test_factor.py

"""
Tests for Factor terms.
"""
from functools import partial
from itertools import product
from nose_parameterized import parameterized
from unittest import TestCase

from toolz import compose
from numpy import (
    apply_along_axis,
    arange,
    array,
    datetime64,
    empty,
    eye,
    log1p,
    nan,
    ones,
    rot90,
    where,
)
from numpy.random import randn, seed
import pandas as pd

from zipline.errors import UnknownRankMethod
from zipline.lib.labelarray import LabelArray
from zipline.lib.rank import masked_rankdata_2d
from zipline.lib.normalize import naive_grouped_rowwise_apply as grouped_apply
from zipline.pipeline import Classifier, Factor, Filter
from zipline.pipeline.factors import (
    Returns,
    RSI,
)
from zipline.testing import (
    check_allclose,
    check_arrays,
    parameter_space,
    permute_rows,
)
from zipline.testing.fixtures import ZiplineTestCase
from zipline.testing.predicates import assert_equal
from zipline.utils.numpy_utils import (
    categorical_dtype,
    datetime64ns_dtype,
    float64_dtype,
    int64_dtype,
    NaTns,
)
from zipline.utils.math_utils import nanmean, nanstd

from .base import BasePipelineTestCase


class F(Factor):
    dtype = float64_dtype
    inputs = ()
    window_length = 0


class OtherF(Factor):
    dtype = float64_dtype
    inputs = ()
    window_length = 0


class C(Classifier):
    dtype = int64_dtype
    missing_value = -1
    inputs = ()
    window_length = 0


class OtherC(Classifier):
    dtype = int64_dtype
    missing_value = -1
    inputs = ()
    window_length = 0


class Mask(Filter):
    inputs = ()
    window_length = 0


for_each_factor_dtype = parameterized.expand([
    ('datetime64[ns]', datetime64ns_dtype),
    ('float', float64_dtype),
])


class FactorTestCase(BasePipelineTestCase):

    def init_instance_fixtures(self):
        super(FactorTestCase, self).init_instance_fixtures()
        self.f = F()

    def test_bad_input(self):
        with self.assertRaises(UnknownRankMethod):
            self.f.rank("not a real rank method")

    @parameter_space(method_name=['isnan', 'notnan', 'isfinite'])
    def test_float64_only_ops(self, method_name):
        class NotFloat(Factor):
            dtype = datetime64ns_dtype
            inputs = ()
            window_length = 0

        nf = NotFloat()
        meth = getattr(nf, method_name)
        with self.assertRaises(TypeError):
            meth()

    @parameter_space(custom_missing_value=[-1, 0])
    def test_isnull_int_dtype(self, custom_missing_value):

        class CustomMissingValue(Factor):
            dtype = int64_dtype
            window_length = 0
            missing_value = custom_missing_value
            inputs = ()

        factor = CustomMissingValue()

        data = arange(25).reshape(5, 5)
        data[eye(5, dtype=bool)] = custom_missing_value

        self.check_terms(
            {
                'isnull': factor.isnull(),
                'notnull': factor.notnull(),
            },
            {
                'isnull': eye(5, dtype=bool),
                'notnull': ~eye(5, dtype=bool),
            },
            initial_workspace={factor: data},
            mask=self.build_mask(ones((5, 5))),
        )

    def test_isnull_datetime_dtype(self):
        class DatetimeFactor(Factor):
            dtype = datetime64ns_dtype
            window_length = 0
            inputs = ()

        factor = DatetimeFactor()

        data = arange(25).reshape(5, 5).astype('datetime64[ns]')
        data[eye(5, dtype=bool)] = NaTns

        self.check_terms(
            {
                'isnull': factor.isnull(),
                'notnull': factor.notnull(),
            },
            {
                'isnull': eye(5, dtype=bool),
                'notnull': ~eye(5, dtype=bool),
            },
            initial_workspace={factor: data},
            mask=self.build_mask(ones((5, 5))),
        )

    @for_each_factor_dtype
    def test_rank_ascending(self, name, factor_dtype):

        f = F(dtype=factor_dtype)

