def test_groups_large2_str(n, seed):
random.seed(seed)
while n == 0:
n = int(random.expovariate(0.0005))
src = ["%x" % random.getrandbits(6) for _ in range(n)]
f0 = dt.Frame({"A": src})
f1 = f0[:, count(), by("A")]
f1.internal.check()
assert f1.nrows == len(set(src))
def py_dt_two_group_proportions_summary(DT,por1,por2):
DT_summary = DT[:,dt.count(),by(f[por1],f[por2])
][:,f[:].extend({'group_tot':dt.sum(f.count)}),by(f[por1])
][:,f[:].extend({'prop':f.count/f.group_tot})
][:,f[:].remove(f[1])
]
return DT_summary
def test_issue_2242(seed):
n = 25000
X = dt.Frame(AGE=[random.randint(1, 50) for i in range(n)],
PAY=[random.choice([True, False]) for i in range(n)])
RES = X[:, dt.math.log((count() + 1) / (sum(f.PAY) + 0.5) - 1), by(f.AGE)]
assert RES.shape == (50, 2)
data = RES.to_list()
assert data[0] == list(range(1, 51))
assert all(isinstance(x, float) for x in data[1])
def test_issue2348():
DT = dt.Frame(A=[1, 2, 3, 1, 2, 3], B=list('akdfnv'),
C=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
D=[11]*6, E=[2]*6)
# Check that these expressions do not crash
DT[:, :, by(f.A), sort(f.A, f.E)]
DT[:, :, by(f.A, f.B), sort(f.A, f.B)]
assert_equals(DT[:, dt.count(), by(f.D), sort(f.E, f.A)],
dt.Frame([[11], [6]],
names=["D", "count"],
stypes=[dt.int32, dt.int64]))
def test_sort_multicolumn1():
# See issue #3141
DT = dt.Frame(A=[111] * 100,
B=['a', 'b'] * 50,
C=['ads', 'adfv', 'adfv', 'adsfv'] * 25)
RES1 = DT[:, dt.count(), dt.by(f.A, f.B, f.C)]
assert_equals(
RES1,
dt.Frame(A=[111, 111, 111, 111],
B=['a', 'a', 'b', 'b'],
C=['adfv', 'ads', 'adfv', 'adsfv'],
count=[25, 25, 25, 25] / dt.int64))
RES2 = DT[:, dt.count(), dt.by(f.B, f.C)]
assert_equals(
RES2,
dt.Frame(B=['a', 'a', 'b', 'b'],
C=['adfv', 'ads', 'adfv', 'adsfv'],
count=[25, 25, 25, 25] / dt.int64))
RES3 = DT[:, dt.count(), dt.by(f.A, f.B)]
assert_equals(
RES3, dt.Frame(A=[111, 111], B=['a', 'b'], count=[50, 50] / dt.int64))
def test_group_empty_frame2():
DT = dt.Frame(A=[])
D1 = DT[:, count(), by(f.A)]
frame_integrity_check(D1)
assert D1.shape == (0, 2)
assert D1.stypes == (DT.stype, dt.int64)
def test_group_boolean2():
DT = dt.Frame(A=[True, False, False] * 500 + [None, True])
DTR = DT[:, count(), by(f.A)]
assert_equals(DTR, dt.Frame(A=[None, False, True], count=[1, 1000, 501],
stypes={"count": dt.int64}))
time_sec=t,
mem_gb=m,
cache=cache,
chk=make_chk(flatten(chk.to_list())),
chk_time_sec=chkt,
on_disk=on_disk)
print(ans.head(3), flush=True)
print(ans.tail(3), flush=True)
del ans
question = 'sum v3 count by id1:id6' # q10
gc.collect()
t_start = timeit.default_timer()
ans = x[:, {
'v3': sum(f.v3),
'count': count()
},
by(f.id1, f.id2, f.id3, f.id4, f.id5, f.id6)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
chk = ans[:, [sum(f.v3), sum(f.count)]]
chkt = timeit.default_timer() - t_start
write_log(task=task,
data=data_name,
in_rows=x.shape[0],
question=question,
out_rows=ans.shape[0],
out_cols=ans.shape[1],
solution=solution,
Here is our basic syntax representation of datatable frame-
DT[I,J,BY|SORT|JOIN]
A sample dataframe created with the column name **languages** and would like to count how many of students are interested in learning each language category using aggregations such as by along with count,min,max,mean etc etc..
