mirror of https://github.com/microsoft/autogen.git
256 lines
9.6 KiB
Python
256 lines
9.6 KiB
Python
'''!
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* Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved.
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* Licensed under the MIT License.
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'''
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import numpy as np
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from scipy.sparse import vstack, issparse
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import pandas as pd
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from .training_log import training_log_reader
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def load_openml_dataset(dataset_id, data_dir=None, random_state=0):
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'''Load dataset from open ML.
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If the file is not cached locally, download it from open ML.
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Args:
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dataset_id: An integer of the dataset id in openml
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data_dir: A string of the path to store and load the data
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random_state: An integer of the random seed for splitting data
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Returns:
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X_train: A 2d numpy array of training data
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X_test: A 2d numpy array of test data
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y_train: A 1d numpy arrya of labels for training data
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y_test: A 1d numpy arrya of labels for test data
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'''
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import os
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import openml
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import pickle
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from sklearn.model_selection import train_test_split
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filename = 'openml_ds' + str(dataset_id) + '.pkl'
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filepath = os.path.join(data_dir, filename)
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if os.path.isfile(filepath):
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print('load dataset from', filepath)
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with open(filepath, 'rb') as f:
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dataset = pickle.load(f)
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else:
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print('download dataset from openml')
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dataset = openml.datasets.get_dataset(dataset_id)
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if not os.path.exists(data_dir):
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os.makedirs(data_dir)
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with open(filepath, 'wb') as f:
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pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)
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print('Dataset name:', dataset.name)
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X, y, * \
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__ = dataset.get_data(
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target=dataset.default_target_attribute, dataset_format='array')
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, random_state=random_state)
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print(
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'X_train.shape: {}, y_train.shape: {};\nX_test.shape: {}, y_test.shape: {}'.format(
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X_train.shape, y_train.shape, X_test.shape, y_test.shape,
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)
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)
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return X_train, X_test, y_train, y_test
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def load_openml_task(task_id, data_dir):
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'''Load task from open ML.
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Use the first fold of the task.
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If the file is not cached locally, download it from open ML.
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Args:
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task_id: An integer of the task id in openml
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data_dir: A string of the path to store and load the data
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Returns:
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X_train: A 2d numpy array of training data
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X_test: A 2d numpy array of test data
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y_train: A 1d numpy arrya of labels for training data
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y_test: A 1d numpy arrya of labels for test data
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'''
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import os
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import openml
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import pickle
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task = openml.tasks.get_task(task_id)
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filename = 'openml_task' + str(task_id) + '.pkl'
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filepath = os.path.join(data_dir, filename)
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if os.path.isfile(filepath):
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print('load dataset from', filepath)
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with open(filepath, 'rb') as f:
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dataset = pickle.load(f)
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else:
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print('download dataset from openml')
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dataset = task.get_dataset()
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with open(filepath, 'wb') as f:
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pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)
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X, y, _, _ = dataset.get_data(task.target_name, dataset_format='array')
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train_indices, test_indices = task.get_train_test_split_indices(
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repeat=0,
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fold=0,
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sample=0,
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)
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X_train = X[train_indices]
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y_train = y[train_indices]
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X_test = X[test_indices]
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y_test = y[test_indices]
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print(
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'X_train.shape: {}, y_train.shape: {},\nX_test.shape: {}, y_test.shape: {}'.format(
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X_train.shape, y_train.shape, X_test.shape, y_test.shape,
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)
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)
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return X_train, X_test, y_train, y_test
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def get_output_from_log(filename, time_budget):
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'''Get output from log file
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Args:
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filename: A string of the log file name
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time_budget: A float of the time budget in seconds
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Returns:
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training_time_list: A list of the finished time of each logged iter
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best_error_list:
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A list of the best validation error after each logged iter
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error_list: A list of the validation error of each logged iter
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config_list:
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A list of the estimator, sample size and config of each logged iter
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logged_metric_list: A list of the logged metric of each logged iter
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'''
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best_config = None
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best_learner = None
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best_val_loss = float('+inf')
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training_duration = 0.0
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training_time_list = []