        # Generated with:
        # data = arange(25).reshape(5, 5).transpose() % 4
        data = array([[0, 1, 2, 3, 0],
                      [1, 2, 3, 0, 1],
                      [2, 3, 0, 1, 2],
                      [3, 0, 1, 2, 3],
                      [0, 1, 2, 3, 0]], dtype=factor_dtype)

        expected_ranks = {
            'ordinal': array([[1., 3., 4., 5., 2.],
                              [2., 4., 5., 1., 3.],
                              [3., 5., 1., 2., 4.],
                              [4., 1., 2., 3., 5.],
                              [1., 3., 4., 5., 2.]]),
            'average': array([[1.5, 3., 4., 5., 1.5],
                              [2.5, 4., 5., 1., 2.5],
                              [3.5, 5., 1., 2., 3.5],
                              [4.5, 1., 2., 3., 4.5],
                              [1.5, 3., 4., 5., 1.5]]),
            'min': array([[1., 3., 4., 5., 1.],
                          [2., 4., 5., 1., 2.],
                          [3., 5., 1., 2., 3.],
                          [4., 1., 2., 3., 4.],
                          [1., 3., 4., 5., 1.]]),
            'max': array([[2., 3., 4., 5., 2.],
                          [3., 4., 5., 1., 3.],
                          [4., 5., 1., 2., 4.],
                          [5., 1., 2., 3., 5.],
                          [2., 3., 4., 5., 2.]]),
            'dense': array([[1., 2., 3., 4., 1.],
                            [2., 3., 4., 1., 2.],
                            [3., 4., 1., 2., 3.],
                            [4., 1., 2., 3., 4.],
                            [1., 2., 3., 4., 1.]]),
        }

        def check(terms):
            self.check_terms(
                terms,
                expected={name: expected_ranks[name] for name in terms},
                initial_workspace={f: data},
                mask=self.build_mask(ones((5, 5))),
            )

        check({meth: f.rank(method=meth) for meth in expected_ranks})
        check({
            meth: f.rank(method=meth, ascending=True)
            for meth in expected_ranks
        })
        # Not passing a method should default to ordinal.
        check({'ordinal': f.rank()})
        check({'ordinal': f.rank(ascending=True)})

    @for_each_factor_dtype
    def test_rank_descending(self, name, factor_dtype):

        f = F(dtype=factor_dtype)

        # Generated with:
        # data = arange(25).reshape(5, 5).transpose() % 4
        data = array([[0, 1, 2, 3, 0],
                      [1, 2, 3, 0, 1],
                      [2, 3, 0, 1, 2],
                      [3, 0, 1, 2, 3],
                      [0, 1, 2, 3, 0]], dtype=factor_dtype)
        expected_ranks = {
            'ordinal': array([[4., 3., 2., 1., 5.],
                              [3., 2., 1., 5., 4.],
                              [2., 1., 5., 4., 3.],
                              [1., 5., 4., 3., 2.],
                              [4., 3., 2., 1., 5.]]),
            'average': array([[4.5, 3., 2., 1., 4.5],
                              [3.5, 2., 1., 5., 3.5],
                              [2.5, 1., 5., 4., 2.5],
                              [1.5, 5., 4., 3., 1.5],
                              [4.5, 3., 2., 1., 4.5]]),
            'min': array([[4., 3., 2., 1., 4.],
                          [3., 2., 1., 5., 3.],
                          [2., 1., 5., 4., 2.],
                          [1., 5., 4., 3., 1.],
                          [4., 3., 2., 1., 4.]]),
            'max': array([[5., 3., 2., 1., 5.],
                          [4., 2., 1., 5., 4.],
                          [3., 1., 5., 4., 3.],
                          [2., 5., 4., 3., 2.],
                          [5., 3., 2., 1., 5.]]),
            'dense': array([[4., 3., 2., 1., 4.],
                            [3., 2., 1., 4., 3.],
                            [2., 1., 4., 3., 2.],
                            [1., 4., 3., 2., 1.],
                            [4., 3., 2., 1., 4.]]),
        }

        def check(terms):
            self.check_terms(
                terms,
                expected={name: expected_ranks[name] for name in terms},
                initial_workspace={f: data},
                mask=self.build_mask(ones((5, 5))),
            )

        check({
            meth: f.rank(method=meth, ascending=False)
            for meth in expected_ranks
        })
        # Not passing a method should default to ordinal.
        check({'ordinal': f.rank(ascending=False)})

    @for_each_factor_dtype
    def test_rank_after_mask(self, name, factor_dtype):

        f = F(dtype=factor_dtype)
        # data = arange(25).reshape(5, 5).transpose() % 4
        data = array([[0, 1, 2, 3, 0],
                      [1, 2, 3, 0, 1],
                      [2, 3, 0, 1, 2],
                      [3, 0, 1, 2, 3],
                      [0, 1, 2, 3, 0]], dtype=factor_dtype)
        mask_data = ~eye(5, dtype=bool)
        initial_workspace = {f: data, Mask(): mask_data}

        terms = {
            "ascending_nomask": f.rank(ascending=True),
            "ascending_mask": f.rank(ascending=True, mask=Mask()),
            "descending_nomask": f.rank(ascending=False),
            "descending_mask": f.rank(ascending=False, mask=Mask()),
        }

