def test_background_sketch(self):
dict_data = [{str(i):1} for i in range(1,10000)]
sa = SArray(dict_data)
s = sa.sketch_summary(background=True, sub_sketch_keys=[str(i ) for i in range(100,200)])
s.sketch_ready() # cannot check the actual value as it depends on the speed of processing
t = s.element_sub_sketch([str(i) for i in range(100, 105)])
self.assertEqual(len(t), 5)
def test_str_sketch(self):
str_data = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", None]
sa = SArray(data=str_data)
sketch = sa.sketch_summary()
with self.assertRaises(RuntimeError):
sketch.min()
with self.assertRaises(RuntimeError):
sketch.max()
with self.assertRaises(RuntimeError):
sketch.sum()
with self.assertRaises(RuntimeError):
sketch.mean()
with self.assertRaises(RuntimeError):
sketch.var()
with self.assertRaises(RuntimeError):
sketch.std()
self.assertAlmostEqual(sketch.num_unique(), 10, delta=3)
self.assertEqual(sketch.num_undefined(), 1)
self.assertEqual(sketch.size(), len(str_data))
with self.assertRaises(RuntimeError):
sketch.quantile(0.5)
self.assertEqual(sketch.frequency_count("1"), 1)
self.assertEqual(sketch.frequency_count("2"), 1)
t = sketch.frequent_items()
self.assertEqual(len(t), 10)
def test_str_sketch(self):
str_data = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", None]
sa = SArray(data=str_data)
sketch = sa.sketch_summary()
with self.assertRaises(RuntimeError):
sketch.min()
with self.assertRaises(RuntimeError):
sketch.max()
with self.assertRaises(RuntimeError):
sketch.sum()
with self.assertRaises(RuntimeError):
sketch.mean()
with self.assertRaises(RuntimeError):
sketch.var()
with self.assertRaises(RuntimeError):
sketch.std()
self.assertAlmostEqual(sketch.num_unique(), 10, delta=3)
self.assertEqual(sketch.num_undefined(), 1)
self.assertEqual(sketch.size(), len(str_data))
with self.assertRaises(RuntimeError):
sketch.quantile(0.5)
self.assertEqual(sketch.frequency_count("1"), 1)
self.assertEqual(sketch.frequency_count("2"), 1)
t = sketch.frequent_items()
self.assertEqual(len(t), 10)
def test_background_sketch(self):
dict_data = [{str(i): 1} for i in range(1, 10000)]
sa = SArray(dict_data)
s = sa.sketch_summary(
background=True, sub_sketch_keys=[str(i) for i in range(100, 200)])
s.sketch_ready(
) # cannot check the actual value as it depends on the speed of processing
t = s.element_sub_sketch([str(i) for i in range(100, 105)])
self.assertEqual(len(t), 5)
def test_vector_sketch(self):
vector_data = [[], [1,2], [3], [4,5,6,7], [8,9,10], None]
sa = SArray(data=vector_data)
sketch = sa.sketch_summary();
self.__validate_sketch_result(sketch, sa)
self.__validate_sketch_result(sketch.element_length_summary(), sa.dropna().item_length())
flattened = list(itertools.chain.from_iterable(list(sa.dropna())))
self.__validate_sketch_result(sketch.element_summary(), SArray(flattened))
fi = sketch.frequent_items()
self.assertEqual(len(fi), 5)
self.assertEqual((fi['[1 2]']), 1)
self.assertEqual((fi['[4 5 6 7]']), 1)
# sub sketch with one key
s = sa.sketch_summary(sub_sketch_keys = 1).element_sub_sketch(1)
expected = sa.vector_slice(1)
self.__validate_sketch_result(s, expected)
# sub sketch with multiple keys
keys = [1,3]
s = sa.sketch_summary(sub_sketch_keys = keys).element_sub_sketch(keys)
self.assertEqual(len(s), len(keys))
for key in keys:
self.assertTrue(s.has_key(key))
expected = sa.vector_slice(key)
