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from datetime import datetime, timezone, timedelta
from random import sample
from typing import List
import numpy as np
import pandas as pd
import pytest
from generalresearch.incite.schemas import empty_dataframe_from_schema
from generalresearch.incite.schemas.admin_responses import (
AdminPOPSchema,
SIX_HOUR_SECONDS,
)
from generalresearch.locales import Localelator
class TestAdminPOPSchema:
schema_df = empty_dataframe_from_schema(AdminPOPSchema)
countries = list(Localelator().get_all_countries())[:5]
dates = [datetime(year=2024, month=1, day=i, tzinfo=None) for i in range(1, 10)]
@classmethod
def assign_valid_vals(cls, df: pd.DataFrame) -> pd.DataFrame:
for c in df.columns:
check_attrs: dict = AdminPOPSchema.columns[c].checks[0].statistics
df[c] = np.random.randint(
check_attrs["min_value"], check_attrs["max_value"], df.shape[0]
)
return df
def test_empty(self):
with pytest.raises(Exception):
AdminPOPSchema.validate(pd.DataFrame())
def test_new_empty_df(self):
df = empty_dataframe_from_schema(AdminPOPSchema)
assert isinstance(df, pd.DataFrame)
assert isinstance(df.index, pd.MultiIndex)
assert df.columns.size == len(AdminPOPSchema.columns)
def test_valid(self):
# (1) Works with raw naive datetime
dates = [
datetime(year=2024, month=1, day=i, tzinfo=None).isoformat()
for i in range(1, 10)
]
df = pd.DataFrame(
index=pd.MultiIndex.from_product(
iterables=[dates, self.countries], names=["index0", "index1"]
),
columns=self.schema_df.columns,
)
df = self.assign_valid_vals(df)
df = AdminPOPSchema.validate(df)
assert isinstance(df, pd.DataFrame)
# (2) Works with isoformat naive datetime
dates = [datetime(year=2024, month=1, day=i, tzinfo=None) for i in range(1, 10)]
df = pd.DataFrame(
index=pd.MultiIndex.from_product(
iterables=[dates, self.countries], names=["index0", "index1"]
),
columns=self.schema_df.columns,
)
df = self.assign_valid_vals(df)
df = AdminPOPSchema.validate(df)
assert isinstance(df, pd.DataFrame)
def test_index_tz_parser(self):
tz_dates = [
datetime(year=2024, month=1, day=i, tzinfo=timezone.utc)
for i in range(1, 10)
]
df = pd.DataFrame(
index=pd.MultiIndex.from_product(
iterables=[tz_dates, self.countries], names=["index0", "index1"]
),
columns=self.schema_df.columns,
)
df = self.assign_valid_vals(df)
# Initially, they're all set with a timezone
timestmaps: List[pd.Timestamp] = [i for i in df.index.get_level_values(0)]
assert all([ts.tz == timezone.utc for ts in timestmaps])
# After validation, the timezone is removed
df = AdminPOPSchema.validate(df)
timestmaps: List[pd.Timestamp] = [i for i in df.index.get_level_values(0)]
assert all([ts.tz is None for ts in timestmaps])
def test_index_tz_no_future_beyond_one_year(self):
now = datetime.now(tz=timezone.utc)
tz_dates = [now + timedelta(days=i * 365) for i in range(1, 10)]
df = pd.DataFrame(
index=pd.MultiIndex.from_product(
iterables=[tz_dates, self.countries], names=["index0", "index1"]
),
columns=self.schema_df.columns,
)
df = self.assign_valid_vals(df)
with pytest.raises(Exception) as cm:
AdminPOPSchema.validate(df)
assert (
"Index 'index0' failed element-wise validator "
"number 0: less_than(" in str(cm.value)
)
def test_index_only_str(self):
# --- float64 to str! ---
df = pd.DataFrame(
