def get_type(value):
from pytracer.core.stats.numpy import StatisticNumpy
from pytracer.core.stats.sklearn import is_sklearn_value
import numpy as np
_type = None
if isinstance(value, int):
_type = TypeValue.INT
elif isinstance(value, float):
_type = TypeValue.FLOAT
elif StatisticNumpy.hasinstance(value):
_type = TypeValue.NUMPY
elif isinstance(value, list):
array = np.array(value, dtype=np.object)
if StatisticNumpy.hasinstance(array):
_type = TypeValue.LIST
else:
_type = TypeValue.OTHER
elif isinstance(value, tuple):
_type = TypeValue.TUPLE
elif isinstance(value, FunctionType):
_type = TypeValue.FUNCTION
elif is_sklearn_value(value):
_type = TypeValue.SKLEARN
else:
_type = TypeValue.OTHER
if not isinstance(value, (str, np.ndarray, np.dtype, type)):
if type(value) not in _type_cache:
logger.warning(f"Unknown type: {type(value)} {value}")
_type_cache.add(type(value))
return _type
def parse_data(self, data):
_data = dict()
for attr in self.__attributes:
for value in data:
if hasattr(value, attr):
if attr in _data:
_data[attr].append(getattr(value, attr))
else:
_data[attr] = [getattr(value, attr)]
if attr in _data:
if (d := _data[attr]) == []:
_data[attr] = StatisticNumpy(np.array(d), empty=True)
else:
_data[attr] = StatisticNumpy(np.array(d))
def parse_data(self, data):
_data = dict()
for attr in self.__attributes:
for value in data:
if hasattr(value, attr):
attr_name = f"MultiTaskLasso.{attr}"
if attr_name in _data:
_data[attr_name].append(getattr(value, attr))
else:
_data[attr_name] = [getattr(value, attr)]
if attr_name in _data:
if (d := _data[attr_name]) == []:
_data[attr_name] = StatisticNumpy(np.array(d), empty=True)
else:
_data[attr_name] = StatisticNumpy(np.array(d))
def parse_data(self, data):
_data = dict()
for attr in self.__attributes:
for value in data:
if hasattr(value, attr):
obj = getattr(value, attr, None)
if obj is None:
_data[attr] = []
continue
if attr in _data:
_data[attr].append(obj)
else:
_data[attr] = [obj]
if attr in _data:
if (d := _data[attr]) == []:
empty = True
else:
empty = False
_data[attr] = StatisticNumpy(np.array(d), empty=empty)
def get_stats(values):
from pytracer.core.stats.numpy import StatisticNumpy
from pytracer.core.stats.sklearn import get_sklearn_stat
import numpy as np
check_type(values)
_type = get_type(values[0])
_stats = None
if _type == TypeValue.INT:
array = np.array(values, dtype=np.int64)
_stats = StatisticNumpy(array)
elif _type == TypeValue.FLOAT:
array = np.array(values, dtype=np.float64)
_stats = StatisticNumpy(array)
elif _type == TypeValue.LIST:
array = np.array(values)
_stats = StatisticNumpy(array)
elif _type == TypeValue.TUPLE:
types = [get_type(t) for t in values[0]]
_stats = []
zipv = zip(*values)
[(t, v) for t, v in zip(types, zipv)]
for ty, v in zip(types, zipv):
if ty == TypeValue.OTHER:
_stats.append(StatisticNumpy(v, empty=True))
else:
_stats.append(StatisticNumpy(np.array(v)))
elif _type == TypeValue.NUMPY:
try:
array = np.array(values)
_stats = StatisticNumpy(array)
except:
logger.debug(f"Could not parse {values}")
_stats = StatisticNumpy(values, empty=True)
elif _type == TypeValue.SKLEARN:
_stats = get_sklearn_stat(values, type(values[0]))
else:
_stats = StatisticNumpy(values, empty=True)
# _stats = values[0]
return _stats