some new features
This commit is contained in:
ilgazca
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57
.venv/lib/python3.12/site-packages/prophet/tests/conftest.py
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57
.venv/lib/python3.12/site-packages/prophet/tests/conftest.py
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from pathlib import Path
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import pandas as pd
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import pytest
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@pytest.fixture(scope="package")
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def daily_univariate_ts() -> pd.DataFrame:
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"""Daily univariate time series with 2 years of data"""
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return pd.read_csv(Path(__file__).parent / "data.csv", parse_dates=["ds"])
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@pytest.fixture(scope="package")
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def subdaily_univariate_ts() -> pd.DataFrame:
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"""Sub-daily univariate time series"""
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return pd.read_csv(Path(__file__).parent / "data2.csv", parse_dates=["ds"])
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@pytest.fixture(scope="package")
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def large_numbers_ts() -> pd.DataFrame:
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"""Univariate time series with large values to test scaling"""
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return pd.read_csv(Path(__file__).parent / "data3.csv", parse_dates=["ds"])
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def pytest_configure(config):
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config.addinivalue_line("markers", "slow: mark tests as slow (include in run with --test-slow)")
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def pytest_addoption(parser):
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parser.addoption("--test-slow", action="store_true", default=False, help="Run slow tests")
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parser.addoption(
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"--backend",
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nargs="+",
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default=["CMDSTANPY"],
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help="Probabilistic Programming Language backend to perform tests with.",
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)
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def pytest_collection_modifyitems(config, items):
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if config.getoption("--test-slow"):
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return
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skip_slow = pytest.mark.skip(reason="Skipped due to the lack of '--test-slow' argument")
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for item in items:
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if "slow" in item.keywords:
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item.add_marker(skip_slow)
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def pytest_generate_tests(metafunc):
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"""
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For each test, if `backend` is used as a fixture, add a parametrization equal to the value of the
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--backend option.
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This is used to re-run the test suite for different probabilistic programming language backends
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(e.g. cmdstanpy, numpyro).
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"""
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if "backend" in metafunc.fixturenames:
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metafunc.parametrize("backend", metafunc.config.getoption("backend"))
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511
.venv/lib/python3.12/site-packages/prophet/tests/data.csv
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511
.venv/lib/python3.12/site-packages/prophet/tests/data.csv
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ds,y
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2012-05-18,38.23
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2012-05-21,34.03
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2012-05-22,31.0
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2012-05-23,32.0
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2012-05-24,33.03
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2012-05-25,31.91
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2012-05-29,28.84
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2012-05-30,28.19
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2012-05-31,29.6
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2012-06-01,27.72
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2012-06-04,26.9
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2012-06-05,25.87
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2012-06-06,26.81
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2012-06-07,26.31
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2012-06-08,27.1
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2012-06-11,27.01
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2012-06-12,27.4
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2012-06-13,27.27
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2012-06-14,28.29
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2012-06-15,30.01
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2012-06-18,31.41
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2012-06-19,31.91
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2012-06-20,31.6
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2012-06-21,31.84
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2012-06-22,33.05
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2012-06-25,32.06
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2012-06-26,33.1
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2012-06-27,32.23
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2012-06-28,31.36
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2012-06-29,31.1
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2012-07-02,30.77
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2012-07-03,31.2
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2012-07-05,31.47
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2012-07-06,31.73
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2012-07-09,32.17
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2012-07-10,31.47
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2012-07-11,30.97
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2012-07-12,30.81
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2012-07-13,30.72
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2012-07-16,28.25
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2012-07-17,28.09
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2012-07-18,29.11
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2012-07-19,29.0
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2012-07-20,28.76
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2012-07-23,28.75
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2012-07-24,28.45
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2012-07-25,29.34
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2012-07-26,26.85
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2012-07-27,23.71
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2012-07-30,23.15
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2012-07-31,21.71
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2012-08-01,20.88
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2012-08-02,20.04
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2012-08-03,21.09
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2012-08-06,21.92
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2012-08-07,20.72
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2012-08-08,20.72
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2012-08-09,21.01
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2012-08-10,21.81
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2012-08-13,21.6
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2012-08-14,20.38
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2012-08-15,21.2
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2012-08-16,19.87
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2012-08-17,19.05
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2012-08-20,20.01
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2012-08-21,19.16
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2012-08-22,19.44
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2012-08-23,19.44
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2012-08-24,19.41
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2012-08-27,19.15
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2012-08-28,19.34
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2012-08-29,19.1
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2012-08-30,19.09
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2012-08-31,18.06
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2012-09-04,17.73
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2012-09-05,18.58
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2012-09-06,18.96
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2012-09-07,18.98
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2012-09-10,18.81
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2012-09-11,19.43
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2012-09-12,20.93
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2012-09-13,20.71
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2012-09-14,22.0
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2012-09-17,21.52
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2012-09-18,21.87
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2012-09-19,23.29
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2012-09-20,22.59
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2012-09-21,22.86
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2012-09-24,20.79
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2012-09-25,20.28
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2012-09-26,20.62
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2012-09-27,20.32
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2012-09-28,21.66
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2012-10-01,21.99
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2012-10-02,22.27
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2012-10-03,21.83
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2012-10-04,21.95
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2012-10-05,20.91
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2012-10-08,20.4
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2012-10-09,20.23
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2012-10-10,19.64
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2012-10-11,19.75
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2012-10-12,19.52
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2012-10-15,19.52
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2012-10-16,19.48
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2012-10-17,19.88
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2012-10-18,18.98
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2012-10-19,19.0
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2012-10-22,19.32
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2012-10-23,19.5
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2012-10-24,23.23
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2012-10-25,22.56
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2012-10-26,21.94
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2012-10-31,21.11
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2012-11-01,21.21
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2012-11-02,21.18
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2012-11-05,21.25
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2012-11-06,21.17
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2012-11-07,20.47
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2012-11-08,19.99
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2012-11-09,19.21
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2012-11-12,20.07
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2012-11-13,19.86
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2012-11-14,22.36
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2012-11-15,22.17
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2012-11-16,23.56
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2012-11-19,22.92
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2012-11-20,23.1
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2012-11-21,24.32
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2012-11-23,24.0
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2012-11-26,25.94
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2012-11-27,26.15
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2012-11-28,26.36
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2012-11-29,27.32
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2012-11-30,28.0
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2012-12-03,27.04
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2012-12-04,27.46
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2012-12-05,27.71
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2012-12-06,26.97
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2012-12-07,27.49
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2012-12-10,27.84
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2012-12-11,27.98
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2012-12-12,27.58
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2012-12-13,28.24
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2012-12-14,26.81
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2012-12-17,26.75
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2012-12-18,27.71
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2012-12-19,27.41
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2012-12-20,27.36
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2012-12-21,26.26
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2012-12-24,26.93
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2012-12-26,26.51
|
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2012-12-27,26.05
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2012-12-28,25.91
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2012-12-31,26.62
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2013-01-02,28.0
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2013-01-03,27.77
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2013-01-04,28.76
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2013-01-07,29.42
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2013-01-08,29.06
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2013-01-09,30.59
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2013-01-10,31.3
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2013-01-11,31.72
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2013-01-14,30.95
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2013-01-15,30.1
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2013-01-16,29.85
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2013-01-17,30.14
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2013-01-18,29.66
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2013-01-22,30.73
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2013-01-23,30.82
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2013-01-24,31.08
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2013-01-25,31.54
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2013-01-28,32.47
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2013-01-29,30.79
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2013-01-30,31.24
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2013-01-31,30.98
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2013-02-01,29.73
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2013-02-04,28.11
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2013-02-05,28.64
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2013-02-06,29.05
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2013-02-07,28.65
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2013-02-08,28.55
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2013-02-11,28.26
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2013-02-12,27.37
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2013-02-13,27.91
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2013-02-14,28.5
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2013-02-15,28.32
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2013-02-19,28.93