self.__validate_sketch_result(s[key], expected)
indexes = range(0,10)
s = sa.sketch_summary(sub_sketch_keys = indexes).element_sub_sketch()
self.assertEqual(len(s), len(indexes))
def test_list_sketch(self):
list_data = [[], [1,2],[1,2], ['a', 'a', 'a', 'b'], [ 1 ,1 , 2], None]
sa = SArray(list_data)
self.__validate_nested_sketch_result(sa)
sketch = sa.sketch_summary();
self.assertEqual(sketch.num_unique(), 4)
element_summary = sketch.element_summary()
another_rep = list(itertools.chain.from_iterable(list(sa.dropna())))
self.__validate_sketch_result(element_summary, SArray(another_rep, str))
fi = sketch.frequent_items()
self.assertEqual(len(fi), 4)
self.assertEqual((fi['[1,2]']), 2)
self.assertEqual((fi['["a","a","a","b"]']), 1)
def test_list_sketch(self):
list_data = [[], [1, 2], [1, 2], ['a', 'a', 'a', 'b'], [1, 1, 2], None]
sa = SArray(list_data)
self.__validate_nested_sketch_result(sa)
sketch = sa.sketch_summary()
self.assertEqual(sketch.num_unique(), 4)
element_summary = sketch.element_summary()
another_rep = list(itertools.chain.from_iterable(list(sa.dropna())))
self.__validate_sketch_result(element_summary,
SArray(another_rep, str))
fi = sketch.frequent_items()
self.assertEqual(len(fi), 4)
self.assertEqual((fi['[1,2]']), 2)
self.assertEqual((fi['["a","a","a","b"]']), 1)
def test_empty_sketch(self):
int_data = []
sa = SArray(data=int_data)
sketch = sa.sketch_summary()
self.assertTrue(math.isnan(sketch.min()))
self.assertTrue(math.isnan(sketch.max()))
self.assertEquals(sketch.sum(), 0)
self.assertEqual(sketch.mean(), 0)
self.assertEqual(sketch.var(), 0)
self.assertEqual(sketch.std(), 0)
self.assertEqual(sketch.num_unique(), 0)
self.assertEqual(sketch.num_undefined(), 0)
self.assertEqual(sketch.size(), 0)
with self.assertRaises(RuntimeError):
sketch.quantile(0.5)
t = sketch.frequent_items()
self.assertEqual(len(t), 0)
def test_empty_sketch(self):
int_data = []
sa = SArray(data=int_data)
sketch = sa.sketch_summary()
self.assertTrue(math.isnan(sketch.min()))
self.assertTrue(math.isnan(sketch.max()))
self.assertEquals(sketch.sum(), 0)
self.assertEqual(sketch.mean(), 0)
self.assertEqual(sketch.var(), 0)
self.assertEqual(sketch.std(), 0)
self.assertEqual(sketch.num_unique(), 0)
self.assertEqual(sketch.num_undefined(),0)
self.assertEqual(sketch.size(), 0)
with self.assertRaises(RuntimeError):
sketch.quantile(0.5)
t = sketch.frequent_items()
self.assertEqual(len(t), 0)
def __validate_sketch_result(self, sketch, sa, delta=1E-7):
df = pd.DataFrame(list(sa.dropna()))
pds = pd.Series(list(sa.dropna()))
if (sa.dtype() == int or sa.dtype() == float):
if (len(sa) == 0):
self.assertTrue(math.isnan(sketch.min()))
self.assertTrue(math.isnan(sketch.min()))
self.assertEquals(sketch.sum(), 0.0)
self.assertEquals(sketch.mean(), 0.0)
self.assertEquals(sketch.var(), 0.0)
self.assertEquals(sketch.std(), 0.0)
else:
self.assertEquals(sketch.min(), sa.min())
self.assertEquals(sketch.max(), sa.max())
self.assertEquals(sketch.sum(), sa.sum())
self.assertAlmostEqual(sketch.mean(),
sa.dropna().mean(),
delta=delta)
self.assertAlmostEqual(sketch.var(),
sa.dropna().var(),
delta=delta)
self.assertAlmostEqual(sketch.std(),
sa.dropna().std(),
delta=delta)
self.assertAlmostEqual(sketch.quantile(0.5),
df.quantile(0.5)[0],
delta=1)
self.assertEqual(sketch.quantile(0), df.quantile(0)[0])
self.assertEqual(sketch.quantile(1), df.quantile(1)[0])
