def test_txt_append():
fname = "testobj.txt"
obj = "bla"
just.append(obj, "testobj.txt")
try:
assert [x for x in just.iread(fname)] == [obj]
just.append(obj, "testobj.txt")
assert [x for x in just.iread(fname)] == [obj, obj]
finally:
os.remove(fname)
def load_object_per_newline(cls, fname, nrows=None):
"""
Iterates over a file containing an object per line (e.g. .jsonl or .txt).
Will only handle new lines not seen earlier; it detects this by storing the number-of-objects seen.
You should implement `object_to_row(cls, row)` on your class that returns a dictionary.
"""
data = []
name = fname + "_" + normalize_name(cls.__name__)
newline_count = get_newline_count(name)
for i, x in enumerate(just.iread(fname)):
if nrows is None:
if i < newline_count:
continue
row = cls.object_to_row(x)
if row is None:
continue
data.append(row)
# breaking at approx 5 rows
if nrows is not None and i > nrows:
break
if data:
data = pd.DataFrame(data)
if newline_count and nrows is None:
data = pd.concat((data, load_df(name)))
if nrows is None:
data = save_df(data, name)
n = i + 1
save_newline_count(n, name)
else:
data = load_df(name)
if nrows is not None:
data = data.iloc[-nrows:]
return data
def test_txt_iread():
fname = "testobj.txt"
obj = "1\n2\n3\n4\n5"
just.write(obj, "testobj.txt")
try:
assert [x for x in just.iread(fname)] == [x for x in obj.split("\n")]
finally:
os.remove(fname)
def test_newl_iread():
fname = "testobj.newl"
obj = ["1", "2"]
just.write(obj, "testobj.newl")
try:
assert [x for x in just.iread(fname)] == [x for x in obj]
finally:
os.remove(fname)
def test_csv_iread():
fname = "testobj.csv"
obj = [['"a"', '"b"']] + [['"1"', '"2"']] * 99
just.write(obj, "testobj.csv")
try:
assert [x for x in just.iread(fname)] == [x for x in obj]
finally:
os.remove(fname)
def test_csv_iread_error():
fname = "testobj.csv"
obj = [['"a"', '"b"']] + [['"1"', '"2"', '"']] * 100
just.write(obj, "testobj.csv")
try:
list(just.iread(fname))
# should not reach here
except ValueError:
assert True
finally:
os.remove(fname)
def get_training_xy(data_path="./tracktrack/"):
positions = list(just.iread(data_path + "positions.jsonl"))
images = [
matplotlib.image.imread(x) for x in just.glob(data_path + "im*.png")
]
m = min(len(images), len(positions))
X = prep_images(images[-m:])
positions = positions[-m:]
y = np.array(positions)
return X, y
def load(cls, nrows=None, **kwargs):
old_text = ""
results = []
nrows = nrows or float("inf")
for file_path in just.glob("~/nostalgia_data/input/whatsapp/*.txt"):
row = 0
for line in just.iread(file_path):
try:
time = datetime_from_format(line[:offset],
"%d/%m/%Y, %H:%M - ")
except ValueError:
old_text += line + "\n"
continue
line = old_text + line[offset:]
old_text = ""
try:
if line.startswith(
"Messages to this chat and calls are now secured"):
continue
sender, text = line.split(": ", 1)
except ValueError:
print("ERR", line)
continue
if line:
if row > nrows:
break
row += 1
results.append((time, sender, text))
df = pd.DataFrame(results, columns=["time", "sender", "text"])
# hack "order" into minute data
same_minute = df.time == df.shift(1).time
seconds = []
second_prop = 0
for x in same_minute:
if x:
second_prop += 1
else:
second_prop = 0
seconds.append(
pd.Timedelta(seconds=60 * second_prop / (second_prop + 1)))
df["time"] = df["time"] + pd.Series(seconds)
return cls(df)
# https://schema.org/Blog
# "http://schema.org/blogPost"
# http://schema.org/WebPage # http://schema.org/BlogPosting
# exlclude http://schema.org/QAPage
it = 0
imo = 0
nice = 0
wrong = 0
from collections import Counter
import just
c = Counter()
for x in just.iread("/home/pascal/nostal_tmp/person.jsonl"):
if "/Person" in (str(x.get("microdata"))):
it += 1
y = x
score = 0
mc_count = 0
for mc in y["microdata"]:
if mc.get("type") in [
"http://schema.org/ImageObject",
"http://schema.org/QAPage",
"http://schema.org/Movie",
"http://schema.org/videoObject",
"http://schema.org/Organization",
"http://schema.org/VideoObject",
"http://schema.org/Question",
"http://schema.org/CreativeWork",
df = pd.DataFrame(np.random.random((10000, int(random.random() * 500))))
pdc.run_benchmarks([df], save=True)
df = pd.DataFrame({"a": [1]})
inferred = pdc.infer(df)
inferred
pdc.save(df, "test")
pdc.save(df, "~/test")
path = pdc.save(df, "~/test")
pdc.load(path)
# pdc.bench_exceptions = ()
# data_gen = (pd.DataFrame(np.random.random((1000, int(random.random() * 500)))) for i in range(2))
pdc.run_benchmarks(just.iread("~/csvlist.txt"))
pdc.train_model("size")
feats = json.loads(
'{"num_obs": 1000, "num_cols": 289, "num_float_vars": 289, "num_str_vars": 0, "percent_float": 1.0, "percent_str": 0.0, "str_missing_proportion": NaN, "float_missing_proportion": NaN, "cardinality_quantile_proportion_25": 1.0, "cardinality_quantile_proportion_50": 1.0, "cardinality_quantile_proportion_75": 1.0, "float_equal_0_proportion": 0.0, "str_len_quantile_25": NaN, "str_len_quantile_50": NaN, "str_len_quantile_75": NaN}'
)
pdc.predict(
pd.DataFrame(np.random.random((10000, int(random.random() * 500000)))))
# pdc = PandasCompressor("test1")
# d = pdc.get_features(pd.read_csv("/home/pascal/Downloads/results-20190622-143623.csv"))
# f**k = just.glob("/home/pascal/egoroot/tradex/data/cmc/*.csv.gz") * 30
