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TST: Updated for new pandas and made clearer types of values

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This commit is contained in:
Richard Frank
2016-02-02 19:39:14 -05:00
parent f14002a741
commit 069fe5ccda
2 changed files with 31 additions and 31 deletions
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tests/pipeline/test_engine.py
+30 -30
View File
@@ -157,22 +157,23 @@ class ConstantInputTestCase(TestCase):
USEquityPricing.close: 3,
USEquityPricing.high: 4,
}
self.assets = [1, 2, 3]
self.asset_ids = [1, 2, 3]
self.dates = date_range('2014-01', '2014-03', freq='D', tz='UTC')
self.loader = ConstantLoader(
constants=self.constants,
dates=self.dates,
assets=self.assets,
assets=self.asset_ids,
)
self.asset_info = make_simple_equity_info(
self.assets,
self.asset_ids,
start_date=self.dates[0],
end_date=self.dates[-1],
)
environment = TradingEnvironment()
environment.write_data(equities_df=self.asset_info)
self.asset_finder = environment.asset_finder
self.assets = self.asset_finder.retrieve_all(self.asset_ids)
def test_bad_dates(self):
loader = self.loader
@@ -192,7 +193,7 @@ class ConstantInputTestCase(TestCase):
lambda column: loader, self.dates, self.asset_finder,
)
factor = AssetID()
asset = self.assets[0]
asset = self.asset_ids[0]
p = Pipeline(columns={'f': factor}, screen=factor <= asset)
# The crux of this is that when we run the pipeline for a single day
@@ -204,7 +205,7 @@ class ConstantInputTestCase(TestCase):
def test_screen(self):
loader = self.loader
finder = self.asset_finder
assets = array(self.assets)
asset_ids = array(self.asset_ids)
engine = SimplePipelineEngine(
lambda column: loader, self.dates, self.asset_finder,
)
@@ -212,11 +213,11 @@ class ConstantInputTestCase(TestCase):
dates = self.dates[10:10 + num_dates]
factor = AssetID()
for asset in assets:
p = Pipeline(columns={'f': factor}, screen=factor <= asset)
for asset_id in asset_ids:
p = Pipeline(columns={'f': factor}, screen=factor <= asset_id)
result = engine.run_pipeline(p, dates[0], dates[-1])
expected_sids = assets[assets <= asset]
expected_sids = asset_ids[asset_ids <= asset_id]
expected_assets = finder.retrieve_all(expected_sids)
expected_result = DataFrame(
index=MultiIndex.from_product([dates, expected_assets]),
@@ -228,7 +229,6 @@ class ConstantInputTestCase(TestCase):
def test_single_factor(self):
loader = self.loader
finder = self.asset_finder
assets = self.assets
engine = SimplePipelineEngine(
lambda column: loader, self.dates, self.asset_finder,
@@ -252,7 +252,7 @@ class ConstantInputTestCase(TestCase):
result = engine.run_pipeline(p, dates[0], dates[-1])
self.assertEqual(set(result.columns), {'f'})
assert_multi_index_is_product(
self, result.index, dates, finder.retrieve_all(assets)
self, result.index, dates, assets
)
check_arrays(
@@ -263,7 +263,6 @@ class ConstantInputTestCase(TestCase):
def test_multiple_rolling_factors(self):
loader = self.loader
finder = self.asset_finder
assets = self.assets
engine = SimplePipelineEngine(
lambda column: loader, self.dates, self.asset_finder,
@@ -289,7 +288,7 @@ class ConstantInputTestCase(TestCase):
self.assertEqual(set(results.columns), {'short', 'high', 'long'})
assert_multi_index_is_product(
self, results.index, dates, finder.retrieve_all(assets)
self, results.index, dates, assets
)
# row-wise sum over an array whose values are all (1 - 2)
@@ -368,7 +367,7 @@ class ConstantInputTestCase(TestCase):
loader = ConstantLoader(
constants=constants,
dates=self.dates,
assets=self.assets,
assets=self.asset_ids,
)
engine = SimplePipelineEngine(
lambda column: loader, self.dates, self.asset_finder,
@@ -394,7 +393,7 @@ class ConstantInputTestCase(TestCase):
set(result.columns)
)
result_index = self.assets * len(dates_to_test)
result_index = self.asset_ids * len(dates_to_test)
result_shape = (len(result_index),)
check_arrays(
result['sumdiff'],
@@ -433,12 +432,12 @@ class ConstantInputTestCase(TestCase):
Loader1DataSet2.col2: 4}
loader1 = RecordingConstantLoader(constants=constants1,
dates=self.dates,
assets=self.assets)
