catalyst/tests/pipeline/test_events.py

"""
Tests for setting up an EventsLoader and a BlazeEventsLoader.
"""
import re
from unittest import TestCase

import blaze as bz
from nose_parameterized import parameterized
import pandas as pd
from pandas.util.testing import assert_series_equal

from zipline.pipeline.common import (
    ANNOUNCEMENT_FIELD_NAME,
    SID_FIELD_NAME,
    TS_FIELD_NAME
)
from zipline.pipeline.data import DataSet, Column
from zipline.pipeline.loaders.blaze.events import BlazeEventsLoader
from zipline.pipeline.loaders.events import (
    DF_NO_TS_NOT_INFER_TS_ERROR,
    DTINDEX_NOT_INFER_TS_ERROR,
    EventsLoader,
    SERIES_NO_DTINDEX_ERROR,
    WRONG_COLS_ERROR,
    WRONG_MANY_COL_DATA_FORMAT_ERROR,
    WRONG_SINGLE_COL_DATA_FORMAT_ERROR
)
from zipline.utils.memoize import lazyval
from zipline.utils.numpy_utils import datetime64ns_dtype


ABSTRACT_CONCRETE_LOADER_ERROR = 'abstract methods concrete_loader'
ABSTRACT_EXPECTED_COLS_ERROR = 'abstract methods expected_cols'


class EventDataSet(DataSet):
    previous_announcement = Column(datetime64ns_dtype)


class EventDataSetLoader(EventsLoader):
    expected_cols = frozenset([ANNOUNCEMENT_FIELD_NAME])

    def __init__(self,
                 all_dates,
                 events_by_sid,
                 infer_timestamps=False,
                 dataset=EventDataSet):
        super(EventDataSetLoader, self).__init__(
            all_dates,
            events_by_sid,
            infer_timestamps=infer_timestamps,
            dataset=dataset,
        )

    @lazyval
    def previous_announcement_loader(self):
        return self._previous_event_date_loader(
            self.dataset.previous_announcement,
            ANNOUNCEMENT_FIELD_NAME,
        )

    @lazyval
    def next_announcement_loader(self):
        return self._previous_event_date_loader(
            self.dataset.previous_announcement,
            ANNOUNCEMENT_FIELD_NAME,
        )


# Test case just for catching an error when multiple columns are in the wrong
#  data format, so no loader defined.
class EventDataSetLoaderMultipleExpectedCols(EventsLoader):
    expected_cols = frozenset([ANNOUNCEMENT_FIELD_NAME, "other_field"])


class EventDataSetLoaderNoExpectedCols(EventsLoader):

    def __init__(self,
                 all_dates,
                 events_by_sid,
                 infer_timestamps=False,
                 dataset=EventDataSet):
        super(EventDataSetLoaderNoExpectedCols, self).__init__(
            all_dates,
            events_by_sid,
            infer_timestamps=infer_timestamps,
            dataset=dataset,
        )


dtx = pd.date_range('2014-01-01', '2014-01-10')


class EventLoaderTestCase(TestCase):
    def assert_loader_error(self, events_by_sid, error, msg,
                            infer_timestamps, loader):
        with self.assertRaisesRegexp(error, re.escape(msg)):
            loader(
                dtx, events_by_sid, infer_timestamps=infer_timestamps,
            )

    def test_no_expected_cols_defined(self):
        events_by_sid = {0: pd.DataFrame({ANNOUNCEMENT_FIELD_NAME: dtx})}
        self.assert_loader_error(events_by_sid, TypeError,
                                 ABSTRACT_EXPECTED_COLS_ERROR,
                                 True, EventDataSetLoaderNoExpectedCols)

    def test_wrong_cols(self):
        wrong_col_name = 'some_other_col'
        # Test wrong cols (cols != expected)
        events_by_sid = {0: pd.DataFrame({wrong_col_name: dtx})}
        self.assert_loader_error(
            events_by_sid, ValueError, WRONG_COLS_ERROR.format(
                expected_columns=list(EventDataSetLoader.expected_cols),
                sid=0,
                resulting_columns=[wrong_col_name],
            ),
            True,
            EventDataSetLoader
        )