        expected = {
            "ascending_nomask": array([[1., 3., 4., 5., 2.],
                                       [2., 4., 5., 1., 3.],
                                       [3., 5., 1., 2., 4.],
                                       [4., 1., 2., 3., 5.],
                                       [1., 3., 4., 5., 2.]]),
            "descending_nomask": array([[4., 3., 2., 1., 5.],
                                        [3., 2., 1., 5., 4.],
                                        [2., 1., 5., 4., 3.],
                                        [1., 5., 4., 3., 2.],
                                        [4., 3., 2., 1., 5.]]),
            # Diagonal should be all nans, and anything whose rank was less
            # than the diagonal in the unmasked calc should go down by 1.
            "ascending_mask": array([[nan, 2., 3., 4., 1.],
                                     [2., nan, 4., 1., 3.],
                                     [2., 4., nan, 1., 3.],
                                     [3., 1., 2., nan, 4.],
                                     [1., 2., 3., 4., nan]]),
            "descending_mask": array([[nan, 3., 2., 1., 4.],
                                      [2., nan, 1., 4., 3.],
                                      [2., 1., nan, 4., 3.],
                                      [1., 4., 3., nan, 2.],
                                      [4., 3., 2., 1., nan]]),
        }

        self.check_terms(
            terms,
            expected,
            initial_workspace,
            mask=self.build_mask(ones((5, 5))),
        )

    @for_each_factor_dtype
    def test_grouped_rank_ascending(self, name, factor_dtype=float64_dtype):

        f = F(dtype=factor_dtype)
        c = C()
        str_c = C(dtype=categorical_dtype, missing_value=None)

        # Generated with:
        # data = arange(25).reshape(5, 5).transpose() % 4
        data = array([[0, 1, 2, 3, 0],
                      [1, 2, 3, 0, 1],
                      [2, 3, 0, 1, 2],
                      [3, 0, 1, 2, 3],
                      [0, 1, 2, 3, 0]], dtype=factor_dtype)

        # Generated with:
        # classifier_data = arange(25).reshape(5, 5).transpose() % 2
        classifier_data = array([[0, 1, 0, 1, 0],
                                 [1, 0, 1, 0, 1],
                                 [0, 1, 0, 1, 0],
                                 [1, 0, 1, 0, 1],
                                 [0, 1, 0, 1, 0]], dtype=int64_dtype)
        string_classifier_data = LabelArray(
            classifier_data.astype(str).astype(object),
            missing_value=None,
        )

        expected_ranks = {
            'ordinal': array(
                [[1., 1., 3., 2., 2.],
                 [1., 2., 3., 1., 2.],
                 [2., 2., 1., 1., 3.],
                 [2., 1., 1., 2., 3.],
                 [1., 1., 3., 2., 2.]]
            ),
            'average': array(
                [[1.5, 1., 3., 2., 1.5],
                 [1.5, 2., 3., 1., 1.5],
                 [2.5, 2., 1., 1., 2.5],
                 [2.5, 1., 1., 2., 2.5],
                 [1.5, 1., 3., 2., 1.5]]
            ),
            'min': array(
                [[1., 1., 3., 2., 1.],
                 [1., 2., 3., 1., 1.],
                 [2., 2., 1., 1., 2.],
                 [2., 1., 1., 2., 2.],
                 [1., 1., 3., 2., 1.]]
            ),
            'max': array(
                [[2., 1., 3., 2., 2.],
                 [2., 2., 3., 1., 2.],
                 [3., 2., 1., 1., 3.],
                 [3., 1., 1., 2., 3.],
                 [2., 1., 3., 2., 2.]]
            ),
            'dense': array(
                [[1., 1., 2., 2., 1.],
                 [1., 2., 2., 1., 1.],
                 [2., 2., 1., 1., 2.],
                 [2., 1., 1., 2., 2.],
                 [1., 1., 2., 2., 1.]]
            ),
        }

        def check(terms):
            self.check_terms(
                terms,
                expected={name: expected_ranks[name] for name in terms},
                initial_workspace={
                    f: data,
                    c: classifier_data,
                    str_c: string_classifier_data,
                },
                mask=self.build_mask(ones((5, 5))),
            )

        # Not specifying the value of ascending param should default to True
        check({
            meth: f.rank(method=meth, groupby=c)
            for meth in expected_ranks
        })
        check({
            meth: f.rank(method=meth, groupby=str_c)
            for meth in expected_ranks
        })
        check({
            meth: f.rank(method=meth, groupby=c, ascending=True)
            for meth in expected_ranks
        })
        check({
            meth: f.rank(method=meth, groupby=str_c, ascending=True)
            for meth in expected_ranks
        })

        # Not passing a method should default to ordinal
        check({'ordinal': f.rank(groupby=c)})
        check({'ordinal': f.rank(groupby=str_c)})
        check({'ordinal': f.rank(groupby=c, ascending=True)})
        check({'ordinal': f.rank(groupby=str_c, ascending=True)})