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2013-02-20,28.46
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2013-02-21,27.28
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2013-02-22,27.13
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2013-02-25,27.27
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2013-02-26,27.39
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2013-02-27,26.87
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2013-02-28,27.25
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2013-03-01,27.78
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2013-03-04,27.72
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2013-03-05,27.52
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2013-03-06,27.45
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2013-03-07,28.58
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2013-03-08,27.96
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2013-03-11,28.14
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2013-03-12,27.83
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2013-03-13,27.08
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2013-03-14,27.04
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2013-03-15,26.65
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2013-03-18,26.49
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2013-03-19,26.55
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2013-03-20,25.86
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2013-03-21,25.74
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2013-03-22,25.73
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2013-03-25,25.13
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2013-03-26,25.21
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2013-03-27,26.09
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2013-03-28,25.58
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2013-04-01,25.53
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2013-04-02,25.42
|
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2013-04-03,26.25
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2013-04-04,27.07
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2013-04-05,27.39
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2013-04-08,26.85
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2013-04-09,26.59
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2013-04-10,27.57
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2013-04-11,28.02
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2013-04-12,27.4
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2013-04-15,26.52
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2013-04-16,26.92
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2013-04-17,26.63
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2013-04-18,25.69
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2013-04-19,25.73
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2013-04-22,25.97
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2013-04-23,25.98
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2013-04-24,26.11
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2013-04-25,26.14
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2013-04-26,26.85
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2013-04-29,26.98
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2013-04-30,27.77
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2013-05-01,27.43
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2013-05-02,28.97
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2013-05-03,28.31
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2013-05-06,27.57
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2013-05-07,26.89
|
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2013-05-08,27.12
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2013-05-09,27.04
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2013-05-10,26.68
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2013-05-13,26.82
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2013-05-14,27.07
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2013-05-15,26.6
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2013-05-16,26.13
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2013-05-17,26.25
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2013-05-20,25.76
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2013-05-21,25.66
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2013-05-22,25.16
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2013-05-23,25.06
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2013-05-24,24.31
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2013-05-28,24.1
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2013-05-29,23.32
|
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2013-05-30,24.55
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2013-05-31,24.35
|
||||
2013-06-03,23.85
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2013-06-04,23.52
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2013-06-05,22.9
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2013-06-06,22.97
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2013-06-07,23.29
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2013-06-10,24.33
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2013-06-11,24.03
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2013-06-12,23.77
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2013-06-13,23.73
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2013-06-14,23.63
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2013-06-17,24.02
|
||||
2013-06-18,24.21
|
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2013-06-19,24.31
|
||||
2013-06-20,23.9
|
||||
2013-06-21,24.53
|
||||
2013-06-24,23.94
|
||||
2013-06-25,24.25
|
||||
2013-06-26,24.16
|
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2013-06-27,24.66
|
||||
2013-06-28,24.88
|
||||
2013-07-01,24.81
|
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2013-07-02,24.41
|
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2013-07-03,24.52
|
||||
2013-07-05,24.37
|
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2013-07-08,24.71
|
||||
2013-07-09,25.48
|
||||
2013-07-10,25.8
|
||||
2013-07-11,25.81
|
||||
2013-07-12,25.91
|
||||
2013-07-15,26.28
|
||||
2013-07-16,26.32
|
||||
2013-07-17,26.65
|
||||
2013-07-18,26.18
|
||||
2013-07-19,25.88
|
||||
2013-07-22,26.05
|
||||
2013-07-23,26.13
|
||||
2013-07-24,26.51
|
||||
2013-07-25,34.36
|
||||
2013-07-26,34.01
|
||||
2013-07-29,35.43
|
||||
2013-07-30,37.63
|
||||
2013-07-31,36.8
|
||||
2013-08-01,37.49
|
||||
2013-08-02,38.05
|
||||
2013-08-05,39.19
|
||||
2013-08-06,38.55
|
||||
2013-08-07,38.87
|
||||
2013-08-08,38.54
|
||||
2013-08-09,38.5
|
||||
2013-08-12,38.22
|
||||
2013-08-13,37.02
|
||||
2013-08-14,36.65
|
||||
2013-08-15,36.56
|
||||
2013-08-16,37.08
|
||||
2013-08-19,37.81
|
||||
2013-08-20,38.41
|
||||
2013-08-21,38.32
|
||||
2013-08-22,38.55
|
||||
2013-08-23,40.55
|
||||
2013-08-26,41.34
|
||||
2013-08-27,39.64
|
||||
2013-08-28,40.55
|
||||
2013-08-29,41.28
|
||||
2013-08-30,41.29
|
||||
2013-09-03,41.87
|
||||
2013-09-04,41.78
|
||||
2013-09-05,42.66
|
||||
2013-09-06,43.95
|
||||
2013-09-09,44.04
|
||||
2013-09-10,43.6
|
||||
2013-09-11,45.04
|
||||
2013-09-12,44.75
|
||||
2013-09-13,44.31
|
||||
2013-09-16,42.51
|
||||
2013-09-17,45.07
|
||||
2013-09-18,45.23
|
||||
2013-09-19,45.98
|
||||
2013-09-20,47.49
|
||||
2013-09-23,47.19
|
||||
2013-09-24,48.45
|
||||
2013-09-25,49.46
|
||||
2013-09-26,50.39
|
||||
2013-09-27,51.24
|
||||
2013-09-30,50.23
|
||||
2013-10-01,50.42
|
||||
2013-10-02,50.28
|
||||
2013-10-03,49.18
|
||||
2013-10-04,51.04
|
||||
2013-10-07,50.52
|
||||
2013-10-08,47.14
|
||||
2013-10-09,46.77
|
||||
2013-10-10,49.05
|
||||
2013-10-11,49.11
|
||||
2013-10-14,49.51
|
||||
2013-10-15,49.5
|
||||
2013-10-16,51.14
|
||||
2013-10-17,52.21
|
||||
2013-10-18,54.22
|
||||
2013-10-21,53.85
|
||||
2013-10-22,52.68
|
||||
2013-10-23,51.9
|
||||
2013-10-24,52.45
|
||||
2013-10-25,51.95
|
||||
2013-10-28,50.23
|
||||
2013-10-29,49.4
|
||||
2013-10-30,49.01
|
||||
2013-10-31,50.21
|
||||
2013-11-01,49.75
|
||||
2013-11-04,48.22
|
||||
2013-11-05,50.11
|
||||
2013-11-06,49.12
|
||||
2013-11-07,47.56
|
||||
2013-11-08,47.53
|
||||
2013-11-11,46.2
|
||||
2013-11-12,46.61
|
||||
2013-11-13,48.71
|
||||
2013-11-14,48.99
|
||||
2013-11-15,49.01
|
||||
2013-11-18,45.83
|
||||
2013-11-19,46.36
|
||||
2013-11-20,46.43
|
||||
2013-11-21,46.7
|
||||
2013-11-22,46.23
|
||||
2013-11-25,44.82
|
||||
2013-11-26,45.89
|
||||
2013-11-27,46.49
|
||||
2013-11-29,47.01
|
||||
2013-12-02,47.06
|
||||
2013-12-03,46.73
|
||||
2013-12-04,48.62
|
||||
2013-12-05,48.34
|
||||
2013-12-06,47.94
|
||||
2013-12-09,48.84
|
||||
2013-12-10,50.25
|
||||
2013-12-11,49.38
|
||||
2013-12-12,51.83
|
||||
2013-12-13,53.32
|
||||
2013-12-16,53.81
|
||||
2013-12-17,54.86
|
||||
2013-12-18,55.57
|
||||
2013-12-19,55.05
|
||||
2013-12-20,55.12
|
||||
2013-12-23,57.77
|
||||
2013-12-24,57.96
|
||||
2013-12-26,57.73
|
||||
2013-12-27,55.44
|
||||
2013-12-30,53.71
|
||||
2013-12-31,54.65
|
||||
2014-01-02,54.71
|
||||
2014-01-03,54.56
|
||||
2014-01-06,57.2
|
||||
2014-01-07,57.92
|
||||
2014-01-08,58.23
|
||||
2014-01-09,57.22
|
||||
2014-01-10,57.94
|
||||
2014-01-13,55.91
|
||||
2014-01-14,57.74
|
||||
2014-01-15,57.6
|
||||
2014-01-16,57.19
|
||||
2014-01-17,56.3
|
||||
2014-01-21,58.51
|
||||
2014-01-22,57.51
|
||||
2014-01-23,56.63
|
||||
2014-01-24,54.45
|
||||
2014-01-27,53.55
|
||||
2014-01-28,55.14
|
||||
2014-01-29,53.53
|
||||
2014-01-30,61.08
|
||||
2014-01-31,62.57
|
||||
2014-02-03,61.48
|
||||
2014-02-04,62.75
|
||||
2014-02-05,62.19
|
||||
2014-02-06,62.16
|
||||
2014-02-07,64.32
|
||||
2014-02-10,63.55
|
||||
2014-02-11,64.85
|
||||
2014-02-12,64.45
|
||||
2014-02-13,67.33
|
||||
2014-02-14,67.09
|
||||
2014-02-18,67.3
|
||||
2014-02-19,68.06
|
||||
2014-02-20,69.63
|
||||
2014-02-21,68.59
|
||||
2014-02-24,70.78
|
||||
2014-02-25,69.85
|
||||
2014-02-26,69.26
|
||||
2014-02-27,68.94
|
||||
2014-02-28,68.46
|
||||
2014-03-03,67.41
|
||||
2014-03-04,68.8
|
||||
2014-03-05,71.57
|
||||
2014-03-06,70.84
|
||||
2014-03-07,69.8
|
||||
2014-03-10,72.03
|
||||
2014-03-11,70.1
|
||||
2014-03-12,70.88
|
||||
2014-03-13,68.83
|
||||
2014-03-14,67.72
|
||||
2014-03-17,68.74
|
||||
2014-03-18,69.19
|
||||
2014-03-19,68.24
|
||||
2014-03-20,66.97
|
||||
2014-03-21,67.24
|
||||
2014-03-24,64.1
|
||||
2014-03-25,64.89
|
||||
2014-03-26,60.39
|
||||
2014-03-27,60.97
|
||||
2014-03-28,60.01
|
||||
2014-03-31,60.24
|
||||
2014-04-01,62.62
|
||||
2014-04-02,62.72
|
||||
2014-04-03,59.49
|
||||
2014-04-04,56.75
|
||||
2014-04-07,56.95
|
||||
2014-04-08,58.19
|
||||
2014-04-09,62.41
|
||||
2014-04-10,59.16
|
||||
2014-04-11,58.53
|
||||
2014-04-14,58.89
|
||||
2014-04-15,59.09
|
||||
2014-04-16,59.72
|
||||
2014-04-17,58.94
|
||||
2014-04-21,61.24
|
||||
2014-04-22,63.03
|
||||
2014-04-23,61.36
|
||||
2014-04-24,60.87
|
||||
2014-04-25,57.71
|
||||
2014-04-28,56.14
|
||||
2014-04-29,58.15
|
||||
2014-04-30,59.78
|
||||
2014-05-01,61.15
|
||||
2014-05-02,60.46
|
||||
2014-05-05,61.22
|
||||
2014-05-06,58.53
|
||||
2014-05-07,57.39
|
||||
2014-05-08,56.76
|
||||
2014-05-09,57.24
|
||||
2014-05-12,59.83
|
||||
2014-05-13,59.83
|
||||
2014-05-14,59.23
|
||||
2014-05-15,57.92
|
||||
2014-05-16,58.02
|
||||
2014-05-19,59.21
|
||||
2014-05-20,58.56
|
||||
2014-05-21,60.49
|
||||
2014-05-22,60.52
|
||||
2014-05-23,61.35
|
||||
2014-05-27,63.48
|
||||
2014-05-28,63.51
|
||||
2014-05-29,63.83
|
||||
2014-05-30,63.30
|
||||
|
864
.venv/lib/python3.12/site-packages/prophet/tests/data2.csv
Normal file
864
.venv/lib/python3.12/site-packages/prophet/tests/data2.csv
Normal file
@ -0,0 +1,864 @@
|
||||
ds,y
|
||||
2017-01-01 00:05:00,0.0
|
||||
2017-01-01 00:10:00,0.0
|
||||
2017-01-01 00:15:00,0.0
|
||||
2017-01-01 00:20:00,0.0
|
||||
2017-01-01 00:25:00,-0.1
|
||||
2017-01-01 00:30:00,-0.1
|
||||
2017-01-01 00:35:00,-0.1
|
||||
2017-01-01 00:40:00,-0.1
|
||||
2017-01-01 00:45:00,-0.1
|
||||
2017-01-01 00:50:00,-0.1
|
||||
2017-01-01 00:55:00,-0.3
|
||||
2017-01-01 01:00:00,-0.2
|
||||
2017-01-01 01:05:00,-0.3
|
||||
2017-01-01 01:10:00,-0.4
|
||||
2017-01-01 01:15:00,-0.4
|
||||
2017-01-01 01:20:00,-0.3
|
||||
2017-01-01 01:25:00,-0.3
|
||||
2017-01-01 01:30:00,-0.2
|
||||
2017-01-01 01:35:00,-0.3
|
||||
2017-01-01 01:40:00,-0.3
|
||||
2017-01-01 01:45:00,-0.3
|
||||
2017-01-01 01:50:00,-0.3
|
||||
2017-01-01 01:55:00,-0.3
|
||||
2017-01-01 02:00:00,-0.3
|
||||
2017-01-01 02:05:00,-0.3
|
||||
2017-01-01 02:10:00,-0.3
|
||||
2017-01-01 02:15:00,-0.3
|
||||
2017-01-01 02:20:00,-0.3
|
||||
2017-01-01 02:25:00,-0.3
|
||||
2017-01-01 02:30:00,-0.3
|
||||
2017-01-01 02:35:00,-0.3
|
||||
2017-01-01 02:40:00,-0.3
|
||||
2017-01-01 02:45:00,-0.3
|
||||
2017-01-01 02:50:00,-0.3
|
||||
2017-01-01 02:55:00,-0.3
|
||||
2017-01-01 03:00:00,-0.3
|
||||
2017-01-01 03:05:00,-0.3
|
||||
2017-01-01 03:10:00,-0.3
|
||||
2017-01-01 03:15:00,-0.3
|
||||
2017-01-01 03:20:00,-0.3
|
||||
2017-01-01 03:25:00,-0.4
|
||||
2017-01-01 03:30:00,-0.6
|
||||
2017-01-01 03:35:00,-0.4
|
||||
2017-01-01 03:40:00,-0.3
|
||||
2017-01-01 03:45:00,-0.4
|
||||
2017-01-01 03:50:00,-0.7
|
||||
2017-01-01 03:55:00,-0.8
|
||||
2017-01-01 04:00:00,-0.4
|
||||
2017-01-01 04:05:00,-0.3
|
||||
2017-01-01 04:10:00,-0.4
|
||||
2017-01-01 04:15:00,-0.4
|
||||
2017-01-01 04:20:00,-0.4
|
||||
2017-01-01 04:25:00,-0.5
|
||||
2017-01-01 04:30:00,-0.5
|
||||
2017-01-01 04:35:00,-0.5
|
||||
2017-01-01 04:40:00,-0.4
|
||||
2017-01-01 04:45:00,-0.5
|
||||
2017-01-01 04:50:00,-0.5
|
||||
2017-01-01 04:55:00,-0.5
|
||||
2017-01-01 05:00:00,-0.6
|
||||
2017-01-01 05:05:00,-0.9
|
||||
2017-01-01 05:10:00,-0.9
|
||||
2017-01-01 05:15:00,-1.2
|
||||
2017-01-01 05:20:00,-1.4
|
||||
2017-01-01 05:25:00,-1.8
|
||||
2017-01-01 05:30:00,-2.0
|
||||
2017-01-01 05:35:00,-2.2
|
||||
2017-01-01 05:40:00,-1.6
|
||||
2017-01-01 05:45:00,-1.2
|
||||
2017-01-01 05:50:00,-1.2
|
||||
2017-01-01 05:55:00,-1.4
|
||||
2017-01-01 06:00:00,-1.2
|
||||
2017-01-01 06:05:00,-0.9
|
||||
2017-01-01 06:10:00,-0.9
|
||||
2017-01-01 06:15:00,-0.9
|
||||
2017-01-01 06:20:00,-0.9
|
||||
2017-01-01 06:25:00,-0.9
|
||||
2017-01-01 06:30:00,-1.2
|
||||
2017-01-01 06:35:00,-1.1
|
||||
2017-01-01 06:40:00,-1.2
|
||||
2017-01-01 06:45:00,-1.3
|
||||
2017-01-01 06:50:00,-1.4
|
||||
2017-01-01 06:55:00,-1.7
|
||||
2017-01-01 07:00:00,-1.7
|
||||
2017-01-01 07:05:00,-1.7
|
||||
2017-01-01 07:10:00,-1.8
|
||||
2017-01-01 07:15:00,-2.4
|
||||
2017-01-01 07:20:00,-2.9
|
||||
2017-01-01 07:25:00,-3.2
|
||||
2017-01-01 07:30:00,-3.4
|
||||
2017-01-01 07:35:00,-3.6
|
||||
2017-01-01 07:40:00,-3.6
|
||||
2017-01-01 07:45:00,-3.5
|
||||
2017-01-01 07:50:00,-3.5
|
||||
2017-01-01 07:55:00,-3.5
|
||||
2017-01-01 08:00:00,-3.6
|
||||
2017-01-01 08:05:00,-3.7
|
||||
2017-01-01 08:10:00,-3.6
|
||||
2017-01-01 08:15:00,-3.6
|
||||
2017-01-01 08:20:00,-3.8
|
||||
2017-01-01 08:25:00,-4.0
|
||||
2017-01-01 08:30:00,-3.9
|
||||
2017-01-01 08:35:00,-3.9
|
||||
2017-01-01 08:40:00,-4.1
|
||||
2017-01-01 08:45:00,-4.0
|
||||
2017-01-01 08:50:00,-4.1
|
||||
2017-01-01 08:55:00,-4.1
|
||||
2017-01-01 09:00:00,-4.2
|
||||
2017-01-01 09:05:00,-4.1
|
||||
2017-01-01 09:10:00,-4.2
|
||||
2017-01-01 09:15:00,-4.1
|
||||
2017-01-01 09:20:00,-4.0
|
||||
2017-01-01 09:25:00,-4.0
|
||||
2017-01-01 09:30:00,-4.0
|
||||
2017-01-01 09:35:00,-4.1
|
||||
2017-01-01 09:40:00,-4.1
|
||||
2017-01-01 09:45:00,-4.2
|
||||
2017-01-01 09:50:00,-4.3
|
||||
2017-01-01 09:55:00,-4.4
|
||||
2017-01-01 10:00:00,-4.5
|
||||
2017-01-01 10:05:00,-4.6
|
||||
2017-01-01 10:10:00,-4.7
|
||||
2017-01-01 10:15:00,-4.6
|
||||
2017-01-01 10:20:00,-4.6
|
||||
2017-01-01 10:25:00,-4.6
|
||||
2017-01-01 10:30:00,-4.5
|
||||
2017-01-01 10:35:00,-4.6
|
||||
2017-01-01 10:40:00,-4.6
|
||||
2017-01-01 10:45:00,-4.6
|
||||
2017-01-01 10:50:00,-4.6
|
||||
2017-01-01 10:55:00,-4.7
|
||||
2017-01-01 11:00:00,-4.7
|
||||
2017-01-01 11:05:00,-4.6
|
||||
2017-01-01 11:10:00,-4.5
|
||||
2017-01-01 11:15:00,-4.7
|
||||
2017-01-01 11:20:00,-4.7
|
||||
2017-01-01 11:25:00,-4.8
|
||||
2017-01-01 11:30:00,-4.8
|
||||
2017-01-01 11:35:00,-4.8
|
||||
2017-01-01 11:40:00,-4.8
|
||||
2017-01-01 11:45:00,-4.7
|
||||
2017-01-01 11:50:00,-4.6
|
||||
2017-01-01 11:55:00,-4.6
|
||||
2017-01-01 12:00:00,-4.8
|
||||
2017-01-01 12:05:00,-4.9
|
||||
2017-01-01 12:10:00,-4.9
|
||||
2017-01-01 12:15:00,-4.9
|
||||
2017-01-01 12:20:00,-5.0
|
||||
2017-01-01 12:25:00,-4.9
|
||||
2017-01-01 12:30:00,-4.9
|
||||
2017-01-01 12:35:00,-5.0
|
||||
2017-01-01 12:40:00,-5.1
|
||||
2017-01-01 12:45:00,-5.3
|
||||
2017-01-01 12:50:00,-5.5
|
||||
2017-01-01 12:55:00,-5.7
|
||||
2017-01-01 13:00:00,-5.8
|
||||
2017-01-01 13:05:00,-5.9
|
||||
2017-01-01 13:10:00,-5.9
|
||||
2017-01-01 13:15:00,-6.1
|
||||
2017-01-01 13:20:00,-6.1
|
||||
2017-01-01 13:25:00,-6.1
|
||||
2017-01-01 13:30:00,-6.2
|
||||
2017-01-01 13:35:00,-6.3
|
||||
2017-01-01 13:40:00,-6.4
|
||||
2017-01-01 13:45:00,-6.5
|
||||
2017-01-01 13:50:00,-6.6
|
||||
2017-01-01 13:55:00,-6.7
|
||||
2017-01-01 14:00:00,-6.7
|
||||
2017-01-01 14:05:00,-6.7
|
||||
2017-01-01 14:10:00,-6.6
|
||||
2017-01-01 14:15:00,-6.7
|
||||
2017-01-01 14:20:00,-6.7
|
||||
2017-01-01 14:25:00,-6.6
|
||||
2017-01-01 14:30:00,-6.7
|
||||
2017-01-01 14:35:00,-6.6
|
||||
2017-01-01 14:40:00,-6.6
|
||||
2017-01-01 14:45:00,-6.4
|
||||
2017-01-01 14:50:00,-6.5
|
||||
2017-01-01 14:55:00,-6.5
|
||||
2017-01-01 15:00:00,-6.4
|
||||
2017-01-01 15:05:00,-6.4
|
||||
