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226 行
5.4 KiB
Python
226 行
5.4 KiB
Python
import pandas as pd
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from ludwig.utils.dataset_utils import get_repeatable_train_val_test_split
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def test_get_repeatable_train_val_test_split():
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# Test adding split with stratify
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df = pd.DataFrame(
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[
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[0, 0],
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[1, 0],
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[2, 0],
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[3, 0],
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[4, 0],
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[5, 1],
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[6, 1],
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[7, 1],
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[8, 1],
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[9, 1],
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[10, 0],
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[11, 0],
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[12, 0],
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[13, 0],
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[14, 0],
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[15, 1],
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[16, 1],
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[17, 1],
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[18, 1],
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[19, 1],
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],
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columns=["input", "target"],
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)
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split_df = get_repeatable_train_val_test_split(df, "target", random_seed=42)
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assert split_df.equals(
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pd.DataFrame(
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[
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[7, 1, 0],
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[16, 1, 0],
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[5, 1, 0],
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[14, 0, 0],
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[19, 1, 0],
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[6, 1, 0],
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[11, 0, 0],
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[18, 1, 0],
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[1, 0, 0],
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[10, 0, 0],
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[2, 0, 0],
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[15, 1, 0],
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[0, 0, 0],
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[17, 1, 1],
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[12, 0, 1],
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[8, 1, 2],
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[4, 0, 2],
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[13, 0, 2],
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[3, 0, 2],
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[9, 1, 2],
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],
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columns=["input", "target", "split"],
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)
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)
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# Test adding split without stratify
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df = pd.DataFrame(
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[
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[0, 0],
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[1, 0],
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[2, 0],
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[3, 0],
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[4, 0],
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[5, 1],
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[6, 1],
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[7, 1],
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[8, 1],
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[9, 1],
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[10, 0],
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[11, 0],
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[12, 0],
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[13, 0],
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[14, 0],
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[15, 1],
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[16, 1],
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[17, 1],
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[18, 1],
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[19, 1],
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],
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columns=["input", "target"],
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)
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split_df = get_repeatable_train_val_test_split(df, random_seed=42)
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assert split_df.equals(
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pd.DataFrame(
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[
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[3, 0, 0],
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[4, 0, 0],
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[5, 1, 0],
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[7, 1, 0],
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[8, 1, 0],
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[10, 0, 0],
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[11, 0, 0],
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[12, 0, 0],
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[13, 0, 0],
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[14, 0, 0],
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[15, 1, 0],
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[16, 1, 0],
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[18, 1, 0],
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[19, 1, 0],
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[0, 0, 1],
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[17, 1, 1],
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[1, 0, 2],
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[2, 0, 2],
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[9, 1, 2],
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[6, 1, 2],
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],
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columns=["input", "target", "split"],
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)
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)
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# Test needing no change
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df = pd.DataFrame(
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[
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[0, 0, 0],
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[1, 0, 0],
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[2, 0, 0],
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[5, 1, 0],
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[6, 1, 0],
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[7, 1, 0],
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[10, 0, 0],
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[11, 0, 0],
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[14, 0, 0],
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[15, 1, 0],
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[16, 1, 0],
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[18, 1, 0],
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[19, 1, 0],
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[12, 0, 1],
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[17, 1, 1],
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[3, 0, 2],
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[4, 0, 2],
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[8, 1, 2],
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[9, 1, 2],
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[13, 0, 2],
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],
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columns=["input", "target", "split"],
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)
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split_df = get_repeatable_train_val_test_split(df, "target", random_seed=42)
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assert split_df.equals(
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pd.DataFrame(
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[
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[0, 0, 0],
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[1, 0, 0],
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[2, 0, 0],
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[5, 1, 0],
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[6, 1, 0],
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[7, 1, 0],
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[10, 0, 0],
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[11, 0, 0],
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[14, 0, 0],
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[15, 1, 0],
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[16, 1, 0],
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[18, 1, 0],
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[19, 1, 0],
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[12, 0, 1],
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[17, 1, 1],
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[3, 0, 2],
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[4, 0, 2],
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[8, 1, 2],
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[9, 1, 2],
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[13, 0, 2],
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],
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columns=["input", "target", "split"],
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)
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)
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# Test adding only validation split
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df = pd.DataFrame(
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[
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[0, 0, 0],
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[1, 0, 0],
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[2, 0, 0],
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[5, 1, 0],
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[6, 1, 0],
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[7, 1, 0],
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[10, 0, 0],
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[11, 0, 0],
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[14, 0, 0],
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[15, 1, 0],
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[16, 1, 0],
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[18, 1, 0],
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[19, 1, 0],
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[12, 0, 0],
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[17, 1, 0],
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[3, 0, 2],
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[4, 0, 2],
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[8, 1, 2],
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[9, 1, 2],
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[13, 0, 2],
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],
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columns=["input", "target", "split"],
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)
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split_df = get_repeatable_train_val_test_split(df, "target", random_seed=42)
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assert split_df.equals(
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pd.DataFrame(
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[
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[0, 0, 0],
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[1, 0, 0],
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[2, 0, 0],
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[5, 1, 0],
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[6, 1, 0],
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[7, 1, 0],
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[10, 0, 0],
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[11, 0, 0],
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[14, 0, 0],
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[16, 1, 0],
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[19, 1, 0],
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[12, 0, 0],
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[17, 1, 0],
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[15, 1, 1],
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[18, 1, 1],
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[3, 0, 2],
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[4, 0, 2],
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[8, 1, 2],
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[9, 1, 2],
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[13, 0, 2],
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],
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columns=["input", "target", "split"],
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)
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)
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