Yes, its correct we should use a function called **count** to caluclate the number of observations and let us see how it works below.
prog_lang_dt = dt.Frame(languages= ['html', 'R', 'R', 'html', 'R', 'javascript',
'R', 'javascript', 'html'])
prog_lang_dt
prog_lang_dt[:,count(),by(f.languages)]
If we would like like to rename a count column as total it can be done as follows,
prog_lang_dt[:,{'total':count()},by(f.languages)]
**count** can also take a column name as argument and report how many of non-missing entries in that specific column. for this example we will create a new dataframe as Y.
data = """
id|charges|payment_method
634-VHG|28|Cheque
365-DQC|33.5|Credit card
264-PPR|631|--
845-AJO|42.3|
789-KPO|56.9|Bank Transfer
"""
def create_data(
X: dt.Frame = None
) -> Union[str, List[str], dt.Frame, List[dt.Frame], np.ndarray,
List[np.ndarray], pd.DataFrame, List[pd.DataFrame], Dict[
str, str], # {data set names : paths}
Dict[str, dt.Frame], # {data set names : dt frames}
Dict[str, np.ndarray], # {data set names : np arrays}
Dict[str, pd.DataFrame], # {data set names : pd frames}
]:
# Download files
# Location in DAI file system where we will save the data set
temp_path = os.path.join(user_dir(), config.contrib_relative_directory)
os.makedirs(temp_path, exist_ok=True)
# URL of desired data, this comes from the City of Seattle
link_basics = "https://datasets.imdbws.com/title.basics.tsv.gz"
link_ratings = "https://datasets.imdbws.com/title.ratings.tsv.gz"
link_episodes = "https://datasets.imdbws.com/title.episode.tsv.gz"
# Download the files
file_basics = download(link_basics, dest_path=temp_path)
file_ratings = download(link_ratings, dest_path=temp_path)
file_episodes = download(link_episodes, dest_path=temp_path)
# get COVID19 new cases data from Our World in Data github
basics = dt.fread(file_basics, fill=True)
ratings = dt.fread(file_ratings, fill=True)
episodes = dt.fread(file_episodes, na_strings=['\\N'], fill=True)
# remove files
os.remove(file_basics)
os.remove(file_ratings)
os.remove(file_episodes)
# Create Title with Ratings dataset
# join titles with non-null ratings
ratings = ratings[~dt.isna(dt.f.averageRating), :]
ratings.key = "tconst"
basics_ratings = basics[:, :, dt.join(ratings)]
# Create Episodes dataset
episodes = episodes[~dt.isna(dt.f.seasonNumber)
& ~dt.isna(dt.f.episodeNumber), :]
episode_ratings = episodes[:, :, dt.join(ratings)]
episode_ratings.names = {
'tconst': 'episodeTconst',
'parentTconst': 'tconst',
'averageRating': 'episodeAverageRating',
'numVotes': 'episodeNumVotes'
}
basics_ratings.key = 'tconst'
title_episode_ratings = episode_ratings[:, :, dt.join(basics_ratings)]
# enumerate series episodes from 1 to N
title_episode_ratings = title_episode_ratings[:, :,
dt.sort(
dt.f.tconst, dt.f.
seasonNumber, dt.f.
episodeNumber)]
result = title_episode_ratings[:, dt.count(),
dt.by(dt.f.tconst)][:,
'count'].to_list()
from itertools import chain
cumcount = chain.from_iterable([i + 1 for i in range(n)]
for n in result[0])
title_episode_ratings['episodeSequence'] = dt.Frame(tuple(cumcount))
# return datasets
return {
f"imdb_title_ratings": basics_ratings,
f"imdb_episode_ratings": title_episode_ratings
}
# Output:
# dataset with downsampled majority class
from sklearn.utils import resample
import pandas as pd
from datatable import f, count, sort, by, min, max, rbind
# parameters
target_col = "Known_Fraud"
times = 5
random_seed = 123
new_dataset_name = "new_dataset_name_with_downsampled_majority"
# counts by target groups
g = X[:, {"count": count()}, by(target_col)]
if not g.shape[1] == 2:
raise ValueError(
"Not a binary target - target column must contain exactly 2 values.")
# find sizes and target values for minority and majority class partitions
n_minority = g[:, min(f.count)][0, 0]
n_majority = g[:, max(f.count)][0, 0]
target_minority = g[f.count == n_minority, target_col][0, 0]
target_majority = g[f.count == n_majority, target_col][0, 0]
# validate that times indeed downsamples majority class
if times * n_minority >= n_majority:
raise ValueError(
"Downsampling coefficient `times` is too large: downsampled dataset results in inflated majority class."