2017-01-01 15:10:00,-6.3
|
||||
2017-01-01 15:15:00,-6.3
|
||||
2017-01-01 15:20:00,-6.4
|
||||
2017-01-01 15:25:00,-6.5
|
||||
2017-01-01 15:30:00,-6.6
|
||||
2017-01-01 15:35:00,-6.6
|
||||
2017-01-01 15:40:00,-6.6
|
||||
2017-01-01 15:45:00,-6.6
|
||||
2017-01-01 15:50:00,-6.5
|
||||
2017-01-01 15:55:00,-6.4
|
||||
2017-01-01 16:00:00,-6.3
|
||||
2017-01-01 16:05:00,-6.3
|
||||
2017-01-01 16:10:00,-6.2
|
||||
2017-01-01 16:15:00,-6.1
|
||||
2017-01-01 16:20:00,-6.0
|
||||
2017-01-01 16:25:00,-5.9
|
||||
2017-01-01 16:30:00,-5.8
|
||||
2017-01-01 16:35:00,-5.7
|
||||
2017-01-01 16:40:00,-5.4
|
||||
2017-01-01 16:45:00,-5.3
|
||||
2017-01-01 16:50:00,-5.1
|
||||
2017-01-01 16:55:00,-5.0
|
||||
2017-01-01 17:00:00,-4.8
|
||||
2017-01-01 17:05:00,-4.6
|
||||
2017-01-01 17:10:00,-4.3
|
||||
2017-01-01 17:15:00,-4.1
|
||||
2017-01-01 17:20:00,-3.9
|
||||
2017-01-01 17:25:00,-3.6
|
||||
2017-01-01 17:30:00,-3.3
|
||||
2017-01-01 17:35:00,-3.1
|
||||
2017-01-01 17:40:00,-2.8
|
||||
2017-01-01 17:45:00,-2.7
|
||||
2017-01-01 17:50:00,-2.4
|
||||
2017-01-01 17:55:00,-2.0
|
||||
2017-01-01 18:00:00,-1.6
|
||||
2017-01-01 18:05:00,-1.3
|
||||
2017-01-01 18:10:00,-1.1
|
||||
2017-01-01 18:15:00,-0.9
|
||||
2017-01-01 18:20:00,-0.7
|
||||
2017-01-01 18:25:00,-0.4
|
||||
2017-01-01 18:30:00,-0.4
|
||||
2017-01-01 18:35:00,-0.2
|
||||
2017-01-01 18:40:00,0.0
|
||||
2017-01-01 18:45:00,0.3
|
||||
2017-01-01 18:50:00,0.6
|
||||
2017-01-01 18:55:00,0.6
|
||||
2017-01-01 19:00:00,1.0
|
||||
2017-01-01 19:05:00,1.0
|
||||
2017-01-01 19:10:00,1.1
|
||||
2017-01-01 19:15:00,1.3
|
||||
2017-01-01 19:20:00,1.0
|
||||
2017-01-01 19:25:00,1.2
|
||||
2017-01-01 19:30:00,1.3
|
||||
2017-01-01 19:35:00,0.9
|
||||
2017-01-01 19:40:00,1.1
|
||||
2017-01-01 19:45:00,1.3
|
||||
2017-01-01 19:50:00,1.5
|
||||
2017-01-01 19:55:00,1.3
|
||||
2017-01-01 20:00:00,1.6
|
||||
2017-01-01 20:05:00,1.6
|
||||
2017-01-01 20:10:00,1.8
|
||||
2017-01-01 20:15:00,1.4
|
||||
2017-01-01 20:20:00,1.4
|
||||
2017-01-01 20:25:00,1.6
|
||||
2017-01-01 20:30:00,1.6
|
||||
2017-01-01 20:35:00,1.5
|
||||
2017-01-01 20:40:00,1.5
|
||||
2017-01-01 20:45:00,1.8
|
||||
2017-01-01 20:50:00,1.6
|
||||
2017-01-01 20:55:00,1.7
|
||||
2017-01-01 21:00:00,1.5
|
||||
2017-01-01 21:05:00,1.8
|
||||
2017-01-01 21:10:00,1.6
|
||||
2017-01-01 21:15:00,1.7
|
||||
2017-01-01 21:20:00,1.9
|
||||
2017-01-01 21:25:00,1.6
|
||||
2017-01-01 21:30:00,1.8
|
||||
2017-01-01 21:35:00,1.8
|
||||
2017-01-01 21:40:00,1.5
|
||||
2017-01-01 21:45:00,1.6
|
||||
2017-01-01 21:50:00,1.6
|
||||
2017-01-01 21:55:00,1.4
|
||||
2017-01-01 22:00:00,1.1
|
||||
2017-01-01 22:05:00,1.5
|
||||
2017-01-01 22:10:00,1.5
|
||||
2017-01-01 22:15:00,1.6
|
||||
2017-01-01 22:20:00,1.5
|
||||
2017-01-01 22:25:00,1.1
|
||||
2017-01-01 22:30:00,1.0
|
||||
2017-01-01 22:35:00,1.0
|
||||
2017-01-01 22:40:00,1.1
|
||||
2017-01-01 22:45:00,1.1
|
||||
2017-01-01 22:50:00,0.7
|
||||
2017-01-01 22:55:00,0.6
|
||||
2017-01-01 23:00:00,0.5
|
||||
2017-01-01 23:05:00,0.3
|
||||
2017-01-01 23:10:00,0.5
|
||||
2017-01-01 23:15:00,0.2
|
||||
2017-01-01 23:20:00,0.2
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||||
2017-01-01 23:25:00,0.0
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2017-01-01 23:30:00,-0.2
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2017-01-01 23:35:00,-0.3
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2017-01-01 23:40:00,-0.5
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2017-01-01 23:45:00,-0.7
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2017-01-01 23:50:00,-1.1
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||||
2017-01-01 23:55:00,-1.3
|
||||
2017-01-02 00:00:00,-1.4
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2017-01-02 00:05:00,-1.7
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2017-01-02 00:10:00,-2.1
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2017-01-02 00:15:00,-2.4
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2017-01-02 00:20:00,-2.6
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2017-01-02 00:25:00,-2.9
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2017-01-02 00:30:00,-3.2
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2017-01-02 00:35:00,-3.5
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2017-01-02 00:40:00,-3.9
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2017-01-02 00:45:00,-4.1
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2017-01-02 00:50:00,-4.2
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2017-01-02 00:55:00,-4.4
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2017-01-02 01:00:00,-4.6
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2017-01-02 01:05:00,-4.7
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2017-01-02 01:10:00,-5.0
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2017-01-02 01:15:00,-5.1
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2017-01-02 01:20:00,-4.8
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2017-01-02 01:25:00,-4.7
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2017-01-02 01:30:00,-4.5
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2017-01-02 01:35:00,-4.0
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2017-01-02 01:40:00,-3.6
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2017-01-02 01:45:00,-3.1
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2017-01-02 01:50:00,-3.0
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2017-01-02 01:55:00,-3.0
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2017-01-02 02:00:00,-3.0
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2017-01-02 02:05:00,-2.9
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2017-01-02 02:10:00,-3.0
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2017-01-02 02:15:00,-2.9
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2017-01-02 02:20:00,-3.0
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2017-01-02 02:25:00,-3.0
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2017-01-02 02:30:00,-3.0
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2017-01-02 02:35:00,-3.0
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2017-01-02 02:40:00,-3.2
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2017-01-02 02:45:00,-3.5
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2017-01-02 02:50:00,-3.7
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2017-01-02 02:55:00,-3.5
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2017-01-02 03:00:00,-3.5
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2017-01-02 03:05:00,-3.4
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2017-01-02 03:10:00,-3.3
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2017-01-02 03:15:00,-3.2
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2017-01-02 03:20:00,-3.2
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2017-01-02 03:25:00,-3.3
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2017-01-02 03:30:00,-3.3
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2017-01-02 03:35:00,-3.3
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2017-01-02 03:40:00,-3.4
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2017-01-02 03:45:00,-3.4
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2017-01-02 03:50:00,-3.4
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2017-01-02 03:55:00,-3.5
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2017-01-02 04:00:00,-3.5
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2017-01-02 04:05:00,-3.5
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2017-01-02 04:10:00,-3.5
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2017-01-02 04:15:00,-3.6
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2017-01-02 04:20:00,-3.6
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2017-01-02 04:25:00,-3.8
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2017-01-02 04:30:00,-3.8
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2017-01-02 04:35:00,-3.8
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2017-01-02 04:40:00,-3.9
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2017-01-02 04:45:00,-3.9
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2017-01-02 04:50:00,-3.9
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2017-01-02 04:55:00,-3.9
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2017-01-02 05:00:00,-3.9
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2017-01-02 05:05:00,-3.9
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2017-01-02 05:10:00,-3.9
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2017-01-02 05:15:00,-4.0
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2017-01-02 05:20:00,-3.9
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2017-01-02 05:25:00,-4.0
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2017-01-02 05:30:00,-4.2
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2017-01-02 05:35:00,-4.2
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2017-01-02 05:40:00,-4.4
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2017-01-02 05:45:00,-4.4
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2017-01-02 05:50:00,-4.4
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2017-01-02 05:55:00,-4.4
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2017-01-02 06:00:00,-4.4
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2017-01-02 06:05:00,-5.3
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2017-01-02 06:10:00,-5.2
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2017-01-02 06:15:00,-5.3
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2017-01-02 06:20:00,-5.2
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2017-01-02 06:25:00,-5.0
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2017-01-02 06:30:00,-4.9
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2017-01-02 06:35:00,-4.8
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2017-01-02 06:40:00,-4.8
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2017-01-02 06:45:00,-4.7
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2017-01-02 06:50:00,-4.7
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2017-01-02 06:55:00,-4.8
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2017-01-02 07:00:00,-4.7
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2017-01-02 07:05:00,-4.7
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2017-01-02 07:10:00,-4.7
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2017-01-02 07:15:00,-5.0
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2017-01-02 07:20:00,-5.0
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2017-01-02 07:25:00,-4.9
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2017-01-02 07:30:00,-4.8
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2017-01-02 07:35:00,-4.8
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2017-01-02 07:40:00,-4.7
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2017-01-02 07:45:00,-4.6
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2017-01-02 07:50:00,-4.6
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2017-01-02 07:55:00,-4.7
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2017-01-02 08:00:00,-4.6
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2017-01-02 08:05:00,-4.6
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2017-01-02 08:10:00,-4.5
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2017-01-02 08:15:00,-4.5
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2017-01-02 08:20:00,-4.5
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2017-01-02 08:25:00,-4.5
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2017-01-02 08:30:00,-4.5
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2017-01-02 08:35:00,-4.5
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2017-01-02 08:40:00,-4.6
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2017-01-02 08:45:00,-4.6
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2017-01-02 08:50:00,-4.6
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2017-01-02 08:55:00,-4.6
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2017-01-02 09:00:00,-4.6
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2017-01-02 09:05:00,-4.6
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2017-01-02 09:10:00,-4.5
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2017-01-02 09:15:00,-4.5
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2017-01-02 09:20:00,-4.5
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2017-01-02 09:25:00,-4.5
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2017-01-02 09:30:00,-4.5
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2017-01-02 09:35:00,-4.5
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2017-01-02 09:40:00,-4.5
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2017-01-02 09:45:00,-4.5
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2017-01-02 09:50:00,-4.4
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2017-01-02 09:55:00,-4.4
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2017-01-02 10:00:00,-4.4
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2017-01-02 10:05:00,-4.5
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2017-01-02 10:10:00,-4.5
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2017-01-02 10:15:00,-4.4
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2017-01-02 10:20:00,-4.5
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2017-01-02 10:25:00,-4.5
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2017-01-02 10:30:00,-4.5
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2017-01-02 10:35:00,-4.5
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2017-01-02 10:40:00,-4.5
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2017-01-02 10:45:00,-4.5
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2017-01-02 10:50:00,-4.5
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2017-01-02 10:55:00,-4.4
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2017-01-02 11:00:00,-4.4
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2017-01-02 11:05:00,-4.5
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2017-01-02 11:10:00,-4.5
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2017-01-02 11:15:00,-4.5
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2017-01-02 11:20:00,-4.5
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2017-01-02 11:25:00,-4.5
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2017-01-02 11:30:00,-4.5
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2017-01-02 11:35:00,-4.5
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2017-01-02 11:40:00,-4.5
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2017-01-02 11:45:00,-4.6
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2017-01-02 11:50:00,-4.6
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2017-01-02 11:55:00,-4.6
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2017-01-02 12:00:00,-4.6
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2017-01-02 12:05:00,-4.7
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2017-01-02 12:10:00,-4.8
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2017-01-02 12:15:00,-4.8
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2017-01-02 12:20:00,-4.9
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2017-01-02 12:25:00,-5.0
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2017-01-02 12:30:00,-5.3
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2017-01-02 12:35:00,-5.5
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2017-01-02 12:40:00,-5.5
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2017-01-02 12:45:00,-5.6
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2017-01-02 12:50:00,-5.9
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2017-01-02 12:55:00,-6.1
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2017-01-02 13:00:00,-6.0
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2017-01-02 13:05:00,-6.1
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2017-01-02 13:10:00,-6.1
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2017-01-02 13:15:00,-6.0
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2017-01-02 13:20:00,-5.7
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2017-01-02 13:25:00,-5.5
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2017-01-02 13:30:00,-5.3
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2017-01-02 13:35:00,-5.2
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2017-01-02 13:40:00,-5.1
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2017-01-02 13:45:00,-5.0
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2017-01-02 13:50:00,-5.0
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2017-01-02 13:55:00,-5.0
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2017-01-02 14:00:00,-4.9
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2017-01-02 14:05:00,-4.9
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2017-01-02 14:10:00,-5.0
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2017-01-02 14:15:00,-4.9
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2017-01-02 14:20:00,-4.9
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2017-01-02 14:25:00,-4.9
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2017-01-02 14:30:00,-4.9
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2017-01-02 14:35:00,-4.9
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2017-01-02 14:40:00,-5.0
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2017-01-02 14:45:00,-4.9
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2017-01-02 14:50:00,-4.9
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2017-01-02 14:55:00,-5.0
