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208 lines
7.7 KiB
Python
208 lines
7.7 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 compatibility
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import pytest
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import numpy as np
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import pandas as pd
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from pandas.testing import assert_series_equal
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from eland.tests.common import TestData
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class TestDataFrameMetrics(TestData):
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funcs = ["max", "min", "mean", "sum"]
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extended_funcs = ["median", "mad", "var", "std"]
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@pytest.mark.parametrize("numeric_only", [False, None])
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def test_flights_metrics(self, numeric_only):
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pd_flights = self.pd_flights()
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ed_flights = self.ed_flights()
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for func in self.funcs:
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# Pandas v1.0 doesn't support mean() on datetime
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# Pandas and Eland don't support sum() on datetime
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if not numeric_only:
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dtype_include = (
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[np.number, np.datetime64]
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if func not in ("mean", "sum")
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else [np.number]
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)
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pd_flights = pd_flights.select_dtypes(include=dtype_include)
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ed_flights = ed_flights.select_dtypes(include=dtype_include)
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pd_metric = getattr(pd_flights, func)(numeric_only=numeric_only)
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ed_metric = getattr(ed_flights, func)(numeric_only=numeric_only)
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assert_series_equal(pd_metric, ed_metric)
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def test_flights_extended_metrics(self):
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pd_flights = self.pd_flights()
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ed_flights = self.ed_flights()
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# Test on reduced set of data for more consistent
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# median behaviour + better var, std test for sample vs population
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pd_flights = pd_flights[["AvgTicketPrice"]]
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ed_flights = ed_flights[["AvgTicketPrice"]]
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import logging
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logger = logging.getLogger("elasticsearch")
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logger.addHandler(logging.StreamHandler())
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logger.setLevel(logging.DEBUG)
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for func in self.extended_funcs:
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pd_metric = getattr(pd_flights, func)(
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**({"numeric_only": True} if func != "mad" else {})
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)
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ed_metric = getattr(ed_flights, func)(numeric_only=True)
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pd_value = pd_metric["AvgTicketPrice"]
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ed_value = ed_metric["AvgTicketPrice"]
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assert (ed_value * 0.9) <= pd_value <= (ed_value * 1.1) # +/-10%
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def test_flights_extended_metrics_nan(self):
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pd_flights = self.pd_flights()
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ed_flights = self.ed_flights()
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# Test on single row to test NaN behaviour of sample std/variance
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pd_flights_1 = pd_flights[pd_flights.FlightNum == "9HY9SWR"][["AvgTicketPrice"]]
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ed_flights_1 = ed_flights[ed_flights.FlightNum == "9HY9SWR"][["AvgTicketPrice"]]
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for func in self.extended_funcs:
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pd_metric = getattr(pd_flights_1, func)()
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ed_metric = getattr(ed_flights_1, func)()
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assert_series_equal(
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pd_metric, ed_metric, check_exact=False, check_less_precise=True
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)
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# Test on zero rows to test NaN behaviour of sample std/variance
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pd_flights_0 = pd_flights[pd_flights.FlightNum == "XXX"][["AvgTicketPrice"]]
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ed_flights_0 = ed_flights[ed_flights.FlightNum == "XXX"][["AvgTicketPrice"]]
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for func in self.extended_funcs:
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pd_metric = getattr(pd_flights_0, func)()
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ed_metric = getattr(ed_flights_0, func)()
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assert_series_equal(
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pd_metric, ed_metric, check_exact=False, check_less_precise=True
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)
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def test_ecommerce_selected_non_numeric_source_fields(self):
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# None of these are numeric
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columns = [
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"category",
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"currency",
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"customer_birth_date",
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"customer_first_name",
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"user",
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]
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pd_ecommerce = self.pd_ecommerce()[columns]
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ed_ecommerce = self.ed_ecommerce()[columns]
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for func in self.funcs:
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assert_series_equal(
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getattr(pd_ecommerce, func)(numeric_only=True),
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getattr(ed_ecommerce, func)(numeric_only=True),
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check_less_precise=True,
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)
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def test_ecommerce_selected_mixed_numeric_source_fields(self):
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# Some of these are numeric
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columns = [
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"category",
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"currency",
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"taxless_total_price",
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"customer_birth_date",
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"total_quantity",
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"customer_first_name",
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"user",
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]
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pd_ecommerce = self.pd_ecommerce()[columns]
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ed_ecommerce = self.ed_ecommerce()[columns]
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for func in self.funcs:
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assert_series_equal(
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getattr(pd_ecommerce, func)(numeric_only=True),
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getattr(ed_ecommerce, func)(numeric_only=True),
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check_less_precise=True,
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)
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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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pd_ecommerce = self.pd_ecommerce()[columns]
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ed_ecommerce = self.ed_ecommerce()[columns]
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for func in self.funcs:
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assert_series_equal(
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getattr(pd_ecommerce, func)(numeric_only=True),
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getattr(ed_ecommerce, func)(numeric_only=True),
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check_less_precise=True,
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)
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def test_flights_datetime_metrics_agg(self):
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ed_timestamps = self.ed_flights()[["timestamp"]]
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expected_values = {
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"timestamp": {
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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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"mad": pd.NaT,
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"median": pd.NaT,
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"std": pd.NaT,
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"sum": pd.NaT,
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"var": pd.NaT,
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"nunique": 12236,
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}
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}
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ed_metrics = ed_timestamps.agg(self.funcs + self.extended_funcs + ["nunique"])
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assert ed_metrics.to_dict() == expected_values
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@pytest.mark.parametrize("agg", ["mean", "min", "max"])
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def test_flights_datetime_metrics_single_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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"nunique": 12236,
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}
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ed_metric = ed_timestamps.agg([agg])
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assert ed_metric.dtypes["timestamp"] == np.dtype("datetime64[ns]")
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assert ed_metric["timestamp"][0] == expected_values[agg]
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@pytest.mark.parametrize("agg", ["mean", "min", "max"])
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def test_flights_datetime_metrics_agg_func(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)(numeric_only=False)
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assert ed_metric.dtype == np.dtype("datetime64[ns]")
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assert ed_metric[0] == expected_values[agg]
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