# Copyright 2019-2023 IBM Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import urllib.request
import zipfile
import numpy as np
import pandas as pd
import lale.datasets.data_schemas
from lale.datasets.util import download_data_cache_dir
download_data_dir = download_data_cache_dir / "uci" / "download_data"
download_data_url = "http://archive.ics.uci.edu/static/public"
[docs]def download(dataset_id, zip_name, contents_files):
zip_url = f"{download_data_url}/{dataset_id}/{zip_name}"
data_dir = os.path.join(download_data_dir, dataset_id)
if not os.path.exists(data_dir):
os.makedirs(data_dir)
full_file_names = [os.path.join(data_dir, base) for base in contents_files]
def all_downloaded():
for full in full_file_names:
if not os.path.exists(full):
return False
return True
if not all_downloaded():
with tempfile.NamedTemporaryFile(suffix=".zip") as tmp_zip_file:
# this request is to a string that begins with a hardcoded http url, so does not risk leaking local data
urllib.request.urlretrieve(zip_url, tmp_zip_file.name) # nosec
with zipfile.ZipFile(tmp_zip_file.name) as myzip:
for full, base in zip(full_file_names, contents_files):
if not os.path.exists(full):
myzip.extract(base, data_dir)
assert all_downloaded()
return full_file_names
[docs]def tsv_to_Xy(file_name, target_col, schema_orig, index_col=None):
data_all = pd.read_csv(file_name, sep="\t", index_col=index_col)
row_schema_X = [
col_schema
for col_schema in schema_orig["items"]["items"]
if col_schema["description"] != target_col
]
columns_X = [col_schema["description"] for col_schema in row_schema_X]
data_X = data_all.loc[:, columns_X]
nrows, ncols_X = data_X.shape
schema_X = {
**schema_orig,
"minItems": nrows,
"maxItems": nrows,
"items": {
"type": "array",
"minItems": ncols_X,
"maxItems": ncols_X,
"items": row_schema_X,
},
}
data_X = lale.datasets.data_schemas.add_schema(data_X, schema_X)
row_schema_y = [
col_schema
for col_schema in schema_orig["items"]["items"]
if col_schema["description"] == target_col
]
data_y = data_all[target_col]
schema_y = {
**schema_orig,
"minItems": nrows,
"maxItems": nrows,
"items": row_schema_y[0],
}
data_y = lale.datasets.data_schemas.add_schema(data_y, schema_y)
return data_X, data_y
[docs]def fetch_drugslib():
files = download(
"461",
"drug+review+dataset+druglib+com.zip",
["drugLibTest_raw.tsv", "drugLibTrain_raw.tsv"],
)
target_col = "rating"
json_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"type": "array",
"items": {
"type": "array",
"minItems": 8,
"maxItems": 8,
"items": [
# index: {"description": "reviewID", "type": "integer", "minimum": 0},
{"description": "urlDrugName", "type": "string"},
{
"description": "rating",
"type": "integer",
"enum": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
},
{
"description": "effectiveness",
"anyOf": [{"type": "string"}, {"enum": [np.nan]}],
},
{
"description": "sideEffects",
"anyOf": [{"type": "string"}, {"enum": [np.nan]}],
},
{
"description": "condition",
"anyOf": [{"type": "string"}, {"enum": [np.nan]}],
},
{
"description": "benefitsReview",
"anyOf": [{"type": "string"}, {"enum": [np.nan]}],
},
{
"description": "sideEffectsReview",
"anyOf": [{"type": "string"}, {"enum": [np.nan]}],
},
{
"description": "commentsReview",
"anyOf": [{"type": "string"}, {"enum": [np.nan]}],
},
],
},
}
test_X, test_y = tsv_to_Xy(files[0], target_col, json_schema, index_col=[0])
train_X, train_y = tsv_to_Xy(files[1], target_col, json_schema, index_col=[0])
return train_X, train_y, test_X, test_y
[docs]def fetch_household_power_consumption():
file_name = download(
"235",
"individual+household+electric+power+consumption.zip",
["household_power_consumption.txt"],
)
df = pd.read_csv(file_name[0], sep=";")
return df