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2017-01-02 15:00:00,-4.9
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2017-01-02 15:05:00,-4.9
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2017-01-02 15:10:00,-4.9
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2017-01-02 15:15:00,-4.9
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2017-01-02 15:20:00,-4.9
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2017-01-02 15:25:00,-4.9
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2017-01-02 15:30:00,-4.9
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2017-01-02 15:35:00,-4.9
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2017-01-02 15:40:00,-4.9
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2017-01-02 15:45:00,-4.9
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2017-01-02 15:50:00,-4.9
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2017-01-02 15:55:00,-4.9
|
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2017-01-02 16:00:00,-4.9
|
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2017-01-02 16:05:00,-4.9
|
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2017-01-02 16:10:00,-4.9
|
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2017-01-02 16:15:00,-4.9
|
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2017-01-02 16:20:00,-4.9
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2017-01-02 16:25:00,-4.8
|
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2017-01-02 16:30:00,-4.8
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2017-01-02 16:35:00,-4.7
|
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2017-01-02 16:40:00,-4.8
|
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2017-01-02 16:45:00,-4.8
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||||
2017-01-02 16:50:00,-4.8
|
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2017-01-02 16:55:00,-4.9
|
||||
2017-01-02 17:00:00,-4.8
|
||||
2017-01-02 17:05:00,-4.8
|
||||
2017-01-02 17:10:00,-4.8
|
||||
2017-01-02 17:15:00,-4.8
|
||||
2017-01-02 17:20:00,-4.7
|
||||
2017-01-02 17:25:00,-4.7
|
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2017-01-02 17:30:00,-4.7
|
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2017-01-02 17:35:00,-4.7
|
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2017-01-02 17:40:00,-4.7
|
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2017-01-02 17:45:00,-4.6
|
||||
2017-01-02 17:50:00,-4.7
|
||||
2017-01-02 17:55:00,-4.7
|
||||
2017-01-02 18:00:00,-4.5
|
||||
2017-01-02 18:05:00,-4.6
|
||||
2017-01-02 18:10:00,-4.5
|
||||
2017-01-02 18:15:00,-4.4
|
||||
2017-01-02 18:20:00,-4.6
|
||||
2017-01-02 18:25:00,-4.6
|
||||
2017-01-02 18:30:00,-4.5
|
||||
2017-01-02 18:35:00,-4.4
|
||||
2017-01-02 18:40:00,-4.4
|
||||
2017-01-02 18:45:00,-4.4
|
||||
2017-01-02 18:50:00,-4.3
|
||||
2017-01-02 18:55:00,-4.2
|
||||
2017-01-02 19:00:00,-4.2
|
||||
2017-01-02 19:05:00,-4.2
|
||||
2017-01-02 19:10:00,-4.2
|
||||
2017-01-02 19:15:00,-4.1
|
||||
2017-01-02 19:20:00,-4.2
|
||||
2017-01-02 19:25:00,-4.2
|
||||
2017-01-02 19:30:00,-4.1
|
||||
2017-01-02 19:35:00,-3.9
|
||||
2017-01-02 19:40:00,-3.9
|
||||
2017-01-02 19:45:00,-4.1
|
||||
2017-01-02 19:50:00,-4.2
|
||||
2017-01-02 19:55:00,-4.0
|
||||
2017-01-02 20:00:00,-4.0
|
||||
2017-01-02 20:05:00,-4.1
|
||||
2017-01-02 20:10:00,-4.0
|
||||
2017-01-02 20:15:00,-4.1
|
||||
2017-01-02 20:20:00,-4.1
|
||||
2017-01-02 20:25:00,-4.0
|
||||
2017-01-02 20:30:00,-4.2
|
||||
2017-01-02 20:35:00,-4.1
|
||||
2017-01-02 20:40:00,-4.1
|
||||
2017-01-02 20:45:00,-4.2
|
||||
2017-01-02 20:50:00,-4.1
|
||||
2017-01-02 20:55:00,-4.3
|
||||
2017-01-02 21:00:00,-4.3
|
||||
2017-01-02 21:05:00,-4.4
|
||||
2017-01-02 21:10:00,-4.5
|
||||
2017-01-02 21:15:00,-4.4
|
||||
2017-01-02 21:20:00,-4.2
|
||||
2017-01-02 21:25:00,-4.5
|
||||
2017-01-02 21:30:00,-4.4
|
||||
2017-01-02 21:35:00,-4.2
|
||||
2017-01-02 21:40:00,-4.3
|
||||
2017-01-02 21:45:00,-4.3
|
||||
2017-01-02 21:50:00,-4.2
|
||||
2017-01-02 21:55:00,-4.2
|
||||
2017-01-02 22:00:00,-4.3
|
||||
2017-01-02 22:05:00,-4.2
|
||||
2017-01-02 22:10:00,-4.3
|
||||
2017-01-02 22:15:00,-4.4
|
||||
2017-01-02 22:20:00,-4.3
|
||||
2017-01-02 22:25:00,-4.3
|
||||
2017-01-02 22:30:00,-4.0
|
||||
2017-01-02 22:35:00,-4.3
|
||||
2017-01-02 22:40:00,-4.1
|
||||
2017-01-02 22:45:00,-4.2
|
||||
2017-01-02 22:50:00,-4.0
|
||||
2017-01-02 22:55:00,-3.9
|
||||
2017-01-02 23:00:00,-4.0
|
||||
2017-01-02 23:05:00,-4.1
|
||||
2017-01-02 23:10:00,-4.1
|
||||
2017-01-02 23:15:00,-4.0
|
||||
2017-01-02 23:20:00,-4.1
|
||||
2017-01-02 23:25:00,-4.2
|
||||
2017-01-02 23:30:00,-4.3
|
||||
2017-01-02 23:35:00,-4.2
|
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2017-01-02 23:40:00,-4.3
|
||||
2017-01-02 23:45:00,-4.3
|
||||
2017-01-02 23:50:00,-4.3
|
||||
2017-01-02 23:55:00,-4.4
|
||||
2017-01-03 00:00:00,-4.5
|
||||
2017-01-03 00:05:00,-4.5
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||||
2017-01-03 00:10:00,-4.5
|
||||
2017-01-03 00:15:00,-4.5
|
||||
2017-01-03 00:20:00,-4.6
|
||||
2017-01-03 00:25:00,-4.6
|
||||
2017-01-03 00:30:00,-4.5
|
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2017-01-03 00:35:00,-4.6
|
||||
2017-01-03 00:40:00,-4.6
|
||||
2017-01-03 00:45:00,-4.5
|
||||
2017-01-03 00:50:00,-4.5
|
||||
2017-01-03 00:55:00,-4.6
|
||||
2017-01-03 01:00:00,-4.5
|
||||
2017-01-03 01:05:00,-4.6
|
||||
2017-01-03 01:10:00,-4.7
|
||||
2017-01-03 01:15:00,-4.7
|
||||
2017-01-03 01:20:00,-4.7
|
||||
2017-01-03 01:25:00,-4.9
|
||||
2017-01-03 01:30:00,-4.9
|
||||
2017-01-03 01:35:00,-4.9
|
||||
2017-01-03 01:40:00,-5.0
|
||||
2017-01-03 01:45:00,-5.0
|
||||
2017-01-03 01:50:00,-5.2
|
||||
2017-01-03 01:55:00,-5.2
|
||||
2017-01-03 02:00:00,-5.5
|
||||
2017-01-03 02:05:00,-5.3
|
||||
2017-01-03 02:10:00,-5.2
|
||||
2017-01-03 02:15:00,-5.2
|
||||
2017-01-03 02:20:00,-5.9
|
||||
2017-01-03 02:25:00,-6.4
|
||||
2017-01-03 02:30:00,-6.5
|
||||
2017-01-03 02:35:00,-6.0
|
||||
2017-01-03 02:40:00,-5.8
|
||||
2017-01-03 02:45:00,-5.5
|
||||
2017-01-03 02:50:00,-5.4
|
||||
2017-01-03 02:55:00,-5.5
|
||||
2017-01-03 03:00:00,-6.3
|
||||
2017-01-03 03:05:00,-6.3
|
||||
2017-01-03 03:10:00,-6.8
|
||||
2017-01-03 03:15:00,-6.3
|
||||
2017-01-03 03:20:00,-5.8
|
||||
2017-01-03 03:25:00,-6.8
|
||||
2017-01-03 03:30:00,-6.2
|
||||
2017-01-03 03:35:00,-5.7
|
||||
2017-01-03 03:40:00,-5.4
|
||||
2017-01-03 03:45:00,-5.3
|
||||
2017-01-03 03:50:00,-5.3
|
||||
2017-01-03 03:55:00,-5.2
|
||||
2017-01-03 04:00:00,-5.3
|
||||
2017-01-03 04:05:00,-5.3
|
||||
2017-01-03 04:10:00,-5.2
|
||||
2017-01-03 04:15:00,-5.2
|
||||
2017-01-03 04:20:00,-5.6
|
||||
2017-01-03 04:25:00,-6.1
|
||||
2017-01-03 04:30:00,-6.1
|
||||
2017-01-03 04:35:00,-6.1
|
||||
2017-01-03 04:40:00,-6.0
|
||||
2017-01-03 04:45:00,-5.8
|
||||
2017-01-03 04:50:00,-5.6
|
||||
2017-01-03 04:55:00,-5.7
|
||||
2017-01-03 05:00:00,-5.6
|
||||
2017-01-03 05:05:00,-6.1
|
||||
2017-01-03 05:10:00,-5.8
|
||||
2017-01-03 05:15:00,-5.9
|
||||
2017-01-03 05:20:00,-5.8
|
||||
2017-01-03 05:25:00,-6.3
|
||||
2017-01-03 05:30:00,-6.4
|
||||
2017-01-03 05:35:00,-6.5
|
||||
2017-01-03 05:40:00,-6.5
|
||||
2017-01-03 05:45:00,-5.9
|
||||
2017-01-03 05:50:00,-5.7
|
||||
2017-01-03 05:55:00,-5.8
|
||||
2017-01-03 06:00:00,-6.0
|
||||
2017-01-03 06:05:00,-6.3
|
||||
2017-01-03 06:10:00,-6.7
|
||||
2017-01-03 06:15:00,-6.6
|
||||
2017-01-03 06:20:00,-6.5
|
||||
2017-01-03 06:25:00,-6.4
|
||||
2017-01-03 06:30:00,-6.1
|
||||
2017-01-03 06:35:00,-6.3
|
||||
2017-01-03 06:40:00,-6.2
|
||||
2017-01-03 06:45:00,-6.1
|
||||
2017-01-03 06:50:00,-6.1
|
||||
2017-01-03 06:55:00,-6.0
|
||||
2017-01-03 07:00:00,-6.0
|
||||
2017-01-03 07:05:00,-6.2
|
||||
2017-01-03 07:10:00,-6.4
|
||||
2017-01-03 07:15:00,-6.2
|
||||
2017-01-03 07:20:00,-6.1
|
||||
2017-01-03 07:25:00,-5.9
|
||||
2017-01-03 07:30:00,-5.9
|
||||
2017-01-03 07:35:00,-5.9
|
||||
2017-01-03 07:40:00,-6.2
|
||||
2017-01-03 07:45:00,-6.4
|
||||
2017-01-03 07:50:00,-6.2
|
||||
2017-01-03 07:55:00,-6.0
|
||||
2017-01-03 08:00:00,-5.9
|
||||
2017-01-03 08:05:00,-5.9
|
||||
2017-01-03 08:10:00,-5.8
|
||||
2017-01-03 08:15:00,-5.8
|
||||
2017-01-03 08:20:00,-5.8
|
||||
2017-01-03 08:25:00,-5.8
|
||||
2017-01-03 08:30:00,-6.0
|
||||
2017-01-03 08:35:00,-5.9
|
||||
2017-01-03 08:40:00,-5.9
|
||||
2017-01-03 08:45:00,-5.8
|
||||
2017-01-03 08:50:00,-5.8
|
||||
2017-01-03 08:55:00,-5.7
|
||||
2017-01-03 09:00:00,-5.8
|
||||
2017-01-03 09:05:00,-5.8
|
||||
2017-01-03 09:10:00,-6.0
|
||||
2017-01-03 09:15:00,-6.1
|
||||
2017-01-03 09:20:00,-6.0
|
||||
2017-01-03 09:25:00,-5.9
|
||||
2017-01-03 09:30:00,-6.0
|
||||
2017-01-03 09:35:00,-6.0
|
||||
2017-01-03 09:40:00,-6.1
|
||||
2017-01-03 09:45:00,-6.2
|
||||
2017-01-03 09:50:00,-6.1
|
||||
2017-01-03 09:55:00,-6.3
|
||||
2017-01-03 10:00:00,-6.3
|
||||
2017-01-03 10:05:00,-6.1
|
||||
2017-01-03 10:10:00,-6.0
|
||||
2017-01-03 10:15:00,-5.9
|
||||
2017-01-03 10:20:00,-5.8
|
||||
2017-01-03 10:25:00,-5.7
|
||||
2017-01-03 10:30:00,-5.7
|
||||
2017-01-03 10:35:00,-5.8
|
||||
2017-01-03 10:40:00,-5.6
|
||||
2017-01-03 10:45:00,-5.6
|
||||
2017-01-03 10:50:00,-5.6
|
||||
2017-01-03 10:55:00,-5.6
|
||||
2017-01-03 11:00:00,-5.5
|
||||
2017-01-03 11:05:00,-5.6
|
||||
2017-01-03 11:10:00,-5.7
|
||||
2017-01-03 11:15:00,-5.7
|
||||
2017-01-03 11:20:00,-5.8
|
||||
2017-01-03 11:25:00,-5.7
|
||||
2017-01-03 11:30:00,-5.6
|
||||
2017-01-03 11:35:00,-5.5
|
||||
2017-01-03 11:40:00,-5.3
|
||||
2017-01-03 11:45:00,-5.2
|
||||
2017-01-03 11:50:00,-5.1
|
||||
2017-01-03 11:55:00,-5.0
|
||||
2017-01-03 12:00:00,-5.1
|
||||
2017-01-03 12:05:00,-5.0
|
||||
2017-01-03 12:10:00,-5.0
|
||||
2017-01-03 12:15:00,-5.0
|
||||
2017-01-03 12:20:00,-4.8
|
||||
2017-01-03 12:25:00,-4.8
|
||||
2017-01-03 12:30:00,-4.7
|
||||
2017-01-03 12:35:00,-4.6
|
||||
2017-01-03 12:40:00,-4.5
|
||||
2017-01-03 12:45:00,-4.4
|
||||
2017-01-03 12:50:00,-4.5
|
||||
2017-01-03 12:55:00,-4.6
|
||||
2017-01-03 13:00:00,-4.6
|
||||
2017-01-03 13:05:00,-4.6
|
||||
2017-01-03 13:10:00,-4.5
|
||||
2017-01-03 13:15:00,-4.5
|
||||
2017-01-03 13:20:00,-4.5
|
||||
2017-01-03 13:25:00,-4.3
|
||||
2017-01-03 13:30:00,-4.3
|
||||
2017-01-03 13:35:00,-4.3
|
||||
2017-01-03 13:40:00,-4.2
|
||||
2017-01-03 13:45:00,-4.2
|
||||
2017-01-03 13:50:00,-4.2
|
||||
2017-01-03 13:55:00,-4.2
|
||||
2017-01-03 14:00:00,-4.3
|
||||
2017-01-03 14:05:00,-4.3
|
||||
2017-01-03 14:10:00,-4.3
|
||||
2017-01-03 14:15:00,-4.3
|
||||
2017-01-03 14:20:00,-4.3
|
||||
2017-01-03 14:25:00,-4.3
|
||||
2017-01-03 14:30:00,-4.4
|
||||
2017-01-03 14:35:00,-4.4
|
||||
2017-01-03 14:40:00,-4.4
|
||||
2017-01-03 14:45:00,-4.5
|
||||
2017-01-03 14:50:00,-4.6
|
||||
2017-01-03 14:55:00,-4.5
|
||||
2017-01-03 15:00:00,-4.5
|
||||
2017-01-03 15:05:00,-4.5
|
||||
2017-01-03 15:10:00,-4.5
|
||||
2017-01-03 15:15:00,-4.5
|
||||
2017-01-03 15:20:00,-4.5
|
||||
2017-01-03 15:25:00,-4.5
|
||||
2017-01-03 15:30:00,-4.5
|
||||
2017-01-03 15:35:00,-4.5
|
||||
2017-01-03 15:40:00,-4.5
|
||||
2017-01-03 15:45:00,-4.6
|
||||
2017-01-03 15:50:00,-4.6
|
||||
2017-01-03 15:55:00,-4.5
|
||||
2017-01-03 16:00:00,-4.6
|
||||
2017-01-03 16:05:00,-4.5
|
||||
2017-01-03 16:10:00,-4.3
|
||||
2017-01-03 16:15:00,-4.2
|
||||
2017-01-03 16:20:00,-4.3
|
||||
2017-01-03 16:25:00,-4.2
|
||||
2017-01-03 16:30:00,-4.1
|
||||
2017-01-03 16:35:00,-4.0
|
||||
2017-01-03 16:40:00,-3.9
|
||||
2017-01-03 16:45:00,-3.8
|
||||
2017-01-03 16:50:00,-3.7
|
||||
2017-01-03 16:55:00,-3.7
|
||||
2017-01-03 17:00:00,-3.4
|
||||
2017-01-03 17:05:00,-3.3
|
||||
2017-01-03 17:10:00,-3.5
|
||||
2017-01-03 17:15:00,-3.4
|
||||
2017-01-03 17:20:00,-3.3
|
||||
2017-01-03 17:25:00,-3.2
|
||||
2017-01-03 17:30:00,-3.1
|
||||
2017-01-03 17:35:00,-3.0
|
||||
2017-01-03 17:40:00,-2.7
|
||||
2017-01-03 17:45:00,-2.6
|
||||
2017-01-03 17:50:00,-2.2
|
||||
2017-01-03 17:55:00,-2.4
|
||||
2017-01-03 18:00:00,-2.4
|
||||
2017-01-03 18:05:00,-2.7
|
||||
2017-01-03 18:10:00,-2.7
|
||||
2017-01-03 18:15:00,-2.6
|
||||
2017-01-03 18:20:00,-2.7
|
||||
2017-01-03 18:25:00,-2.5
|
||||
2017-01-03 18:30:00,-2.5
|
||||
2017-01-03 18:35:00,-2.6
|
||||
2017-01-03 18:40:00,-2.6
|
||||
2017-01-03 18:45:00,-2.6
|
||||
2017-01-03 18:50:00,-2.9
|
||||
2017-01-03 18:55:00,-2.7
|
||||
2017-01-03 19:00:00,-2.5
|
||||
2017-01-03 19:05:00,-2.3
|
||||
2017-01-03 19:10:00,-2.3
|
||||
2017-01-03 19:15:00,-2.3
|
||||
2017-01-03 19:20:00,-2.3
|
||||
2017-01-03 19:25:00,-2.2
|
||||
2017-01-03 19:30:00,-2.1
|
||||
2017-01-03 19:35:00,-2.3
|
||||
2017-01-03 19:40:00,-2.2
|
||||
2017-01-03 19:45:00,-2.0
|
||||
2017-01-03 19:50:00,-1.9
|
||||
2017-01-03 19:55:00,-1.8
|
||||
2017-01-03 20:00:00,-1.8
|
||||
2017-01-03 20:05:00,-1.9
|
||||
2017-01-03 20:10:00,-1.8
|
||||
2017-01-03 20:15:00,-1.6
|
||||
2017-01-03 20:20:00,-1.5
|
||||
2017-01-03 20:25:00,-1.1
|
||||
2017-01-03 20:30:00,-1.6
|
||||
2017-01-03 20:35:00,-2.2
|
||||
2017-01-03 20:40:00,-2.2
|
||||
2017-01-03 20:45:00,-2.3
|
||||
2017-01-03 20:50:00,-2.4
|
||||
2017-01-03 20:55:00,-2.4
|
||||
2017-01-03 21:00:00,-2.4
|
||||
2017-01-03 21:05:00,-2.3
|
||||
2017-01-03 21:10:00,-2.4
|
||||
2017-01-03 21:15:00,-2.5
|
||||
2017-01-03 21:20:00,-2.3
|
||||
2017-01-03 21:25:00,-2.1
|
||||
2017-01-03 21:30:00,-2.2
|
||||
2017-01-03 21:35:00,-2.2
|
||||
2017-01-03 21:40:00,-2.3
|
||||
2017-01-03 21:45:00,-2.3
|
||||
2017-01-03 21:50:00,-2.3
|
||||
2017-01-03 21:55:00,-2.3
|
||||
2017-01-03 22:00:00,-2.4
|
||||
2017-01-03 22:05:00,-2.3
|
||||
2017-01-03 22:10:00,-2.3
|
||||
2017-01-03 22:15:00,-2.4
|
||||
2017-01-03 22:20:00,-2.4
|
||||
2017-01-03 22:25:00,-2.5
|
||||
2017-01-03 22:30:00,-2.5
|
||||
2017-01-03 22:35:00,-2.7
|
||||
2017-01-03 22:40:00,-2.7
|
||||
2017-01-03 22:45:00,-2.8
|
||||
2017-01-03 22:50:00,-2.8
|
||||
2017-01-03 22:55:00,-2.8
|
||||
2017-01-03 23:00:00,-2.8
|
||||
2017-01-03 23:05:00,-2.8
|
||||
2017-01-03 23:10:00,-2.8
|
||||
2017-01-03 23:15:00,-2.7
|
||||
2017-01-03 23:20:00,-2.7
|
||||
2017-01-03 23:25:00,-2.6
|
||||
2017-01-03 23:30:00,-2.6
|
||||
2017-01-03 23:35:00,-2.5
|
||||
2017-01-03 23:40:00,-2.5
|
||||
2017-01-03 23:45:00,-2.4
|
||||
2017-01-03 23:50:00,-2.4
|
||||
2017-01-03 23:55:00,-2.4
|
||||
|
71
.venv/lib/python3.12/site-packages/prophet/tests/data3.csv
Normal file
71
.venv/lib/python3.12/site-packages/prophet/tests/data3.csv
Normal file
@ -0,0 +1,71 @@
|
||||
ds,y
|
||||
2023-03-02,623031970.0
|
||||
2023-03-06,623032040.0
|
||||
2023-03-07,623032054.0
|
||||
2023-03-08,623032091.0
|
||||
2023-03-09,623032123.0
|
||||
2023-03-10,623032152.0
|
||||
2023-03-11,623032177.0
|
||||
2023-03-12,623032184.0
|
||||
2023-03-13,623032193.0
|
||||
2023-03-16,623032296.0
|
||||
2023-03-17,623032316.0
|
||||
2023-03-18,623032328.0
|
||||
2023-03-19,623032339.0
|
||||
2023-03-20,623032352.0
|
||||
2023-03-21,623032385.0
|
||||
2023-03-22,623032410.0
|
||||
2023-03-23,623032427.0
|
||||
2023-03-25,623032479.0
|
||||
2023-03-26,623032496.0
|
||||
2023-03-27,623032506.0
|
||||
2023-03-28,623032533.0
|
||||
2023-03-29,623032598.0
|
||||
2023-03-30,623032643.0
|
||||
2023-03-31,623032681.0
|
||||
2023-04-01,623032727.0
|
||||
2023-04-02,623032756.0
|
||||
2023-04-03,623032767.0
|
||||
2023-04-04,623032799.0
|
||||
2023-04-05,623032843.0
|
||||
2023-04-06,623032890.0
|
||||
2023-04-07,623032934.0
|
||||
2023-04-08,623032954.0
|
||||
2023-04-09,623032959.0
|
||||
2023-04-10,623032964.0
|
||||
2023-04-11,623032997.0
|
||||
2023-04-12,623033041.0
|
||||
2023-04-13,623033062.0
|
||||
2023-04-14,623033095.0
|
||||
2023-04-15,623033122.0
|
||||
2023-04-16,623033163.0
|
||||
2023-04-17,623033190.0
|
||||
2023-04-18,623033227.0
|
||||
2023-04-19,623033258.0
|
||||
2023-04-20,623033294.0
|
||||
2023-04-21,623033329.0
|
||||
2023-04-22,623033361.0
|
||||
2023-04-23,623033385.0
|
||||
2023-04-24,623033397.0
|
||||
2023-04-25,623033419.0
|
||||
2023-04-26,623033440.0
|
||||
2023-04-27,623033482.0
|
||||
2023-04-28,623033535.0
|
||||
2023-04-29,623033575.0
|
||||
2023-04-30,623033600.0
|
||||
2023-05-01,623033610.0
|
||||
2023-05-02,623033632.0
|
||||
2023-05-03,623033666.0
|
||||
2023-05-04,623033704.0
|
||||
2023-05-05,623033714.0
|
||||
2023-05-06,623033752.0
|
||||
2023-05-07,623033760.0
|
||||
2023-05-08,623033769.0
|
||||
2023-05-09,623033784.0
|
||||
2023-05-10,623033823.0
|
||||
2023-05-11,623033853.0
|
||||
2023-05-12,623034010.0
|
||||
2023-05-13,623034041.0
|
||||
2023-05-14,623034060.0
|
||||
2023-05-15,623034068.0
|
||||
2023-05-16,623034084.0
|
||||
|
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@ -0,0 +1,403 @@
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
import datetime
|
||||
import itertools
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
from prophet import Prophet, diagnostics
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def ts_short(daily_univariate_ts):
|
||||
return daily_univariate_ts.head(100)
|
||||
|
||||
|
||||
class CustomParallelBackend:
|
||||
def map(self, func, *iterables):
|
||||
results = [func(*args) for args in zip(*iterables)]
|
||||
return results
|
||||
|
||||
|
||||
PARALLEL_METHODS = [None, "processes", "threads", CustomParallelBackend()]
|
||||
try:
|
||||
from dask.distributed import Client
|
||||
|
||||
client = Client(processes=False) # noqa
|
||||
PARALLEL_METHODS.append("dask")
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
@diagnostics.register_performance_metric
|
||||
def mase(df, w):
|
||||
"""Mean absolute scale error
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df: Cross-validation results dataframe.
|
||||
w: Aggregation window size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with columns horizon and mase.