)
# ~ 3b ~
# Investigate Unknown HPO Codes
# For each code listed in 'hpo_codes_not_found', investigate the code to
# determine how to reconcile the invalid code. In some instances, you can
# add a new record in `rd3_phenotype`, but some codes may have changed or
# are no longer used. If you are still unable to find an answer, contact
# the data provider.
#
# prep "HPO codes to verify" dataset
hpoCodes = dt.Frame(hpo_codes_not_found, types={
'id': str,
'hpo': str
})[:, {
'frequency': count(),
'url': None,
'status': 'not.found',
'action': None
},
by(f.hpo)]
# set url for HPO code if it exists
hpoCodes['url'] = dt.Frame([
f'http://purl.obolibrary.org/obo/{d}' for d in hpoCodes['hpo'].to_list()[0]
])
# manually check each link and search for code. Either add the code or follow
# up with SolveRD project data coordinators.
rbind(fread('data/unknown_hpo_codes.csv'),
hpoCodes).to_csv('data/unknown_hpo_codes.csv')
'TMIN': "temp_min",
'TMAX': "temp_max"}
weather_dt
weather_dt.key="stop_date"
# count the number of missing values
policia_dt.countna()
del policia_dt[:,['county_name', 'state']]
# glance
policia_dt
policia_dt[:,count(),by(f.driver_gender)]
policia_tidy_dt = policia_dt[~dt.isna(f.driver_gender),:]
policia_tidy_dt[:,count(),by(f.violation)
][:,f[:].extend({'grand_tot':dt.sum(f.count)})
][:,f[:].extend({'prop':f.count/f.grand_tot})
][:,f[:].remove(f.grand_tot),sort(-f.prop)
]
# custom function to generate a summary report per a single group column
def py_dt_one_group_proportions_summary(DT,por):
DT_summary = DT[:,dt.count(),by(f[por])
][:,f[:].extend({'grand_tot':dt.sum(f.count)})
][:,f[:].extend({'prop':f.count/f.grand_tot})
gains = defaultdict(list)
for experimento in os.listdir(f'../experimentos/'):
for file in os.listdir(f'../experimentos/{experimento}/'):
if file.endswith('importance.csv'):
with open(f'../experimentos/{experimento}/{file}') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
weights[row['variable']].append(float(row['weight']))
gains[row['variable']].append(float(row['gain']))
weights = pad_dict_list(weights)
gains = pad_dict_list(gains)
df_weights = dt.Frame(weights).mean().to_pandas().T
df_gains = dt.Frame(gains).mean().to_pandas().T
df_count = dt.Frame(weights)[:, dt.count(dt.f[:])].to_pandas().T
df = pd.DataFrame(columns=['variable', 'weight', 'gain'])
df['variable'] = df_weights.index.values
df['gain'] = df_gains[0].values
df['count'] = df_count[0].values
df['weight'] = df_weights[0].values
df = df.sort_values(by=['gain', 'count', 'weight'], ascending=False)
df.to_csv('../analisis_feature_importance.csv', index=False)
penguins_dt[:, update(temp=f.body_mass_g == dt.max(f.body_mass_g)), by(f.sex)]
# step - 2 : finding a max value of body_mass of penguins per sex
penguins_dt[f.temp == 1, f[:].remove(f.temp)]
# step - 1 : finding a min value of body_mass of penguins per sex
penguins_dt[:, update(temp=f.body_mass_g == dt.min(f.body_mass_g)), by(f.sex)]
penguins_dt[f.temp == 1, f[:].remove(f.temp)]
del penguins_dt["temp"]
penguins_tidy_dt = penguins_dt[~dt.isna(f.sex), :]
penguins_year_island = penguins_tidy_dt[:, {
'total': count()
}, by(f.year, f.island)]
penguins_year = penguins_year_island[:, {
'gr_total': dt.sum(f.total)
}, by(f.year)]
penguins_year
penguins_year.key = "year"
penguins_year_island = penguins_year_island[:, :, dt.join(penguins_year)]
penguins_year_island[:, update(perc=f.total / f.gr_total)]
penguins_year_island
def score(self,
actual: np.array,
predicted: np.array,
sample_weight: typing.Optional[np.array] = None,
labels: typing.Optional[List[any]] = None,
X: typing.Optional[dt.Frame] = None,
**kwargs) -> float:
# Get the logger if it exists
logger = self.get_experiment_logger()
# hard-coded as access to experiment parameters (such as self.tgc) not yet available
tgc = ["Store", "Dept"]
# tgc = ["state"]
# tgc = None
# enable weighted average over TS R2 scores: weighted based on TS share of rows
isR2AverageWeighted = False
# obtain a scorer for metric to use
scorer = self.get_scorer()
if tgc is None or not all(col in X.names for col in tgc):
loggerinfo(
logger,
f"TS R2 computes single R2 on {X.nrows} rows as either tgc {tgc} is not defined or incorrect."