    @for_each_factor_dtype
    def test_grouped_rank_descending(self, name, factor_dtype):

        f = F(dtype=factor_dtype)
        c = C()
        str_c = C(dtype=categorical_dtype, missing_value=None)

        # Generated with:
        # data = arange(25).reshape(5, 5).transpose() % 4
        data = array([[0, 1, 2, 3, 0],
                      [1, 2, 3, 0, 1],
                      [2, 3, 0, 1, 2],
                      [3, 0, 1, 2, 3],
                      [0, 1, 2, 3, 0]], dtype=factor_dtype)

        # Generated with:
        # classifier_data = arange(25).reshape(5, 5).transpose() % 2
        classifier_data = array([[0, 1, 0, 1, 0],
                                 [1, 0, 1, 0, 1],
                                 [0, 1, 0, 1, 0],
                                 [1, 0, 1, 0, 1],
                                 [0, 1, 0, 1, 0]], dtype=int64_dtype)

        string_classifier_data = LabelArray(
            classifier_data.astype(str).astype(object),
            missing_value=None,
        )

        expected_ranks = {
            'ordinal': array(
                [[2., 2., 1., 1., 3.],
                 [2., 1., 1., 2., 3.],
                 [1., 1., 3., 2., 2.],
                 [1., 2., 3., 1., 2.],
                 [2., 2., 1., 1., 3.]]
            ),
            'average': array(
                [[2.5, 2., 1., 1., 2.5],
                 [2.5, 1., 1., 2., 2.5],
                 [1.5, 1., 3., 2., 1.5],
                 [1.5, 2., 3., 1., 1.5],
                 [2.5, 2., 1., 1., 2.5]]
            ),
            'min': array(
                [[2., 2., 1., 1., 2.],
                 [2., 1., 1., 2., 2.],
                 [1., 1., 3., 2., 1.],
                 [1., 2., 3., 1., 1.],
                 [2., 2., 1., 1., 2.]]
            ),
            'max': array(
                [[3., 2., 1., 1., 3.],
                 [3., 1., 1., 2., 3.],
                 [2., 1., 3., 2., 2.],
                 [2., 2., 3., 1., 2.],
                 [3., 2., 1., 1., 3.]]
            ),
            'dense': array(
                [[2., 2., 1., 1., 2.],
                 [2., 1., 1., 2., 2.],
                 [1., 1., 2., 2., 1.],
                 [1., 2., 2., 1., 1.],
                 [2., 2., 1., 1., 2.]]
            ),
        }

        def check(terms):
            self.check_terms(
                terms,
                expected={name: expected_ranks[name] for name in terms},
                initial_workspace={
                    f: data,
                    c: classifier_data,
                    str_c: string_classifier_data,
                },
                mask=self.build_mask(ones((5, 5))),
            )

        check({
            meth: f.rank(method=meth, groupby=c, ascending=False)
            for meth in expected_ranks
        })
        check({
            meth: f.rank(method=meth, groupby=str_c, ascending=False)
            for meth in expected_ranks
        })

        # Not passing a method should default to ordinal
        check({'ordinal': f.rank(groupby=c, ascending=False)})
        check({'ordinal': f.rank(groupby=str_c, ascending=False)})

    @parameterized.expand([
        # Test cases computed by doing:
        # from numpy.random import seed, randn
        # from talib import RSI
        # seed(seed_value)
        # data = abs(randn(15, 3))
        # expected = [RSI(data[:, i])[-1] for i in range(3)]
        (100, array([41.032913785966, 51.553585468393, 51.022005016446])),
        (101, array([43.506969935466, 46.145367530182, 50.57407044197])),
        (102, array([46.610102205934, 47.646892444315, 52.13182788538])),
    ])
    def test_rsi(self, seed_value, expected):

        rsi = RSI()

        today = datetime64(1, 'ns')
        assets = arange(3)
        out = empty((3,), dtype=float)

        seed(seed_value)  # Seed so we get deterministic results.
        test_data = abs(randn(15, 3))

        out = empty((3,), dtype=float)
        rsi.compute(today, assets, out, test_data)

        check_allclose(expected, out)

    @parameterized.expand([
        (100, 15),
        (101, 4),
        (102, 100),
        ])
    def test_returns(self, seed_value, window_length):

        returns = Returns(window_length=window_length)

        today = datetime64(1, 'ns')
        assets = arange(3)
        out = empty((3,), dtype=float)

        seed(seed_value)  # Seed so we get deterministic results.
        test_data = abs(randn(window_length, 3))

