/
data.py
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/
data.py
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'''!
* Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
'''
import numpy as np
from scipy.sparse import vstack, issparse
import pandas as pd
from .training_log import training_log_reader
from datetime import datetime
def load_openml_dataset(dataset_id, data_dir=None, random_state=0):
'''Load dataset from open ML.
If the file is not cached locally, download it from open ML.
Args:
dataset_id: An integer of the dataset id in openml
data_dir: A string of the path to store and load the data
random_state: An integer of the random seed for splitting data
Returns:
X_train: A 2d numpy array of training data
X_test: A 2d numpy array of test data
y_train: A 1d numpy arrya of labels for training data
y_test: A 1d numpy arrya of labels for test data
'''
import os
import openml
import pickle
from sklearn.model_selection import train_test_split
filename = 'openml_ds' + str(dataset_id) + '.pkl'
filepath = os.path.join(data_dir, filename)
if os.path.isfile(filepath):
print('load dataset from', filepath)
with open(filepath, 'rb') as f:
dataset = pickle.load(f)
else:
print('download dataset from openml')
dataset = openml.datasets.get_dataset(dataset_id)
if not os.path.exists(data_dir):
os.makedirs(data_dir)
with open(filepath, 'wb') as f:
pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)
print('Dataset name:', dataset.name)
X, y, * \
__ = dataset.get_data(
target=dataset.default_target_attribute, dataset_format='array')
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=random_state)
print(
'X_train.shape: {}, y_train.shape: {};\nX_test.shape: {}, y_test.shape: {}'.format(
X_train.shape, y_train.shape, X_test.shape, y_test.shape,
)
)
return X_train, X_test, y_train, y_test
def load_openml_task(task_id, data_dir):
'''Load task from open ML.
Use the first fold of the task.
If the file is not cached locally, download it from open ML.
Args:
task_id: An integer of the task id in openml
data_dir: A string of the path to store and load the data
Returns:
X_train: A 2d numpy array of training data
X_test: A 2d numpy array of test data
y_train: A 1d numpy arrya of labels for training data
y_test: A 1d numpy arrya of labels for test data
'''
import os
import openml
import pickle
task = openml.tasks.get_task(task_id)
filename = 'openml_task' + str(task_id) + '.pkl'
filepath = os.path.join(data_dir, filename)
if os.path.isfile(filepath):
print('load dataset from', filepath)
with open(filepath, 'rb') as f:
dataset = pickle.load(f)
else:
print('download dataset from openml')
dataset = task.get_dataset()
with open(filepath, 'wb') as f:
pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)
X, y, _, _ = dataset.get_data(task.target_name, dataset_format='array')
train_indices, test_indices = task.get_train_test_split_indices(
repeat=0,
fold=0,
sample=0,
)
X_train = X[train_indices]
y_train = y[train_indices]
X_test = X[test_indices]
y_test = y[test_indices]
print(
'X_train.shape: {}, y_train.shape: {},\nX_test.shape: {}, y_test.shape: {}'.format(
X_train.shape, y_train.shape, X_test.shape, y_test.shape,
)
)
return X_train, X_test, y_train, y_test
def get_output_from_log(filename, time_budget):
'''Get output from log file
Args:
filename: A string of the log file name
time_budget: A float of the time budget in seconds
Returns:
training_time_list: A list of the finished time of each logged iter
best_error_list:
A list of the best validation error after each logged iter
error_list: A list of the validation error of each logged iter
config_list:
A list of the estimator, sample size and config of each logged iter
logged_metric_list: A list of the logged metric of each logged iter
'''
best_config = None
best_learner = None
best_val_loss = float('+inf')
training_duration = 0.0
training_time_list = []
config_list = []
best_error_list = []
error_list = []
logged_metric_list = []
best_config_list = []
with training_log_reader(filename) as reader:
for record in reader.records():
time_used = record.total_search_time
training_duration = time_used
val_loss = record.validation_loss
config = record.config
learner = record.learner.split('_')[0]
sample_size = record.sample_size
train_loss = record.logged_metric
if time_used < time_budget:
if val_loss < best_val_loss:
best_val_loss = val_loss
best_config = config
best_learner = learner
best_config_list.append(best_config)
training_time_list.append(training_duration)
best_error_list.append(best_val_loss)
logged_metric_list.append(train_loss)
error_list.append(val_loss)
config_list.append({"Current Learner": learner,
