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PyCaret: On CI, only use model arima, providing a smaller MASE #300

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PyCaret: On CI, only use model arima, providing a smaller MASE #300

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10 changes: 6 additions & 4 deletions topic/machine-learning/pycaret/automl_timeseries_forecasting_with_pycaret.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1084,12 +1084,14 @@
"# all available models are included by default)\n",
"# - \"fold\" defines the number of folds to use for cross-validation.\n",
"\n",
"# Note: This is only relevant if we are executing automated tests\n",
"# On CI/testing, only evaluate a single cheap model.\n",
"# # Alternatives: arima, ets, et_cds_dt, exp_smooth, naive.\n",
"if \"PYTEST_CURRENT_TEST\" in os.environ:\n",
" best_models = compare_models(sort=\"MASE\",\n",
" include=[\"ets\", \"et_cds_dt\", \"naive\"],\n",
" n_select=3)\n",
"# If we are not in an automated test, compare all available models\n",
" include=[\"ets\"],\n",
" n_select=1)\n",
"\n",
"# When not on CI/testing, compare all available models.\n",
"else:\n",
" best_models = compare_models(sort=\"MASE\", n_select=3)"
]
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9 changes: 7 additions & 2 deletions topic/machine-learning/pycaret/automl_timeseries_forecasting_with_pycaret.py
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Expand Up @@ -82,10 +82,15 @@ def fetch_data():

def run_experiment(data):
setup(data = data, fh=15, target="total_sales", index="month", log_experiment=True)

# On CI/testing, only evaluate a single cheap model.
# Alternatives: arima, ets, et_cds_dt, exp_smooth, naive.
if "PYTEST_CURRENT_TEST" in os.environ:
best_models = compare_models(sort="MASE",
include=["arima", "ets", "exp_smooth"],
n_select=3)
include=["ets"],
n_select=1)

# When not on CI/testing, compare all available models.
else:
best_models = compare_models(sort="MASE", n_select=3)

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