83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
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from typing import Tuple
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import pandas as pd
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from pandas import DataFrame
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from sklearn.model_selection import train_test_split
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def split_stratified_into_train_val_test(
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df_input,
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target_colname="z",
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stratify_colname="y",
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frac_train=0.6,
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frac_val=0.15,
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frac_test=0.25,
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random_state=None,
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) -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:
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"""
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Splits a Pandas dataframe into three subsets (train, val, and test)
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following fractional ratios provided by the user, where each subset is
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stratified by the values in a specific column (that is, each subset has
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the same relative frequency of the values in the column). It performs this
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splitting by running train_test_split() twice.
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Parameters
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----------
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df_input : Pandas dataframe
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Input dataframe to be split.
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stratify_colname : str
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The name of the column that will be used for stratification. Usually
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this column would be for the label.
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frac_train : float
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frac_val : float
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frac_test : float
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The ratios with which the dataframe will be split into train, val, and
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test data. The values should be expressed as float fractions and should
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sum to 1.0.
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random_state : int, None, or RandomStateInstance
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Value to be passed to train_test_split().
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Returns
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-------
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df_train, df_val, df_test :
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Dataframes containing the three splits.
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"""
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if frac_train + frac_val + frac_test != 1.0:
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raise ValueError(
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"fractions %f, %f, %f do not add up to 1.0"
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% (frac_train, frac_val, frac_test)
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)
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if stratify_colname not in df_input.columns:
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raise ValueError("%s is not a column in the dataframe" % (stratify_colname))
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if target_colname not in df_input.columns:
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raise ValueError("%s is not a column in the dataframe" % (target_colname))
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X = df_input # Contains all columns.
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y = df_input[[target_colname]] # Dataframe of just the column on which to stratify.
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z = df_input[[stratify_colname]]
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# Split original dataframe into train and temp dataframes.
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df_train, df_temp, y_train, y_temp = train_test_split(
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X, y, stratify=z, test_size=(1.0 - frac_train), random_state=random_state
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)
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if frac_val <= 0:
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assert len(df_input) == len(df_train) + len(df_temp)
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return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp
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# Split the temp dataframe into val and test dataframes.
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relative_frac_test = frac_test / (frac_val + frac_test)
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df_val, df_test, y_val, y_test = train_test_split(
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df_temp,
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y_temp,
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stratify=df_temp[[stratify_colname]],
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test_size=relative_frac_test,
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random_state=random_state,
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)
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assert len(df_input) == len(df_train) + len(df_val) + len(df_test)
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return df_train, df_val, df_test, y_train, y_val, y_test
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