"Current Sample": sample_size,
"Current Hyper-parameters": record.config,
"Best Learner": best_learner,
"Best Hyper-parameters": best_config})
return (training_time_list, best_error_list, error_list, config_list,
logged_metric_list)
def concat(X1, X2):
'''concatenate two matrices vertically
'''
if isinstance(X1, pd.DataFrame) or isinstance(X1, pd.Series):
df = pd.concat([X1, X2], sort=False)
df.reset_index(drop=True, inplace=True)
if isinstance(X1, pd.DataFrame):
cat_columns = X1.select_dtypes(
include='category').columns
if len(cat_columns):
df[cat_columns] = df[cat_columns].astype('category')
return df
if issparse(X1):
return vstack((X1, X2))
else:
return np.concatenate([X1, X2])
class DataTransformer:
'''transform X, y
'''
def fit_transform(self, X, y, task):
if isinstance(X, pd.DataFrame):
X = X.copy()
n = X.shape[0]
cat_columns, num_columns, datetime_columns = [], [], []
drop = False
for column in X.columns:
# sklearn\utils\validation.py needs int/float values
if X[column].dtype.name in ('object', 'category'):
if X[column].nunique() == 1 or X[column].nunique(
dropna=True) == n - X[column].isnull().sum():
X.drop(columns=column, inplace=True)
drop = True
elif X[column].dtype.name == 'category':
current_categories = X[column].cat.categories
if '__NAN__' not in current_categories:
X[column] = X[column].cat.add_categories(
'__NAN__').fillna('__NAN__')
cat_columns.append(column)
else:
X[column] = X[column].fillna('__NAN__')
cat_columns.append(column)
else:
# print(X[column].dtype.name)
if X[column].nunique(dropna=True) < 2:
X.drop(columns=column, inplace=True)
drop = True
else:
if X[column].dtype.name == 'datetime64[ns]':
tmp_dt = X[column].dt
new_columns_dict = {f'year_{column}': tmp_dt.year, f'month_{column}': tmp_dt.month,
f'day_{column}': tmp_dt.day, f'hour_{column}': tmp_dt.hour,
f'minute_{column}': tmp_dt.minute, f'second_{column}': tmp_dt.second,
f'dayofweek_{column}': tmp_dt.dayofweek,
f'dayofyear_{column}': tmp_dt.dayofyear,
f'quarter_{column}': tmp_dt.quarter}
for new_col_name in new_columns_dict.keys():
if new_col_name not in X.columns and \
new_columns_dict.get(new_col_name).nunique(dropna=False) >= 2:
X[new_col_name] = new_columns_dict.get(new_col_name)
num_columns.append(new_col_name)
X[column] = X[column].map(datetime.toordinal)
datetime_columns.append(column)
del tmp_dt
else:
X[column] = X[column].fillna(np.nan)
num_columns.append(column)
X = X[cat_columns + num_columns]
if cat_columns:
X[cat_columns] = X[cat_columns].astype('category')
if num_columns:
X_num = X[num_columns]
if drop and np.issubdtype(X_num.columns.dtype, np.integer):
X_num.columns = range(X_num.shape[1])
else:
drop = False
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
self.transformer = ColumnTransformer([(
'continuous',
SimpleImputer(missing_values=np.nan, strategy='median'),
X_num.columns)])
X[num_columns] = self.transformer.fit_transform(X_num)
self._cat_columns, self._num_columns, self._datetime_columns = \
cat_columns, num_columns, datetime_columns
self._drop = drop
if task == 'regression':
self.label_transformer = None
else:
from sklearn.preprocessing import LabelEncoder
self.label_transformer = LabelEncoder()
y = self.label_transformer.fit_transform(y)
return X, y
def transform(self, X):
X = X.copy()
if isinstance(X, pd.DataFrame):
cat_columns, num_columns, datetime_columns = self._cat_columns, \
self._num_columns, self._datetime_columns
if datetime_columns:
for column in datetime_columns:
tmp_dt = X[column].dt
new_columns_dict = {f'year_{column}': tmp_dt.year, f'month_{column}': tmp_dt.month,
f'day_{column}': tmp_dt.day, f'hour_{column}': tmp_dt.hour,
f'minute_{column}': tmp_dt.minute, f'second_{column}': tmp_dt.second,
f'dayofweek_{column}': tmp_dt.dayofweek,
f'dayofyear_{column}': tmp_dt.dayofyear,
f'quarter_{column}': tmp_dt.quarter}
for new_col_name in new_columns_dict.keys():
if new_col_name not in X.columns and \
new_columns_dict.get(new_col_name).nunique(dropna=False) >= 2:
X[new_col_name] = new_columns_dict.get(new_col_name)
X[column] = X[column].map(datetime.toordinal)
del tmp_dt
X = X[cat_columns + num_columns].copy()
for column in cat_columns:
if X[column].dtype.name == 'object':
X[column] = X[column].fillna('__NAN__')
elif X[column].dtype.name == 'category':
current_categories = X[column].cat.categories
if '__NAN__' not in current_categories:
X[column] = X[column].cat.add_categories(
'__NAN__').fillna('__NAN__')
if cat_columns:
X[cat_columns] = X[cat_columns].astype('category')
if num_columns:
X_num = X[num_columns].fillna(np.nan)
if self._drop:
X_num.columns = range(X_num.shape[1])
X[num_columns] = self.transformer.transform(X_num)
return X