)
return scorer.score(actual, predicted, sample_weight, labels,
**kwargs)
else:
tgc_values = X[:, {
"weight": count() / X.nrows,
"r2": 0.0
}, by(tgc)]
loggerinfo(
logger,
f"TS R2 computes multiple R2 on {X.nrows} rows, tgc {tgc} with weighting is {isR2AverageWeighted}."
)
none_values = [None] * X.nrows
X = cbind(
X[:, tgc],
Frame(actual=actual,
predicted=predicted,
sample_weight=sample_weight
if sample_weight is not None else none_values))
for i in range(0, tgc_values.nrows):
current_tgc = tgc_values[i, :]
current_tgc.key = tgc
ts_frame = X[:, :, join(current_tgc)][~isna(f.r2), :]
r2_score = scorer.score(
ts_frame['actual'].to_numpy(),
ts_frame['predicted'].to_numpy(),
ts_frame['sample_weight'].to_numpy()
if sample_weight is not None else None, labels, **kwargs)
tgc_values[i, f.r2] = r2_score
loggerinfo(
logger,
f"TS R2 = {r2_score} on {ts_frame.nrows} rows, tgc = {current_tgc[0, tgc].to_tuples()}"
)
if isR2AverageWeighted:
# return np.average(tgc_values["r2"].to_numpy(), weights=tgc_values["weight"].to_numpy())
return tgc_values[:, mean(f.r2 * f.weight)][0, 0]
else:
return tgc_values[:, mean(f.r2)][0, 0]
weather_dt = dt.fread(
'https://assets.datacamp.com/production/repositories/1497/datasets/02f3fb2d4416d3f6626e1117688e0386784e8e55/weather.csv',
na_strings=[""])
policia_dt
weather_dt
# count the number of missing values
policia_dt.countna()
del policia_dt[:, ['county_name', 'state']]
policia_dt
policia_dt[:, count(), by(f.driver_gender)]
policia_tidy_dt = policia_dt[~dt.isna(f.driver_gender), :]
policia_tidy_df = policia_tidy_dt.to_pandas()
policia_tidy_df.info()
policia_tidy_df
policia_tidy_dt[:, count(),
by(f.violation)][:, f[:].extend({'grand_tot': dt.sum(
f.count)})][:, f[:].extend({'prop': f.count / f.grand_tot}
)][:, f[:].remove(f.grand_tot),
sort(-f.prop)]
def test_group_empty_frame3():
DT = dt.Frame(A=[], stype=dt.float32)
D2 = DT[:, count(f.A), by(f.A)]
frame_integrity_check(D2)
assert D2.shape == (0, 2)
assert D2.stypes == (DT.stype, dt.int64)
def test_count_with_i():
# See issue 1316
DT = dt.Frame(A=range(100))
assert DT[:5, count()][0, 0] == 5
assert DT[-12:, count()][0, 0] == 12
assert DT[::3, count()][0, 0] == 34
def test_group_boolean3():
DT = dt.Frame(A=[True] * 1234)
DTR = DT[:, count(), by(f.A)]
assert_equals(DTR,
dt.Frame(A=[True], count=[1234], stypes={"count": dt.int64}))
def test_count_2d_array_integer():
a_in = [[9, 8, 2, 3, None, None, 3, 0, 5, 5, 8, None, 1],
[0, 1, 0, 5, 3, 8, 1, 0, 2, 5, None, 8, 1]]
a_reduce = count(a_in)
assert a_reduce == 2
from datatable import f,by,count,update,sort,join
import re
dt.options.display.head_nrows=4
dt.options.display.tail_nrows=4
dt.init_styles()
# Importign data
amigos_info_dt = dt.fread('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-08/friends_info.csv')
amigos_dt = dt.fread('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-08/friends.csv')
# Glance
amigos_info_dt
# Seasons
amigos_info_dt[:,count(),by(f.season)]
# Unique episodes per a season
amigos_info_dt[:,count(),by(f.season,f.episode)
][:,{'unique_episodes':count()},by(f.season)
]
# average views and ratings per season
amigos_info_dt[:,dt.mean(f[-2:]),by(f.season)]
# Highest rating title
amigos_info_dt[f.imdb_rating==dt.max(f.imdb_rating),:]
# lowest rating title
amigos_info_dt[f.imdb_rating==dt.min(f.imdb_rating),:]
#ans = x[:, {"r2": cor(v1, v2)^2}, by(f.id2, f.id4)]
#print(ans.shape, flush=True)
#t = timeit.default_timer() - t_start
#m = memory_usage()
#t_start = timeit.default_timer()
#chk = ans[:, sum(f.r2)]
#chkt = timeit.default_timer() - t_start
#write_log(task=task, data=data_name, in_rows=x.shape[0], question=question, out_rows=ans.shape[0], out_cols=ans.shape[1], solution=solution, version=ver, git=git, fun=fun, run=2, time_sec=t, mem_gb=m, cache=cache, chk=make_chk(flatten(chk.to_list())), chk_time_sec=chkt)
#print(ans.head(3).to_pandas(), flush=True)
#print(ans.tail(3).to_pandas(), flush=True)
#del ans
question = "sum v3 count by id1:id6" # q10
gc.collect()
t_start = timeit.default_timer()
ans = x[:, {"v3": sum(f.v3), "count": count()}, by(f.id1, f.id2, f.id3, f.id4, f.id5, f.id6)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
chk = ans[:, [sum(f.v3), sum(f.count)]]
chkt = timeit.default_timer() - t_start
