_to_df(data_file),
strategy=self.missing_values)
# make a dict of {header: column values}, JSONify, return
data_dict = data_df.to_dict(orient='list', into=dict)
jsonified = json.dumps(data_dict)
return jsonified
def _process_data(self, fit=True):
"""
read and return data as x and y
@return: list of separate x and y
"""
assert isinstance(self.target, list), "provide target(s) as a list in the yaml file"
assert len(self.target) > 0, "please provide at least a target to predict"
try:
dataset = pd.read_csv(self.data_path)
logger.info(f"dataset shape: {dataset.shape}")
attributes = list(dataset.columns)
logger.info(f"dataset attributes: {attributes}")
# handle missing values in the dataset
preprocess_props = self.dataset_props.get('preprocess', None)
if preprocess_props:
# preprocessing strategy: mean, median, mode etc..
strategy = preprocess_props.get('missing_values')
if strategy:
dataset = handle_missing_values(dataset,
strategy=strategy)
if any(col not in attributes for col in self.target):
raise Exception("chosen target(s) to predict must exist in the dataset")
y = pd.concat([dataset.pop(x) for x in self.target], axis=1)
x = _reshape(dataset.to_numpy())
y = _reshape(y.to_numpy())
logger.info(f"y shape: {y.shape} and x shape: {x.shape}")
if not fit:
return x, y
split_options = self.dataset_props.get('split')
test_size = split_options.get('test_size')
shuffle = split_options.get('shuffle')
stratify = split_options.get('stratify')
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=test_size,
shuffle=shuffle,
stratify=None if not stratify or stratify.lower() == "none" else stratify)
return x_train, y_train, x_test, y_test
except Exception as e:
logger.exception(f"error occured while preparing the data: {e.args}")
def _process_data(self, target='fit'):
"""
read and return data as x and y
@return: list of separate x and y
"""
assert isinstance(self.target,
list), "provide target(s) as a list in the yaml file"
if self.model_type != "clustering":
assert len(
self.target) > 0, "please provide at least a target to predict"
try:
read_data_options = self.dataset_props.get('read_data_options',
None)
dataset = pd.read_csv(
self.data_path) if not read_data_options else pd.read_csv(
self.data_path, **read_data_options)
logger.info(f"dataset shape: {dataset.shape}")
attributes = list(dataset.columns)
logger.info(f"dataset attributes: {attributes}")
# handle missing values in the dataset
preprocess_props = self.dataset_props.get('preprocess', None)
if preprocess_props:
# handle encoding
encoding = preprocess_props.get('encoding')
if encoding:
encoding_type = encoding.get('type', None)
column = encoding.get('column', None)
if column in attributes:
dataset, classes_map = encode(
df=dataset,
encoding_type=encoding_type.lower(),
column=column)
if classes_map:
self.dataset_props[
'label_encoding_classes'] = classes_map
logger.info(
f"adding classes_map to dataset props: \n{classes_map}"
)
logger.info(
f"shape of the dataset after encoding => {dataset.shape}"
)
# preprocessing strategy: mean, median, mode etc..
strategy = preprocess_props.get('missing_values')
if strategy:
dataset = handle_missing_values(dataset, strategy=strategy)
logger.info(
f"shape of the dataset after handling missing values => {dataset.shape}"
)
if target == 'predict' or target == 'fit_cluster':
x = _reshape(dataset.to_numpy())
if not preprocess_props:
return x
scaling_props = preprocess_props.get('scale', None)
if not scaling_props:
return x
else:
scaling_method = scaling_props.get('method', None)
return normalize(x, method=scaling_method)
if any(col not in attributes for col in self.target):
raise Exception(
"chosen target(s) to predict must exist in the dataset")
y = pd.concat([dataset.pop(x) for x in self.target], axis=1)
x = _reshape(dataset.to_numpy())
y = _reshape(y.to_numpy())
logger.info(f"y shape: {y.shape} and x shape: {x.shape}")
# handle data scaling
if preprocess_props:
scaling_props = preprocess_props.get('scale', None)
if scaling_props:
scaling_method = scaling_props.get('method', None)
scaling_target = scaling_props.get('target', None)
if scaling_target == 'all':
x = normalize(x, method=scaling_method)
y = normalize(y, method=scaling_method)
elif scaling_target == 'inputs':
x = normalize(x, method=scaling_method)
elif scaling_target == 'outputs':
y = normalize(y, method=scaling_method)
if target == 'evaluate':
return x, y
split_options = self.dataset_props.get('split', None)
if not split_options:
return x, y, None, None
test_size = split_options.get('test_size')
shuffle = split_options.get('shuffle')
stratify = split_options.get('stratify')
x_train, x_test, y_train, y_test = train_test_split(
x,
y,
test_size=test_size,
shuffle=shuffle,
stratify=None
if not stratify or stratify.lower() == "default" else stratify)
return x_train, y_train, x_test, y_test
except Exception as e:
logger.exception(
f"error occured while preparing the data: {e.args}")
