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HrovatinAVHopp
Hrovatin
authored and
AVHopp
committed
Improve naming
1 parent f88892c commit 33b6e3d

5 files changed

+35
-38
lines changed

‎benchmarks/domains/arylhalides_tl_substance.py

+6-6
Original file line numberDiff line numberDiff line change
@@ -32,8 +32,8 @@ def get_data() -> pd.DataFrame:
3232

3333
data = get_data()
3434

35-
test_task = "1-iodo-4-methoxybenzene"
36-
source_task = [
35+
target_tasks = ["1-iodo-4-methoxybenzene"]
36+
source_tasks = [
3737
# Dissimilar source task
3838
"1-chloro-4-(trifluoromethyl)benzene"
3939
]
@@ -66,16 +66,16 @@ def space_data() -> (
6666

6767
task_param = TaskParameter(
6868
name="aryl_halide",
69-
values=[test_task] + source_task,
70-
active_values=[test_task],
69+
values=target_tasks + source_tasks,
70+
active_values=target_tasks,
7171
)
7272

7373
objective = SingleTargetObjective(NumericalTarget(name="yield", mode="MAX"))
7474
searchspace = SearchSpace.from_product(parameters=[*data_params, task_param])
7575
searchspace_nontl = SearchSpace.from_product(parameters=data_params)
7676

77-
lookup = data.query(f'aryl_halide=="{test_task}"').copy(deep=True)
78-
initial_data = data.query("aryl_halide.isin(@source_task)", engine="python").copy(
77+
lookup = data.query("aryl_halide.isin(@target_tasks)").copy(deep=True)
78+
initial_data = data.query("aryl_halide.isin(@source_tasks)", engine="python").copy(
7979
deep=True
8080
)
8181

‎benchmarks/domains/direct_arylation_tl_temp.py ‎benchmarks/domains/direct_arylation_tl_temperature.py

-3
Original file line numberDiff line numberDiff line change
@@ -56,9 +56,6 @@ def space_data() -> (
5656
SubstanceParameter(
5757
name=substance,
5858
data=dict(zip(data[substance], data[f"{substance}_SMILES"])),
59-
# Instead of using RDKIT as in paper the
60-
# RDKIT2DDESCRIPTORS is used due to deprecation of
61-
# the former
6259
encoding="RDKIT2DDESCRIPTORS",
6360
)
6461
for substance in ["Solvent", "Base", "Ligand"]

‎benchmarks/domains/easom_tl_noise.py

+9-9
Original file line numberDiff line numberDiff line change
@@ -66,15 +66,15 @@ def get_data() -> pd.DataFrame:
6666
Returns:
6767
Data for benchmark.
6868
"""
69-
test_functions = {
70-
"Test_Function": lambda x: Easom(x, negate=True),
71-
"Training_Function": lambda x: Easom(x, noise_std=0.05, negate=True),
69+
functions = {
70+
"Target_Function": lambda x: Easom(x, negate=True),
71+
"Source_Function": lambda x: Easom(x, noise_std=0.05, negate=True),
7272
}
7373

7474
grid = np.meshgrid(*[points for points in _grid_locations().values()])
7575

7676
lookups = []
77-
for function_name, function in test_functions.items():
77+
for function_name, function in functions.items():
7878
lookup = pd.DataFrame(
7979
{f"x{d}": grid_d.ravel() for d, grid_d in enumerate(grid)}
8080
)
@@ -88,8 +88,8 @@ def get_data() -> pd.DataFrame:
8888

8989
data = get_data()
9090

91-
test_task = "Test_Function"
92-
source_task = "Training_Function"
91+
target_task = "Target_Function"
92+
source_task = "Source_Function"
9393

9494

9595
def space_data() -> (
@@ -117,15 +117,15 @@ def space_data() -> (
117117

118118
task_param = TaskParameter(
119119
name="Function",
120-
values=[test_task, source_task],
121-
active_values=[test_task],
120+
values=[target_task, source_task],
121+
active_values=[target_task],
122122
)
123123

124124
objective = SingleTargetObjective(target=NumericalTarget(name="Target", mode="MAX"))
125125
searchspace = SearchSpace.from_product(parameters=[*data_params, task_param])
126126
searchspace_nontl = SearchSpace.from_product(parameters=data_params)
127127

128-
lookup = data.query(f'Function=="{test_task}"').copy(deep=True)
128+
lookup = data.query(f'Function=="{target_task}"').copy(deep=True)
129129
initial_data = data.query(f'Function=="{source_task}"', engine="python").copy(
130130
deep=True
131131
)

‎benchmarks/domains/hartmann_tl_inverted_noise.py

+10-10
Original file line numberDiff line numberDiff line change
@@ -18,7 +18,7 @@
1818
ConvergenceBenchmarkSettings,
1919
)
2020

21-
DIMENSION = 3 # input dimensionality of the test function
21+
DIMENSION = 3 # input dimensionality of the function
2222
POINTS_PER_DIM = 5 # number of grid points per input dimension
2323
BOUNDS = Hartmann(dim=DIMENSION).bounds
2424

