reduced benchmark execution time
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parent
75e019efc0
commit
9f32be3bdf
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@ -35,12 +35,12 @@ def mlflow_context():
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@given(
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@given(
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data=st.data(),
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data=st.data(),
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feature_size=st.integers(min_value=1, max_value=10),
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feature_size=st.integers(min_value=1, max_value=10),
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width=st.integers(min_value=MINIMUM_NETWORK_DIMENSION, max_value=200),
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width=st.integers(min_value=MINIMUM_NETWORK_DIMENSION, max_value=20),
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height=st.integers(min_value=MINIMUM_NETWORK_DIMENSION, max_value=200),
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height=st.integers(min_value=MINIMUM_NETWORK_DIMENSION, max_value=20),
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num_iterations=st.integers(min_value=10, max_value=1000),
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num_iterations=st.integers(min_value=10, max_value=50),
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initial_learning_rate=st.floats(min_value=1e-3, max_value=1.0, allow_nan=False, allow_infinity=False)
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initial_learning_rate=st.floats(min_value=1e-3, max_value=1.0, allow_nan=False, allow_infinity=False)
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)
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)
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@settings(max_examples=20, deadline=None, suppress_health_check=(HealthCheck.too_slow,))
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@settings(max_examples=10, deadline=None, suppress_health_check=(HealthCheck.too_slow,))
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def benchmark_kohonen_networks_performance_mlflow(mlflow_context, data, feature_size, width, height, num_iterations, initial_learning_rate):
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def benchmark_kohonen_networks_performance_mlflow(mlflow_context, data, feature_size, width, height, num_iterations, initial_learning_rate):
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np.random.seed(42)
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np.random.seed(42)
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timestamp = datetime.datetime.now(datetime.UTC).strftime('%Y-%m-%dT%H:%M:%SZ')
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timestamp = datetime.datetime.now(datetime.UTC).strftime('%Y-%m-%dT%H:%M:%SZ')
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