Module imodels.util.data_util
Expand source code
import os.path
from os.path import join as oj
from typing import Tuple
import numpy as np
import pandas as pd
import requests
import sklearn.datasets
from scipy.sparse import issparse
from sklearn.datasets import fetch_openml
def define_openml_outcomes(y, data_id: str):
if data_id == '59': # ionosphere, positive is "good" class
y = (y == 'g').astype(int)
if data_id == '183': # abalone, need to convert strings to floats
y = y.astype(float)
return y
def clean_feat_names(feature_names):
# shouldn't start with a digit
return ['X_' + x if x[0].isdigit()
else x
for x in feature_names]
def clean_features(X):
if issparse(X):
X = X.toarray()
try:
return X.astype(float)
except:
for j in range(X.shape[1]):
try:
X[:, j].astype(float)
except:
# non-numeric get replaced with numerical values
classes, X[:, j] = np.unique(X[:, j], return_inverse=True)
return X.astype(float)
def get_clean_dataset(dataset: str = None, data_source: str = 'imodels', data_path='data') -> Tuple[
np.ndarray, np.ndarray, list]:
"""Return
Parameters
----------
dataset: str
csv_file path or dataset id if data_source is specified
data_source: str
options: 'imodels', 'pmlb', 'sklearn', 'openml', 'synthetic'
boolean - whether dataset is a pmlb dataset name
Returns
-------
X, y, feature_names
"""
assert data_source in ['imodels', 'pmlb', 'sklearn', 'openml', 'synthetic'], data_source + ' not correct'
if data_source == 'imodels':
if not dataset.endswith('csv'):
dataset = dataset + '.csv'
if not os.path.isfile(dataset):
download_imodels_dataset(dataset, data_path)
df = pd.read_csv(oj(data_path, 'imodels_data', dataset))
X, y = df.iloc[:, :-1].values, df.iloc[:, -1].values
feature_names = df.columns.values[:-1]
return np.nan_to_num(X.astype('float32')), y, clean_feat_names(feature_names)
elif data_source == 'pmlb':
from pmlb import fetch_data
feature_names = list(
fetch_data(dataset, return_X_y=False, local_cache_dir=oj(data_path, 'pmlb_data')).columns)
feature_names.remove('target')
X, y = fetch_data(dataset, return_X_y=True, local_cache_dir=oj(data_path, 'pmlb_data'))
if np.unique(y).size == 2: # if binary classification, ensure that the classes are 0 and 1
y -= np.min(y)
return clean_features(X), y, clean_feat_names(feature_names)
elif data_source == 'sklearn':
if dataset == 'diabetes':
data = sklearn.datasets.load_diabetes()
elif dataset == 'california_housing':
data = sklearn.datasets.fetch_california_housing(data_home=oj(data_path, 'sklearn_data'))
return data['data'], data['target'], clean_feat_names(data['feature_names'])
elif data_source == 'openml': # note this api might change in newer sklearn - should give dataset-id not name
data = sklearn.datasets.fetch_openml(data_id=dataset, data_home=oj(data_path, 'openml_data'))
X, y, feature_names = data['data'], data['target'], clean_feat_names(data['feature_names'])
if isinstance(X, pd.DataFrame):
X = X.values
if isinstance(y, pd.Series):
y = y.values
y = define_openml_outcomes(y, dataset)
return clean_features(X), y, clean_feat_names(feature_names)
elif data_source == 'synthetic':
if dataset == 'friedman1':
X, y = sklearn.datasets.make_friedman1(n_samples=200, n_features=10)
elif dataset == 'friedman2':
X, y = sklearn.datasets.make_friedman2(n_samples=200)
elif dataset == 'friedman3':
X, y = sklearn.datasets.make_friedman3(n_samples=200)
return X, y, ['X_' + str(i + 1) for i in range(X.shape[1])]
def get_openml_dataset(data_id: int) -> pd.DataFrame:
dataset = fetch_openml(data_id=data_id, as_frame=False)
X = dataset.data
if issparse(X):
X = X.toarray()
y = (dataset.target == dataset.target[0]).astype(int)
feature_names = dataset.feature_names
target_name = dataset.target_names
if target_name[0].lower() == 'class':
target_name = [dataset.target[0]]
X_df = pd.DataFrame(X, columns=feature_names)
y_df = pd.DataFrame(y, columns=target_name)
return pd.concat((X_df, y_df), axis=1)
def download_imodels_dataset(dataset_fname, data_path: str):
dataset_fname = dataset_fname.split('/')[-1] # remove anything about the path
download_path = f'https://raw.githubusercontent.com/csinva/imodels-data/master/data_cleaned/{dataset_fname}'
r = requests.get(download_path)
if r.status_code == 404:
raise Exception(f'404 Error for dataset {dataset_fname} (see valid files at https://github.com/csinva/imodels-data/tree/master/data_cleaned)')
os.makedirs(oj(data_path, 'imodels_data'), exist_ok=True)
with open(oj(data_path, 'imodels_data', dataset_fname), 'w') as f:
f.write(r.text)
Functions
def clean_feat_names(feature_names)
-
Expand source code
def clean_feat_names(feature_names): # shouldn't start with a digit return ['X_' + x if x[0].isdigit() else x for x in feature_names]
def clean_features(X)
-
Expand source code