write_log(task=task, data=data_name, in_rows=x.shape[0], question=question, out_rows=ans.shape[0], out_cols=ans.shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk(flatten(chk.to_list())), chk_time_sec=chkt)
del ans
gc.collect()
t_start = timeit.default_timer()
ans = x[:, {"v3": sum(f.v3), "count": count()}, by(f.id1, f.id2, f.id3, f.id4, f.id5, f.id6)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
def test_groups_large1():
n = 251 * 4000
xs = [(i * 19) % 251 for i in range(n)]
f0 = dt.Frame({"A": xs})
f1 = f0[:, count(), by("A")]
assert f1.to_list() == [list(range(251)), [4000] * 251]
def create_data(
X: dt.Frame = None
) -> Union[str, List[str], dt.Frame, List[dt.Frame], np.ndarray,
List[np.ndarray], pd.DataFrame, List[pd.DataFrame], Dict[
str, str], # {data set names : paths}
Dict[str, dt.Frame], # {data set names : dt frames}
Dict[str, np.ndarray], # {data set names : np arrays}
Dict[str, pd.DataFrame], # {data set names : pd frames}
]:
# define date column and forecast horizon
date_col = 'date'
group_by_cols = ["state"]
forecast_len = 7
# state codes lookup table
us_state_codes = dt.Frame(
code=[
'AL', 'AK', 'AS', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'DC',
'FL', 'GA', 'GU', 'HI', 'ID', 'IL', 'IN', 'IA', 'KS', 'KY',
'LA', 'ME', 'MD', 'MA', 'MI', 'MN', 'MS', 'MO', 'MT', 'NE',
'NV', 'NH', 'NJ', 'NM', 'NY', 'NC', 'ND', 'MP', 'OH', 'OK',
'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VT',
'VI', 'VA', 'WA', 'WV', 'WI', 'WY'
],
state=[
'Alabama', 'Alaska', 'American Samoa', 'Arizona', 'Arkansas',
'California', 'Colorado', 'Connecticut', 'Delaware',
'District of Columbia', 'Florida', 'Georgia', 'Guam', 'Hawaii',
'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky',
'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan',
'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska',
'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico',
'New York', 'North Carolina', 'North Dakota',
'Northern Mariana Islands', 'Ohio', 'Oklahoma', 'Oregon',
'Pennsylvania', 'Puerto Rico', 'Rhode Island',
'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah',
'Vermont', 'Virgin Islands', 'Virginia', 'Washington',
'West Virginia', 'Wisconsin', 'Wyoming'
])
us_state_codes.key = "state"
# get states population lookup table
us_states_pop = dt.fread(
"http://www2.census.gov/programs-surveys/popest/datasets/2010-2019/national/totals/nst-est2019-alldata.csv"
)
us_states_pop.names = {'NAME': 'state', 'POPESTIMATE2019': 'pop'}
us_states_pop = us_states_pop[dt.f.STATE > 0, :]
us_states_pop.key = "state"
# join state codes and population into single lookup table
us_states_pop[:, dt.update(code=dt.g.code), dt.join(us_state_codes)]
us_states_pop.key = "code"
# US Covid Tracking API: https://covidtracking.com/data/api
us_states = dt.fread(
"https://covidtracking.com/api/v1/states/daily.csv")
# remove deprecated fields
deprecated = [
'checkTimeEt', 'commercialScore', 'dateChecked', 'dateModified',
'grade', 'hash', 'hospitalized', 'negativeIncrease',
'negativeRegularScore', 'negativeScore', 'posNeg', 'positiveScore',
'score', 'total'
]
us_states = us_states[:, list(set(us_states.names) - set(deprecated))]
us_states.names = {'state': 'code'}
series_cols = [
"positive", "negative", "hospitalizedCumulative",
"inIcuCumulative", "onVentilatorCumulative", "recovered", "death"
]
aggs = {f"{col}100k": f[col] / (g.pop / 100000) for col in series_cols}
us_states[:,
dt.update(
state=g.state, pop=g.pop, pop100k=g.pop / 10000, **aggs),
join(us_states_pop)]
us_states = us_states[~dt.isna(dt.f.state), :]
# produce lag of 1 unit and add as new feature for each shift column
series_cols.extend([col + "100k" for col in series_cols])
aggs = {f"{col}_yesterday": shift(f[col]) for col in series_cols}
us_states[:, update(**aggs), sort(date_col), by(group_by_cols)]
# update NA lags
aggs = {f"{col}_yesterday": 0 for col in series_cols}
us_states[isna(f[f"{series_cols[0]}_yesterday"]), update(**aggs)]
aggs = {
f"{col}_daily": f[col] - f[f"{col}_yesterday"]
for col in series_cols
}
us_states[:, update(**aggs), sort(date_col), by(group_by_cols)]
for col in series_cols:
del us_states[:, f[f"{col}_yesterday"]]
# validate dataset
if us_states[:, count(),
by(dt.f.state, f.date)][f.count > 1, :].shape[0] > 1:
raise ValueError(
"Found duplicate elements for the same date and state.")