        # Calculate the expected returns
        expected = (test_data[-1] - test_data[0]) / test_data[0]

        out = empty((3,), dtype=float)
        returns.compute(today, assets, out, test_data)

        check_allclose(expected, out)

    def gen_ranking_cases():
        seeds = range(int(1e4), int(1e5), int(1e4))
        methods = ('ordinal', 'average')
        use_mask_values = (True, False)
        set_missing_values = (True, False)
        ascending_values = (True, False)
        return product(
            seeds,
            methods,
            use_mask_values,
            set_missing_values,
            ascending_values,
        )

    @parameterized.expand(gen_ranking_cases())
    def test_masked_rankdata_2d(self,
                                seed_value,
                                method,
                                use_mask,
                                set_missing,
                                ascending):
        eyemask = ~eye(5, dtype=bool)
        nomask = ones((5, 5), dtype=bool)

        seed(seed_value)
        asfloat = (randn(5, 5) * seed_value)
        asdatetime = (asfloat).copy().view('datetime64[ns]')

        mask = eyemask if use_mask else nomask
        if set_missing:
            asfloat[:, 2] = nan
            asdatetime[:, 2] = NaTns

        float_result = masked_rankdata_2d(
            data=asfloat,
            mask=mask,
            missing_value=nan,
            method=method,
            ascending=True,
        )
        datetime_result = masked_rankdata_2d(
            data=asdatetime,
            mask=mask,
            missing_value=NaTns,
            method=method,
            ascending=True,
        )

        check_arrays(float_result, datetime_result)

    def test_normalizations_hand_computed(self):
        """
        Test the hand-computed example in factor.demean.
        """
        f = self.f
        m = Mask()
        c = C()
        str_c = C(dtype=categorical_dtype, missing_value=None)

        factor_data = array(
            [[1.0, 2.0, 3.0, 4.0],
             [1.5, 2.5, 3.5, 1.0],
             [2.0, 3.0, 4.0, 1.5],
             [2.5, 3.5, 1.0, 2.0]],
        )
        filter_data = array(
            [[False, True, True, True],
             [True, False, True, True],
             [True, True, False, True],
             [True, True, True, False]],
            dtype=bool,
        )
        classifier_data = array(
            [[1, 1, 2, 2],
             [1, 1, 2, 2],
             [1, 1, 2, 2],
             [1, 1, 2, 2]],
            dtype=int64_dtype,
        )
        string_classifier_data = LabelArray(
            classifier_data.astype(str).astype(object),
            missing_value=None,
        )

        terms = {
            'vanilla': f.demean(),
            'masked': f.demean(mask=m),
            'grouped': f.demean(groupby=c),
            'grouped_str': f.demean(groupby=str_c),
            'grouped_masked': f.demean(mask=m, groupby=c),
            'grouped_masked_str': f.demean(mask=m, groupby=str_c),
        }
        expected = {
            'vanilla': array(
                [[-1.500, -0.500,  0.500,  1.500],
                 [-0.625,  0.375,  1.375, -1.125],
                 [-0.625,  0.375,  1.375, -1.125],
                 [0.250,   1.250, -1.250, -0.250]],
            ),
            'masked': array(
                [[nan,    -1.000,  0.000,  1.000],
                 [-0.500,    nan,  1.500, -1.000],
                 [-0.166,  0.833,    nan, -0.666],
                 [0.166,   1.166, -1.333,    nan]],
            ),
            'grouped': array(
                [[-0.500, 0.500, -0.500,  0.500],
                 [-0.500, 0.500,  1.250, -1.250],
                 [-0.500, 0.500,  1.250, -1.250],
                 [-0.500, 0.500, -0.500,  0.500]],
            ),
            'grouped_masked': array(
                [[nan,     0.000, -0.500,  0.500],
                 [0.000,     nan,  1.250, -1.250],
                 [-0.500,  0.500,    nan,  0.000],
                 [-0.500,  0.500,  0.000,    nan]]
            )
        }
        # Changing the classifier dtype shouldn't affect anything.
        expected['grouped_str'] = expected['grouped']
        expected['grouped_masked_str'] = expected['grouped_masked']

        self.check_terms(
            terms,
            expected,
            initial_workspace={
                f: factor_data,
                c: classifier_data,
                str_c: string_classifier_data,
                m: filter_data,
            },
            mask=self.build_mask(self.ones_mask(shape=factor_data.shape)),
            # The hand-computed values aren't very precise (in particular,
            # we truncate repeating decimals at 3 places) This is just
            # asserting that the example isn't misleading by being totally
            # wrong.
            check=partial(check_allclose, atol=0.001),
        )

    @parameter_space(
        seed_value=range(1, 2),
        normalizer_name_and_func=[
            ('demean', lambda row: row - nanmean(row)),
            ('zscore', lambda row: (row - nanmean(row)) / nanstd(row)),
        ],
        add_nulls_to_factor=(False, True,),
    )
    def test_normalizations_randomized(self,
                                       seed_value,
                                       normalizer_name_and_func,
                                       add_nulls_to_factor):

        name, func = normalizer_name_and_func

        shape = (7, 7)