|
||||
"""
|
||||
e = (df['y'] - df['yhat'])
|
||||
d = np.abs(np.diff(df['y'])).sum()/(df['y'].shape[0]-1)
|
||||
se = np.abs(e/d)
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'mase': se})
|
||||
return diagnostics.rolling_mean_by_h(
|
||||
x=se.values, h=df['horizon'].values, w=w, name='mase'
|
||||
)
|
||||
|
||||
class TestCrossValidation:
|
||||
@pytest.mark.parametrize("parallel_method", PARALLEL_METHODS)
|
||||
def test_cross_validation(self, ts_short, parallel_method, backend):
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(ts_short)
|
||||
# Calculate the number of cutoff points(k)
|
||||
horizon = pd.Timedelta("4 days")
|
||||
period = pd.Timedelta("10 days")
|
||||
initial = pd.Timedelta("115 days")
|
||||
df_cv = diagnostics.cross_validation(
|
||||
m, horizon="4 days", period="10 days", initial="115 days", parallel=parallel_method
|
||||
)
|
||||
assert len(np.unique(df_cv["cutoff"])) == 3
|
||||
assert max(df_cv["ds"] - df_cv["cutoff"]) == horizon
|
||||
assert min(df_cv["cutoff"]) >= min(ts_short["ds"]) + initial
|
||||
dc = df_cv["cutoff"].diff()
|
||||
dc = dc[dc > pd.Timedelta(0)].min()
|
||||
assert dc >= period
|
||||
assert (df_cv["cutoff"] < df_cv["ds"]).all()
|
||||
# Each y in df_cv and ts_short with same ds should be equal
|
||||
df_merged = pd.merge(df_cv, ts_short, "left", on="ds")
|
||||
assert np.sum((df_merged["y_x"] - df_merged["y_y"]) ** 2) == pytest.approx(0.0)
|
||||
df_cv = diagnostics.cross_validation(
|
||||
m, horizon="4 days", period="10 days", initial="135 days"
|
||||
)
|
||||
assert len(np.unique(df_cv["cutoff"])) == 1
|
||||
with pytest.raises(ValueError):
|
||||
diagnostics.cross_validation(m, horizon="10 days", period="10 days", initial="140 days")
|
||||
|
||||
def test_bad_parallel_methods(self, ts_short, backend):
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(ts_short)
|
||||
# invalid alias
|
||||
with pytest.raises(ValueError, match="'parallel' should be one"):
|
||||
diagnostics.cross_validation(m, horizon="4 days", parallel="bad")
|
||||
# no map method
|
||||
with pytest.raises(ValueError, match="'parallel' should be one"):
|
||||
diagnostics.cross_validation(m, horizon="4 days", parallel=object())
|
||||
|
||||
def test_check_single_cutoff_forecast_func_calls(self, ts_short, monkeypatch, backend):
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(ts_short)
|
||||
|
||||
def mock_predict(df, model, cutoff, horizon, predict_columns):
|
||||
nonlocal n_calls
|
||||
n_calls = n_calls + 1
|
||||
return pd.DataFrame(
|
||||
{
|
||||
"ds": pd.date_range(start="2012-09-17", periods=3),
|
||||
"yhat": np.arange(16, 19),
|
||||
"yhat_lower": np.arange(15, 18),
|
||||
"yhat_upper": np.arange(17, 20),
|
||||
"y": np.arange(16.5, 19.5),
|
||||
"cutoff": [datetime.date(2012, 9, 15)] * 3,
|
||||
}
|
||||
)
|
||||
|
||||
monkeypatch.setattr(diagnostics, "single_cutoff_forecast", mock_predict)
|
||||
# cross validation with 3 and 7 forecasts
|
||||
for args, forecasts in (
|
||||
(["4 days", "10 days", "115 days"], 3),
|
||||
(["4 days", "4 days", "115 days"], 7),
|
||||
):
|
||||
n_calls = 0
|
||||
_ = diagnostics.cross_validation(m, *args)
|
||||
# check single forecast function called expected number of times
|
||||
assert n_calls == forecasts
|
||||
|
||||
@pytest.mark.parametrize("extra_output_columns", ["trend", ["trend"]])
|
||||
def test_check_extra_output_columns_cross_validation(self, ts_short, backend, extra_output_columns):
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(ts_short)
|
||||
df_cv = diagnostics.cross_validation(
|
||||
m,
|
||||
horizon="1 days",
|
||||
period="1 days",
|
||||
initial="140 days",
|
||||
extra_output_columns=extra_output_columns
|
||||
)
|
||||
assert "trend" in df_cv.columns
|
||||
|
||||
@pytest.mark.parametrize("growth", ["logistic", "flat"])
|
||||
def test_cross_validation_logistic_or_flat_growth(self, growth, ts_short, backend):
|
||||
df = ts_short.copy()
|
||||
if growth == "logistic":
|
||||
df["cap"] = 40
|
||||
m = Prophet(growth=growth, stan_backend=backend).fit(df)
|
||||
df_cv = diagnostics.cross_validation(
|
||||
m, horizon="1 days", period="1 days", initial="140 days"
|
||||
)
|
||||
assert len(np.unique(df_cv["cutoff"])) == 2
|
||||
assert (df_cv["cutoff"] < df_cv["ds"]).all()
|
||||
df_merged = pd.merge(df_cv, ts_short, "left", on="ds")
|
||||
assert np.sum((df_merged["y_x"] - df_merged["y_y"]) ** 2) == pytest.approx(0.0)
|
||||
|
||||
def test_cross_validation_extra_regressors(self, ts_short, backend):
|
||||
df = ts_short.copy()
|
||||
df["extra"] = range(df.shape[0])
|
||||
df["is_conditional_week"] = np.arange(df.shape[0]) // 7 % 2
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.add_seasonality(name="monthly", period=30.5, fourier_order=5)
|
||||
m.add_seasonality(
|
||||
name="conditional_weekly",
|
||||
period=7,
|
||||
fourier_order=3,
|
||||
prior_scale=2.0,
|
||||
condition_name="is_conditional_week",
|
||||
)
|
||||
m.add_regressor("extra")
|
||||
m.fit(df)
|
||||
df_cv = diagnostics.cross_validation(
|
||||
m, horizon="4 days", period="4 days", initial="135 days"
|
||||
)
|
||||
assert len(np.unique(df_cv["cutoff"])) == 2
|
||||
period = pd.Timedelta("4 days")
|
||||
dc = df_cv["cutoff"].diff()
|
||||
dc = dc[dc > pd.Timedelta(0)].min()
|
||||
assert dc >= period
|
||||
assert (df_cv["cutoff"] < df_cv["ds"]).all()
|
||||
df_merged = pd.merge(df_cv, ts_short, "left", on="ds")
|
||||
assert np.sum((df_merged["y_x"] - df_merged["y_y"]) ** 2) == pytest.approx(0.0)
|
||||
|
||||
def test_cross_validation_default_value_check(self, ts_short, backend):
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(ts_short)
|
||||
# Default value of initial should be equal to 3 * horizon
|
||||
df_cv1 = diagnostics.cross_validation(m, horizon="32 days", period="10 days")
|
||||
df_cv2 = diagnostics.cross_validation(
|
||||
m, horizon="32 days", period="10 days", initial="96 days"
|
||||
)
|
||||
assert ((df_cv1["y"] - df_cv2["y"]) ** 2).sum() == pytest.approx(0.0)
|
||||
assert ((df_cv1["yhat"] - df_cv2["yhat"]) ** 2).sum() == pytest.approx(0.0)
|
||||
|
||||
def test_cross_validation_custom_cutoffs(self, ts_short, backend):
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(ts_short)
|
||||
# When specify a list of cutoffs
|
||||
# the cutoff dates in df_cv are those specified
|
||||
df_cv1 = diagnostics.cross_validation(
|
||||
m,
|
||||
horizon="32 days",
|
||||
period="10 days",
|
||||
cutoffs=[pd.Timestamp("2012-07-31"), pd.Timestamp("2012-08-31")],
|
||||
)
|
||||
assert len(df_cv1["cutoff"].unique()) == 2
|
||||
|
||||
def test_cross_validation_uncertainty_disabled(self, ts_short, backend):
|
||||
df = ts_short.copy()
|
||||
for uncertainty in [0, False]:
|
||||
m = Prophet(uncertainty_samples=uncertainty, stan_backend=backend)
|
||||
m.fit(df, algorithm="Newton")
|
||||
df_cv = diagnostics.cross_validation(
|
||||
m, horizon="4 days", period="4 days", initial="115 days"
|
||||
)
|
||||
expected_cols = ["ds", "yhat", "y", "cutoff"]
|
||||
assert all(col in expected_cols for col in df_cv.columns.tolist())
|
||||
df_p = diagnostics.performance_metrics(df_cv)
|
||||
assert "coverage" not in df_p.columns
|
||||
|
||||
|
||||
class TestPerformanceMetrics:
|
||||
def test_performance_metrics(self, ts_short, backend):
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(ts_short)
|
||||
df_cv = diagnostics.cross_validation(
|
||||
m, horizon="4 days", period="10 days", initial="90 days"
|
||||
)
|
||||
# Aggregation level none
|
||||
df_none = diagnostics.performance_metrics(df_cv, rolling_window=-1)
|
||||
assert set(df_none.columns) == {
|
||||
"horizon",
|
||||
"coverage",
|
||||
"mae",
|
||||
"mape",
|
||||
"mdape",
|
||||
"mse",
|
||||
"rmse",
|
||||
"smape",
|
||||
}
|
||||
assert df_none.shape[0] == 16
|
||||
# Aggregation level 0
|
||||
df_0 = diagnostics.performance_metrics(df_cv, rolling_window=0)
|
||||
assert len(df_0) == 4
|
||||
assert len(df_0["horizon"].unique()) == 4
|
||||
# Aggregation level 0.2
|
||||
df_horizon = diagnostics.performance_metrics(df_cv, rolling_window=0.2)
|
||||
assert len(df_horizon) == 4
|
||||
assert len(df_horizon["horizon"].unique()) == 4
|
||||
# Aggregation level all
|
||||
df_all = diagnostics.performance_metrics(df_cv, rolling_window=1)
|
||||
assert df_all.shape[0] == 1
|
||||
for metric in ["mse", "mape", "mae", "coverage"]:
|
||||
assert df_all[metric].values[0] == pytest.approx(df_none[metric].mean())
|
||||
assert df_all["mdape"].values[0] == pytest.approx(df_none["mdape"].median())
|
||||
# Custom list of metrics
|
||||
df_horizon = diagnostics.performance_metrics(
|
||||
df_cv,
|
||||
metrics=["coverage", "mse", "mase"],
|
||||
)
|
||||
assert set(df_horizon.columns) == {"coverage", "mse", "mase","horizon"}
|
||||
# Skip MAPE
|
||||
df_cv.loc[0, "y"] = 0.0
|
||||
df_horizon = diagnostics.performance_metrics(
|
||||
df_cv,
|
||||
metrics=["coverage", "mape"],
|
||||
)
|
||||
assert set(df_horizon.columns) == {"coverage", "horizon"}
|
||||
# Handle zero y and yhat
|
||||
df_cv["y"] = 0.0
|
||||
df_cv["yhat"] = 0.0
|
||||
df_horizon = diagnostics.performance_metrics(
|
||||
df_cv,
|
||||
)
|
||||
assert set(df_horizon.columns) == {"coverage", "horizon", "mae", "mdape", "mse", "rmse", "smape"}
|
||||
df_horizon = diagnostics.performance_metrics(
|
||||
df_cv,
|
||||
metrics=["mape"],
|
||||
)
|
||||
assert df_horizon is None
|
||||
# List of metrics containing non-valid metrics
|
||||
with pytest.raises(ValueError):
|
||||
diagnostics.performance_metrics(
|
||||
df_cv,
|
||||
metrics=["mse", "error_metric"],
|
||||
)
|
||||
|
||||
def test_rolling_mean(self):
|
||||
x = np.arange(10)
|
||||
h = np.arange(10)
|
||||
df = diagnostics.rolling_mean_by_h(x=x, h=h, w=1, name="x")
|
||||
assert np.array_equal(x, df["x"].values)
|
||||
assert np.array_equal(h, df["horizon"].values)
|
||||
|
||||
df = diagnostics.rolling_mean_by_h(x, h, w=4, name="x")
|
||||
assert np.allclose(x[3:] - 1.5, df["x"].values)
|
||||
assert np.array_equal(np.arange(3, 10), df["horizon"].values)
|
||||
|
||||
h = np.array([1.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 7.0, 7.0])
|
||||
x_true = np.array([1.0, 5.0, 22.0 / 3])
|
||||
h_true = np.array([3.0, 4.0, 7.0])
|
||||
df = diagnostics.rolling_mean_by_h(x, h, w=3, name="x")
|
||||
assert np.allclose(x_true, df["x"].values)
|
||||
assert np.array_equal(h_true, df["horizon"].values)
|
||||
|
||||
df = diagnostics.rolling_mean_by_h(x, h, w=10, name="x")
|
||||
assert np.allclose(np.array([7.0]), df["horizon"].values)
|
||||
assert np.allclose(np.array([4.5]), df["x"].values)
|
||||
|
||||
def test_rolling_median(self):
|
||||
x = np.arange(10)
|
||||
h = np.arange(10)
|
||||
df = diagnostics.rolling_median_by_h(x=x, h=h, w=1, name="x")
|
||||
assert np.array_equal(x, df["x"].values)
|
||||
assert np.array_equal(h, df["horizon"].values)
|
||||
|
||||
df = diagnostics.rolling_median_by_h(x, h, w=4, name="x")
|
||||
x_true = x[3:] - 1.5
|
||||
assert np.allclose(x_true, df["x"].values)
|
||||
assert np.array_equal(np.arange(3, 10), df["horizon"].values)
|
||||
|
||||
h = np.array([1.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 7.0, 7.0])
|
||||
x_true = np.array([1.0, 5.0, 8.0])
|
||||
h_true = np.array([3.0, 4.0, 7.0])
|
||||
df = diagnostics.rolling_median_by_h(x, h, w=3, name="x")
|
||||
assert np.allclose(x_true, df["x"].values)
|
||||
assert np.array_equal(h_true, df["horizon"].values)
|
||||
|
||||
df = diagnostics.rolling_median_by_h(x, h, w=10, name="x")
|
||||
assert np.allclose(np.array([7.0]), df["horizon"].values)
|
||||
assert np.allclose(np.array([4.5]), df["x"].values)
|
||||
|
||||
|
||||
class TestProphetCopy:
|
||||
@pytest.fixture(scope="class")
|
||||
def data(self, daily_univariate_ts):
|
||||
df = daily_univariate_ts.copy()
|
||||
df["cap"] = 200.0
|
||||
df["binary_feature"] = [0] * 255 + [1] * 255
|
||||
return df
|
||||
|
||||
def test_prophet_copy(self, data, backend):
|
||||
# These values are created except for its default values
|
||||
holiday = pd.DataFrame({"ds": pd.to_datetime(["2016-12-25"]), "holiday": ["x"]})
|
||||
products = itertools.product(
|
||||
["linear", "logistic"], # growth
|
||||
[None, pd.to_datetime(["2016-12-25"])], # changepoints
|
||||
[3], # n_changepoints
|
||||
[0.9], # changepoint_range
|
||||
[True, False], # yearly_seasonality
|
||||
[True, False], # weekly_seasonality
|
||||
[True, False], # daily_seasonality
|
||||
[None, holiday], # holidays
|
||||
["additive", "multiplicative"], # seasonality_mode
|
||||
[1.1], # seasonality_prior_scale
|
||||
[1.1], # holidays_prior_scale
|
||||
[0.1], # changepoint_prior_scale
|
||||
[100], # mcmc_samples
|
||||
[0.9], # interval_width
|
||||
[200], # uncertainty_samples
|
||||
)
|
||||
# Values should be copied correctly
|
||||
for product in products:
|
||||
m1 = Prophet(*product, stan_backend=backend)
|
||||
m1.country_holidays = "US"
|
||||
m1.history = m1.setup_dataframe(data.copy(), initialize_scales=True)
|
||||
m1.set_auto_seasonalities()
|
||||
m2 = diagnostics.prophet_copy(m1)
|
||||
assert m1.growth == m2.growth
|
||||
assert m1.n_changepoints == m2.n_changepoints
|
||||
assert m1.changepoint_range == m2.changepoint_range
|
||||
if m1.changepoints is None:
|
||||
assert m1.changepoints == m2.changepoints
|
||||
else:
|
||||
assert m1.changepoints.equals(m2.changepoints)
|
||||
assert False == m2.yearly_seasonality
|
||||
assert False == m2.weekly_seasonality
|
||||
assert False == m2.daily_seasonality
|
||||
assert m1.yearly_seasonality == ("yearly" in m2.seasonalities)
|
||||
assert m1.weekly_seasonality == ("weekly" in m2.seasonalities)
|
||||
assert m1.daily_seasonality == ("daily" in m2.seasonalities)
|
||||
if m1.holidays is None:
|
||||
assert m1.holidays == m2.holidays
|
||||
else:
|
||||
assert (m1.holidays == m2.holidays).values.all()
|
||||
assert m1.country_holidays == m2.country_holidays
|
||||
assert m1.holidays_mode == m2.holidays_mode
|
||||
assert m1.seasonality_mode == m2.seasonality_mode
|
||||
assert m1.seasonality_prior_scale == m2.seasonality_prior_scale
|
||||
assert m1.changepoint_prior_scale == m2.changepoint_prior_scale
|
||||
assert m1.holidays_prior_scale == m2.holidays_prior_scale
|
||||
assert m1.mcmc_samples == m2.mcmc_samples
|
||||
assert m1.interval_width == m2.interval_width
|
||||
assert m1.uncertainty_samples == m2.uncertainty_samples
|
||||
|
||||
def test_prophet_copy_custom(self, data, backend):
|
||||
changepoints = pd.date_range("2012-06-15", "2012-09-15")
|
||||
cutoff = pd.Timestamp("2012-07-25")
|
||||
m1 = Prophet(changepoints=changepoints, stan_backend=backend)
|
||||
m1.add_seasonality("custom", 10, 5)
|
||||
m1.add_regressor("binary_feature")
|
||||
m1.fit(data)
|
||||
m2 = diagnostics.prophet_copy(m1, cutoff=cutoff)
|
||||
changepoints = changepoints[changepoints < cutoff]
|
||||
assert (changepoints == m2.changepoints).all()
|
||||
assert "custom" in m2.seasonalities
|
||||
assert "binary_feature" in m2.extra_regressors
|
||||
984
.venv/lib/python3.12/site-packages/prophet/tests/test_prophet.py
Normal file
984
.venv/lib/python3.12/site-packages/prophet/tests/test_prophet.py
Normal file
@ -0,0 +1,984 @@
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
from prophet import Prophet
|
||||
from prophet.utilities import warm_start_params
|
||||
|
||||
|
||||
def train_test_split(ts_data: pd.DataFrame, n_test_rows: int) -> pd.DataFrame:
|
||||
train = ts_data.head(ts_data.shape[0] - n_test_rows)
|
||||
test = ts_data.tail(n_test_rows)
|
||||
return train.reset_index(), test.reset_index()
|
||||
|
||||
|
||||
def rmse(predictions, targets) -> float:
|
||||
return np.sqrt(np.mean((predictions - targets) ** 2))
|
||||
|
||||
|
||||
class TestProphetFitPredictDefault:
|
||||
@pytest.mark.parametrize(
|
||||
"scaling,expected",
|
||||
[("absmax", 10.64), ("minmax", 11.13)],
|
||||
ids=["absmax", "minmax"]
|
||||
)
|
||||
def test_fit_predict(self, daily_univariate_ts, backend, scaling, expected):
|
||||
test_days = 30
|
||||
train, test = train_test_split(daily_univariate_ts, test_days)
|
||||
forecaster = Prophet(stan_backend=backend, scaling=scaling)
|
||||
forecaster.fit(train, seed=1237861298)
|
||||
np.random.seed(876543987)
|
||||
future = forecaster.make_future_dataframe(test_days, include_history=False)
|
||||
future = forecaster.predict(future)
|
||||
res = rmse(future["yhat"], test["y"])
