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Yizhou Chi 2024-09-04 17:52:02 +08:00
parent fcd1ba66a6
commit ab8a1d6824
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129
expo/dataset.py
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@ -1,12 +1,14 @@
import openml
from pathlib import Path
from sklearn.model_selection import train_test_split
import os
import json
import yaml
import pandas as pd
from expo.insights.solution_designer import SolutionDesigner
import asyncio
import json
import os
from pathlib import Path
import openml
import pandas as pd
import yaml
from sklearn.model_selection import train_test_split
from expo.insights.solution_designer import SolutionDesigner
BASE_USER_REQUIREMENT = """\
This is a {datasetname} dataset. Your goal is to predict the target column `{target_col}`.
@ -59,14 +61,12 @@ OPENML_DATASET_IDS = [
41980,
42225,
531,
# cls
41143,
31,
42733,
41162,
1067,
# multi cls
40498,
40982,
@ -79,14 +79,15 @@ CUSTOM_DATASETS = [
("04_titanic", "Survived"),
("05_house-prices-advanced-regression-techniques", "SalePrice"),
("06_santander-customer-transaction-prediction", "target"),
("07_icr-identify-age-related-conditions", "Class")
("07_icr-identify-age-related-conditions", "Class"),
]
def get_split_dataset_path(dataset_name, config):
datasets_dir = config['datasets_dir']
if dataset_name in config['datasets']:
dataset = config['datasets'][dataset_name]
data_path = os.path.join(datasets_dir, dataset['dataset'])
datasets_dir = config["datasets_dir"]
if dataset_name in config["datasets"]:
dataset = config["datasets"][dataset_name]
data_path = os.path.join(datasets_dir, dataset["dataset"])
split_datasets = {
"train": os.path.join(data_path, "split_train.csv"),
"dev": os.path.join(data_path, "split_dev.csv"),
@ -98,32 +99,39 @@ def get_split_dataset_path(dataset_name, config):
}
return split_datasets
else:
raise ValueError(f"Dataset {dataset_name} not found in config file. Available datasets: {config['datasets'].keys()}")
raise ValueError(
f"Dataset {dataset_name} not found in config file. Available datasets: {config['datasets'].keys()}"
)
def get_user_requirement(task_name, config):
datasets_dir = config['datasets_dir']
if task_name in config['datasets']:
dataset = config['datasets'][task_name]
data_path = os.path.join(datasets_dir, dataset['dataset'])
user_requirement = dataset['user_requirement']
datasets_dir = config["datasets_dir"]
if task_name in config["datasets"]:
dataset = config["datasets"][task_name]
data_path = os.path.join(datasets_dir, dataset["dataset"])
user_requirement = dataset["user_requirement"]
return data_path, user_requirement
else:
raise ValueError(f"Dataset {task_name} not found in config file. Available datasets: {config['datasets'].keys()}")
raise ValueError(
f"Dataset {task_name} not found in config file. Available datasets: {config['datasets'].keys()}"
)
def save_datasets_dict_to_yaml(datasets_dict):
with open("datasets.yaml", "w") as file:
yaml.dump(datasets_dict, file)
def create_dataset_dict(dataset):
dataset_dict = {
"dataset": dataset.name,
"user_requirement": dataset.create_base_requirement(),
"metric": dataset.get_metric(),
"target_col": dataset.target_col
"target_col": dataset.target_col,
}
return dataset_dict
def generate_task_requirement(task_name, data_config):
user_requirement = get_user_requirement(task_name, data_config)
split_dataset_path = get_split_dataset_path(task_name, data_config)
@ -132,19 +140,23 @@ def generate_task_requirement(task_name, data_config):
test_path = split_dataset_path["test_wo_target"]
work_dir = data_config["work_dir"]
output_dir = f"{work_dir}/{task_name}"
user_requirement = TASK_PROMPT.format(user_requirement=user_requirement,
train_path=train_path, dev_path=dev_path, test_path=test_path,
output_dir=output_dir)
user_requirement = TASK_PROMPT.format(
user_requirement=user_requirement,
train_path=train_path,
dev_path=dev_path,
test_path=test_path,
output_dir=output_dir,
)
print(user_requirement)
return user_requirement
class ExpDataset:
description : str = None
metadata : dict = None
dataset_dir : str = None
target_col : str = None
name : str = None
description: str = None
metadata: dict = None
dataset_dir: str = None
target_col: str = None
name: str = None
def __init__(self, name, dataset_dir, **kwargs):
self.name = name
@ -154,18 +166,23 @@ class ExpDataset:
self.save_dataset(target_col=self.target_col)
def check_dataset_exists(self):