        # All Trues.
        nomask = self.ones_mask(shape=shape)
        # Falses on main diagonal.
        eyemask = self.eye_mask(shape=shape)
        # Falses on other diagonal.
        eyemask90 = rot90(eyemask)
        # Falses on both diagonals.
        xmask = eyemask & eyemask90

        # Block of random data.
        factor_data = self.randn_data(seed=seed_value, shape=shape)
        if add_nulls_to_factor:
            factor_data = where(eyemask, factor_data, nan)

        # Cycles of 0, 1, 2, 0, 1, 2, ...
        classifier_data = (
            (self.arange_data(shape=shape, dtype=int64_dtype) + seed_value) % 3
        )
        # With -1s on main diagonal.
        classifier_data_eyenulls = where(eyemask, classifier_data, -1)
        # With -1s on opposite diagonal.
        classifier_data_eyenulls90 = where(eyemask90, classifier_data, -1)
        # With -1s on both diagonals.
        classifier_data_xnulls = where(xmask, classifier_data, -1)

        f = self.f
        c = C()
        c_with_nulls = OtherC()
        m = Mask()
        method = getattr(f, name)
        terms = {
            'vanilla': method(),
            'masked': method(mask=m),
            'grouped': method(groupby=c),
            'grouped_with_nulls': method(groupby=c_with_nulls),
            'both': method(mask=m, groupby=c),
            'both_with_nulls': method(mask=m, groupby=c_with_nulls),
        }

        expected = {
            'vanilla': apply_along_axis(func, 1, factor_data,),
            'masked': where(
                eyemask,
                grouped_apply(factor_data, eyemask, func),
                nan,
            ),
            'grouped': grouped_apply(
                factor_data,
                classifier_data,
                func,
            ),
            # If the classifier has nulls, we should get NaNs in the
            # corresponding locations in the output.
            'grouped_with_nulls': where(
                eyemask90,
                grouped_apply(factor_data, classifier_data_eyenulls90, func),
                nan,
            ),
            # Passing a mask with a classifier should behave as though the
            # classifier had nulls where the mask was False.
            'both': where(
                eyemask,
                grouped_apply(
                    factor_data,
                    classifier_data_eyenulls,
                    func,
                ),
                nan,
            ),
            'both_with_nulls': where(
                xmask,
                grouped_apply(
                    factor_data,
                    classifier_data_xnulls,
                    func,
                ),
                nan,
            )
        }

        self.check_terms(
            terms=terms,
            expected=expected,
            initial_workspace={
                f: factor_data,
                c: classifier_data,
                c_with_nulls: classifier_data_eyenulls90,
                Mask(): eyemask,
            },
            mask=self.build_mask(nomask),
        )

    @parameter_space(method_name=['demean', 'zscore'])
    def test_cant_normalize_non_float(self, method_name):
        class DateFactor(Factor):
            dtype = datetime64ns_dtype
            inputs = ()
            window_length = 0

        d = DateFactor()
        with self.assertRaises(TypeError) as e:
            getattr(d, method_name)()

        errmsg = str(e.exception)
        expected = (
            "{normalizer}() is only defined on Factors of dtype float64,"
            " but it was called on a Factor of dtype datetime64[ns]."
        ).format(normalizer=method_name)

        self.assertEqual(errmsg, expected)

    @parameter_space(seed=[1, 2, 3])
    def test_quantiles_unmasked(self, seed):
        permute = partial(permute_rows, seed)

        shape = (6, 6)

        # Shuffle the input rows to verify that we don't depend on the order.
        # Take the log to ensure that we don't depend on linear scaling or
        # integrality of inputs
        factor_data = permute(log1p(arange(36, dtype=float).reshape(shape)))

