mirror of
https://github.com/microsoft/autogen.git
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569908fbe6
* console log handler * version update * doc * skippable steps * notebook update * constraint sign * doc for constraints * bug fix: define-by-run and unflatten_hierarchical * const * handle nested space in indexof() * test grid search * test suggestion * model test * >1 ckpts * always increase iter count * log total # iterations * security patch * make iter_per_learner consistent
139 lines
3.5 KiB
Python
139 lines
3.5 KiB
Python
import sys
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import pytest
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import pickle
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import shutil
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@pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")
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def test_hf_data():
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from flaml import AutoML
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import requests
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from datasets import load_dataset
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try:
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train_dataset = load_dataset("glue", "mrpc", split="train[:1%]").to_pandas()
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dev_dataset = (
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load_dataset("glue", "mrpc", split="train[1%:2%]").to_pandas().iloc[:4]
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)
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test_dataset = (
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load_dataset("glue", "mrpc", split="test[2%:3%]").to_pandas().iloc[:4]
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)
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except requests.exceptions.ConnectionError:
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return
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custom_sent_keys = ["sentence1", "sentence2"]
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label_key = "label"
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X_train = train_dataset[custom_sent_keys]
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y_train = train_dataset[label_key]
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X_val = dev_dataset[custom_sent_keys]
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y_val = dev_dataset[label_key]
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X_test = test_dataset[custom_sent_keys]
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automl = AutoML()
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automl_settings = {
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"gpu_per_trial": 0,
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"max_iter": 3,
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"time_budget": 10,
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"task": "seq-classification",
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"metric": "accuracy",
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"log_file_name": "seqclass.log",
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}
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automl_settings["custom_hpo_args"] = {
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"model_path": "google/electra-small-discriminator",
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"output_dir": "test/data/output/",
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"ckpt_per_epoch": 5,
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"fp16": False,
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}
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automl.fit(
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X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
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)
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automl = AutoML()
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automl.retrain_from_log(
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X_train=X_train,
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y_train=y_train,
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train_full=True,
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record_id=0,
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**automl_settings
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)
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with open("automl.pkl", "wb") as f:
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pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL)
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with open("automl.pkl", "rb") as f:
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automl = pickle.load(f)
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shutil.rmtree("test/data/output/")
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automl.predict(X_test)
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automl.predict(["test test", "test test"])
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automl.predict(
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[
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["test test", "test test"],
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["test test", "test test"],
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["test test", "test test"],
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]
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)
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automl.predict_proba(X_test)
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print(automl.classes_)
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def _test_custom_data():
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from flaml import AutoML
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import requests
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import pandas as pd
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try:
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train_dataset = pd.read_csv("data/input/train.tsv", delimiter="\t", quoting=3)
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dev_dataset = pd.read_csv("data/input/dev.tsv", delimiter="\t", quoting=3)
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test_dataset = pd.read_csv("data/input/test.tsv", delimiter="\t", quoting=3)
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except requests.exceptions.ConnectionError:
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pass
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custom_sent_keys = ["#1 String", "#2 String"]
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label_key = "Quality"
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X_train = train_dataset[custom_sent_keys]
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y_train = train_dataset[label_key]
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X_val = dev_dataset[custom_sent_keys]
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y_val = dev_dataset[label_key]
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X_test = test_dataset[custom_sent_keys]
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automl = AutoML()
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automl_settings = {
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"gpu_per_trial": 0,
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"max_iter": 3,
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"time_budget": 5,
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"task": "seq-classification",
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"metric": "accuracy",
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}
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automl_settings["custom_hpo_args"] = {
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"model_path": "google/electra-small-discriminator",
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"output_dir": "data/output/",
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"ckpt_per_epoch": 1,
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}
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automl.fit(
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X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
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)
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automl.predict(X_test)
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automl.predict(["test test"])
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automl.predict(
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[
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["test test", "test test"],
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["test test", "test test"],
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["test test", "test test"],
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]
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)
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if __name__ == "__main__":
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test_hf_data()
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