Computation times¶
+00:00.000 total execution time for 1 file from examples/api:
+Example |
+Time |
+Mem (MB) |
+
---|---|---|
Parallel Coordinates ( |
+00:00.000 |
+0.0 |
+
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a/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip and b/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip differ diff --git a/development/_modules/deepcave/runs/converters/bohb.html b/development/_modules/deepcave/runs/converters/bohb.html index e33ae475..634e6a46 100644 --- a/development/_modules/deepcave/runs/converters/bohb.html +++ b/development/_modules/deepcave/runs/converters/bohb.html @@ -732,7 +732,6 @@
X (np.ndarray [n_samples, n_features (config + instance features)]) – Training samples.
Tuple
[ndarray
, Optional
[ndarray
]]
Tuple
[ndarray
, Optional
[ndarray
]]
X (np.ndarray) – [n_samples, n_features (config)]
Tuple
[ndarray
, ndarray
]
Tuple
[ndarray
, ndarray
]
None
None
Calculate mean and sigma of predictions
X (ndarray
) – input data shaped [NxK] for N samples and K parameters
X (ndarray
) – input data shaped [NxK] for N samples and K parameters
Tuple
[ndarray
, ndarray
]
Tuple
[ndarray
, ndarray
]
means, stds
diff --git a/development/api/deepcave.evaluators.epm.utils.html b/development/api/deepcave.evaluators.epm.utils.html index 29fb9a7a..5f8fe722 100644 --- a/development/api/deepcave.evaluators.epm.utils.html +++ b/development/api/deepcave.evaluators.epm.utils.html @@ -724,7 +724,7 @@ hyperparameters and instance features.Tuple
[List
[int
], List
[Tuple
[float
, float
]]]
Tuple
[List
[int
], List
[Tuple
[float
, float
]]]
None
None
Call self as a function.
Union
[List
[Component
], Component
]
Union
[List
[Component
], Component
]
Call self as a function.
List
[Component
]
List
[Component
]
Call self as a function.
Union
[List
[Component
], Component
]
Union
[List
[Component
], Component
]
RuntimeError – _description_
None
None
NotMergeableError – If runs are not compatible, an error is thrown.
None
None
RuntimeError – _description_
None
None
NotMergeableError – If runs are not compatible, an error is thrown.
None
None
RuntimeError – _description_
None
None
run_path (str) – Path of a run.
None
None
working_directory (Union[Path, str]) – Directory to be set.
None
None
Updates the internal run and group instances but only if a hash changed.
None
None
NotMergeableError – If runs can not be merged, an error is thrown.
None
None
NotValidRunError – If directory can not be transformed into a run, an error is thrown.
Optional
[AbstractRun
]
Optional
[AbstractRun
]
str
str
Tuple
[List
[float
], List
[float
], List
[float
], List
[int
], List
[int
]]
Tuple
[List
[float
], List
[float
], List
[float
], List
[int
], List
[int
]]
RuntimeError – If optimize is not lower or upper.
None
None
RuntimeError – If number of costs does not match number of objectives.
None
None
Based on a path, return a new Run object.
Clear all cache and reset to defaults
None
None
Retrieve value for a specific key
Optional
[Any
]
Optional
[Any
]
Check whether cache has specific key
bool
bool
Reads content from a file and load into cache as dictionary.
None
None
None
None
Updates cache to a specific value
None
None
Write content of cache into file.
None
None
Deserialize a dataframe from a string.
TypeVar
(TYPE
)
TypeVar
(TYPE
)
Serialize a dataframe to a string.
str
str
Iterator[Configuration] – Border configurations.
Iterator
[Configuration
]
Iterator
[Configuration
]
Iterator[Configuration] – Random configurations.
Iterator
[Configuration
]
Iterator
[Configuration
]
None
None
rst_to_md
(filename)
str
+str
file_to_hash
(filename)
str
str
string_to_hash
(string)
str
str
create_table
(output[, fixed, head, striped, mb])
Table
Table
get_slider_marks
([strings, steps, access_all])
Dict
[int
, str
]
Dict
[int
, str
]
List
[Dict
[str
, Any
]]
List
[Dict
[str
, Any
]]
get_logger
(logger_name)
Logger
Logger
Removes all caches.
None
None
Removes all caches for the given run.
