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import os |
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import numpy as np |
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import pandas as pd |
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from hyperactive import Hyperactive |
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from hyperactive.dashboards import ProgressBoard |
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from hyperactive.dashboards.progress_board.streamlit_backend import StreamlitBackend |
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from hyperactive.dashboards.progress_board.progress_io import ProgressIO |
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def test_progress_io_0(): |
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search_id = "test_model" |
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_io_ = ProgressIO("./") |
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_io_.get_filter_file_path(search_id) |
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def test_progress_io_1(): |
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search_id = "test_model" |
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_io_ = ProgressIO("./") |
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_io_.get_progress_data_path(search_id) |
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def test_progress_io_2(): |
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search_id = "test_model" |
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_io_ = ProgressIO("./") |
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_io_.remove_filter(search_id) |
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filter_ = _io_.load_filter(search_id) |
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assert filter_ is None |
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def test_progress_io_3(): |
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search_id = "test_model" |
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_io_ = ProgressIO("./") |
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_io_.remove_progress(search_id) |
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progress_ = _io_.load_progress(search_id) |
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assert progress_ is None |
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def test_progress_io_4(): |
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search_id = "test_model" |
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search_space = { |
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"x1": np.arange(-100, 101, 1), |
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} |
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_io_ = ProgressIO("./") |
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_io_.remove_progress(search_id) |
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_io_.create_filter(search_id, search_space) |
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progress_ = _io_.load_filter(search_id) |
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assert progress_ is not None |
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def test_filter_data_0(): |
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search_id1 = "test_model1" |
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search_id2 = "test_model2" |
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search_id3 = "test_model3" |
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search_ids = [search_id1, search_id2, search_id3] |
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def objective_function(opt): |
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score = -opt["x1"] * opt["x1"] |
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return score |
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search_space = { |
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"x1": np.arange(-100, 101, 1), |
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} |
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hyper = Hyperactive() |
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hyper.add_search(objective_function, search_space, n_iter=200) |
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hyper.run() |
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search_data = hyper.results(objective_function) |
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indices = list(search_space.keys()) + ["score"] |
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filter_dict = { |
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"parameter": indices, |
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"lower bound": "---", |
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"upper bound": "---", |
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} |
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filter_df = pd.DataFrame(filter_dict) |
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threshold = -1000 |
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filter_df["lower bound"].iloc[1] = threshold |
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board = StreamlitBackend(search_ids) |
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progress_data = board.filter_data(search_data, filter_df) |
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assert not np.all(search_data["score"].values >= threshold) |
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assert np.all(progress_data["score"].values >= threshold) |
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def test_streamlit_backend_0(): |
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search_id1 = "test_model1" |
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search_id2 = "test_model2" |
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search_id3 = "test_model3" |
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search_ids = [search_id1, search_id2, search_id3] |
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board = StreamlitBackend(search_ids) |
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progress_data = board.get_progress_data(search_id1) |
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assert progress_data is None |
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View Code Duplication |
def test_streamlit_backend_1(): |
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search_id1 = "test_model1" |
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search_id2 = "test_model2" |
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search_id3 = "test_model3" |
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search_ids = [search_id1, search_id2, search_id3] |
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board = StreamlitBackend(search_ids) |
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def objective_function(opt): |
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score = -opt["x1"] * opt["x1"] |
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return score |
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search_space = { |
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"x1": np.arange(-100, 101, 1), |
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} |
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hyper = Hyperactive() |
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hyper.add_search(objective_function, search_space, n_iter=200) |
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hyper.run() |
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search_data = hyper.results(objective_function) |
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search_data["nth_iter"] = 0 |
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search_data["score_best"] = 0 |
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search_data["nth_process"] = 0 |
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pyplot_fig = board.pyplot(search_data) |
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assert pyplot_fig is not None |
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View Code Duplication |
def test_streamlit_backend_2(): |
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search_id1 = "test_model1" |
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search_id2 = "test_model2" |
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search_id3 = "test_model3" |
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search_ids = [search_id1, search_id2, search_id3] |
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board = StreamlitBackend(search_ids) |
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def objective_function(opt): |
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score = -opt["x1"] * opt["x1"] |
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return score |
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search_space = { |
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"x1": np.arange(-100, 101, 1), |
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} |
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hyper = Hyperactive() |
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hyper.add_search(objective_function, search_space, n_iter=200) |
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hyper.run() |
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search_data = hyper.results(objective_function) |
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search_data["nth_iter"] = 0 |
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search_data["score_best"] = 0 |
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search_data["nth_process"] = 0 |
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plotly_fig = board.plotly(search_data, search_id1) |
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assert plotly_fig is not None |
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def test_streamlit_backend_3(): |
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search_id1 = "test_model1" |
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search_id2 = "test_model2" |
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search_id3 = "test_model3" |
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search_ids = [search_id1, search_id2, search_id3] |
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board = StreamlitBackend(search_ids) |
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df_empty = pd.DataFrame() |
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board.pyplot(df_empty) |
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def test_streamlit_backend_4(): |
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search_id1 = "test_model1" |
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search_id2 = "test_model2" |
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search_id3 = "test_model3" |
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search_ids = [search_id1, search_id2, search_id3] |
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board = StreamlitBackend(search_ids) |
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df_empty = pd.DataFrame([], columns=["nth_iter", "score_best", "nth_process"]) |
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board.pyplot(df_empty) |
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def test_streamlit_backend_3(): |
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search_id1 = "test_model1" |
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search_id2 = "test_model2" |
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search_id3 = "test_model3" |
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search_ids = [search_id1, search_id2, search_id3] |
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board = StreamlitBackend(search_ids) |
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df_empty = pd.DataFrame() |
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board.plotly(df_empty, search_id1) |
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def test_streamlit_backend_4(): |
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search_id1 = "test_model1" |
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search_id2 = "test_model2" |
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search_id3 = "test_model3" |
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search_ids = [search_id1, search_id2, search_id3] |
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board = StreamlitBackend(search_ids) |
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df_empty = pd.DataFrame([], columns=["nth_iter", "score_best", "nth_process"]) |
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board.plotly(df_empty, search_id1) |
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SimonBlanke /
Hyperactive
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