def clean_features(X): if issparse(X): X = X.toarray() try: return X.astype(float) except: for j in range(X.shape[1]): try: X[:, j].astype(float) except: # non-numeric get replaced with numerical values classes, X[:, j] = np.unique(X[:, j], return_inverse=True) return X.astype(float)
def define_openml_outcomes(y, data_id: str)
-
Expand source code
def define_openml_outcomes(y, data_id: str): if data_id == '59': # ionosphere, positive is "good" class y = (y == 'g').astype(int) if data_id == '183': # abalone, need to convert strings to floats y = y.astype(float) return y
def download_imodels_dataset(dataset_fname, data_path: str)
-
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def download_imodels_dataset(dataset_fname, data_path: str): dataset_fname = dataset_fname.split('/')[-1] # remove anything about the path download_path = f'https://raw.githubusercontent.com/csinva/imodels-data/master/data_cleaned/{dataset_fname}' r = requests.get(download_path) if r.status_code == 404: raise Exception(f'404 Error for dataset {dataset_fname} (see valid files at https://github.com/csinva/imodels-data/tree/master/data_cleaned)') os.makedirs(oj(data_path, 'imodels_data'), exist_ok=True) with open(oj(data_path, 'imodels_data', dataset_fname), 'w') as f: f.write(r.text)
def get_clean_dataset(dataset: str = None, data_source: str = 'imodels', data_path='data') ‑> Tuple[numpy.ndarray, numpy.ndarray, list]
-
Return
Parameters
dataset
:str
- csv_file path or dataset id if data_source is specified
data_source
:str
- options: 'imodels', 'pmlb', 'sklearn', 'openml', 'synthetic' boolean - whether dataset is a pmlb dataset name
Returns
X, y, feature_names
Expand source code
def get_clean_dataset(dataset: str = None, data_source: str = 'imodels', data_path='data') -> Tuple[ np.ndarray, np.ndarray, list]: """Return Parameters ---------- dataset: str csv_file path or dataset id if data_source is specified data_source: str options: 'imodels', 'pmlb', 'sklearn', 'openml', 'synthetic' boolean - whether dataset is a pmlb dataset name Returns ------- X, y, feature_names """ assert data_source in ['imodels', 'pmlb', 'sklearn', 'openml', 'synthetic'], data_source + ' not correct' if data_source == 'imodels': if not dataset.endswith('csv'): dataset = dataset + '.csv' if not os.path.isfile(dataset): download_imodels_dataset(dataset, data_path) df = pd.read_csv(oj(data_path, 'imodels_data', dataset)) X, y = df.iloc[:, :-1].values, df.iloc[:, -1].values feature_names = df.columns.values[:-1] return np.nan_to_num(X.astype('float32')), y, clean_feat_names(feature_names) elif data_source == 'pmlb': from pmlb import fetch_data feature_names = list( fetch_data(dataset, return_X_y=False, local_cache_dir=oj(data_path, 'pmlb_data')).columns) feature_names.remove('target') X, y = fetch_data(dataset, return_X_y=True, local_cache_dir=oj(data_path, 'pmlb_data')) if np.unique(y).size == 2: # if binary classification, ensure that the classes are 0 and 1 y -= np.min(y) return clean_features(X), y, clean_feat_names(feature_names) elif data_source == 'sklearn': if dataset == 'diabetes': data = sklearn.datasets.load_diabetes() elif dataset == 'california_housing': data = sklearn.datasets.fetch_california_housing(data_home=oj(data_path, 'sklearn_data')) return data['data'], data['target'], clean_feat_names(data['feature_names']) elif data_source == 'openml': # note this api might change in newer sklearn - should give dataset-id not name data = sklearn.datasets.fetch_openml(data_id=dataset, data_home=oj(data_path, 'openml_data')) X, y, feature_names = data['data'], data['target'], clean_feat_names(data['feature_names']) if isinstance(X, pd.DataFrame): X = X.values if isinstance(y, pd.Series): y = y.values y = define_openml_outcomes(y, dataset) return clean_features(X), y, clean_feat_names(feature_names) elif data_source == 'synthetic': if dataset == 'friedman1': X, y = sklearn.datasets.make_friedman1(n_samples=200, n_features=10) elif dataset == 'friedman2': X, y = sklearn.datasets.make_friedman2(n_samples=200) elif dataset == 'friedman3': X, y = sklearn.datasets.make_friedman3(n_samples=200) return X, y, ['X_' + str(i + 1) for i in range(X.shape[1])]
def get_openml_dataset(data_id: int) ‑> pandas.core.frame.DataFrame
-
Expand source code
def get_openml_dataset(data_id: int) -> pd.DataFrame: dataset = fetch_openml(data_id=data_id, as_frame=False) X = dataset.data if issparse(X): X = X.toarray() y = (dataset.target == dataset.target[0]).astype(int) feature_names = dataset.feature_names target_name = dataset.target_names if target_name[0].lower() == 'class': target_name = [dataset.target[0]] X_df = pd.DataFrame(X, columns=feature_names) y_df = pd.DataFrame(y, columns=target_name) return pd.concat((X_df, y_df), axis=1)