self.assertEqual(sketch.frequent_items(),
SArray(pds).sketch_summary().frequent_items())
for item in pds.value_counts().index:
self.assertEqual(sketch.frequency_count(item),
pds.value_counts()[item])
self.assertAlmostEqual(sketch.num_unique(),
len(sa.unique()),
delta=3)
else:
with self.assertRaises(RuntimeError):
sketch.quantile((0.5))
self.assertEqual(sketch.num_undefined(), sa.num_missing())
self.assertEqual(sketch.size(), len(sa))
self.assertEqual(sketch.sketch_ready(), True)
self.assertEqual(sketch.num_elements_processed(), sketch.size())
def test_dict_sketch_int_value(self):
dict_data = [{}, {'a':1, 'b':2}, {'a':1, 'b':2}, {'a':3, 'c':1}, {'a': 1, 'b': 2, 'c': 3}, None]
sa = SArray(data=dict_data)
self.__validate_nested_sketch_result(sa)
sketch = sa.sketch_summary()
self.assertEqual(sketch.num_unique(), 4)
fi = sketch.frequent_items()
self.assertEqual(len(fi), 4)
self.assertEqual((fi['{"a":1, "b":2}']), 2)
self.assertEqual((fi['{"a":3, "c":1}']), 1)
# Get dict key sketch
key_summary = sketch.dict_key_summary()
another_rep = list(itertools.chain.from_iterable(list(sa.dict_keys().dropna())))
self.__validate_sketch_result(key_summary, SArray(another_rep))
# Get dict value sketch
value_summary = sketch.dict_value_summary()
another_rep = list(itertools.chain.from_iterable(list(sa.dict_values().dropna())))
self.__validate_sketch_result(value_summary, SArray(another_rep))
# sub sketch with one key
s = sa.sketch_summary(sub_sketch_keys ='a').element_sub_sketch('a')
expected = sa.unpack(column_name_prefix="")['a']
self.__validate_sketch_result(s, expected)
s = sa.sketch_summary(sub_sketch_keys ='Nonexist').element_sub_sketch('Nonexist')
self.assertEqual(s.num_undefined(), len(sa))
# sub sketch with multiple keys
keys = ['a', 'b']
s = sa.sketch_summary(sub_sketch_keys =keys).element_sub_sketch(keys)
self.assertEqual(len(s), len(keys))
for key in keys:
self.assertTrue(s.has_key(key))
expected = sa.unpack(column_name_prefix="")[key]
self.__validate_sketch_result(s[key], expected)
def _combine(task):
'''
The actual code that will be ran inside of a task to combine all results and add the parameter
column to the final SFrame(s).
'''
# Initialize empty SFrames for each output.
for out_name in task.get_outputs():
task.outputs[out_name] = _SFrame()
params_to_outputs = task.params[_COMBINE_PARAMETER_NAME]
for params, path in params_to_outputs:
for out_name in task.get_outputs():
try:
cur_result = _SFrame(_path_join(path, out_name))
except IOError:
_log.info("No output for %s with parameters: %s " % (out_name, str(params)))
continue
# Add the 'Parameters' column and append to previous results.
cur_result['parameters'] = _SArray.from_const(params, len(cur_result))
cur_result.__materialize__()
task.outputs[out_name] = task.outputs[out_name].append(cur_result)
def rating_prediction_switch(self, dataset, dataset_switch, model_manager, force):
from graphlab.data_structures.sframe import SFrame
from graphlab.data_structures.sarray import SArray
import os
for folder in dataset.folders:
rating_prediction_file = self._get_rating_prediction_file(dataset_switch, folder)
class_prediction_file = self._get_class_prediction_file(dataset_switch, folder)
if os.path.exists(rating_prediction_file) and not force:
print "Model " + self.id + " in " + dataset_switch.id + " " + folder.id + " already tested."
