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Time in model training benchmark #995

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Nov 28, 2022
Merged

Time in model training benchmark #995

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2 changes: 1 addition & 1 deletion .github/workflows/metrics.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -39,12 +39,12 @@ jobs:
- name: Compare performance
run: |
echo "## Model Benchmark" >> report.md
echo "<details>\n<summary>Show benchmark results</summary>\n" >> report.md
python tests/metrics/compareMetrics.py >> report.md
- name: Publish report
env:
REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
echo "<details>\n<summary>Model training plots</summary>\n" >> report.md
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I'd prefer to have an option to also collapse the regular metrics - no hard feelings and no blocker for sure 👍

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Totally, agree, in the current setting there are too many metrics. Lets agree in our weekly meeting on the few key metrics that we always display and make all the others collabsable. I am just afraid that people do not check their metrics if its not visible that their pr reduces performance.

echo "## Model Training" >> report.md
echo "### PeytonManning" >> report.md
cml asset publish tests/metrics/PeytonManning.svg --md >> report.md
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10 changes: 10 additions & 0 deletions tests/test_model_performance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
import logging
import os
import pathlib
import time

import pandas as pd
import plotly.graph_objects as go
Expand Down Expand Up @@ -92,9 +93,12 @@ def test_PeytonManning():
df = pd.read_csv(PEYTON_FILE)
m = NeuralProphet(early_stopping=True)
df_train, df_test = m.split_df(df=df, freq="D", valid_p=0.2)
start = time.time()
metrics = m.fit(df_train, validation_df=df_test, freq="D")
end = time.time()

accuracy_metrics = metrics.to_dict("records")[-1]
accuracy_metrics["time"] = round(end - start, 2)
with open(os.path.join(DIR, "tests", "metrics", "PeytonManning.json"), "w") as outfile:
json.dump(accuracy_metrics, outfile)

Expand All @@ -112,9 +116,12 @@ def test_YosemiteTemps():
early_stopping=True,
)
df_train, df_test = m.split_df(df=df, freq="5min", valid_p=0.2)
start = time.time()
metrics = m.fit(df_train, validation_df=df_test, freq="5min")
end = time.time()

accuracy_metrics = metrics.to_dict("records")[-1]
accuracy_metrics["time"] = round(end - start, 2)
with open(os.path.join(DIR, "tests", "metrics", "YosemiteTemps.json"), "w") as outfile:
json.dump(accuracy_metrics, outfile)

Expand All @@ -125,9 +132,12 @@ def test_AirPassengers():
df = pd.read_csv(AIR_FILE)
m = NeuralProphet(seasonality_mode="multiplicative", early_stopping=True)
df_train, df_test = m.split_df(df=df, freq="MS", valid_p=0.2)
start = time.time()
metrics = m.fit(df_train, validation_df=df_test, freq="MS")
end = time.time()

accuracy_metrics = metrics.to_dict("records")[-1]
accuracy_metrics["time"] = round(end - start, 2)
with open(os.path.join(DIR, "tests", "metrics", "AirPassengers.json"), "w") as outfile:
json.dump(accuracy_metrics, outfile)

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