|
||||
# Higher threshold due to cmdstan 2.33.1 producing numerical differences for macOS Intel (ARM is fine).
|
||||
assert res == pytest.approx(expected, 0.1), "backend: {}".format(forecaster.stan_backend)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"scaling,expected",
|
||||
[("absmax", 23.44), ("minmax", 11.29)],
|
||||
ids=["absmax", "minmax"]
|
||||
)
|
||||
def test_fit_predict_newton(self, daily_univariate_ts, backend, scaling, expected):
|
||||
test_days = 30
|
||||
train, test = train_test_split(daily_univariate_ts, test_days)
|
||||
forecaster = Prophet(stan_backend=backend, scaling=scaling)
|
||||
forecaster.fit(train, algorithm="Newton", seed=1237861298)
|
||||
np.random.seed(876543987)
|
||||
future = forecaster.make_future_dataframe(test_days, include_history=False)
|
||||
future = forecaster.predict(future)
|
||||
res = rmse(future["yhat"], test["y"])
|
||||
assert res == pytest.approx(expected, 0.01), "backend: {}".format(forecaster.stan_backend)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"scaling,expected",
|
||||
[("absmax", 127.01), ("minmax", 93.45)],
|
||||
ids=["absmax", "minmax"]
|
||||
)
|
||||
def test_fit_predict_large_numbers(self, large_numbers_ts, backend, scaling, expected):
|
||||
test_days = 30
|
||||
train, test = train_test_split(large_numbers_ts, test_days)
|
||||
forecaster = Prophet(stan_backend=backend, scaling=scaling)
|
||||
forecaster.fit(train, seed=1237861298)
|
||||
np.random.seed(876543987)
|
||||
future = forecaster.make_future_dataframe(test_days, include_history=False)
|
||||
future = forecaster.predict(future)
|
||||
res = rmse(future["yhat"], test["y"])
|
||||
assert res == pytest.approx(expected, 0.01), "backend: {}".format(forecaster.stan_backend)
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_fit_predict_sampling(self, daily_univariate_ts, backend):
|
||||
test_days = 30
|
||||
train, test = train_test_split(daily_univariate_ts, test_days)
|
||||
forecaster = Prophet(mcmc_samples=500, stan_backend=backend)
|
||||
# chains adjusted from 4 to 7 to satisfy test for cmdstanpy
|
||||
forecaster.fit(train, seed=1237861298, chains=7, show_progress=False)
|
||||
np.random.seed(876543987)
|
||||
future = forecaster.make_future_dataframe(test_days, include_history=False)
|
||||
future = forecaster.predict(future)
|
||||
# this gives ~ 215.77
|
||||
res = rmse(future["yhat"], test["y"])
|
||||
assert 236 < res < 193, "backend: {}".format(forecaster.stan_backend)
|
||||
|
||||
def test_fit_predict_no_seasons(self, daily_univariate_ts, backend):
|
||||
test_days = 30
|
||||
train, _ = train_test_split(daily_univariate_ts, test_days)
|
||||
forecaster = Prophet(
|
||||
weekly_seasonality=False, yearly_seasonality=False, stan_backend=backend
|
||||
)
|
||||
forecaster.fit(train)
|
||||
future = forecaster.make_future_dataframe(test_days, include_history=False)
|
||||
result = forecaster.predict(future)
|
||||
assert (future.ds == result.ds).all()
|
||||
|
||||
def test_fit_predict_no_changepoints(self, daily_univariate_ts, backend):
|
||||
test_days = daily_univariate_ts.shape[0] // 2
|
||||
train, future = train_test_split(daily_univariate_ts, test_days)
|
||||
forecaster = Prophet(n_changepoints=0, stan_backend=backend)
|
||||
forecaster.fit(train)
|
||||
forecaster.predict(future)
|
||||
assert forecaster.params is not None
|
||||
assert forecaster.n_changepoints == 0
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_fit_predict_no_changepoints_mcmc(self, daily_univariate_ts, backend):
|
||||
test_days = daily_univariate_ts.shape[0] // 2
|
||||
train, future = train_test_split(daily_univariate_ts, test_days)
|
||||
forecaster = Prophet(n_changepoints=0, mcmc_samples=100, stan_backend=backend)
|
||||
forecaster.fit(train, show_progress=False)
|
||||
forecaster.predict(future)
|
||||
assert forecaster.params is not None
|
||||
assert forecaster.n_changepoints == 0
|
||||
|
||||
def test_fit_changepoint_not_in_history(self, daily_univariate_ts, backend):
|
||||
train = daily_univariate_ts[
|
||||
(daily_univariate_ts["ds"] < "2013-01-01") | (daily_univariate_ts["ds"] > "2014-01-01")
|
||||
]
|
||||
future = pd.DataFrame({"ds": daily_univariate_ts["ds"]})
|
||||
prophet = Prophet(changepoints=["2013-06-06"], stan_backend=backend)
|
||||
forecaster = prophet
|
||||
forecaster.fit(train)
|
||||
forecaster.predict(future)
|
||||
assert forecaster.params is not None
|
||||
assert forecaster.n_changepoints == 1
|
||||
|
||||
def test_fit_predict_duplicates(self, daily_univariate_ts, backend):
|
||||
"""
|
||||
The underlying model should still fit successfully when there are duplicate dates in the history.
|
||||
The model essentially sees this as multiple observations for the same time value, and fits the parameters
|
||||
accordingly.
|
||||
"""
|
||||
train, test = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)
|
||||
repeated_obs = train.copy()
|
||||
repeated_obs["y"] += 10
|
||||
train = pd.concat([train, repeated_obs])
|
||||
forecaster = Prophet(stan_backend=backend)
|
||||
forecaster.fit(train)
|
||||
forecaster.predict(test)
|
||||
|
||||
def test_fit_predict_constant_history(self, daily_univariate_ts, backend):
|
||||
"""
|
||||
When the training data history is constant, Prophet should predict the same value for all future dates.
|
||||
"""
|
||||
for constant in [0, 20]:
|
||||
train, test = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)
|
||||
train["y"] = constant
|
||||
forecaster = Prophet(stan_backend=backend)
|
||||
forecaster.fit(train)
|
||||
result = forecaster.predict(test)
|
||||
assert result["yhat"].values[-1] == constant
|
||||
|
||||
def test_fit_predict_uncertainty_disabled(self, daily_univariate_ts, backend):
|
||||
test_days = daily_univariate_ts.shape[0] // 2
|
||||
train, future = train_test_split(daily_univariate_ts, test_days)
|
||||
for uncertainty in [0, False]:
|
||||
forecaster = Prophet(uncertainty_samples=uncertainty, stan_backend=backend)
|
||||
forecaster.fit(train)
|
||||
result = forecaster.predict(future)
|
||||
expected_cols = [
|
||||
"ds",
|
||||
"trend",
|
||||
"additive_terms",
|
||||
"multiplicative_terms",
|
||||
"weekly",
|
||||
"yhat",
|
||||
]
|
||||
assert all(col in expected_cols for col in result.columns.tolist())
|
||||
|
||||
|
||||
class TestProphetDataPrep:
|
||||
def test_setup_dataframe(self, daily_univariate_ts, backend):
|
||||
"""Test that the columns 't' and 'y_scaled' are added to the dataframe."""
|
||||
train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)
|
||||
m = Prophet(stan_backend=backend)
|
||||
history = m.setup_dataframe(train, initialize_scales=True)
|
||||
|
||||
assert "t" in history
|
||||
assert history["t"].min() == 0.0
|
||||
assert history["t"].max() == 1.0
|
||||
|
||||
assert "y_scaled" in history
|
||||
assert history["y_scaled"].max() == 1.0
|
||||
|
||||
def test_setup_dataframe_ds_column(self, daily_univariate_ts, backend):
|
||||
"""Test case where 'ds' exists as an index name and column. Prophet should use the column."""
|
||||
train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)
|
||||
train.index = pd.to_datetime(["1970-01-01" for _ in range(train.shape[0])])
|
||||
train.index.rename("ds", inplace=True)
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(train)
|
||||
assert np.all(m.history["ds"].values == train["ds"].values)
|
||||
|
||||
def test_logistic_floor(self, daily_univariate_ts, backend):
|
||||
"""Test the scaling of y with logistic growth and a floor/cap."""
|
||||
train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)
|
||||
train["floor"] = 10.0
|
||||
train["cap"] = 80.0
|
||||
m = Prophet(growth="logistic", stan_backend=backend)
|
||||
m.fit(train)
|
||||
assert m.logistic_floor
|
||||
assert "floor" in m.history
|
||||
assert m.history["y_scaled"][0] == 1.0
|
||||
for col in ["y", "floor", "cap"]:
|
||||
train[col] += 10.0
|
||||
m2 = Prophet(growth="logistic", stan_backend=backend)
|
||||
m2.fit(train)
|
||||
assert m2.history["y_scaled"][0] == pytest.approx(1.0, 0.01)
|
||||
|
||||
def test_logistic_floor_minmax(self, daily_univariate_ts, backend):
|
||||
"""Test the scaling of y with logistic growth and a floor/cap."""
|
||||
train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)
|
||||
train["floor"] = 10.0
|
||||
train["cap"] = 80.0
|
||||
m = Prophet(growth="logistic", stan_backend=backend, scaling="minmax")
|
||||
m.fit(train)
|
||||
assert m.logistic_floor
|
||||
assert "floor" in m.history
|
||||
assert m.history["y_scaled"].min() > 0.0
|
||||
assert m.history["y_scaled"].max() < 1.0
|
||||
for col in ["y", "floor", "cap"]:
|
||||
train[col] += 10.0
|
||||
m2 = Prophet(growth="logistic", stan_backend=backend, scaling="minmax")
|
||||
m2.fit(train)
|
||||
assert m2.history["y_scaled"].min() > 0.0
|
||||
assert m2.history["y_scaled"].max() < 1.0
|
||||
# Check that the scaling is the same
|
||||
assert m2.history['y_scaled'].mean() == m.history['y_scaled'].mean()
|
||||
|
||||
def test_make_future_dataframe(self, daily_univariate_ts, backend):
|
||||
train = daily_univariate_ts.head(468 // 2)
|
||||
forecaster = Prophet(stan_backend=backend)
|
||||
forecaster.fit(train)
|
||||
future = forecaster.make_future_dataframe(periods=3, freq="D", include_history=False)
|
||||
correct = pd.DatetimeIndex(["2013-04-26", "2013-04-27", "2013-04-28"])
|
||||
assert len(future) == 3
|
||||
assert np.all(future["ds"].values == correct.values)
|
||||
|
||||
future = forecaster.make_future_dataframe(periods=3, freq=pd.tseries.offsets.MonthEnd(1), include_history=False)
|
||||
correct = pd.DatetimeIndex(["2013-04-30", "2013-05-31", "2013-06-30"])
|
||||
assert len(future) == 3
|
||||
assert np.all(future["ds"].values == correct.values)
|
||||
|
||||
def test_make_future_dataframe_include_history(self, daily_univariate_ts, backend):
|
||||
train = daily_univariate_ts.head(468 // 2).copy()
|
||||
#cover history with NAs
|
||||
train.loc[train.sample(10).index, "y"] = np.nan
|
||||
|
||||
forecaster = Prophet(stan_backend=backend)
|
||||
forecaster.fit(train)
|
||||
future = forecaster.make_future_dataframe(periods=3, freq="D", include_history=True)
|
||||
|
||||
assert len(future) == train.shape[0] + 3
|
||||
|
||||
class TestProphetTrendComponent:
|
||||
def test_invalid_growth_input(self, backend):
|
||||
msg = 'Parameter "growth" should be "linear", ' '"logistic" or "flat".'
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Prophet(growth="constant", stan_backend=backend)
|
||||
|
||||
def test_growth_init(self, daily_univariate_ts, backend):
|
||||
model = Prophet(growth="logistic", stan_backend=backend)
|
||||
train = daily_univariate_ts.iloc[:468].copy()
|
||||
train["cap"] = train["y"].max()
|
||||
|
||||
history = model.setup_dataframe(train, initialize_scales=True)
|
||||
|
||||
k, m = model.linear_growth_init(history)
|
||||
assert k == pytest.approx(0.3055671)
|
||||
assert m == pytest.approx(0.5307511)
|
||||
|
||||
k, m = model.logistic_growth_init(history)
|
||||
assert k == pytest.approx(1.507925, abs=1e-4)
|
||||
assert m == pytest.approx(-0.08167497, abs=1e-4)
|
||||
|
||||
k, m = model.flat_growth_init(history)
|
||||
assert k == 0
|
||||
assert m == pytest.approx(0.49335657, abs=1e-4)
|
||||
|
||||
def test_growth_init_minmax(self, daily_univariate_ts, backend):
|
||||
model = Prophet(growth="logistic", stan_backend=backend, scaling="minmax")
|
||||
train = daily_univariate_ts.iloc[:468].copy()
|
||||
train["cap"] = train["y"].max()
|
||||
|
||||
history = model.setup_dataframe(train, initialize_scales=True)
|
||||
|
||||
k, m = model.linear_growth_init(history)
|
||||
assert k == pytest.approx(0.4053406)
|
||||
assert m == pytest.approx(0.3775322)
|
||||
|
||||
k, m = model.logistic_growth_init(history)
|
||||
assert k == pytest.approx(1.782523, abs=1e-4)
|
||||
assert m == pytest.approx(0.280521, abs=1e-4)
|
||||
|
||||
k, m = model.flat_growth_init(history)
|
||||
assert k == 0
|
||||
assert m == pytest.approx(0.32792770, abs=1e-4)
|
||||
|
||||
@pytest.mark.parametrize("scaling",["absmax","minmax"])
|
||||
def test_flat_growth(self, backend, scaling):
|
||||
m = Prophet(growth="flat", stan_backend=backend, scaling=scaling)
|
||||
x = np.linspace(0, 2 * np.pi, 8 * 7)
|
||||
history = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.date_range(start="2020-01-01", periods=8 * 7, freq="d"),
|
||||
"y": 30 + np.sin(x * 8.0),
|
||||
}
|
||||
)
|
||||
m.fit(history)
|
||||
future = m.make_future_dataframe(10, include_history=True)
|
||||
fcst = m.predict(future)
|
||||
m_ = m.params["m"][0, 0]
|
||||
k = m.params["k"][0, 0]
|
||||
assert k == pytest.approx(0.0)
|
||||
assert fcst["trend"].unique()[0] == pytest.approx((m_ * m.y_scale) + m.y_min)
|
||||
assert np.round((m_ * m.y_scale) + m.y_min) == 30.0
|
||||
|
||||
def test_piecewise_linear(self, backend):
|
||||
model = Prophet(stan_backend=backend)
|
||||
|
||||
t = np.arange(11.0)
|
||||
m = 0
|
||||
k = 1.0
|
||||
deltas = np.array([0.5])
|
||||
changepoint_ts = np.array([5])
|
||||
|
||||
y = model.piecewise_linear(t, deltas, k, m, changepoint_ts)
|
||||
y_true = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.5, 8.0, 9.5, 11.0, 12.5])
|
||||
assert (y - y_true).sum() == 0.0
|
||||
|
||||
t = t[8:]
|
||||
y_true = y_true[8:]
|
||||
y = model.piecewise_linear(t, deltas, k, m, changepoint_ts)
|
||||
assert (y - y_true).sum() == 0.0
|
||||
|
||||
def test_piecewise_logistic(self, backend):
|
||||
model = Prophet(stan_backend=backend)
|
||||
|
||||
t = np.arange(11.0)
|
||||
cap = np.ones(11) * 10
|
||||
m = 0
|
||||
k = 1.0
|
||||
deltas = np.array([0.5])
|
||||
changepoint_ts = np.array([5])
|
||||
|
||||
y = model.piecewise_logistic(t, cap, deltas, k, m, changepoint_ts)
|
||||
y_true = np.array(
|
||||
[
|
||||
5.000000,
|
||||
7.310586,
|
||||
8.807971,
|
||||
9.525741,
|
||||
9.820138,
|
||||
9.933071,
|
||||
9.984988,
|
||||
9.996646,
|
||||
9.999252,
|
||||
9.999833,
|
||||
9.999963,
|
||||
]
|
||||
)
|
||||
|
||||
t = t[8:]
|
||||
y_true = y_true[8:]
|
||||
cap = cap[8:]
|
||||
y = model.piecewise_logistic(t, cap, deltas, k, m, changepoint_ts)
|
||||
assert (y - y_true).sum() == pytest.approx(0.0, abs=1e-5)
|
||||
|
||||
def test_flat_trend(self, backend):
|
||||
model = Prophet(stan_backend=backend)
|
||||
t = np.arange(11)
|
||||
m = 0.5
|
||||
y = model.flat_trend(t, m)
|
||||
y_true = np.array([0.5] * 11)
|
||||
assert (y - y_true).sum() == 0.0
|
||||
t = t[8:]
|
||||
y_true = y_true[8:]
|
||||
y = model.flat_trend(t, m)
|
||||
assert (y - y_true).sum() == 0.0
|
||||
|
||||
def test_get_changepoints(self, daily_univariate_ts, backend):
|
||||
"""
|
||||
By default, Prophet uses the first 80% of the history to detect changepoints.