continue
cf_predictions = model_manager.get_predictions(dataset, folder)
if self.id == 'best':
test_sframe = folder.test_sframe
target = test_sframe.select_column(key = 'rating')
rating_predictions = map(lambda t, *p: self._get_best_prediction(t, *p), target, *cf_predictions)
rating_array = SArray(rating_predictions)
rating_array.save(filename = rating_prediction_file)
else:
sf = SFrame.read_csv(class_prediction_file, header = True, quote_char = '"',
column_type_hints = [int, str])
switch_predictions = sf.select_column(key = 'x')
index_switch_predictions = model_manager.get_index_model(switch_predictions)
rating_predictions = map(lambda t, *p: self._get_switch_prediction(t, *p),
index_switch_predictions, *cf_predictions)
rating_array = SArray(rating_predictions)
rating_array.save(filename = rating_prediction_file)
def test_sketch_int(self):
int_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None]
sa = SArray(data=int_data)
self.__validate_sketch_result(sa.sketch_summary(), sa)
def test_large_value_sketch(self):
sa = SArray([1234567890 for i in range(100)])
sk = sa.sketch_summary()
self.__validate_sketch_result(sa.sketch_summary(), sa, 1E-5)
def test_cancelation(self):
sa = SArray(range(1, 10000))
s = sa.sketch_summary(background=True)
s.cancel()
def test_large_value_sketch(self):
sa = SArray([1234567890 for i in range(100)])
sk = sa.sketch_summary();
self.__validate_sketch_result(sa.sketch_summary(), sa, 1E-5)
def test_dict_sketch_str_value(self):
# Dict value sketch type should be auto inferred
dict_data = [{'a':'b', 'b':'c'}, {'a':'b', 'b':'c'}, {'a':'d', 'b':'4'}, None]
sa = SArray(data=dict_data)
self.__validate_nested_sketch_result(sa)
sketch = sa.sketch_summary()
fi = sketch.frequent_items()
self.assertEqual(len(fi), 2)
self.assertEqual(fi['{"a":"b", "b":"c"}'], 2)
self.assertEqual(fi['{"a":"d", "b":"4"}'], 1)
# Get dict key sketch
key_summary = sketch.dict_key_summary()
another_rep = list(itertools.chain.from_iterable(list(sa.dict_keys().dropna())))
self.__validate_sketch_result(key_summary, SArray(another_rep))
# Get dict value sketch
value_summary = sketch.dict_value_summary()
another_rep = list(itertools.chain.from_iterable(list(sa.dict_values().dropna())))
self.__validate_sketch_result(value_summary, SArray(another_rep))
# sub sketch with one key
s = sa.sketch_summary(sub_sketch_keys ='a').element_sub_sketch('a')
expected = sa.unpack(column_name_prefix="")['a']
self.__validate_sketch_result(s, expected)
s = sa.sketch_summary(sub_sketch_keys ='Nonexist').element_sub_sketch('Nonexist')
self.assertEqual(s.num_undefined(), len(sa))
# sub sketch with multiple keys
keys = ['a', 'b']
s = sa.sketch_summary(sub_sketch_keys =keys).element_sub_sketch(keys)
self.assertEqual(len(s), len(keys))
for key in keys:
self.assertTrue(s.has_key(key))
expected = sa.unpack(column_name_prefix="")[key]
self.__validate_sketch_result(s[key], expected)
# allow pass in empty keys, which will retrieve all keys
s = sa.sketch_summary(sub_sketch_keys =keys).element_sub_sketch()
self.assertEqual(len(s), len(keys))
for key in keys:
self.assertTrue(s.has_key(key))
expected = sa.unpack(column_name_prefix="")[key]
self.__validate_sketch_result(s[key], expected)
def test_sketch_int(self):
int_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None]
sa = SArray(data=int_data)
self.__validate_sketch_result(sa.sketch_summary(), sa)
def test_cancelation(self):
sa = SArray(range(1,10000))
s = sa.sketch_summary(background=True)
s.cancel()
def test_dict_sketch_str_value(self):
# Dict value sketch type should be auto inferred
dict_data = [{
'a': 'b',
'b': 'c'
}, {
'a': 'b',
'b': 'c'
}, {
'a': 'd',
'b': '4'
}, None]
sa = SArray(data=dict_data)
self.__validate_nested_sketch_result(sa)
sketch = sa.sketch_summary()
fi = sketch.frequent_items()
self.assertEqual(len(fi), 2)
self.assertEqual(fi['{"a":"b", "b":"c"}'], 2)
self.assertEqual(fi['{"a":"d", "b":"4"}'], 1)