assets=self.asset_ids)
constants2 = {Loader2DataSet.col1: 5,
Loader2DataSet.col2: 6}
loader2 = RecordingConstantLoader(constants=constants2,
dates=self.dates,
assets=self.assets)
assets=self.asset_ids)
engine = SimplePipelineEngine(
lambda column:
@@ -517,7 +516,7 @@ class FrameInputTestCase(TestCase):
cls.env = TradingEnvironment()
day = cls.env.trading_day
cls.assets = Int64Index([1, 2, 3])
cls.asset_ids = [1, 2, 3]
cls.dates = date_range(
'2015-01-01',
'2015-01-31',
@@ -526,12 +525,13 @@ class FrameInputTestCase(TestCase):
)
asset_info = make_simple_equity_info(
cls.assets,
cls.asset_ids,
start_date=cls.dates[0],
end_date=cls.dates[-1],
)
cls.env.write_data(equities_df=asset_info)
cls.asset_finder = cls.env.asset_finder
cls.assets = cls.asset_finder.retrieve_all(cls.asset_ids)
@classmethod
def tearDownClass(cls):
@@ -546,7 +546,7 @@ class FrameInputTestCase(TestCase):
return DataFrame(data, columns=self.assets, index=self.dates)
def test_compute_with_adjustments(self):
dates, assets = self.dates, self.assets
dates, asset_ids = self.dates, self.asset_ids
low, high = USEquityPricing.low, USEquityPricing.high
apply_idxs = [3, 10, 16]
@@ -557,7 +557,7 @@ class FrameInputTestCase(TestCase):
[
dict(
kind=MULTIPLY,
sid=assets[1],
sid=asset_ids[1],
value=2.0,
start_date=None,
end_date=apply_date(0, offset=-1),
@@ -565,7 +565,7 @@ class FrameInputTestCase(TestCase):
),
dict(
kind=MULTIPLY,
sid=assets[1],
sid=asset_ids[1],
value=3.0,
start_date=None,
end_date=apply_date(1, offset=-1),
@@ -573,7 +573,7 @@ class FrameInputTestCase(TestCase):
),
dict(
kind=MULTIPLY,
sid=assets[1],
sid=asset_ids[1],
value=5.0,
start_date=None,
end_date=apply_date(2, offset=-1),
@@ -643,7 +643,7 @@ class SyntheticBcolzTestCase(TestCase):
asset_lifetime=8,
)
cls.last_asset_end = cls.asset_info['end_date'].max()
cls.all_assets = cls.asset_info.index
cls.all_asset_ids = cls.asset_info.index
cls.env.write_data(equities_df=cls.asset_info)
cls.finder = cls.env.asset_finder
@@ -659,7 +659,7 @@ class SyntheticBcolzTestCase(TestCase):
table = cls.writer.write(
cls.temp_dir.getpath('testdata.bcolz'),
cls.calendar,
cls.all_assets,
cls.all_asset_ids,
)
cls.pipeline_loader = USEquityPricingLoader(
@@ -711,7 +711,7 @@ class SyntheticBcolzTestCase(TestCase):
self.finder,
)
window_length = 5
assets = self.all_assets
asset_ids = self.all_asset_ids
dates = date_range(
self.first_asset_start + self.trading_day,
self.last_asset_end,
@@ -735,7 +735,7 @@ class SyntheticBcolzTestCase(TestCase):
# **previous** day's data.
expected_raw = rolling_mean(
self.writer.expected_values_2d(
dates - self.trading_day, assets, 'close',
dates - self.trading_day, asset_ids, 'close',
),
window_length,
min_periods=1,
@@ -745,7 +745,7 @@ class SyntheticBcolzTestCase(TestCase):
# Truncate off the extra rows needed to compute the SMAs.
expected_raw[window_length:],
index=dates_to_test, # dates_to_test is dates[window_length:]
columns=self.finder.retrieve_all(assets),
columns=self.finder.retrieve_all(asset_ids),
)
self.write_nans(expected)
result = results['sma'].unstack()
@@ -763,7 +763,7 @@ class SyntheticBcolzTestCase(TestCase):
self.finder,
)
window_length = 5
assets = self.all_assets
asset_ids = self.all_asset_ids
dates = date_range(
self.first_asset_start + self.trading_day,
self.last_asset_end,
@@ -785,9 +785,9 @@ class SyntheticBcolzTestCase(TestCase):
# We expect NaNs when the asset was undefined, otherwise 0 everywhere,
# since the input is always increasing.
expected = DataFrame(
data=zeros((len(dates_to_test), len(assets)), dtype=float),
data=zeros((len(dates_to_test), len(asset_ids)), dtype=float),
index=dates_to_test,
columns=self.finder.retrieve_all(assets),
columns=self.finder.retrieve_all(asset_ids),
)
self.write_nans(expected)
result = results['drawdown'].unstack()
tests/pipeline/test_pipeline_algo.py
+1 -1
View File
@@ -401,7 +401,7 @@ class PipelineAlgorithmTestCase(TestCase):
'ratio': array([], dtype=float),
'sid': array([], dtype=int),
},
index=DatetimeIndex([], tz='UTC'),
index=DatetimeIndex([]),
columns=['effective_date', 'ratio', 'sid'],
)
dividends = DataFrame({