# determine threshold to split train and test based on forecast horizon
dates = dt.unique(us_states[:, date_col])
split_date = dates[-(forecast_len + 1):, :, dt.sort(date_col)][0, 0]
test_date = dates[-1, :, dt.sort(date_col)][0, 0]
# split data to honor forecast horizon in test set
df = us_states[date_col].to_pandas()
train = us_states[df[date_col] <= split_date, :]
test = us_states[df[date_col] > split_date, :]
return {
f"covidtracking_daily_{split_date}_by_us_states_train": train,
f"covidtracking_daily_{test_date}_by_us_states_test": test
}
def test_count_array_integer():
a_in = [9, 8, 2, 3, None, None, 3, 0, 5, 5, 8, None, 1]
a_reduce = count(a_in)
assert a_reduce == 10
def create_data(
X: dt.Frame = None
) -> Union[str, List[str], dt.Frame, List[dt.Frame], np.ndarray,
List[np.ndarray], pd.DataFrame, List[pd.DataFrame]]:
import os
from h2oaicore.systemutils_more import download
from h2oaicore.systemutils import config
import bz2
def extract_bz2(file, output_file):
zipfile = bz2.BZ2File(file)
data = zipfile.read()
open(output_file, 'wb').write(data)
temp_path = os.path.join(config.data_directory,
config.contrib_relative_directory, "airlines")
os.makedirs(temp_path, exist_ok=True)
# specify which years are used for training and testing
training = [2007]
testing = [2008]
# download and unzip files
files = []
for f in ["%d.csv.bz2" % year for year in training + testing]:
link = "http://stat-computing.org/dataexpo/2009/%s" % f
file = download(link, dest_path=temp_path)
output_file = file.replace(".bz2", "")
extract_bz2(file, output_file)
files.append(output_file)
# parse with datatable
X = dt.rbind(*[dt.fread(x) for x in files])
# add date
date_col = 'Date'
X[:, date_col] = dt.f['Year'] * 10000 + dt.f['Month'] * 100 + dt.f[
'DayofMonth']
cols_to_keep = ['Date']
# add number of flights in/out for each airport per given interval
timeslice_mins = 60
for name, new_col, col, group in [
("out", "CRSDepTime_mod", "CRSDepTime", "Origin"),
("in", "CRSArrTime_mod", "CRSArrTime", "Dest")
]:
X[:, new_col] = X[:, dt.f[col] // timeslice_mins]
group_cols = [date_col, group, new_col]
new_name = 'flights_%s' % name
flights = X[:, {new_name: dt.count()}, dt.by(*group_cols)]
flights.key = group_cols
cols_to_keep.append(new_name)
X = X[:, :, dt.join(flights)]
# Fill NaNs with 0s
X[dt.isna(dt.f['DepDelay']), 'DepDelay'] = 0
cols_to_keep.extend([
'DepDelay',
'Year',
'Month',
'DayofMonth',
'DayOfWeek',
'CRSDepTime',
'UniqueCarrier',
'FlightNum',
'TailNum',
'CRSElapsedTime',
'Origin',
'Dest',
'Distance',
# Leaks for delay
# 'DepTime',
# 'ArrTime', #'CRSArrTime',
# 'ActualElapsedTime',
# 'AirTime', #'ArrDelay', #'DepDelay',
# 'TaxiIn', #'TaxiOut', #'Cancelled', #'CancellationCode', #'Diverted', #'CarrierDelay',
# #'WeatherDelay', #'NASDelay', #'SecurityDelay', #'LateAircraftDelay',
])
X = X[:, cols_to_keep]
# Join in some extra info
join_files = [('UniqueCarrier', 'carriers.csv', 'Code'),
('Origin', 'airports.csv', 'iata'),
('Dest', 'airports.csv', 'iata'),
('TailNum', 'plane-data.csv', 'tailnum')]
for join_key, file, col in join_files:
file = download('http://stat-computing.org/dataexpo/2009/%s' %
file,
dest_path=temp_path)
X_join = dt.fread(file, fill=True)
X_join.names = {col: join_key}
X_join.names = [join_key] + [
join_key + "_" + x for x in X_join.names if x != join_key
]
X_join.key = join_key
X = X[:, :, dt.join(X_join)]
del X[:, join_key]
split = False