        f = self.f

        # Apply the same shuffle we applied to the input rows to our
        # expectations. Doing it this way makes it obvious that our
        # expectation corresponds to our input, while still testing against
        # a range of input orderings.
        permuted_array = compose(permute, partial(array, dtype=int64_dtype))
        self.check_terms(
            terms={
                '2': f.quantiles(bins=2),
                '3': f.quantiles(bins=3),
                '6': f.quantiles(bins=6),
            },
            initial_workspace={
                f: factor_data,
            },
            expected={
                # The values in the input are all increasing, so the first half
                # of each row should be in the bottom bucket, and the second
                # half should be in the top bucket.
                '2': permuted_array([[0, 0, 0, 1, 1, 1],
                                     [0, 0, 0, 1, 1, 1],
                                     [0, 0, 0, 1, 1, 1],
                                     [0, 0, 0, 1, 1, 1],
                                     [0, 0, 0, 1, 1, 1],
                                     [0, 0, 0, 1, 1, 1]]),
                # Similar for three buckets.
                '3': permuted_array([[0, 0, 1, 1, 2, 2],
                                     [0, 0, 1, 1, 2, 2],
                                     [0, 0, 1, 1, 2, 2],
                                     [0, 0, 1, 1, 2, 2],
                                     [0, 0, 1, 1, 2, 2],
                                     [0, 0, 1, 1, 2, 2]]),
                # In the limiting case, we just have every column different.
                '6': permuted_array([[0, 1, 2, 3, 4, 5],
                                     [0, 1, 2, 3, 4, 5],
                                     [0, 1, 2, 3, 4, 5],
                                     [0, 1, 2, 3, 4, 5],
                                     [0, 1, 2, 3, 4, 5],
                                     [0, 1, 2, 3, 4, 5]]),
            },
            mask=self.build_mask(self.ones_mask(shape=shape)),
        )

    @parameter_space(seed=[1, 2, 3])
    def test_quantiles_masked(self, seed):
        permute = partial(permute_rows, seed)

        # 7 x 7 so that we divide evenly into 2/3/6-tiles after including the
        # nan value in each row.
        shape = (7, 7)

        # Shuffle the input rows to verify that we don't depend on the order.
        # Take the log to ensure that we don't depend on linear scaling or
        # integrality of inputs
        factor_data = permute(log1p(arange(49, dtype=float).reshape(shape)))
        factor_data_w_nans = where(
            permute(rot90(self.eye_mask(shape=shape))),
            factor_data,
            nan,
        )
        mask_data = permute(self.eye_mask(shape=shape))

        f = F()
        f_nans = OtherF()
        m = Mask()

        # Apply the same shuffle we applied to the input rows to our
        # expectations. Doing it this way makes it obvious that our
        # expectation corresponds to our input, while still testing against
        # a range of input orderings.
        permuted_array = compose(permute, partial(array, dtype=int64_dtype))

        self.check_terms(
            terms={
                '2_masked': f.quantiles(bins=2, mask=m),
                '3_masked': f.quantiles(bins=3, mask=m),
                '6_masked': f.quantiles(bins=6, mask=m),
                '2_nans': f_nans.quantiles(bins=2),
                '3_nans': f_nans.quantiles(bins=3),
                '6_nans': f_nans.quantiles(bins=6),
            },
            initial_workspace={
                f: factor_data,
                f_nans: factor_data_w_nans,
                m: mask_data,
            },
            expected={
                # Expected results here are the same as in
                # test_quantiles_unmasked, except with diagonals of -1s
                # interpolated to match the effects of masking and/or input
                # nans.
                '2_masked': permuted_array([[-1, 0,  0,  0,  1,  1,  1],
                                            [0, -1,  0,  0,  1,  1,  1],
                                            [0,  0, -1,  0,  1,  1,  1],
                                            [0,  0,  0, -1,  1,  1,  1],
                                            [0,  0,  0,  1, -1,  1,  1],
                                            [0,  0,  0,  1,  1, -1,  1],
                                            [0,  0,  0,  1,  1,  1, -1]]),
                '3_masked': permuted_array([[-1, 0,  0,  1,  1,  2,  2],
                                            [0, -1,  0,  1,  1,  2,  2],
                                            [0,  0, -1,  1,  1,  2,  2],
                                            [0,  0,  1, -1,  1,  2,  2],
                                            [0,  0,  1,  1, -1,  2,  2],
                                            [0,  0,  1,  1,  2, -1,  2],
                                            [0,  0,  1,  1,  2,  2, -1]]),
                '6_masked': permuted_array([[-1, 0,  1,  2,  3,  4,  5],
                                            [0, -1,  1,  2,  3,  4,  5],
                                            [0,  1, -1,  2,  3,  4,  5],
                                            [0,  1,  2, -1,  3,  4,  5],
                                            [0,  1,  2,  3, -1,  4,  5],
                                            [0,  1,  2,  3,  4, -1,  5],
                                            [0,  1,  2,  3,  4,  5, -1]]),
                '2_nans': permuted_array([[0,  0,  0,  1,  1,  1, -1],
                                          [0,  0,  0,  1,  1, -1,  1],
                                          [0,  0,  0,  1, -1,  1,  1],
                                          [0,  0,  0, -1,  1,  1,  1],
                                          [0,  0, -1,  0,  1,  1,  1],
                                          [0, -1,  0,  0,  1,  1,  1],
                                          [-1, 0,  0,  0,  1,  1,  1]]),
                '3_nans': permuted_array([[0,  0,  1,  1,  2,  2, -1],
                                          [0,  0,  1,  1,  2, -1,  2],
                                          [0,  0,  1,  1, -1,  2,  2],
                                          [0,  0,  1, -1,  1,  2,  2],
                                          [0,  0, -1,  1,  1,  2,  2],
                                          [0, -1,  0,  1,  1,  2,  2],
                                          [-1, 0,  0,  1,  1,  2,  2]]),
                '6_nans': permuted_array([[0,  1,  2,  3,  4,  5, -1],
                                          [0,  1,  2,  3,  4, -1,  5],
                                          [0,  1,  2,  3, -1,  4,  5],
                                          [0,  1,  2, -1,  3,  4,  5],
                                          [0,  1, -1,  2,  3,  4,  5],
                                          [0, -1,  1,  2,  3,  4,  5],
                                          [-1, 0,  1,  2,  3,  4,  5]]),
            },
            mask=self.build_mask(self.ones_mask(shape=shape)),
        )