|
||||
"""
|
||||
train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)
|
||||
m = Prophet(stan_backend=backend)
|
||||
history = m.setup_dataframe(train, initialize_scales=True)
|
||||
m.history = history
|
||||
m.set_changepoints()
|
||||
cp = m.changepoints_t
|
||||
assert cp.shape[0] == m.n_changepoints
|
||||
assert len(cp.shape) == 1
|
||||
assert cp.min() > 0
|
||||
cp_indx = int(np.ceil(0.8 * history.shape[0]))
|
||||
assert cp.max() <= history["t"].values[cp_indx]
|
||||
|
||||
def test_set_changepoint_range(self, daily_univariate_ts, backend):
|
||||
train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)
|
||||
m = Prophet(changepoint_range=0.4, stan_backend=backend)
|
||||
history = m.setup_dataframe(train, initialize_scales=True)
|
||||
m.history = history
|
||||
m.set_changepoints()
|
||||
cp = m.changepoints_t
|
||||
assert cp.shape[0] == m.n_changepoints
|
||||
assert len(cp.shape) == 1
|
||||
assert cp.min() > 0
|
||||
cp_indx = int(np.ceil(0.4 * history.shape[0]))
|
||||
cp.max() <= history["t"].values[cp_indx]
|
||||
for out_of_range in [-0.1, 2]:
|
||||
with pytest.raises(ValueError):
|
||||
m = Prophet(changepoint_range=out_of_range, stan_backend=backend)
|
||||
|
||||
def test_get_zero_changepoints(self, daily_univariate_ts, backend):
|
||||
train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)
|
||||
m = Prophet(n_changepoints=0, stan_backend=backend)
|
||||
history = m.setup_dataframe(train, initialize_scales=True)
|
||||
m.history = history
|
||||
m.set_changepoints()
|
||||
cp = m.changepoints_t
|
||||
assert cp.shape[0] == 1
|
||||
assert cp[0] == 0
|
||||
|
||||
def test_override_n_changepoints(self, daily_univariate_ts, backend):
|
||||
train = daily_univariate_ts.head(20).copy()
|
||||
m = Prophet(n_changepoints=15, stan_backend=backend)
|
||||
history = m.setup_dataframe(train, initialize_scales=True)
|
||||
m.history = history
|
||||
m.set_changepoints()
|
||||
assert m.n_changepoints == 15
|
||||
cp = m.changepoints_t
|
||||
assert cp.shape[0] == 15
|
||||
|
||||
|
||||
class TestProphetSeasonalComponent:
|
||||
def test_fourier_series_weekly(self, daily_univariate_ts):
|
||||
mat = Prophet.fourier_series(daily_univariate_ts["ds"], 7, 3)
|
||||
# These are from the R forecast package directly.
|
||||
true_values = np.array([0.7818315, 0.6234898, 0.9749279, -0.2225209, 0.4338837, -0.9009689])
|
||||
assert np.sum((mat[0] - true_values) ** 2) == pytest.approx(0.0)
|
||||
|
||||
def test_fourier_series_yearly(self, daily_univariate_ts):
|
||||
mat = Prophet.fourier_series(daily_univariate_ts["ds"], 365.25, 3)
|
||||
# These are from the R forecast package directly.
|
||||
true_values = np.array(
|
||||
[0.7006152, -0.7135393, -0.9998330, 0.01827656, 0.7262249, 0.6874572]
|
||||
)
|
||||
assert np.sum((mat[0] - true_values) ** 2) == pytest.approx(0.0)
|
||||
|
||||
def test_auto_weekly_seasonality(self, daily_univariate_ts, backend):
|
||||
# Should be enabled
|
||||
train = daily_univariate_ts.head(15)
|
||||
m = Prophet(stan_backend=backend)
|
||||
assert m.weekly_seasonality == "auto"
|
||||
m.fit(train)
|
||||
assert "weekly" in m.seasonalities
|
||||
assert m.seasonalities["weekly"] == {
|
||||
"period": 7,
|
||||
"fourier_order": 3,
|
||||
"prior_scale": 10.0,
|
||||
"mode": "additive",
|
||||
"condition_name": None,
|
||||
}
|
||||
# Should be disabled due to too short history
|
||||
train = daily_univariate_ts.head(9)
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(train)
|
||||
assert "weekly" not in m.seasonalities
|
||||
m = Prophet(weekly_seasonality=True, stan_backend=backend)
|
||||
m.fit(train)
|
||||
assert "weekly" in m.seasonalities
|
||||
# Should be False due to weekly spacing
|
||||
train = daily_univariate_ts.iloc[::7, :]
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(train)
|
||||
assert "weekly" not in m.seasonalities
|
||||
m = Prophet(weekly_seasonality=2, seasonality_prior_scale=3.0, stan_backend=backend)
|
||||
m.fit(daily_univariate_ts)
|
||||
assert m.seasonalities["weekly"] == {
|
||||
"period": 7,
|
||||
"fourier_order": 2,
|
||||
"prior_scale": 3.0,
|
||||
"mode": "additive",
|
||||
"condition_name": None,
|
||||
}
|
||||
|
||||
def test_auto_yearly_seasonality(self, daily_univariate_ts, backend):
|
||||
# Should be enabled
|
||||
m = Prophet(stan_backend=backend)
|
||||
assert m.yearly_seasonality == "auto"
|
||||
m.fit(daily_univariate_ts)
|
||||
assert "yearly" in m.seasonalities
|
||||
assert m.seasonalities["yearly"] == {
|
||||
"period": 365.25,
|
||||
"fourier_order": 10,
|
||||
"prior_scale": 10.0,
|
||||
"mode": "additive",
|
||||
"condition_name": None,
|
||||
}
|
||||
# Should be disabled due to too short history
|
||||
train = daily_univariate_ts.head(240)
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(train)
|
||||
assert "yearly" not in m.seasonalities
|
||||
m = Prophet(yearly_seasonality=True, stan_backend=backend)
|
||||
m.fit(train)
|
||||
assert "yearly" in m.seasonalities
|
||||
m = Prophet(yearly_seasonality=7, seasonality_prior_scale=3.0, stan_backend=backend)
|
||||
m.fit(daily_univariate_ts)
|
||||
assert m.seasonalities["yearly"] == {
|
||||
"period": 365.25,
|
||||
"fourier_order": 7,
|
||||
"prior_scale": 3.0,
|
||||
"mode": "additive",
|
||||
"condition_name": None,
|
||||
}
|
||||
|
||||
def test_auto_daily_seasonality(self, daily_univariate_ts, subdaily_univariate_ts, backend):
|
||||
# Should be enabled
|
||||
m = Prophet(stan_backend=backend)
|
||||
assert m.daily_seasonality == "auto"
|
||||
m.fit(subdaily_univariate_ts)
|
||||
assert "daily" in m.seasonalities
|
||||
assert m.seasonalities["daily"] == {
|
||||
"period": 1,
|
||||
"fourier_order": 4,
|
||||
"prior_scale": 10.0,
|
||||
"mode": "additive",
|
||||
"condition_name": None,
|
||||
}
|
||||
# Should be disabled due to too short history
|
||||
train = subdaily_univariate_ts.head(430)
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(train)
|
||||
assert "daily" not in m.seasonalities
|
||||
m = Prophet(daily_seasonality=True, stan_backend=backend)
|
||||
m.fit(train)
|
||||
assert "daily" in m.seasonalities
|
||||
m = Prophet(daily_seasonality=7, seasonality_prior_scale=3.0, stan_backend=backend)
|
||||
m.fit(subdaily_univariate_ts)
|
||||
assert m.seasonalities["daily"] == {
|
||||
"period": 1,
|
||||
"fourier_order": 7,
|
||||
"prior_scale": 3.0,
|
||||
"mode": "additive",
|
||||
"condition_name": None,
|
||||
}
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.fit(daily_univariate_ts)
|
||||
assert "daily" not in m.seasonalities
|
||||
|
||||
def test_set_seasonality_mode(self, backend):
|
||||
# Setting attribute
|
||||
m = Prophet(stan_backend=backend)
|
||||
assert m.seasonality_mode == "additive"
|
||||
m = Prophet(seasonality_mode="multiplicative", stan_backend=backend)
|
||||
assert m.seasonality_mode == "multiplicative"
|
||||
with pytest.raises(ValueError):
|
||||
Prophet(seasonality_mode="batman", stan_backend=backend)
|
||||
|
||||
def test_set_holidays_mode(self, backend):
|
||||
# Setting attribute
|
||||
m = Prophet(stan_backend=backend)
|
||||
assert m.holidays_mode == "additive"
|
||||
m = Prophet(seasonality_mode="multiplicative", stan_backend=backend)
|
||||
assert m.holidays_mode == "multiplicative"
|
||||
m = Prophet(holidays_mode="multiplicative", stan_backend=backend)
|
||||
assert m.holidays_mode == "multiplicative"
|
||||
with pytest.raises(ValueError):
|
||||
Prophet(holidays_mode="batman", stan_backend=backend)
|
||||
|
||||
def test_seasonality_modes(self, daily_univariate_ts, backend):
|
||||
# Model with holidays, seasonalities, and extra regressors
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2016-12-25"]),
|
||||
"holiday": ["xmas"],
|
||||
"lower_window": [-1],
|
||||
"upper_window": [0],
|
||||
}
|
||||
)
|
||||
m = Prophet(seasonality_mode="multiplicative", holidays=holidays, stan_backend=backend)
|
||||
m.add_seasonality("monthly", period=30, mode="additive", fourier_order=3)
|
||||
m.add_regressor("binary_feature", mode="additive")
|
||||
m.add_regressor("numeric_feature")
|
||||
# Construct seasonal features
|
||||
df = daily_univariate_ts.copy()
|
||||
df["binary_feature"] = [0] * 255 + [1] * 255
|
||||
df["numeric_feature"] = range(510)
|
||||
df = m.setup_dataframe(df, initialize_scales=True)
|
||||
m.history = df.copy()
|
||||
m.set_auto_seasonalities()
|
||||
seasonal_features, prior_scales, component_cols, modes = m.make_all_seasonality_features(df)
|
||||
assert sum(component_cols["additive_terms"]) == 7
|
||||
assert sum(component_cols["multiplicative_terms"]) == 29
|
||||
assert set(modes["additive"]) == {
|
||||
"monthly",
|
||||
"binary_feature",
|
||||
"additive_terms",
|
||||
"extra_regressors_additive",
|
||||
}
|
||||
assert set(modes["multiplicative"]) == {
|
||||
"weekly",
|
||||
"yearly",
|
||||
"xmas",
|
||||
"numeric_feature",
|
||||
"multiplicative_terms",
|
||||
"extra_regressors_multiplicative",
|
||||
"holidays",
|
||||
}
|
||||
|
||||
|
||||
class TestProphetCustomSeasonalComponent:
|
||||
def test_custom_monthly_seasonality(self, backend):
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.add_seasonality(name="monthly", period=30, fourier_order=5, prior_scale=2.0)
|
||||
assert m.seasonalities["monthly"] == {
|
||||
"period": 30,
|
||||
"fourier_order": 5,
|
||||
"prior_scale": 2.0,
|
||||
"mode": "additive",
|
||||
"condition_name": None,
|
||||
}
|
||||
|
||||
def test_duplicate_component_names(self, backend):
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2017-01-02"]),
|
||||
"holiday": ["special_day"],
|
||||
"prior_scale": [4.0],
|
||||
}
|
||||
)
|
||||
m = Prophet(holidays=holidays, stan_backend=backend)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
m.add_seasonality(name="special_day", period=30, fourier_order=5)
|
||||
with pytest.raises(ValueError):
|
||||
m.add_seasonality(name="trend", period=30, fourier_order=5)
|
||||
m.add_seasonality(name="weekly", period=30, fourier_order=5)
|
||||
|
||||
def test_custom_fourier_order(self, backend):
|
||||
"""Fourier order cannot be <= 0"""
|
||||
m = Prophet(stan_backend=backend)
|
||||
with pytest.raises(ValueError):
|
||||
m.add_seasonality(name="weekly", period=7, fourier_order=0)
|
||||
with pytest.raises(ValueError):
|
||||
m.add_seasonality(name="weekly", period=7, fourier_order=-1)
|
||||
|
||||
def test_custom_priors(self, daily_univariate_ts, backend):
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2017-01-02"]),
|
||||
"holiday": ["special_day"],
|
||||
"prior_scale": [4.0],
|
||||
}
|
||||
)
|
||||
m = Prophet(
|
||||
holidays=holidays,
|
||||
yearly_seasonality=False,
|
||||
seasonality_mode="multiplicative",
|
||||
stan_backend=backend,
|
||||
)
|
||||
m.add_seasonality(
|
||||
name="monthly", period=30, fourier_order=5, prior_scale=2.0, mode="additive"
|
||||
)
|
||||
m.fit(daily_univariate_ts)
|
||||
assert m.seasonalities["monthly"]["mode"] == "additive"
|
||||
assert m.seasonalities["weekly"]["mode"] == "multiplicative"
|
||||
seasonal_features, prior_scales, component_cols, modes = m.make_all_seasonality_features(
|
||||
m.history
|
||||
)
|
||||
assert sum(component_cols["monthly"]) == 10
|
||||
assert sum(component_cols["special_day"]) == 1
|
||||
assert sum(component_cols["weekly"]) == 6
|
||||
assert sum(component_cols["additive_terms"]) == 10
|
||||
assert sum(component_cols["multiplicative_terms"]) == 7
|
||||
|
||||
if seasonal_features.columns[0] == "monthly_delim_1":
|
||||
true = [2.0] * 10 + [10.0] * 6 + [4.0]
|
||||
assert sum(component_cols["monthly"][:10]) == 10
|
||||
assert sum(component_cols["weekly"][10:16]) == 6
|
||||
else:
|
||||
true = [10.0] * 6 + [2.0] * 10 + [4.0]
|
||||
assert sum(component_cols["weekly"][:6]) == 6
|
||||
assert sum(component_cols["monthly"][6:16]) == 10
|
||||
assert prior_scales == true
|
||||
|
||||
def test_conditional_custom_seasonality(self, daily_univariate_ts, backend):
|
||||
m = Prophet(weekly_seasonality=False, yearly_seasonality=False, stan_backend=backend)
|
||||
m.add_seasonality(
|
||||
name="conditional_weekly",
|
||||
period=7,
|
||||
fourier_order=3,
|
||||
prior_scale=2.0,
|
||||
condition_name="is_conditional_week",
|
||||
)
|
||||
m.add_seasonality(name="normal_monthly", period=30.5, fourier_order=5, prior_scale=2.0)
|
||||
df = daily_univariate_ts.copy()
|
||||
with pytest.raises(ValueError):
|
||||
# Require all conditions names in df
|
||||
m.fit(df)
|
||||
df["is_conditional_week"] = [0] * 255 + [2] * 255
|
||||
with pytest.raises(ValueError):
|
||||
# Require boolean compatible values
|
||||
m.fit(df)
|
||||
df["is_conditional_week"] = [0] * 255 + [1] * 255
|
||||
m.fit(df)
|
||||
assert m.seasonalities["conditional_weekly"] == {
|
||||
"period": 7,
|
||||
"fourier_order": 3,
|
||||
"prior_scale": 2.0,
|
||||
"mode": "additive",
|
||||
"condition_name": "is_conditional_week",
|
||||
}
|
||||
assert m.seasonalities["normal_monthly"]["condition_name"] is None
|
||||
seasonal_features, prior_scales, component_cols, modes = m.make_all_seasonality_features(
|
||||
m.history
|
||||
)
|
||||
# Confirm that only values without is_conditional_week has non zero entries
|
||||
condition_cols = [
|
||||
c for c in seasonal_features.columns if c.startswith("conditional_weekly")
|
||||
]
|
||||
assert np.array_equal(
|
||||
(seasonal_features[condition_cols] != 0).any(axis=1).values,
|
||||
df["is_conditional_week"].values,
|
||||
)
|
||||
|
||||
|
||||
class TestProphetHolidays:
|
||||
def test_holidays_lower_window(self, backend):
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2016-12-25"]),
|
||||
"holiday": ["xmas"],
|
||||
"lower_window": [-1],
|
||||
"upper_window": [0],
|
||||
}
|
||||
)
|
||||
model = Prophet(holidays=holidays, stan_backend=backend)
|
||||
df = pd.DataFrame({"ds": pd.date_range("2016-12-20", "2016-12-31")})
|
||||
feats, priors, names = model.make_holiday_features(df["ds"], model.holidays)
|
||||
assert feats.shape == (df.shape[0], 2)
|
||||
assert (feats.sum(axis=0) - np.array([1.0, 1.0])).sum() == 0.0
|
||||
assert priors == [10.0, 10.0] # Default prior
|
||||
assert names == ["xmas"]
|
||||
|
||||
def test_holidays_upper_window(self, backend):
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2016-12-25"]),
|
||||
"holiday": ["xmas"],
|
||||
"lower_window": [-1],
|
||||
"upper_window": [10],
|
||||
}
|
||||
)
|
||||
m = Prophet(holidays=holidays, stan_backend=backend)
|
||||
df = pd.DataFrame({"ds": pd.date_range("2016-12-20", "2016-12-31")})
|
||||
feats, priors, names = m.make_holiday_features(df["ds"], m.holidays)
|
||||
# 12 columns generated even though only 8 overlap
|
||||
assert feats.shape == (df.shape[0], 12)
|
||||
assert priors == [10.0 for _ in range(12)]
|
||||
assert names == ["xmas"]
|
||||
|
||||
def test_holidays_priors(self, backend):
|
||||
# Check prior specifications
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2016-12-25", "2017-12-25"]),
