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117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
# Licensed to Elasticsearch B.V. under one or more contributor
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# license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright
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# ownership. Elasticsearch B.V. licenses this file to you under
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# the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# File called _pytest for PyCharm compatability
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from datetime import timedelta
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import numpy as np
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import pandas as pd
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import pytest
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from tests.common import TestData
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class TestSeriesMetrics(TestData):
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all_funcs = ["max", "min", "mean", "sum", "nunique", "var", "std", "mad"]
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timestamp_funcs = ["max", "min", "mean", "nunique"]
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def assert_almost_equal_for_agg(self, func, pd_metric, ed_metric):
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if func in ("nunique", "var", "mad"):
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np.testing.assert_almost_equal(pd_metric, ed_metric, decimal=-3)
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else:
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np.testing.assert_almost_equal(pd_metric, ed_metric, decimal=2)
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def test_flights_metrics(self):
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pd_flights = self.pd_flights()["AvgTicketPrice"]
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ed_flights = self.ed_flights()["AvgTicketPrice"]
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for func in self.all_funcs:
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pd_metric = getattr(pd_flights, func)()
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ed_metric = getattr(ed_flights, func)()
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self.assert_almost_equal_for_agg(func, pd_metric, ed_metric)
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def test_flights_timestamp(self):
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pd_flights = self.pd_flights()["timestamp"]
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ed_flights = self.ed_flights()["timestamp"]
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for func in self.timestamp_funcs:
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pd_metric = getattr(pd_flights, func)()
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ed_metric = getattr(ed_flights, func)()
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if func == "nunique":
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print(pd_metric, ed_metric)
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self.assert_almost_equal_for_agg(func, pd_metric, ed_metric)
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elif func == "mean":
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offset = timedelta(seconds=0.001)
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assert (ed_metric - offset) < pd_metric < (ed_metric + offset)
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else:
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assert pd_metric == ed_metric
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def test_ecommerce_selected_non_numeric_source_fields(self):
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# None of these are numeric, will result in NaNs
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column = "category"
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ed_ecommerce = self.ed_ecommerce()[column]
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for func in self.all_funcs:
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if func == "nunique": # nunique never returns 'NaN'
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continue
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ed_metric = getattr(ed_ecommerce, func)(numeric_only=False)
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print(func, ed_metric)
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assert np.isnan(ed_metric)
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def test_ecommerce_selected_all_numeric_source_fields(self):
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# All of these are numeric
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columns = ["total_quantity", "taxful_total_price", "taxless_total_price"]
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for column in columns:
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pd_ecommerce = self.pd_ecommerce()[column]
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ed_ecommerce = self.ed_ecommerce()[column]
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for func in self.all_funcs:
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pd_metric = getattr(pd_ecommerce, func)()
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ed_metric = getattr(ed_ecommerce, func)(
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**({"numeric_only": True} if (func != "nunique") else {})
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)
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self.assert_almost_equal_for_agg(func, pd_metric, ed_metric)
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@pytest.mark.parametrize("agg", ["mean", "min", "max"])
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def test_flights_datetime_metrics_agg(self, agg):
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ed_timestamps = self.ed_flights()["timestamp"]
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expected_values = {
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"min": pd.Timestamp("2018-01-01 00:00:00"),
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"mean": pd.Timestamp("2018-01-21 19:20:45.564438232"),
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"max": pd.Timestamp("2018-02-11 23:50:12"),
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}
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ed_metric = getattr(ed_timestamps, agg)()
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assert ed_metric == expected_values[agg]
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def test_flights_datetime_median_metric(self):
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ed_series = self.ed_flights_small()["timestamp"]
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median = ed_series.median()
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assert isinstance(median, pd.Timestamp)
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assert (
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pd.to_datetime("2018-01-01 10:00:00.000")
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<= median
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<= pd.to_datetime("2018-01-01 12:00:00.000")
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)
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