# Get dict key sketch
key_summary = sketch.dict_key_summary()
another_rep = list(
itertools.chain.from_iterable(list(sa.dict_keys().dropna())))
self.__validate_sketch_result(key_summary, SArray(another_rep))
# Get dict value sketch
value_summary = sketch.dict_value_summary()
another_rep = list(
itertools.chain.from_iterable(list(sa.dict_values().dropna())))
self.__validate_sketch_result(value_summary, SArray(another_rep))
# sub sketch with one key
s = sa.sketch_summary(sub_sketch_keys='a').element_sub_sketch('a')
expected = sa.unpack(column_name_prefix="")['a']
self.__validate_sketch_result(s, expected)
s = sa.sketch_summary(
sub_sketch_keys='Nonexist').element_sub_sketch('Nonexist')
self.assertEqual(s.num_undefined(), len(sa))
# sub sketch with multiple keys
keys = ['a', 'b']
s = sa.sketch_summary(sub_sketch_keys=keys).element_sub_sketch(keys)
self.assertEqual(len(s), len(keys))
for key in keys:
self.assertTrue(s.has_key(key))
expected = sa.unpack(column_name_prefix="")[key]
self.__validate_sketch_result(s[key], expected)
# allow pass in empty keys, which will retrieve all keys
s = sa.sketch_summary(sub_sketch_keys=keys).element_sub_sketch()
self.assertEqual(len(s), len(keys))
for key in keys:
self.assertTrue(s.has_key(key))
expected = sa.unpack(column_name_prefix="")[key]
self.__validate_sketch_result(s[key], expected)
def test_dict_sketch_int_value(self):
dict_data = [{}, {
'a': 1,
'b': 2
}, {
'a': 1,
'b': 2
}, {
'a': 3,
'c': 1
}, {
'a': 1,
'b': 2,
'c': 3
}, None]
sa = SArray(data=dict_data)
self.__validate_nested_sketch_result(sa)
sketch = sa.sketch_summary()
self.assertEqual(sketch.num_unique(), 4)
fi = sketch.frequent_items()
self.assertEqual(len(fi), 4)
self.assertEqual((fi['{"a":1, "b":2}']), 2)
self.assertEqual((fi['{"a":3, "c":1}']), 1)
# Get dict key sketch
key_summary = sketch.dict_key_summary()
another_rep = list(
itertools.chain.from_iterable(list(sa.dict_keys().dropna())))
self.__validate_sketch_result(key_summary, SArray(another_rep))
# Get dict value sketch
value_summary = sketch.dict_value_summary()
another_rep = list(
itertools.chain.from_iterable(list(sa.dict_values().dropna())))
self.__validate_sketch_result(value_summary, SArray(another_rep))
# sub sketch with one key
s = sa.sketch_summary(sub_sketch_keys='a').element_sub_sketch('a')
expected = sa.unpack(column_name_prefix="")['a']
self.__validate_sketch_result(s, expected)
s = sa.sketch_summary(
sub_sketch_keys='Nonexist').element_sub_sketch('Nonexist')
self.assertEqual(s.num_undefined(), len(sa))
# sub sketch with multiple keys
keys = ['a', 'b']
s = sa.sketch_summary(sub_sketch_keys=keys).element_sub_sketch(keys)
self.assertEqual(len(s), len(keys))
for key in keys:
self.assertTrue(s.has_key(key))
expected = sa.unpack(column_name_prefix="")[key]
self.__validate_sketch_result(s[key], expected)
def test_sketch_float(self):
int_data = [1.2, 3,.4, 6.789, None]
sa = SArray(data=int_data)
self.__validate_sketch_result(sa.sketch_summary(), sa)
def __repr__(self):
"""
Emits a brief summary of all the statistics as a string.
"""
fields = [
['size', 'Length' , 'Yes'],
['min', 'Min' , 'Yes'],
['max', 'Max' , 'Yes'],
['mean', 'Mean' , 'Yes'],
['sum', 'Sum' , 'Yes'],
['var', 'Variance' , 'Yes'],
['std', 'Standard Deviation' , 'Yes'],
['num_undefined', '# Missing Values' , 'Yes',],
['num_unique', '# unique values', 'No' ]
]
s = '\n'
result = []
for field in fields:
try:
method_to_call = getattr(self, field[0])
result.append([field[1], str(method_to_call()), field[2]])
except:
pass
sf = SArray(result).unpack(column_name_prefix = "")
sf.rename({'0': 'item', '1':'value', '2': 'is exact'})
s += sf.__str__(footer=False)
s += "\n"
s += "\nMost frequent items:\n"
frequent = self.frequent_items()
sorted_freq = sorted(frequent.iteritems(), key=operator.itemgetter(1), reverse=True)
if len(sorted_freq) == 0:
s += " -- All elements appear with less than 0.01% frequency -- \n"
else:
sorted_freq = sorted_freq[:10]
sf = SFrame()
sf.add_column(SArray(['count']), 'value')
for elem in sorted_freq:
sf.add_column(SArray([elem[1]]), str(elem[0]))
s += sf.__str__(footer=False) + "\n"
s += "\n"
try:
# print quantiles
t = self.quantile(0)
s += "Quantiles: \n"
sf = SFrame()
for q in [0.0,0.01,0.05,0.25,0.5,0.75,0.95,0.99,1.00]:
sf.add_column(SArray([self.quantile(q)]), str(int(q * 100)) + '%')
s += sf.__str__(footer=False) + "\n"
except:
pass
try:
t_k = self.dict_key_summary()
t_v = self.dict_value_summary()
s += "\n******** Dictionary Element Key Summary ********\n"
s += t_k.__repr__()
s += "\n******** Dictionary Element Value Summary ********\n"
s += t_v.__repr__() + '\n'
except:
pass
try:
t_k = self.element_summary()
s += "\n******** Element Summary ********\n"
s += t_k.__repr__() + '\n'
except:
pass
return s.expandtabs(8)
def test_sketch_float(self):
int_data = [1.2, 3, .4, 6.789, None]
sa = SArray(data=int_data)
self.__validate_sketch_result(sa.sketch_summary(), sa)
def test_vector_sketch(self):
vector_data = [[], [1, 2], [3], [4, 5, 6, 7], [8, 9, 10], None]
sa = SArray(data=vector_data)
sketch = sa.sketch_summary()
self.__validate_sketch_result(sketch, sa)
self.__validate_sketch_result(sketch.element_length_summary(),
sa.dropna().item_length())
flattened = list(itertools.chain.from_iterable(list(sa.dropna())))
self.__validate_sketch_result(sketch.element_summary(),
SArray(flattened))
fi = sketch.frequent_items()
self.assertEqual(len(fi), 5)
self.assertEqual((fi['[1 2]']), 1)
self.assertEqual((fi['[4 5 6 7]']), 1)
# sub sketch with one key
s = sa.sketch_summary(sub_sketch_keys=1).element_sub_sketch(1)
expected = sa.vector_slice(1)
self.__validate_sketch_result(s, expected)
# sub sketch with multiple keys
keys = [1, 3]
s = sa.sketch_summary(sub_sketch_keys=keys).element_sub_sketch(keys)
self.assertEqual(len(s), len(keys))
for key in keys:
self.assertTrue(s.has_key(key))
expected = sa.vector_slice(key)
self.__validate_sketch_result(s[key], expected)
indexes = range(0, 10)
s = sa.sketch_summary(sub_sketch_keys=indexes).element_sub_sketch()
self.assertEqual(len(s), len(indexes))
def __repr__(self):
"""
Emits a brief summary of all the statistics as a string.
"""
fields = [['size', 'Length', 'Yes'], ['min', 'Min', 'Yes'],
['max', 'Max', 'Yes'], ['mean', 'Mean', 'Yes'],
['sum', 'Sum', 'Yes'], ['var', 'Variance', 'Yes'],
['std', 'Standard Deviation', 'Yes'],
[
'num_undefined',
'# Missing Values',
'Yes',
], ['num_unique', '# unique values', 'No']]
s = '\n'
result = []
for field in fields:
try:
method_to_call = getattr(self, field[0])
result.append([field[1], str(method_to_call()), field[2]])
except:
pass
sf = SArray(result).unpack(column_name_prefix="")
sf.rename({'0': 'item', '1': 'value', '2': 'is exact'})
s += sf.__str__(footer=False)
s += "\n"
s += "\nMost frequent items:\n"
frequent = self.frequent_items()
sorted_freq = sorted(frequent.iteritems(),
key=operator.itemgetter(1),
reverse=True)
if len(sorted_freq) == 0:
s += " -- All elements appear with less than 0.01% frequency -- \n"
else:
sorted_freq = sorted_freq[:10]
sf = SFrame()
sf.add_column(SArray(['count']), 'value')
for elem in sorted_freq:
sf.add_column(SArray([elem[1]]), str(elem[0]))
s += sf.__str__(footer=False) + "\n"
s += "\n"
try:
# print quantiles
t = self.quantile(0)
s += "Quantiles: \n"
sf = SFrame()
for q in [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.00]:
sf.add_column(SArray([self.quantile(q)]),
str(int(q * 100)) + '%')
s += sf.__str__(footer=False) + "\n"
except:
pass
try:
t_k = self.dict_key_summary()
t_v = self.dict_value_summary()
s += "\n******** Dictionary Element Key Summary ********\n"
s += t_k.__repr__()
s += "\n******** Dictionary Element Value Summary ********\n"
s += t_v.__repr__() + '\n'
except:
pass
try:
t_k = self.element_summary()
s += "\n******** Element Summary ********\n"
s += t_k.__repr__() + '\n'
except:
pass
return s.expandtabs(8)