|
||||
"holiday": ["xmas", "xmas"],
|
||||
"lower_window": [-1, -1],
|
||||
"upper_window": [0, 0],
|
||||
"prior_scale": [5.0, 5.0],
|
||||
}
|
||||
)
|
||||
m = Prophet(holidays=holidays, stan_backend=backend)
|
||||
df = pd.DataFrame({"ds": pd.date_range("2016-12-20", "2016-12-31")})
|
||||
feats, priors, names = m.make_holiday_features(df["ds"], m.holidays)
|
||||
assert priors == [5.0, 5.0]
|
||||
assert names == ["xmas"]
|
||||
# 2 different priors
|
||||
holidays2 = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2012-06-06", "2013-06-06"]),
|
||||
"holiday": ["seans-bday"] * 2,
|
||||
"lower_window": [0] * 2,
|
||||
"upper_window": [1] * 2,
|
||||
"prior_scale": [8] * 2,
|
||||
}
|
||||
)
|
||||
holidays2 = pd.concat((holidays, holidays2), sort=True)
|
||||
m = Prophet(holidays=holidays2, stan_backend=backend)
|
||||
feats, priors, names = m.make_holiday_features(df["ds"], m.holidays)
|
||||
pn = zip(priors, [s.split("_delim_")[0] for s in feats.columns])
|
||||
for t in pn:
|
||||
assert t in [(8.0, "seans-bday"), (5.0, "xmas")]
|
||||
holidays2 = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2012-06-06", "2013-06-06"]),
|
||||
"holiday": ["seans-bday"] * 2,
|
||||
"lower_window": [0] * 2,
|
||||
"upper_window": [1] * 2,
|
||||
}
|
||||
)
|
||||
holidays2 = pd.concat((holidays, holidays2), sort=True)
|
||||
feats, priors, names = Prophet(
|
||||
holidays=holidays2, holidays_prior_scale=4, stan_backend=backend
|
||||
).make_holiday_features(df["ds"], holidays2)
|
||||
assert set(priors) == {4.0, 5.0}
|
||||
|
||||
def test_holidays_bad_priors(self, backend):
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2016-12-25", "2016-12-27"]),
|
||||
"holiday": ["xmasish", "xmasish"],
|
||||
"lower_window": [-1, -1],
|
||||
"upper_window": [0, 0],
|
||||
"prior_scale": [5.0, 6.0],
|
||||
}
|
||||
)
|
||||
df = pd.DataFrame({"ds": pd.date_range("2016-12-20", "2016-12-31")})
|
||||
with pytest.raises(ValueError):
|
||||
Prophet(holidays=holidays, stan_backend=backend).make_holiday_features(
|
||||
df["ds"], holidays
|
||||
)
|
||||
|
||||
def test_fit_with_holidays(self, daily_univariate_ts, backend):
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2012-06-06", "2013-06-06"]),
|
||||
"holiday": ["seans-bday"] * 2,
|
||||
"lower_window": [0] * 2,
|
||||
"upper_window": [1] * 2,
|
||||
}
|
||||
)
|
||||
model = Prophet(holidays=holidays, uncertainty_samples=0, stan_backend=backend)
|
||||
model.fit(daily_univariate_ts).predict()
|
||||
|
||||
def test_fit_predict_with_country_holidays(self, daily_univariate_ts, backend):
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2012-06-06", "2013-06-06"]),
|
||||
"holiday": ["seans-bday"] * 2,
|
||||
"lower_window": [0] * 2,
|
||||
"upper_window": [1] * 2,
|
||||
}
|
||||
)
|
||||
# Test with holidays and country_holidays
|
||||
model = Prophet(holidays=holidays, uncertainty_samples=0, stan_backend=backend)
|
||||
model.add_country_holidays(country_name="US")
|
||||
model.fit(daily_univariate_ts).predict()
|
||||
# There are training holidays missing in the test set
|
||||
train = daily_univariate_ts.head(154)
|
||||
future = daily_univariate_ts.tail(355)
|
||||
model = Prophet(uncertainty_samples=0, stan_backend=backend)
|
||||
model.add_country_holidays(country_name="US")
|
||||
model.fit(train).predict(future)
|
||||
# There are test holidays missing in the training set
|
||||
train = daily_univariate_ts.tail(355)
|
||||
model = Prophet(uncertainty_samples=0, stan_backend=backend)
|
||||
model.add_country_holidays(country_name="US")
|
||||
model.fit(train)
|
||||
future = model.make_future_dataframe(periods=60, include_history=False)
|
||||
model.predict(future)
|
||||
|
||||
def test_subdaily_holidays(self, subdaily_univariate_ts, backend):
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2017-01-02"]),
|
||||
"holiday": ["special_day"],
|
||||
}
|
||||
)
|
||||
m = Prophet(holidays=holidays, stan_backend=backend)
|
||||
m.fit(subdaily_univariate_ts)
|
||||
fcst = m.predict()
|
||||
assert sum(fcst["special_day"] == 0) == 575
|
||||
|
||||
|
||||
|
||||
class TestProphetRegressors:
|
||||
def test_added_regressors(self, daily_univariate_ts, backend):
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.add_regressor("binary_feature", prior_scale=0.2)
|
||||
m.add_regressor("numeric_feature", prior_scale=0.5)
|
||||
m.add_regressor("numeric_feature2", prior_scale=0.5, mode="multiplicative")
|
||||
m.add_regressor("binary_feature2", standardize=True)
|
||||
df = daily_univariate_ts.copy()
|
||||
df["binary_feature"] = ["0"] * 255 + ["1"] * 255
|
||||
df["numeric_feature"] = range(510)
|
||||
df["numeric_feature2"] = range(510)
|
||||
with pytest.raises(ValueError):
|
||||
# Require all regressors in df
|
||||
m.fit(df)
|
||||
df["binary_feature2"] = [1] * 100 + [0] * 410
|
||||
m.fit(df)
|
||||
# Check that standardizations are correctly set
|
||||
assert m.extra_regressors["binary_feature"] == {
|
||||
"prior_scale": 0.2,
|
||||
"mu": 0,
|
||||
"std": 1,
|
||||
"standardize": "auto",
|
||||
"mode": "additive",
|
||||
}
|
||||
assert m.extra_regressors["numeric_feature"]["prior_scale"] == 0.5
|
||||
assert m.extra_regressors["numeric_feature"]["mu"] == 254.5
|
||||
assert m.extra_regressors["numeric_feature"]["std"] == pytest.approx(147.368585, abs=1e-5)
|
||||
assert m.extra_regressors["numeric_feature2"]["mode"] == "multiplicative"
|
||||
assert m.extra_regressors["binary_feature2"]["prior_scale"] == 10.0
|
||||
assert m.extra_regressors["binary_feature2"]["mu"] == pytest.approx(0.1960784, abs=1e-5)
|
||||
assert m.extra_regressors["binary_feature2"]["std"] == pytest.approx(0.3974183, abs=1e-5)
|
||||
# Check that standardization is done correctly
|
||||
df2 = m.setup_dataframe(df.copy())
|
||||
assert df2["binary_feature"][0] == 0
|
||||
assert df2["numeric_feature"][0] == pytest.approx(-1.726962, abs=1e-4)
|
||||
assert df2["binary_feature2"][0] == pytest.approx(2.022859, abs=1e-4)
|
||||
# Check that feature matrix and prior scales are correctly constructed
|
||||
seasonal_features, prior_scales, component_cols, modes = m.make_all_seasonality_features(
|
||||
df2
|
||||
)
|
||||
assert seasonal_features.shape[1] == 30
|
||||
names = ["binary_feature", "numeric_feature", "binary_feature2"]
|
||||
true_priors = [0.2, 0.5, 10.0]
|
||||
for i, name in enumerate(names):
|
||||
assert name in seasonal_features
|
||||
assert sum(component_cols[name]) == 1
|
||||
assert sum(np.array(prior_scales) * component_cols[name]) == true_priors[i]
|
||||
# Check that forecast components are reasonable
|
||||
future = pd.DataFrame(
|
||||
{
|
||||
"ds": ["2014-06-01"],
|
||||
"binary_feature": [0],
|
||||
"numeric_feature": [10],
|
||||
"numeric_feature2": [10],
|
||||
}
|
||||
)
|
||||
# future dataframe also requires regressor values
|
||||
with pytest.raises(ValueError):
|
||||
m.predict(future)
|
||||
future["binary_feature2"] = 0
|
||||
fcst = m.predict(future)
|
||||
assert fcst.shape[1] == 37
|
||||
assert fcst["binary_feature"][0] == 0
|
||||
assert fcst["extra_regressors_additive"][0] == pytest.approx(
|
||||
fcst["numeric_feature"][0] + fcst["binary_feature2"][0]
|
||||
)
|
||||
assert fcst["extra_regressors_multiplicative"][0] == pytest.approx(
|
||||
fcst["numeric_feature2"][0]
|
||||
)
|
||||
assert fcst["additive_terms"][0] == pytest.approx(
|
||||
fcst["yearly"][0] + fcst["weekly"][0] + fcst["extra_regressors_additive"][0]
|
||||
)
|
||||
assert fcst["multiplicative_terms"][0] == pytest.approx(
|
||||
fcst["extra_regressors_multiplicative"][0]
|
||||
)
|
||||
assert fcst["yhat"][0] == pytest.approx(
|
||||
fcst["trend"][0] * (1 + fcst["multiplicative_terms"][0]) + fcst["additive_terms"][0]
|
||||
)
|
||||
|
||||
def test_constant_regressor(self, daily_univariate_ts, backend):
|
||||
df = daily_univariate_ts.copy()
|
||||
df["constant_feature"] = 0
|
||||
m = Prophet(stan_backend=backend)
|
||||
m.add_regressor("constant_feature")
|
||||
m.fit(df)
|
||||
assert m.extra_regressors["constant_feature"]["std"] == 1
|
||||
|
||||
|
||||
class TestProphetWarmStart:
|
||||
def test_fit_warm_start(self, daily_univariate_ts, backend):
|
||||
m = Prophet(stan_backend=backend).fit(daily_univariate_ts.iloc[:500])
|
||||
m2 = Prophet(stan_backend=backend).fit(
|
||||
daily_univariate_ts.iloc[:510], init=warm_start_params(m)
|
||||
)
|
||||
assert len(m2.params["delta"][0]) == 25
|
||||
|
||||
def test_sampling_warm_start(self, daily_univariate_ts, backend):
|
||||
m = Prophet(mcmc_samples=100, stan_backend=backend).fit(
|
||||
daily_univariate_ts.iloc[:500], show_progress=False
|
||||
)
|
||||
m2 = Prophet(mcmc_samples=100, stan_backend=backend).fit(
|
||||
daily_univariate_ts.iloc[:510], init=warm_start_params(m), show_progress=False
|
||||
)
|
||||
assert m2.params["delta"].shape == (200, 25)
|
||||
@ -0,0 +1,142 @@
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
from prophet import Prophet
|
||||
from prophet.serialize import PD_DATAFRAME, PD_SERIES, model_from_json, model_to_json
|
||||
|
||||
|
||||
class TestSerialize:
|
||||
def test_simple_serialize(self, daily_univariate_ts, backend):
|
||||
m = Prophet(stan_backend=backend)
|
||||
df = daily_univariate_ts.head(daily_univariate_ts.shape[0] - 30)
|
||||
m.fit(df)
|
||||
|
||||
future = m.make_future_dataframe(2, include_history=False)
|
||||
fcst = m.predict(future)
|
||||
|
||||
model_str = model_to_json(m)
|
||||
# Make sure json doesn't get too large in the future
|
||||
assert len(model_str) < 200000
|
||||
m2 = model_from_json(model_str)
|
||||
|
||||
# Check that m and m2 are equal
|
||||
assert m.__dict__.keys() == m2.__dict__.keys()
|
||||
for k, v in m.__dict__.items():
|
||||
if k in ["stan_fit", "stan_backend"]:
|
||||
continue
|
||||
if k == "params":
|
||||
assert v.keys() == m2.params.keys()
|
||||
for kk, vv in v.items():
|
||||
assert np.array_equal(vv, m2.params[kk])
|
||||
elif k in PD_SERIES and v is not None:
|
||||
assert v.equals(m2.__dict__[k])
|
||||
elif k in PD_DATAFRAME and v is not None:
|
||||
pd.testing.assert_frame_equal(v, m2.__dict__[k], check_index_type=False)
|
||||
elif k == "changepoints_t":
|
||||
assert np.array_equal(v, m.__dict__[k])
|
||||
else:
|
||||
assert v == m2.__dict__[k]
|
||||
assert m2.stan_fit is None
|
||||
assert m2.stan_backend is None
|
||||
|
||||
# Check that m2 makes the same forecast
|
||||
future2 = m2.make_future_dataframe(2, include_history=False)
|
||||
fcst2 = m2.predict(future2)
|
||||
|
||||
assert np.array_equal(fcst["yhat"].values, fcst2["yhat"].values)
|
||||
|
||||
def test_full_serialize(self, daily_univariate_ts, backend):
|
||||
# Construct a model with all attributes
|
||||
holidays = pd.DataFrame(
|
||||
{
|
||||
"ds": pd.to_datetime(["2012-06-06", "2013-06-06"]),
|
||||
"holiday": ["seans-bday"] * 2,
|
||||
"lower_window": [0] * 2,
|
||||
"upper_window": [1] * 2,
|
||||
}
|
||||
)
|
||||
# Test with holidays and country_holidays
|
||||
m = Prophet(
|
||||
holidays=holidays,
|
||||
seasonality_mode="multiplicative",
|
||||
changepoints=["2012-07-01", "2012-10-01", "2013-01-01"],
|
||||
stan_backend=backend,
|
||||
)
|
||||
m.add_country_holidays(country_name="US")
|
||||
m.add_seasonality(
|
||||
name="conditional_weekly",
|
||||
period=7,
|
||||
fourier_order=3,
|
||||
prior_scale=2.0,
|
||||
condition_name="is_conditional_week",
|
||||
)
|
||||
m.add_seasonality(name="normal_monthly", period=30.5, fourier_order=5, prior_scale=2.0)
|
||||
df = daily_univariate_ts.copy()
|
||||
df["is_conditional_week"] = [0] * 255 + [1] * 255
|
||||
m.add_regressor("binary_feature", prior_scale=0.2)
|
||||
m.add_regressor("numeric_feature", prior_scale=0.5)
|
||||
m.add_regressor("numeric_feature2", prior_scale=0.5, mode="multiplicative")
|
||||
m.add_regressor("binary_feature2", standardize=True)
|
||||
df["binary_feature"] = ["0"] * 255 + ["1"] * 255
|
||||
df["numeric_feature"] = range(510)
|
||||
df["numeric_feature2"] = range(510)
|
||||
df["binary_feature2"] = [1] * 100 + [0] * 410
|
||||
|
||||
train = df.head(400)
|
||||
test = df.tail(100)
|
||||
|
||||
m.fit(train)
|
||||
fcst = m.predict(test)
|
||||
# Serialize!
|
||||
m2 = model_from_json(model_to_json(m))
|
||||
|
||||
# Check that m and m2 are equal
|
||||
assert m.__dict__.keys() == m2.__dict__.keys()
|
||||
for k, v in m.__dict__.items():
|
||||
if k in ["stan_fit", "stan_backend"]:
|
||||
continue
|
||||
if k == "params":
|
||||
assert v.keys() == m2.params.keys()
|
||||
for kk, vv in v.items():
|
||||
assert np.array_equal(vv, m2.params[kk])
|
||||
elif k in PD_SERIES and v is not None:
|
||||
assert v.equals(m2.__dict__[k])
|
||||
elif k in PD_DATAFRAME and v is not None:
|
||||
pd.testing.assert_frame_equal(v, m2.__dict__[k], check_index_type=False)
|
||||
elif k == "changepoints_t":
|
||||
assert np.array_equal(v, m.__dict__[k])
|
||||
else:
|
||||
assert v == m2.__dict__[k]
|
||||
assert m2.stan_fit is None
|
||||
assert m2.stan_backend is None
|
||||
|
||||
# Check that m2 makes the same forecast
|
||||
fcst2 = m2.predict(test)
|
||||
assert np.array_equal(fcst["yhat"].values, fcst2["yhat"].values)
|
||||
|
||||
def test_backwards_compatibility(self):
|
||||
old_versions = {
|
||||
"0.6.1.dev0": (29.3669923968994, "fb"),
|
||||
"0.7.1": (29.282810844704414, "fb"),
|
||||
"1.0.1": (29.282810844704414, ""),
|
||||
}
|
||||
for v, (pred_val, v_str) in old_versions.items():
|
||||
fname = Path(__file__).parent / f"serialized_model_v{v}.json"
|
||||
with open(fname, "r") as fin:
|
||||
model_str = json.load(fin)
|
||||
# Check that deserializes
|
||||
m = model_from_json(model_str)
|
||||
assert json.loads(model_str)[f"__{v_str}prophet_version"] == v
|
||||
# Predict
|
||||
future = m.make_future_dataframe(10)
|
||||
fcst = m.predict(future)
|
||||
assert fcst["yhat"].values[-1] == pytest.approx(pred_val)
|
||||
@ -0,0 +1,27 @@
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
import numpy as np
|
||||
|
||||
from prophet import Prophet
|
||||
from prophet.utilities import regressor_coefficients
|
||||
|
||||
|
||||
class TestUtilities:
|
||||
def test_regressor_coefficients(self, daily_univariate_ts, backend):
|
||||
m = Prophet(stan_backend=backend)
|
||||
df = daily_univariate_ts.copy()
|
||||
np.random.seed(123)
|
||||
df["regr1"] = np.random.normal(size=df.shape[0])
|
||||
df["regr2"] = np.random.normal(size=df.shape[0])
|
||||
m.add_regressor("regr1", mode="additive")
|
||||
m.add_regressor("regr2", mode="multiplicative")
|
||||
m.fit(df)
|
||||
|
||||
coefs = regressor_coefficients(m)
|
||||
assert coefs.shape == (2, 6)
|
||||
# No MCMC sampling, so lower and upper should be the same as mean
|
||||
assert np.array_equal(coefs["coef_lower"].values, coefs["coef"].values)
|
||||
assert np.array_equal(coefs["coef_upper"].values, coefs["coef"].values)
|
||||
Reference in New Issue
Block a user