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187 lines
5.4 KiB
Python
187 lines
5.4 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. 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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from datetime import datetime
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from importlib.util import find_spec
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import pandas as pd
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import pytest
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from superset.exceptions import InvalidPostProcessingError
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from superset.utils.core import DTTM_ALIAS
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from superset.utils.pandas_postprocessing import prophet
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from tests.unit_tests.fixtures.dataframes import prophet_df
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def test_prophet_valid():
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df = prophet(df=prophet_df, time_grain="P1M", periods=3, confidence_interval=0.9)
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columns = {column for column in df.columns}
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assert columns == {
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DTTM_ALIAS,
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"a__yhat",
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"a__yhat_upper",
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"a__yhat_lower",
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"a",
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"b__yhat",
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"b__yhat_upper",
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"b__yhat_lower",
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"b",
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}
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assert df[DTTM_ALIAS].iloc[0].to_pydatetime() == datetime(2018, 12, 31)
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assert df[DTTM_ALIAS].iloc[-1].to_pydatetime() == datetime(2022, 3, 31)
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assert len(df) == 7
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df = prophet(df=prophet_df, time_grain="P1M", periods=5, confidence_interval=0.9)
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assert df[DTTM_ALIAS].iloc[0].to_pydatetime() == datetime(2018, 12, 31)
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assert df[DTTM_ALIAS].iloc[-1].to_pydatetime() == datetime(2022, 5, 31)
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assert len(df) == 9
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df = prophet(
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df=pd.DataFrame(
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{
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"__timestamp": [datetime(2022, 1, 2), datetime(2022, 1, 9)],
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"x": [1, 1],
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}
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),
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time_grain="P1W",
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periods=1,
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confidence_interval=0.9,
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)
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assert df[DTTM_ALIAS].iloc[-1].to_pydatetime() == datetime(2022, 1, 16)
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assert len(df) == 3
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df = prophet(
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df=pd.DataFrame(
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{
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"__timestamp": [datetime(2022, 1, 2), datetime(2022, 1, 9)],
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"x": [1, 1],
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}
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),
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time_grain="1969-12-28T00:00:00Z/P1W",
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periods=1,
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confidence_interval=0.9,
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)
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assert df[DTTM_ALIAS].iloc[-1].to_pydatetime() == datetime(2022, 1, 16)
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assert len(df) == 3
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df = prophet(
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df=pd.DataFrame(
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{
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"__timestamp": [datetime(2022, 1, 3), datetime(2022, 1, 10)],
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"x": [1, 1],
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}
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),
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time_grain="1969-12-29T00:00:00Z/P1W",
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periods=1,
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confidence_interval=0.9,
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)
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assert df[DTTM_ALIAS].iloc[-1].to_pydatetime() == datetime(2022, 1, 17)
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assert len(df) == 3
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df = prophet(
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df=pd.DataFrame(
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{
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"__timestamp": [datetime(2022, 1, 8), datetime(2022, 1, 15)],
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"x": [1, 1],
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}
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),
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time_grain="P1W/1970-01-03T00:00:00Z",
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periods=1,
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confidence_interval=0.9,
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)
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assert df[DTTM_ALIAS].iloc[-1].to_pydatetime() == datetime(2022, 1, 22)
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assert len(df) == 3
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def test_prophet_valid_zero_periods():
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df = prophet(df=prophet_df, time_grain="P1M", periods=0, confidence_interval=0.9)
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columns = {column for column in df.columns}
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assert columns == {
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DTTM_ALIAS,
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"a__yhat",
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"a__yhat_upper",
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"a__yhat_lower",
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"a",
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"b__yhat",
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"b__yhat_upper",
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"b__yhat_lower",
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"b",
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}
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assert df[DTTM_ALIAS].iloc[0].to_pydatetime() == datetime(2018, 12, 31)
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assert df[DTTM_ALIAS].iloc[-1].to_pydatetime() == datetime(2021, 12, 31)
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assert len(df) == 4
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def test_prophet_import():
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dynamic_module = find_spec("prophet")
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if dynamic_module is None:
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with pytest.raises(InvalidPostProcessingError):
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prophet(df=prophet_df, time_grain="P1M", periods=3, confidence_interval=0.9)
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def test_prophet_missing_temporal_column():
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df = prophet_df.drop(DTTM_ALIAS, axis=1)
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with pytest.raises(InvalidPostProcessingError):
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prophet(
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df=df,
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time_grain="P1M",
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periods=3,
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confidence_interval=0.9,
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)
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def test_prophet_incorrect_confidence_interval():
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with pytest.raises(InvalidPostProcessingError):
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prophet(
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df=prophet_df,
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time_grain="P1M",
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periods=3,
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confidence_interval=0.0,
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)
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with pytest.raises(InvalidPostProcessingError):
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prophet(
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df=prophet_df,
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time_grain="P1M",
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periods=3,
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confidence_interval=1.0,
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)
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def test_prophet_incorrect_periods():
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with pytest.raises(InvalidPostProcessingError):
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prophet(
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df=prophet_df,
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time_grain="P1M",
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periods=-1,
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confidence_interval=0.8,
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)
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def test_prophet_incorrect_time_grain():
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with pytest.raises(InvalidPostProcessingError):
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prophet(
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df=prophet_df,
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time_grain="yearly",
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periods=10,
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confidence_interval=0.8,
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)
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