if not split:
filename = os.path.join(
temp_path, "flight_delays_regression_%d-%d.jay" %
(min(training), max(testing)))
X.to_jay(filename)
return filename
else:
# prepare splits (by year) and create binary .jay files for import into Driverless AI
output_files = []
for condition, name in [
((min(training) <= dt.f['Year']) &
(dt.f['Year'] <= max(training)), 'training'),
((min(testing) <= dt.f['Year']) &
(dt.f['Year'] <= max(testing)), 'test'),
]:
X_split = X[condition, :]
filename = os.path.join(temp_path,
"flight_delays_%s.jay" % name)
X_split.to_jay(filename)
output_files.append(filename)
return output_files
def test_count_array_string():
a_in = [None, "blue", "green", "indico", None, None, "orange", "red",
"violet", "yellow", "green", None, "blue"]
a_reduce = count(a_in)
assert a_reduce == 9
ans = x[:, {"r2": corr(f.v1, f.v2)**2}, by(f.id2, f.id4)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
chk = ans[:, sum(f.r2)]
chkt = timeit.default_timer() - t_start
write_log(task=task, data=data_name, in_rows=x.shape[0], question=question, out_rows=ans.shape[0], out_cols=ans.shape[1], solution=solution, version=ver, git=git, fun=fun, run=2, time_sec=t, mem_gb=m, cache=cache, chk=make_chk(flatten(chk.to_list())), chk_time_sec=chkt, on_disk=on_disk)
print(ans.head(3).to_pandas(), flush=True)
print(ans.tail(3).to_pandas(), flush=True)
del ans
question = "sum v3 count by id1:id6" # q10
gc.collect()
t_start = timeit.default_timer()
ans = x[:, {"v3": sum(f.v3), "count": count()}, by(f.id1, f.id2, f.id3, f.id4, f.id5, f.id6)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
chk = ans[:, [sum(f.v3), sum(f.count)]]
chkt = timeit.default_timer() - t_start
write_log(task=task, data=data_name, in_rows=x.shape[0], question=question, out_rows=ans.shape[0], out_cols=ans.shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk(flatten(chk.to_list())), chk_time_sec=chkt, on_disk=on_disk)
del ans
gc.collect()
t_start = timeit.default_timer()
ans = x[:, {"v3": sum(f.v3), "count": count()}, by(f.id1, f.id2, f.id3, f.id4, f.id5, f.id6)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
def create_data(
X: dt.Frame = None
) -> Union[str, List[str], dt.Frame, List[dt.Frame], np.ndarray,
List[np.ndarray], pd.DataFrame, List[pd.DataFrame]]:
import os
from h2oaicore.systemutils_more import download
from h2oaicore.systemutils import config
import bz2
def extract_bz2(file, output_file):
zipfile = bz2.BZ2File(file)
data = zipfile.read()
open(output_file, 'wb').write(data)
temp_path = os.path.join(user_dir(), "recipe_tmp", "airlines")
os.makedirs(temp_path, exist_ok=True)
dt.options.nthreads = 8
# specify which years are used for training and testing
training = list(range(2005, 2008))
testing = [2008]
# download and unzip files
files = []
for f in ["%d.csv.bz2" % year for year in training + testing]:
link = AirlinesData.base_url + "%s" % f
file = download(link, dest_path=temp_path)
output_file = file.replace(".bz2", "")
if not os.path.exists(output_file):
extract_bz2(file, output_file)
files.append(output_file)
# parse with datatable
X = dt.rbind(*[dt.fread(x) for x in files])
# add date
date_col = 'Date'
X[:, date_col] = dt.f['Year'] * 10000 + dt.f['Month'] * 100 + dt.f[
'DayofMonth']
cols_to_keep = ['Date']
# add number of flights in/out for each airport per given interval
timeslice_mins = 60
for name, new_col, col, group in [
("out", "CRSDepTime_mod", "CRSDepTime", "Origin"),