index=pd.MultiIndex.from_product(
iterables=[self.dates, np.random.rand(1, 10)[0]],
names=["index0", "index1"],
),
columns=self.schema_df.columns,
)
df = self.assign_valid_vals(df)
vals = [i for i in df.index.get_level_values(1)]
assert all([isinstance(v, float) for v in vals])
df = AdminPOPSchema.validate(df, lazy=True)
vals = [i for i in df.index.get_level_values(1)]
assert all([isinstance(v, str) for v in vals])
# --- int to str ---
df = pd.DataFrame(
index=pd.MultiIndex.from_product(
iterables=[self.dates, sample(range(100), 20)],
names=["index0", "index1"],
),
columns=self.schema_df.columns,
)
df = self.assign_valid_vals(df)
vals = [i for i in df.index.get_level_values(1)]
assert all([isinstance(v, int) for v in vals])
df = AdminPOPSchema.validate(df, lazy=True)
vals = [i for i in df.index.get_level_values(1)]
assert all([isinstance(v, str) for v in vals])
# a = 1
assert isinstance(df, pd.DataFrame)
def test_invalid_parsing(self):
# (1) Timezones AND as strings will still parse correctly
tz_str_dates = [
datetime(
year=2024, month=1, day=1, minute=i, tzinfo=timezone.utc
).isoformat()
for i in range(1, 10)
]
df = pd.DataFrame(
index=pd.MultiIndex.from_product(
iterables=[tz_str_dates, self.countries],
names=["index0", "index1"],
),
columns=self.schema_df.columns,
)
df = self.assign_valid_vals(df)
df = AdminPOPSchema.validate(df, lazy=True)
assert isinstance(df, pd.DataFrame)
timestmaps: List[pd.Timestamp] = [i for i in df.index.get_level_values(0)]
assert all([ts.tz is None for ts in timestmaps])
# (2) Timezones are removed
dates = [
datetime(year=2024, month=1, day=1, minute=i, tzinfo=timezone.utc)
for i in range(1, 10)
]
df = pd.DataFrame(
index=pd.MultiIndex.from_product(
iterables=[dates, self.countries], names=["index0", "index1"]
),
columns=self.schema_df.columns,
)
df = self.assign_valid_vals(df)
# Has tz before validation, and none after
timestmaps: List[pd.Timestamp] = [i for i in df.index.get_level_values(0)]
assert all([ts.tz is timezone.utc for ts in timestmaps])
df = AdminPOPSchema.validate(df, lazy=True)
timestmaps: List[pd.Timestamp] = [i for i in df.index.get_level_values(0)]
assert all([ts.tz is None for ts in timestmaps])
def test_clipping(self):
df = pd.DataFrame(
index=pd.MultiIndex.from_product(
iterables=[self.dates, self.countries],
names=["index0", "index1"],
),
columns=self.schema_df.columns,
)
df = self.assign_valid_vals(df)
df = AdminPOPSchema.validate(df)
assert df.elapsed_avg.max() < SIX_HOUR_SECONDS
# Now that we know it's valid, break the elapsed avg
df["elapsed_avg"] = np.random.randint(
SIX_HOUR_SECONDS, SIX_HOUR_SECONDS + 10_000, df.shape[0]
)
assert df.elapsed_avg.max() > SIX_HOUR_SECONDS
# Confirm it doesn't fail if the values are greater, and that
# all the values are clipped to the max
df = AdminPOPSchema.validate(df)
assert df.elapsed_avg.eq(SIX_HOUR_SECONDS).all()
def test_rounding(self):
df = pd.DataFrame(
index=pd.MultiIndex.from_product(
iterables=[self.dates, self.countries],
names=["index0", "index1"],
),
columns=self.schema_df.columns,
)
df = self.assign_valid_vals(df)
df["payout_avg"] = 2.123456789900002
assert df.payout_avg.sum() == 95.5555555455001
df = AdminPOPSchema.validate(df)
assert df.payout_avg.sum() == 95.40000000000003
|
