increase skopt runs 10 -> 25

wwu-mmll · Sep 7, 2020 · cd5ddb3 · cd5ddb3
1 parent 3d2686c
commit cd5ddb3
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Showing 7 changed files with 7 additions and 11 deletions.
diff --git a/README.md b/README.md
@@ -31,7 +31,7 @@ or you can read a prolonged introduction on [Arxiv](https://arxiv.org/abs/2002.0
 
 ---
 ## Getting Started
-In order to use PHOTON you only need to have your favourite Python IDE ready.
+In order to use PHOTONAI you only need to have your favourite Python IDE ready.
 Then install the latest stable version simply via pip
 ```
 pip install photonai
@@ -52,7 +52,7 @@ from photonai.optimization import FloatRange, Categorical, IntegerRange
 my_pipe = Hyperpipe('basic_svm_pipe',  # the name of your pipeline
                     # which optimizer PHOTON shall use
                     optimizer='sk_opt',
-                    optimizer_params={'n_configurations': 10},
+                    optimizer_params={'n_configurations': 25},
                     # the performance metrics of your interest
                     metrics=['accuracy', 'precision', 'recall', 'balanced_accuracy'],
                     # after hyperparameter optimization, this metric declares the winner config

diff --git a/examples/advanced/callbacks.py b/examples/advanced/callbacks.py
@@ -10,7 +10,6 @@ def my_monitor(X, y=None, **kwargs):
    debug = True
 
 
-# WE USE THE BREAST CANCER SET FROM SKLEARN
 X, y = load_boston(return_X_y=True)
 
 # DESIGN YOUR PIPELINE

diff --git a/examples/advanced/feature_selection.py b/examples/advanced/feature_selection.py
@@ -4,8 +4,6 @@
 from photonai.base import Hyperpipe, PipelineElement, OutputSettings, Switch
 from photonai.optimization import IntegerRange
 
-
-# WE USE THE BREAST CANCER SET FROM SKLEARN
 X, y = load_boston(return_X_y=True)
 
 # DESIGN YOUR PIPELINE

diff --git a/examples/advanced/regression_with_constraints.py b/examples/advanced/regression_with_constraints.py
@@ -4,7 +4,6 @@
 from photonai.base import Hyperpipe, PipelineElement, OutputSettings
 from photonai.optimization import MinimumPerformance, IntegerRange
 
-# WE USE THE BREAST CANCER SET FROM SKLEARN
 X, y = load_boston(return_X_y=True)
 
 # DESIGN YOUR PIPELINE

diff --git a/examples/basic/jmlr_example.py b/examples/basic/jmlr_example.py
@@ -1,8 +1,8 @@
 from sklearn.datasets import load_breast_cancer
 from sklearn.model_selection import KFold
 
-from photonai.base import Hyperpipe, PipelineElement, Stack, OutputSettings
-from photonai.optimization import FloatRange, IntegerRange, Categorical
+from photonai.base import Hyperpipe, PipelineElement, Stack
+from photonai.optimization import FloatRange, IntegerRange
 
 X, y = load_breast_cancer(return_X_y=True)
 

diff --git a/examples/basic/stack.py b/examples/basic/stack.py
@@ -9,7 +9,7 @@
 
 my_pipe = Hyperpipe('basic_stack_pipe',
                     optimizer='sk_opt',
-                    optimizer_params={'n_configurations': 5},
+                    optimizer_params={'n_configurations': 25},
                     metrics=['accuracy', 'precision', 'recall'],
                     best_config_metric='accuracy',
                     outer_cv=KFold(n_splits=3),
@@ -21,7 +21,7 @@
 
 tree = PipelineElement('DecisionTreeClassifier',
                        hyperparameters={'criterion': ['gini'],
-                                         'min_samples_split': IntegerRange(2, 4)})
+                                        'min_samples_split': IntegerRange(2, 4)})
 
 svc = PipelineElement('LinearSVC',
                       hyperparameters={'C': FloatRange(0.5, 25)})

diff --git a/examples/neural_networks/multi_layer_perceptron_classifier.py b/examples/neural_networks/multi_layer_perceptron_classifier.py
@@ -10,7 +10,7 @@
 # DESIGN YOUR PIPELINE
 my_pipe = Hyperpipe('basic_svm_pipe',
                     optimizer='sk_opt',
-                    optimizer_params={'n_configurations': 10},
+                    optimizer_params={'n_configurations': 25},
                     metrics=['accuracy', 'precision', 'recall', 'balanced_accuracy'],
                     best_config_metric='accuracy',
                     outer_cv=KFold(n_splits=3),