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''' |
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Functions for data preprocessing. |
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:author: Andreas Kanz |
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''' |
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# Imports |
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import numpy as np |
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import pandas as pd |
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from sklearn.base import BaseEstimator, TransformerMixin |
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from sklearn.ensemble import ExtraTreesRegressor |
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from sklearn.experimental import enable_iterative_imputer # noqa |
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from sklearn.impute import (SimpleImputer, IterativeImputer) |
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from sklearn.feature_selection import (f_classif, |
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SelectFromModel, |
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SelectPercentile, |
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VarianceThreshold) |
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from sklearn.linear_model import LassoCV |
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from sklearn.model_selection import train_test_split |
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from sklearn.pipeline import make_pipeline |
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from sklearn.preprocessing import (OneHotEncoder, |
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RobustScaler, |
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MaxAbsScaler) |
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from .utils import (_validate_input_int, |
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_validate_input_range, |
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_validate_input_sum) |
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__all__ = ['feature_selection_pipe', |
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'num_pipe', |
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'cat_pipe', |
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'train_dev_test_split'] |
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class ColumnSelector(BaseEstimator, TransformerMixin): |
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''' |
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Determines and selects numerical and categorical columns from a dataset based on their supposed dtype. Unlike \ |
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sklearn's make_column_selector() missing values are temporarily filled in to allow convert_dtypes() to determine \ |
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the dtype of a column. |
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Parameter: |
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--------- |
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num: default, True |
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Select only numerica Columns. If num = False, only categorical columns are selected. |
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Returns: |
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------- |
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Dataset containing only numerical or categorical data. |
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''' |
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def __init__(self, num=True): |
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self.num = num |
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def fit(self, X, y=None): |
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return self |
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def transform(self, X, y=None): |
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temp = X.fillna(X.mode().iloc[0]).convert_dtypes() |
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if self.num: |
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return X[temp.select_dtypes(include=['number']).columns.tolist()] |
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else: |
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return X[temp.select_dtypes(exclude=['number']).columns.tolist()] |
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class PipeInfo(BaseEstimator, TransformerMixin): |
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''' |
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Prints intermediary information about the dataset from within a pipeline. Include at any point in a Pipeline to |
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print out the shape of the dataset at this point. |
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Parameter: |
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--------- |
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name: string, default None |
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Provide a name for the current step. |
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Returns: |
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------- |
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Data: Data is being passed through. |
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''' |
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def __init__(self, name=None): |
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self.name = name |
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def fit(self, X, y=None): |
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return self |
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def transform(self, X, y=None): |
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print(f'Step: {self.name} --- Shape: {X.shape}') |
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return X |
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def cat_pipe(imputer=SimpleImputer(strategy='most_frequent'), |
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encoder=OneHotEncoder(handle_unknown='ignore'), |
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scaler=MaxAbsScaler(), |
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encoder_info=PipeInfo(name='after encoding')): |
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''' |
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Standard preprocessing operations on categorical data. |
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Parameters: |
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---------- |
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imputer: default, SimpleImputer(strategy='most_frequent') |
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encoder: default, OneHotEncoder(handle_unknown='ignore') |
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Encode categorical features as a one-hot numeric array. |
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scaler: default, MaxAbsScaler() |
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Scale each feature by its maximum absolute value. MaxAbsScaler() does not shift/center the data, and thus does \ |
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not destroy any sparsity. It is recommended to check for outliers before applying MaxAbsScaler(). |
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Returns: |
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------- |
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Pipeline |
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''' |
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cat_pipe = make_pipeline(ColumnSelector(num=False), |
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imputer, |
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encoder, encoder_info, |
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scaler) |
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return cat_pipe |
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def feature_selection_pipe( |
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var_thresh=VarianceThreshold(threshold=0.1), |
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select_from_model=SelectFromModel(LassoCV(cv=4, random_state=408), threshold="0.1*median"), |
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select_percentile=SelectPercentile(f_classif, percentile=95), |
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var_thresh_info=PipeInfo(name='after var_thresh'), |
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select_from_model_info=PipeInfo(name='after select_from_model'), |
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select_percentile_info=PipeInfo(name='after select_percentile')): |
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''' |
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Preprocessing operations for feature selection. |
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Parameters: |
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---------- |
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var_thresh: default, VarianceThreshold(threshold=0.1) |
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Specify a threshold to drop low variance features. |
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select_from_model: default, SelectFromModel(LassoCV(cv=4, random_state=408), threshold="0.1*median") |
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Specify an estimator which is used for selecting features based on importance weights. |
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select_percentile: default, SelectPercentile(f_classif, percentile=95) |
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Specify a score-function and a percentile value of features to keep. |
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Returns: |
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------- |
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Pipeline |
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''' |
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feature_selection_pipe = make_pipeline(var_thresh, var_thresh_info, |
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select_from_model, select_from_model_info, |
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select_percentile, select_percentile_info) |
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return feature_selection_pipe |
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def num_pipe(imputer=IterativeImputer(estimator=ExtraTreesRegressor( |
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n_estimators=25, n_jobs=4, random_state=408), random_state=408), |
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scaler=RobustScaler()): |
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''' |
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Standard preprocessing operations on numerical data. |
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Parameters: |
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---------- |
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imputer: default, IterativeImputer(estimator=ExtraTreesRegressor(n_estimators=25, n_jobs=4, random_state=408), \ |
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random_state=408) |
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scaler: default, RobustScaler() |
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Returns: |
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------- |
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Pipeline |
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''' |
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num_pipe = make_pipeline(ColumnSelector(), |
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imputer, |
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scaler) |
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return num_pipe |
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def train_dev_test_split(data, target, dev_size=0.1, test_size=0.1, stratify=None, random_state=408): |
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''' |
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Split a dataset and a label column into train, dev and test sets. |
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Parameters: |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
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information is used to label the plots. |
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target: string, list, np.array or pd.Series, default None |
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Specify target for correlation. E.g. label column to generate only the correlations between each feature \ |
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and the label. |
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dev_size: float, default 0.1 |
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If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the dev \ |
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split. |
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test_size: float, default 0.1 |
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If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test \ |
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split. |
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stratify: target column, default None |
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If not None, data is split in a stratified fashion, using the input as the class labels. |
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random_state: integer, default 408 |
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Random_state is the seed used by the random number generator. |
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Returns |
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------- |
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tuple: Tuple containing train-dev-test split of inputs. |
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''' |
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# Validate Inputs |
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_validate_input_range(dev_size, 'dev_size', 0, 1) |
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_validate_input_range(test_size, 'test_size', 0, 1) |
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_validate_input_int(random_state, 'random_state') |
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_validate_input_sum(1, 'Dev and test', dev_size, test_size) |
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target_data = [] |
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if isinstance(target, str): |
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target_data = data[target] |
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data = data.drop(target, axis=1) |
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elif isinstance(target, (list, pd.Series, np.ndarray)): |
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target_data = pd.Series(target) |
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X_train, X_dev_test, y_train, y_dev_test = train_test_split(data, target_data, |
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test_size=dev_size+test_size, |
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random_state=random_state, |
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stratify=stratify) |
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if (dev_size == 0) or (test_size == 0): |
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return X_train, X_dev_test, y_train, y_dev_test |
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else: |
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X_dev, X_test, y_dev, y_test = train_test_split(X_dev_test, y_dev_test, |
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test_size=test_size/(dev_size+test_size), |
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random_state=random_state, |
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stratify=y_dev_test) |
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return X_train, X_dev, X_test, y_train, y_dev, y_test |
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akanz1 /
klib