    @parameterized.expand([
        # DataFrame without timestamp column and infer_timestamps = True
        [pd.DataFrame({ANNOUNCEMENT_FIELD_NAME: dtx}), True],
        # DataFrame with timestamp column
        [pd.DataFrame({ANNOUNCEMENT_FIELD_NAME: dtx,
                       TS_FIELD_NAME: dtx}), False],
        # DatetimeIndex with infer_timestamps = True
        [pd.DatetimeIndex(dtx), True],
        # Series with DatetimeIndex as index and infer_timestamps = False
        [pd.Series(dtx, index=dtx), False]
    ])
    def test_conversion_to_df(self, df, infer_timestamps):

        events_by_sid = {0: df}
        loader = EventDataSetLoader(
            dtx,
            events_by_sid,
            infer_timestamps=infer_timestamps,
        )
        self.assertEqual(
            loader.events_by_sid.keys(),
            events_by_sid.keys(),
        )

        if infer_timestamps:
            expected = pd.Series(index=[dtx[0]] * 10, data=dtx,
                                 name=ANNOUNCEMENT_FIELD_NAME)
        else:
            expected = pd.Series(index=dtx, data=dtx,
                                 name=ANNOUNCEMENT_FIELD_NAME)
            expected.index.name = TS_FIELD_NAME
        # Check that index by first given date has been added
        assert_series_equal(
            loader.events_by_sid[0][ANNOUNCEMENT_FIELD_NAME],
            expected,
        )

    @parameterized.expand(
        [
            # DataFrame without timestamp column and infer_timestamps = True
            [
                pd.DataFrame({ANNOUNCEMENT_FIELD_NAME: dtx}),
                False,
                DF_NO_TS_NOT_INFER_TS_ERROR.format(
                    timestamp_column_name=TS_FIELD_NAME,
                    sid=0
                ),
                EventDataSetLoader
            ],
            # DatetimeIndex with infer_timestamps = False
            [
                pd.DatetimeIndex(dtx, name=ANNOUNCEMENT_FIELD_NAME),
                False,
                DTINDEX_NOT_INFER_TS_ERROR.format(sid=0),
                EventDataSetLoader
            ],
            # Series with DatetimeIndex as index and infer_timestamps = False
            [
                pd.Series(dtx, name=ANNOUNCEMENT_FIELD_NAME),
                False,
                SERIES_NO_DTINDEX_ERROR.format(sid=0),
                EventDataSetLoader
            ],
            # Below, 2 cases repeated for infer_timestamps = True and False.
            # Shouldn't make a difference in the outcome.
            # We expected 1 column but got a data structure other than a
            # DataFrame, Series, or DatetimeIndex
            [
                [dtx],
                True,
                WRONG_SINGLE_COL_DATA_FORMAT_ERROR.format(sid=0),
                EventDataSetLoader
            ],
            # We expected multiple columns but got a data structure other
            # than a DataFrame
            [
                [dtx, dtx],
                True,
                WRONG_MANY_COL_DATA_FORMAT_ERROR.format(sid=0),
                EventDataSetLoaderMultipleExpectedCols
            ],
            [
                [dtx],
                False,
                WRONG_SINGLE_COL_DATA_FORMAT_ERROR.format(sid=0),
                EventDataSetLoader
            ],
            # We expected multiple columns but got a data structure other
            # than a DataFrame
            [
                [dtx, dtx],
                False,
                WRONG_MANY_COL_DATA_FORMAT_ERROR.format(sid=0),
                EventDataSetLoaderMultipleExpectedCols
            ]
        ]
    )
    def test_bad_conversion_to_df(self, df, infer_timestamps, msg, loader):
        events_by_sid = {0: df}
        self.assert_loader_error(events_by_sid, ValueError, msg,
                                 infer_timestamps, loader)


class BlazeEventDataSetLoaderNoConcreteLoader(BlazeEventsLoader):
    def __init__(self,
                 expr,
                 dataset=EventDataSet,
                 **kwargs):
        super(
            BlazeEventDataSetLoaderNoConcreteLoader, self
        ).__init__(expr,
                   dataset=dataset,
                   **kwargs)


class BlazeEventLoaderTestCase(TestCase):
    # Blaze loader: need to test failure if no concrete loader
    def test_no_concrete_loader_defined(self):
        with self.assertRaisesRegexp(
                TypeError, re.escape(ABSTRACT_CONCRETE_LOADER_ERROR)
        ):
            BlazeEventDataSetLoaderNoConcreteLoader(
                bz.data(
                    pd.DataFrame({ANNOUNCEMENT_FIELD_NAME: dtx,
                                  SID_FIELD_NAME: 0})
                )
            )