def test_kruskal_stress():
actual_data = chooseData('scurve')
fitted_data = chooseEmbedder('isomap', actual_data)
fitted_data.embed(use_cache=True)
evaluator = StressEvaluator(actual_data.df, 100)
assert evaluator.kruskal(fitted_data.em) > 0
def test_global_ranking_stress():
actual_data = chooseData('scurve')
fitted_data = chooseEmbedder('isomap', actual_data)
fitted_data.embed(use_cache=True)
evaluator = StressEvaluator(actual_data.df, 100)
assert evaluator.middle_ranking(fitted_data.em, 15, is_z_value=False) > 0
def getFigure(data_key, embedder_key, reducer_key):
sc_data = chooseData(data_key)
embedder = chooseEmbedder(embedder_key, sc_data)
embedder.embed()
reducer = chooseReducer(reducer_key, sc_data, embedder)
reducer.reduce()
return px.scatter(reducer.rd,
x='col0',
y='col1',
color=sc_data.color,
title=f'{data_key} | {reducer.class_key}')
def get_embedding(data_key: str, embedder_key: str) -> DataFrame:
"""Get embedding
Args:
data_key (str): Name of dataset.
embedder_key (str): Name of embedder.
Returns:
DataFrame: Embedding results.
"""
sc_data = chooseData(data_key)
embedder = chooseEmbedder(embedder_key, sc_data)
embedder.embed()
return embedder.em
margin=dict(r=20, l=10, b=10, t=10),
)
fig3d.show()
# Create 2d figure After Dinmensionality Reduction
fig2d = visualize_2d(reducer.rd)
fig2d.update_layout(title_text="After Diminsionality Reduction")
fig2d.show()
if __name__ == "__main__":
# Get 3d animations
# Run these in Jupyter or something
which_data = "basic_cluster"
embedder = chooseEmbedder("t_sne", (chooseData(which_data)))
embedder.embed(dim=3, use_cache=True)
reducer = chooseReducer("pca", chooseData(which_data), embedder)
reducer.reduce(dim=2, save_rd=False)
# visualize_3d_to_2d_projection(embedder, reducer)
# Double filter
query1 = "col1<0 & col2>0 & col2>=0.5"
query0 = "*"
# For Dimensionality Reduction to 2D
reducer.setRds(query0=query0, query1=query1)
print(reducer.getRdsDf())
fig2d = px.scatter(
reducer.getRdsDf(),