Merge pull request #92 from automl/development

Version 1.1.3

Author: sarah-segel

CHANGELOG.md

@@ -1,3 +1,11 @@
+# Version 1.1.3
+
+## Bug-Fixes
+- Fix seaborn style name (#82).
+- Remove potential sources of nondeterminism in evaluators by not setting seeds randomly (#75).
+- Exchange SMAC log examples to fix issue with PDP (#54).
+- Fix error when requesting more than 10 colors in a plot (36 colors available now).
+
 # Version 1.1.2
 
 ## Bug-Fixes

Makefile

@@ -2,7 +2,7 @@
 # are usually completed in github actions.
 
 SHELL := /bin/bash
-VERSION := 1.1.2
+VERSION := 1.1.3
 
 NAME := DeepCAVE
 PACKAGE_NAME := deepcave

deepcave/__init__.py

@@ -17,7 +17,7 @@
     "Source Code": "https://github.com/automl/deepcave",
 }
 copyright = f"Copyright {datetime.date.today().strftime('%Y')}, {author}"
-version = "1.1.2"
+version = "1.1.3"
 
 _exec_file = sys.argv[0]
 _exec_files = ["server.py", "worker.py", "sphinx-build"]

deepcave/evaluators/epm/fanova_forest.py

@@ -30,7 +30,7 @@ def __init__(
         instance_features: Optional[np.ndarray] = None,
         pca_components: Optional[int] = 2,
         cutoffs: Tuple[float, float] = (-np.inf, np.inf),
-        seed: Optional[int] = None,
+        seed: int = 0,
     ):
         super().__init__(
             configspace=configspace,

deepcave/evaluators/epm/random_forest.py

@@ -56,7 +56,7 @@ def __init__(
         instance_features: Optional[np.ndarray] = None,
         pca_components: Optional[int] = 2,
         log_y: bool = False,
-        seed: Optional[int] = None,
+        seed: int = 0,
     ):
         self.cs = configspace
         self.log_y = log_y
@@ -295,8 +295,6 @@ def _train(self, X: np.ndarray, Y: np.ndarray) -> None:
         # Now we can start to prepare the data for the pyrfr
         data = self._get_data_container(X, Y.flatten())
         seed = self.seed
-        if seed is None:
-            seed = int(random() * 9999)
 
         rng = regression.default_random_engine(seed)
 

deepcave/evaluators/fanova.py

@@ -30,7 +30,7 @@ def calculate(
         objectives: Optional[Union[Objective, List[Objective]]] = None,
         budget: Optional[Union[int, float]] = None,
         n_trees: int = 16,
-        seed: Optional[int] = None,
+        seed: int = 0,
     ) -> None:
         """
         Get the data wrt budget and trains the forest on the encoded data.
@@ -47,8 +47,8 @@ def calculate(
             Considered budget. By default None. If None, the highest budget is chosen.
         n_trees : int, optional
             How many trees should be used. By default 16.
-        seed : Optional[int], optional
-            Random seed. By default None.
+        seed : int
+            Random seed. By default 0.
         """
         if objectives is None:
             objectives = self.run.get_objectives()

deepcave/evaluators/lpi.py

@@ -30,7 +30,7 @@ def calculate(
         budget: Optional[Union[int, float]] = None,
         continous_neighbors: int = 500,
         n_trees: int = 10,
-        seed: Optional[int] = None,
+        seed: int = 0,
     ) -> None:
         """
         Prepares the data and trains a RandomForest model.
@@ -56,9 +56,6 @@ def calculate(
         self.default = self.cs.get_default_configuration()
         self.incumbent_array = self.incumbent.get_array()
 
-        # Set the seed
-        if seed is None:
-            seed = int(random() * 9999)
         self.seed = seed
         self.rs = np.random.RandomState(seed)
 

deepcave/utils/styled_plot.py

@@ -25,7 +25,7 @@ class StyledPlot:
     """
 
     def __init__(self):
-        plt.style.use("seaborn")
+        plt.style.use("seaborn-v0_8")
 
         # Set MatPlotLib defaults
         if find_executable("latex"):

deepcave/utils/styled_plotty.py

@@ -69,9 +69,12 @@ def hex_to_rgb(hex_string: str) -> Tuple[int, int, int]:
 
 def get_color(id_: int, alpha: float = 1) -> Union[str, Tuple[float, float, float, float]]:
     """
-    Currently (Plotly version 5.3.1) there are 10 possible colors.
+    Using Plotly palette for the first 10 ids and Alphabet palette for the next 26, currently 36 colors are possible.
     """
-    color = px.colors.qualitative.Plotly[id_]
+    if id_ < 10:
+        color = px.colors.qualitative.Plotly[id_]
+    else:
+        color = px.colors.qualitative.Alphabet[id_ - 10]
 
     r, g, b = hex_to_rgb(color)
     return f"rgba({r}, {g}, {b}, {alpha})"

logs/SMAC3v1/mlp/run_1/configspace.json

@@ -1,102 +1,40 @@
 {
   "hyperparameters": [
     {
-      "name": "activation",
-      "type": "categorical",
-      "choices": [
-        "logistic",
-        "tanh",
-        "relu"
-      ],
-      "default": "tanh",
-      "probabilities": null
-    },
-    {
-      "name": "n_layer",
-      "type": "uniform_int",
-      "log": false,
-      "lower": 1,
-      "upper": 5,
-      "default": 1,
-      "q": null
+      "name": "alpha",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 1e-08,
+      "upper": 1.0,
+      "default": 0.001
     },
     {
-      "name": "n_neurons",
+      "name": "batch_size",
       "type": "uniform_int",
       "log": true,
-      "lower": 8,
-      "upper": 1024,
-      "default": 10,
-      "q": null
-    },
-    {
-      "name": "solver",
-      "type": "categorical",
-      "choices": [
-        "lbfgs",
-        "sgd",
-        "adam"
-      ],
-      "default": "adam",
-      "probabilities": null
+      "lower": 4,
+      "upper": 256,
+      "default": 32
     },
     {
-      "name": "batch_size",
+      "name": "depth",
       "type": "uniform_int",
       "log": false,
-      "lower": 30,
-      "upper": 300,
-      "default": 200,
-      "q": null
-    },
-    {
-      "name": "learning_rate",
-      "type": "categorical",
-      "choices": [
-        "constant",
-        "invscaling",
-        "adaptive"
-      ],
-      "default": "constant",
-      "probabilities": null
+      "lower": 1,
+      "upper": 3,
+      "default": 3
     },
     {
       "name": "learning_rate_init",
       "type": "uniform_float",
       "log": true,
-      "lower": 0.0001,
+      "lower": 1e-05,
       "upper": 1.0,
-      "default": 0.001,
-      "q": null
-    }
-  ],
-  "conditions": [
-    {
-      "child": "batch_size",
-      "parent": "solver",
-      "type": "IN",
-      "values": [
-        "sgd",
-        "adam"
-      ]
-    },
-    {
-      "child": "learning_rate",
-      "parent": "solver",
-      "type": "EQ",
-      "value": "sgd"
-    },
-    {
-      "child": "learning_rate_init",
-      "parent": "solver",
-      "type": "IN",
-      "values": [
-        "sgd",
-        "adam"
-      ]
+      "default": 0.001
     }
   ],
+  "conditions": [],
   "forbiddens": [],
-  "python_module_version": "0.4.19",
-  "json_format_version": 0.2
+  "python_module_version": "0.6.1",
+  "json_format_version": 0.4
 }