None
None
None
None
generate_config_code
(register, variables)
List
[Component
]
List
[Component
]
get_hovertext_from_config
(run, config_id)
str
str
Using Plotly palette for the first 10 ids and Alphabet palette for the next 26, currently 36 colors are possible.
Union
[str
, Tuple
[float
, float
, float
, float
]]
Union
[str
, Tuple
[float
, float
, float
, float
]]
Tuple
[int
, int
, int
]
Tuple
[int
, int
, int
]
None
None
create_url
(pathname, inputs)
str
str
parse_url
(pathname)
Optional
[Dict
[str
, Any
]]
Optional
[Dict
[str
, Any
]]
get_latest_change
(st_mtime)
str
str
get_random_string
(length)
str
str
short_string
(value[, length, mode])
str
str
This example shows how to use the plugin parallel coordinates. Note that other plugins use the same interfaces and can be used in the same fashion.
-from deepcave.plugins.objective.parallel_coordinates import ParallelCoordinates
+from deepcave.plugins.objective.parallel_coordinates import ParallelCoordinates
from deepcave.runs.converters.deepcave import DeepCAVERun
@@ -729,14 +729,13 @@
# figure.write_image("examples/api/test.png")
-Total running time of the script: (0 minutes 0.000 seconds)
diff --git a/development/examples/api/sg_execution_times.html b/development/examples/api/sg_execution_times.html
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+00:00.000 total execution time for 1 file from examples/api:
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+Time
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diff --git a/development/examples/record/digits_sklearn.html b/development/examples/record/digits_sklearn.html
index 5d25e595..d759b05b 100644
--- a/development/examples/record/digits_sklearn.html
+++ b/development/examples/record/digits_sklearn.html
@@ -702,7 +702,7 @@
Multi-Layer Perceptron via Sklearn¶
This more advanced example shows how sklearn can be used to record an optimization
process in DeepCAVE format.
-from sklearn.neural_network import MLPClassifier
+from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from ConfigSpace import ConfigurationSpace
from ConfigSpace.hyperparameters import (
@@ -789,14 +789,13 @@
r.end(costs=[score, None])
-Total running time of the script: (0 minutes 0.000 seconds)
diff --git a/development/examples/record/minimal.html b/development/examples/record/minimal.html
index 9f24df42..7de8e246 100644
--- a/development/examples/record/minimal.html
+++ b/development/examples/record/minimal.html
@@ -701,7 +701,7 @@
Record Minimal Run¶
This example shows how DeepCAVE can be used to record a minimal run.
-import numpy as np
+import numpy as np
import ConfigSpace as CS
from deepcave import Recorder, Objective
@@ -727,14 +727,13 @@
r.end(costs=[accuracy, None])
-Total running time of the script: (0 minutes 0.000 seconds)
diff --git a/development/examples/record/mnist_pytorch.html b/development/examples/record/mnist_pytorch.html
index 63e62004..18f31fc3 100644
--- a/development/examples/record/mnist_pytorch.html
+++ b/development/examples/record/mnist_pytorch.html
@@ -702,7 +702,7 @@
Multi-Layer Perceptron via PyTorch¶
This more advanced example incorporates multiple objectives, budgets and statusses to
show the strenghts of DeepCAVE’s recorder.
-from inspect import BoundArguments
+from inspect import BoundArguments
import os
from re import T
import time as t
@@ -982,14 +982,13 @@
r.end(costs=[accuracy_, loss_, elapsed_time], status=status)
-Total running time of the script: (0 minutes 0.000 seconds)
diff --git a/development/examples/record/sg_execution_times.html b/development/examples/record/sg_execution_times.html
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)
+00:00.000
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+Record Minimal Run (minimal.py
)
+00:00.000
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+Multi-Layer Perceptron via PyTorch (mnist_pytorch.py
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+ Computation times — DeepCAVE Documentation 1.1.3 documentation
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+Computation times¶
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+Computation times¶
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+Parallel Coordinates (../examples/api/parallel_coordinates.py
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+Multi-Layer Perceptron via Sklearn (../examples/record/digits_sklearn.py
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+Record Minimal Run (../examples/record/minimal.py
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+Multi-Layer Perceptron via PyTorch (../examples/record/mnist_pytorch.py
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