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config_list = []
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best_error_list = []
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error_list = []
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logged_metric_list = []
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best_config_list = []
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with training_log_reader(filename) as reader:
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for record in reader.records():
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time_used = record.total_search_time
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training_duration = time_used
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val_loss = record.validation_loss
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config = record.config
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learner = record.learner.split('_')[0]
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sample_size = record.sample_size
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train_loss = record.logged_metric
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if time_used < time_budget:
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if val_loss < best_val_loss:
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best_val_loss = val_loss
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best_config = config
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best_learner = learner
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best_config_list.append(best_config)
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training_time_list.append(training_duration)
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best_error_list.append(best_val_loss)
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logged_metric_list.append(train_loss)
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error_list.append(val_loss)
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config_list.append({"Current Learner": learner,
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"Current Sample": sample_size,
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"Current Hyper-parameters": record.config,
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"Best Learner": best_learner,
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"Best Hyper-parameters": best_config})
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return (training_time_list, best_error_list, error_list, config_list,
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logged_metric_list)
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def concat(X1, X2):
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'''concatenate two matrices vertically
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'''
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if isinstance(X1, pd.DataFrame) or isinstance(X1, pd.Series):
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df = pd.concat([X1, X2], sort=False)
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df.reset_index(drop=True, inplace=True)
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if isinstance(X1, pd.DataFrame):
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cat_columns = X1.select_dtypes(
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include='category').columns
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if len(cat_columns):
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df[cat_columns] = df[cat_columns].astype('category')
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return df
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if issparse(X1):
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return vstack((X1, X2))
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else:
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return np.concatenate([X1, X2])
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class DataTransformer:
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'''transform X, y
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'''
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def fit_transform(self, X, y, task):
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if isinstance(X, pd.DataFrame):
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X = X.copy()
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n = X.shape[0]
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cat_columns, num_columns = [], []
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for column in X.columns:
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if X[column].dtype.name in ('object', 'category'):
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if X[column].nunique() == 1 or X[column].nunique(
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dropna=True) == n - X[column].isnull().sum():
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X.drop(columns=column, inplace=True)
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elif X[column].dtype.name == 'category':
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current_categories = X[column].cat.categories
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if '__NAN__' not in current_categories:
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X[column] = X[column].cat.add_categories(
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'__NAN__').fillna('__NAN__')
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cat_columns.append(column)
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else:
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X[column].fillna('__NAN__', inplace=True)
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cat_columns.append(column)
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else:
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# print(X[column].dtype.name)
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if X[column].nunique(dropna=True) < 2:
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X.drop(columns=column, inplace=True)
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else:
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X[column].fillna(np.nan, inplace=True)
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num_columns.append(column)
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X = X[cat_columns + num_columns]
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if cat_columns:
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X[cat_columns] = X[cat_columns].astype('category')
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if num_columns:
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from sklearn.impute import SimpleImputer
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from sklearn.compose import ColumnTransformer
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self.transformer = ColumnTransformer([(
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'continuous',
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SimpleImputer(missing_values=np.nan, strategy='median'),
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num_columns)])
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X[num_columns] = self.transformer.fit_transform(X)
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self._cat_columns, self._num_columns = cat_columns, num_columns
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if task == 'regression':
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self.label_transformer = None
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else:
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from sklearn.preprocessing import LabelEncoder
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self.label_transformer = LabelEncoder()
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y = self.label_transformer.fit_transform(y)
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return X, y
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def transform(self, X):
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if isinstance(X, pd.DataFrame):
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cat_columns, num_columns = self._cat_columns, self._num_columns
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X = X[cat_columns + num_columns].copy()
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for column in cat_columns:
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# print(column, X[column].dtype.name)
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if X[column].dtype.name == 'object':
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X[column].fillna('__NAN__', inplace=True)
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elif X[column].dtype.name == 'category':
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current_categories = X[column].cat.categories
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if '__NAN__' not in current_categories:
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X[column] = X[column].cat.add_categories(
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'__NAN__').fillna('__NAN__')
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if cat_columns:
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X[cat_columns] = X[cat_columns].astype('category')
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if num_columns:
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X[num_columns].fillna(np.nan, inplace=True)
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X[num_columns] = self.transformer.transform(X)
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return X
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