fnames = ["split_train.csv", "split_dev.csv", "split_test.csv",
"split_dev_wo_target.csv", "split_dev_target.csv",
"split_test_wo_target.csv", "split_test_target.csv"]
fnames = [
"split_train.csv",
"split_dev.csv",
"split_test.csv",
"split_dev_wo_target.csv",
"split_dev_target.csv",
"split_test_wo_target.csv",
"split_test_target.csv",
]
for fname in fnames:
if not os.path.exists(Path(self.dataset_dir, self.name, fname)):
return False
return True
def check_datasetinfo_exists(self):
return os.path.exists(Path(self.dataset_dir, self.name, "dataset_info.json"))
def get_raw_dataset(self):
raw_dir = Path(self.dataset_dir, self.name, "raw")
if not os.path.exists(Path(raw_dir, "train.csv")):
@ -173,17 +190,17 @@ class ExpDataset:
else:
df = pd.read_csv(Path(raw_dir, "train.csv"))
return df
def get_dataset_info(self):
raw_df = pd.read_csv(Path(self.dataset_dir, self.name, "raw", "train.csv"))
metadata = {
'NumberOfClasses': raw_df[self.target_col].nunique(),
'NumberOfFeatures': raw_df.shape[1],
'NumberOfInstances': raw_df.shape[0],
'NumberOfInstancesWithMissingValues': int(raw_df.isnull().any(axis=1).sum()),
'NumberOfMissingValues': int(raw_df.isnull().sum().sum()),
'NumberOfNumericFeatures': raw_df.select_dtypes(include=['number']).shape[1],
'NumberOfSymbolicFeatures': raw_df.select_dtypes(include=['object']).shape[1],
"NumberOfClasses": raw_df[self.target_col].nunique(),
"NumberOfFeatures": raw_df.shape[1],
"NumberOfInstances": raw_df.shape[0],
"NumberOfInstancesWithMissingValues": int(raw_df.isnull().any(axis=1).sum()),
"NumberOfMissingValues": int(raw_df.isnull().sum().sum()),
"NumberOfNumericFeatures": raw_df.select_dtypes(include=["number"]).shape[1],
"NumberOfSymbolicFeatures": raw_df.select_dtypes(include=["object"]).shape[1],
}
df_head_text = raw_df.head().to_string(index=False)
@ -193,10 +210,10 @@ class ExpDataset:
"description": "",
"target_col": self.target_col,
"metadata": metadata,
"df_head": df_head_text
"df_head": df_head_text,
}
return dataset_info
def get_metric(self):
dataset_info = self.get_dataset_info()
num_classes = dataset_info["metadata"]["NumberOfClasses"]
@ -216,7 +233,6 @@ class ExpDataset:
return req
def save_dataset(self, target_col):
df = self.get_raw_dataset()
if not self.check_dataset_exists() or self.force_update:
print(f"Saving Dataset {self.name} in {self.dataset_dir}")
@ -249,25 +265,22 @@ class ExpDataset:
def split_and_save(self, df, target_col):
if not target_col:
raise ValueError("Target column not provided")
train, test = train_test_split(df, test_size=1-TRAIN_TEST_SPLIT, random_state=SEED)
train, dev = train_test_split(train, test_size=1-TRAIN_DEV_SPLIT, random_state=SEED)
train, test = train_test_split(df, test_size=1 - TRAIN_TEST_SPLIT, random_state=SEED)
train, dev = train_test_split(train, test_size=1 - TRAIN_DEV_SPLIT, random_state=SEED)
self.save_split_datasets(train, "train")
self.save_split_datasets(dev, "dev", target_col)
self.save_split_datasets(test, "test", target_col)
class OpenMLExpDataset(ExpDataset):
def __init__(self, name, dataset_dir, dataset_id, **kwargs):
self.dataset_id = dataset_id
self.dataset = openml.datasets.get_dataset(self.dataset_id,
download_data=False,
download_qualities=False,
download_features_meta_data=True)
self.dataset = openml.datasets.get_dataset(
self.dataset_id, download_data=False, download_qualities=False, download_features_meta_data=True
)
self.name = self.dataset.name
self.target_col = self.dataset.default_target_attribute
super().__init__(self.name, dataset_dir, target_col=self.target_col, **kwargs)
def get_raw_dataset(self):
dataset = self.dataset
@ -276,7 +289,7 @@ class OpenMLExpDataset(ExpDataset):
os.makedirs(raw_dir, exist_ok=True)
dataset_df.to_csv(Path(raw_dir, "train.csv"), index=False)
return dataset_df
def get_dataset_info(self):
dataset_info = super().get_dataset_info()
dataset = self.dataset
@ -290,12 +303,14 @@ class OpenMLExpDataset(ExpDataset):
# def __init__(self, name, dataset_dir, dataset_name, **kwargs):
# super().__init__(name, dataset_dir, **kwargs)
async def process_dataset(dataset, solution_designer, save_analysis_pool, datasets_dict):
if save_analysis_pool:
asyncio.run(solution_designer.generate_solutions(dataset.get_dataset_info(), dataset.name))
dataset_dict = create_dataset_dict(dataset)
datasets_dict["datasets"][dataset.name] = dataset_dict
if __name__ == "__main__":
datasets_dir = "D:/work/automl/datasets"
force_update = False