("in", "CRSArrTime_mod", "CRSArrTime", "Dest")
]:
X[:, new_col] = X[:, dt.f[col] // timeslice_mins]
group_cols = [date_col, group, new_col]
new_name = 'flights_%s_per_%d_min' % (name, timeslice_mins)
flights = X[:, {new_name: dt.count()}, dt.by(*group_cols)]
flights.key = group_cols
cols_to_keep.append(new_name)
X = X[:, :, dt.join(flights)]
# select flights leaving from SFO only
X = X[dt.f['Origin'] == 'SFO', :]
# Fill NaNs in DepDelay column
X[dt.isna(dt.f['DepDelay']), 'DepDelay'] = 0
# create binary target column
depdelay_threshold_mins = 15
target = 'DepDelay%dm' % depdelay_threshold_mins
X[:, target] = dt.f['DepDelay'] > depdelay_threshold_mins
cols_to_keep.extend([
target,
'Year',
'Month',
'DayofMonth',
'DayOfWeek',
'CRSDepTime',
'UniqueCarrier',
'FlightNum',
'TailNum',
'CRSElapsedTime',
'Origin',
'Dest',
'Distance',
# Leaks for delay
# 'DepTime',
# 'ArrTime', #'CRSArrTime',
# 'ActualElapsedTime',
# 'AirTime', #'ArrDelay', #'DepDelay',
# 'TaxiIn', #'TaxiOut', #'Cancelled', #'CancellationCode', #'Diverted', #'CarrierDelay',
# #'WeatherDelay', #'NASDelay', #'SecurityDelay', #'LateAircraftDelay',
])
X = X[:, cols_to_keep]
# Join in some extra info
join_files = [('UniqueCarrier', 'carriers.csv', 'Code'),
('Origin', 'airports.csv', 'iata'),
('Dest', 'airports.csv', 'iata'),
('TailNum', 'plane-data.csv', 'tailnum')]
for join_key, file, col in join_files:
file = download(
'https://0xdata-public.s3.amazonaws.com/data_recipes_data/%s' %
file,
dest_path=temp_path)
X_join = dt.fread(file, fill=True)
X_join.names = {col: join_key}
X_join.names = [join_key] + [
join_key + "_" + x for x in X_join.names if x != join_key
]
X_join.key = join_key
X = X[:, :, dt.join(X_join)]
del X[:, join_key]
split = True
if not split:
filename = os.path.join(
temp_path, "flight_delays_data_recipe_%d-%d.csv" %
(min(training), max(testing)))
X.to_csv(filename)
return filename
else:
# prepare splits (by year) and create binary .jay files for import into Driverless AI
output_files = []
for condition, name in [
((min(training) <= dt.f['Year']) &
(dt.f['Year'] <= max(training)), 'training'),
((min(testing) <= dt.f['Year']) &
(dt.f['Year'] <= max(testing)), 'test'),
]:
X_split = X[condition, :]
filename = os.path.join(
temp_path, "augmented_flights_%s-%d_%s.csv" %
(X_split[:, 'Year'].min1(), X_split[:,
'Year'].max1(), name))
X_split.to_csv(filename)
output_files.append(filename)
return output_files
#t = timeit.default_timer() - t_start
#m = memory_usage()
#t_start = timeit.default_timer()
#chk = ans[:, sum(f.r2)]
#chkt = timeit.default_timer() - t_start
#write_log(task=task, data=data_name, in_rows=x.shape[0], question=question, out_rows=ans.shape[0], out_cols=ans.shape[1], solution=solution, version=ver, git=git, fun=fun, run=2, time_sec=t, mem_gb=m, cache=cache, chk=make_chk(flatten(chk.to_list())), chk_time_sec=chkt)
#print(ans.head(3).to_pandas(), flush=True)
#print(ans.tail(3).to_pandas(), flush=True)
#del ans
question = "sum v3 count by id1:id6" # q10
gc.collect()
t_start = timeit.default_timer()
ans = x[:, {
"v3": sum(f.v3),
"count": count()
},
by(f.id1, f.id2, f.id3, f.id4, f.id5, f.id6)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
chk = ans[:, [sum(f.v3), sum(f.count)]]
chkt = timeit.default_timer() - t_start
write_log(task=task,
data=data_name,
in_rows=x.shape[0],
question=question,
out_rows=ans.shape[0],
out_cols=ans.shape[1],
solution=solution,