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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.model_selection import train_test_split |
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from sklearn.pipeline import make_pipeline, make_union |
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from sklearn.preprocessing import OneHotEncoder, RobustScaler |
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from .utils import _validate_input_int |
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from .utils import _validate_input_range |
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__all__ = ['train_dev_test_split', |
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'cat_pipe', |
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'num_pipe', |
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'preprocessing_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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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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target = target.name |
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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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class ColumnSelector(BaseEstimator, TransformerMixin): |
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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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def cat_pipe(imputer=SimpleImputer(strategy='most_frequent')): |
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'''Set of standard preprocessing operations on categorical data.''' |
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cat_pipe = make_pipeline(ColumnSelector(num=False), |
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imputer, |
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OneHotEncoder(handle_unknown='ignore')) |
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return cat_pipe |
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def num_pipe(imputer=IterativeImputer( |
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estimator=ExtraTreesRegressor(n_estimators=25, n_jobs=4, random_state=408), random_state=408), |
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scaler=RobustScaler()): |
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'''Set of standard preprocessing operations on numerical data.''' |
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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 preprocessing_pipe(num=True, cat=True): |
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'''Set of standard preprocessing operations on numerical and categorical data. |
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Parameters: |
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---------- |
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num: bool, default True |
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Set to false if no numerical data is in the dataset. |
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cat: bool, default True |
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Set to false if no categorical data is in the dataset. |
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''' |
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pipe = None |
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if num and cat: |
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pipe = make_union(num_pipe(), cat_pipe(), n_jobs=4) |
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elif num: |
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pipe = num_pipe() |
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elif cat: |
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pipe = cat_pipe() |
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return pipe |
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akanz1 /
klib