    def test_quantiles_uneven_buckets(self):
        permute = partial(permute_rows, 5)
        shape = (5, 5)

        factor_data = permute(log1p(arange(25, dtype=float).reshape(shape)))
        mask_data = permute(self.eye_mask(shape=shape))

        f = F()
        m = Mask()

        permuted_array = compose(permute, partial(array, dtype=int64_dtype))
        self.check_terms(
            terms={
                '3_masked': f.quantiles(bins=3, mask=m),
                '7_masked': f.quantiles(bins=7, mask=m),
            },
            initial_workspace={
                f: factor_data,
                m: mask_data,
            },
            expected={
                '3_masked': permuted_array([[-1, 0,  0,  1,  2],
                                            [0, -1,  0,  1,  2],
                                            [0,  0, -1,  1,  2],
                                            [0,  0,  1, -1,  2],
                                            [0,  0,  1,  2, -1]]),
                '7_masked': permuted_array([[-1, 0,  2,  4,  6],
                                            [0, -1,  2,  4,  6],
                                            [0,  2, -1,  4,  6],
                                            [0,  2,  4, -1,  6],
                                            [0,  2,  4,  6, -1]]),
            },
            mask=self.build_mask(self.ones_mask(shape=shape)),
        )

    def test_quantile_helpers(self):
        f = self.f
        m = Mask()

        self.assertIs(f.quartiles(), f.quantiles(bins=4))
        self.assertIs(f.quartiles(mask=m), f.quantiles(bins=4, mask=m))
        self.assertIsNot(f.quartiles(), f.quartiles(mask=m))

        self.assertIs(f.quintiles(), f.quantiles(bins=5))
        self.assertIs(f.quintiles(mask=m), f.quantiles(bins=5, mask=m))
        self.assertIsNot(f.quintiles(), f.quintiles(mask=m))

        self.assertIs(f.deciles(), f.quantiles(bins=10))
        self.assertIs(f.deciles(mask=m), f.quantiles(bins=10, mask=m))
        self.assertIsNot(f.deciles(), f.deciles(mask=m))


class ShortReprTestCase(TestCase):
    """
    Tests for short_repr methods of Factors.
    """

    def test_demean(self):
        r = F().demean().short_repr()
        self.assertEqual(r, "GroupedRowTransform('demean')")

    def test_zscore(self):
        r = F().zscore().short_repr()
        self.assertEqual(r, "GroupedRowTransform('zscore')")


class TestWindowSafety(TestCase):

    def test_zscore_is_window_safe(self):
        self.assertTrue(F().zscore().window_safe)

    def test_demean_is_window_safe_if_input_is_window_safe(self):
        self.assertFalse(F().demean().window_safe)
        self.assertFalse(F(window_safe=False).demean().window_safe)
        self.assertTrue(F(window_safe=True).demean().window_safe)


class TestPostProcessAndToWorkSpaceValue(ZiplineTestCase):
    @parameter_space(dtype_=(float64_dtype, datetime64ns_dtype))
    def test_reversability(self, dtype_):
        class F(Factor):
            inputs = ()
            dtype = dtype_
            window_length = 0

        f = F()
        column_data = array(
            [[0, f.missing_value],
             [1, f.missing_value],
             [2, 3]],
            dtype=dtype_,
        )

        assert_equal(f.postprocess(column_data.ravel()), column_data.ravel())

        # only include the non-missing data
        pipeline_output = pd.Series(
            data=array([0, 1, 2, 3], dtype=dtype_),
            index=pd.MultiIndex.from_arrays([
                [pd.Timestamp('2014-01-01'),
                 pd.Timestamp('2014-01-02'),
                 pd.Timestamp('2014-01-03'),
                 pd.Timestamp('2014-01-03')],
                [0, 0, 0, 1],
            ]),
        )

        assert_equal(
            f.to_workspace_value(pipeline_output, pd.Index([0, 1])),
            column_data,
        )