def createAggData(self):
cand_agg, hist_agg = self.aggregateEnsembleList() # these are dfs
cand_fname = os.path.join(self.dname, 'aggregate_candidates.csv')
df_utils.write(cand_fname, cand_agg)
candidateData = LocalExecutionModule.readSampleFromCsvFile(
cand_fname, askForNumInputs=False)
hist_fname = os.path.join(self.dname, 'aggregate_history.csv')
if len(hist_agg) == 0:
historyData = None
else:
df_utils.write(hist_fname, hist_agg)
historyData = LocalExecutionModule.readSampleFromCsvFile(
hist_fname, askForNumInputs=False)
return candidateData, historyData
def test_rank():
""" Call to order.rank() using hard-coded data written to temp files """
# candidate dataframe to write to cand file
cand_df = pd.DataFrame({
'w': [0.15, 0.15, 0.15, 0.175, 0.175, 0.15, 0.15, 0.15],
'G': [2700, 2500, 2000, 2500, 2500, 1500, 1500, 2000],
'lldg': [0.15, 0.15, 0.25, 0.25, 0.3, 0.15, 0.15, 0.3],
'L': [10039, 9060, 7519, 7358, 6185, 3100, 3454, 8529]
})
cand_fn = pl.Path(sys.path[0], "tmp_cand.csv")
df_utils.write(cand_fn, cand_df)
# dist matrix to be written to dmat.npy file
dmat = np.array(
[[
1.00000000e+01, 2.75151884e-02, 5.10153186e-01, 5.40001829e-01,
9.61868543e-01, 1.18523604e+00, 1.11693144e+00, 7.64581026e-01
],
[
2.75151884e-02, 1.00000000e+01, 3.70385458e-01, 4.64940661e-01,
8.54039200e-01, 8.52817813e-01, 7.94402325e-01, 6.53028010e-01
],
[
5.10153186e-01, 3.70385458e-01, 1.00000000e+01, 2.60486124e-01,
3.48005747e-01, 6.15110612e-01, 5.72261139e-01, 7.70540649e-02
],
[
5.40001829e-01, 4.64940661e-01, 2.60486124e-01, 1.00000000e+01,
8.21307813e-02, 1.02830593e+00, 9.87082757e-01, 3.42180198e-01
],
[
9.61868543e-01, 8.54039200e-01, 3.48005747e-01, 8.21307813e-02,
1.00000000e+01, 1.21791690e+00, 1.18854249e+00, 3.38505599e-01
],
[
1.18523604e+00, 8.52817813e-01, 6.15110612e-01, 1.02830593e+00,
1.21791690e+00, 1.00000000e+01, 1.78792001e-03, 1.06951995e+00
],
[
1.11693144e+00, 7.94402325e-01, 5.72261139e-01, 9.87082757e-01,
1.18854249e+00, 1.78792001e-03, 1.00000000e+01, 1.01646822e+00
],
[
7.64581026e-01, 6.53028010e-01, 7.70540649e-02, 3.42180198e-01,
3.38505599e-01, 1.06951995e+00, 1.01646822e+00, 1.00000000e+01
]])
dmat_fn = pl.Path(sys.path[0], "tmp_dmat.npy")
np.save(dmat_fn, dmat)
# file name dict to be passed to order.rank()
fnames = {"cand": str(cand_fn), "dmat": str(dmat_fn)}
# Make the actual call
fname_ranked = order.rank(fnames, ga_max_attempts=5)
# Ranked results as a dataframe
ret_ranked_df = df_utils.load(fname_ranked)
# Expected ranked results as dataframe
ranked_df = pd.DataFrame({
'w': [0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.175, 0.175],
'G': [2000, 2000, 1500, 1500, 2500, 2700, 2500, 2500],
'lldg': [0.3, 0.25, 0.15, 0.15, 0.15, 0.15, 0.25, 0.3],
'L': [8529, 7519, 3100, 3454, 9060, 10039, 7358, 6185]
})
test_results = ret_ranked_df.equals(ranked_df)
# Clean up tmp files
cand_fn.unlink()
dmat_fn.unlink()
pl.Path(fname_ranked).unlink()
assert test_results