@@ -42,11 +42,11 @@ def get_data() -> pd.DataFrame:
4242
Returns:
4343
Data for benchmark.
4444
"""
45-
test_functions = {
46-
"Test_Function": lambda x: Hartmann(dim=DIMENSION, negate=True)
45+
functions = {
46+
"Target_Function": lambda x: Hartmann(dim=DIMENSION, negate=True)
4747
.forward(torch.tensor(x))
4848
.item(),
49-
"Training_Function": lambda x: Hartmann(
49+
"Source_Function": lambda x: Hartmann(
5050
dim=DIMENSION, negate=False, noise_std=0.15
5151
)
5252
.forward(torch.tensor(x))
@@ -56,7 +56,7 @@ def get_data() -> pd.DataFrame:
5656
grid = np.meshgrid(*[points for points in _grid_locations().values()])
5757

5858
lookups = []
59-
for function_name, function in test_functions.items():
59+
for function_name, function in functions.items():
6060
lookup = pd.DataFrame(
6161
{f"x{d}": grid_d.ravel() for d, grid_d in enumerate(grid)}
6262
)
@@ -70,8 +70,8 @@ def get_data() -> pd.DataFrame:
7070

7171
data = get_data()
7272

73-
test_task = "Test_Function"
74-
source_task = "Training_Function"
73+
target_task = "Target_Function"
74+
source_task = "Source_Function"
7575

7676

7777
def space_data() -> (
@@ -99,15 +99,15 @@ def space_data() -> (
9999

100100
task_param = TaskParameter(
101101
name="Function",
102-
values=[test_task, source_task],
103-
active_values=[test_task],
102+
values=[target_task, source_task],
103+
active_values=[target_task],
104104
)
105105

106106
objective = SingleTargetObjective(target=NumericalTarget(name="Target", mode="MAX"))
107107
searchspace = SearchSpace.from_product(parameters=[*data_params, task_param])
108108
searchspace_nontl = SearchSpace.from_product(parameters=data_params)
109109

110-
lookup = data.query(f'Function=="{test_task}"').copy(deep=True)
110+
lookup = data.query(f'Function=="{target_task}"').copy(deep=True)
111111
initial_data = data.query(f'Function=="{source_task}"', engine="python").copy(
112112
deep=True
113113
)

‎benchmarks/domains/michalewicz_tl_noise.py

+10-10
Original file line numberDiff line numberDiff line change
@@ -18,7 +18,7 @@
1818
ConvergenceBenchmarkSettings,
1919
)
2020

21-
DIMENSION = 4 # input dimensionality of the test function
21+
DIMENSION = 4 # input dimensionality of the function
2222
POINTS_PER_DIM = 15 # number of grid points per input dimension
2323
BOUNDS = Michalewicz(dim=DIMENSION).bounds
2424

@@ -42,11 +42,11 @@ def get_data() -> pd.DataFrame:
4242
Returns:
4343
Data for benchmark.
4444
"""
45-
test_functions = {
46-
"Test_Function": lambda x: Michalewicz(dim=DIMENSION, negate=True)
45+
functions = {
46+
"Target_Function": lambda x: Michalewicz(dim=DIMENSION, negate=True)
4747
.forward(torch.tensor(x))
4848
.item(),
49-
"Training_Function": lambda x: Michalewicz(
49+
"Source_Function": lambda x: Michalewicz(
5050
dim=DIMENSION, noise_std=0.15, negate=True
5151
)
5252
.forward(torch.tensor(x))
@@ -56,7 +56,7 @@ def get_data() -> pd.DataFrame:
5656
grid = np.meshgrid(*[points for points in _grid_locations().values()])
5757

5858
lookups = []
59-
for function_name, function in test_functions.items():
59+
for function_name, function in functions.items():
6060
lookup = pd.DataFrame(
6161
{f"x{d}": grid_d.ravel() for d, grid_d in enumerate(grid)}
6262
)
@@ -70,8 +70,8 @@ def get_data() -> pd.DataFrame:
7070

7171
data = get_data()
7272

73-
test_task = "Test_Function"
74-
source_task = "Training_Function"
73+
target_task = "Target_Function"
74+
source_task = "Source_Function"
7575

7676

7777
def space_data() -> (
@@ -99,15 +99,15 @@ def space_data() -> (
9999

100100
task_param = TaskParameter(
101101
name="Function",
102-
values=[test_task, source_task],
103-
active_values=[test_task],
102+
values=[target_task, source_task],
103+
active_values=[target_task],
104104
)
105105

106106
objective = SingleTargetObjective(target=NumericalTarget(name="Target", mode="MAX"))
107107
searchspace = SearchSpace.from_product(parameters=[*data_params, task_param])
108108
searchspace_nontl = SearchSpace.from_product(parameters=data_params)
109109

110-
lookup = data.query(f'Function=="{test_task}"').copy(deep=True)
110+
lookup = data.query(f'Function=="{target_task}"').copy(deep=True)
111111
initial_data = data.query(f'Function=="{source_task}"', engine="python").copy(
112112
deep=True
113113
)

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