From f9d5091451ce00bc83e8c51e35938fe74b4b5aba Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?L=C3=A9o=20Bouscarrat?= <leo.bouscarrat@euranova.eu>
Date: Wed, 11 Mar 2020 17:16:01 +0100
Subject: [PATCH] Add test for base OmpForest class
---
code/bolsonaro/models/model_parameters.py | 14 ++++++
tests/test_bolsonaro.py | 58 +++++++++++++++++++++++
2 files changed, 72 insertions(+)
create mode 100644 tests/test_bolsonaro.py
diff --git a/code/bolsonaro/models/model_parameters.py b/code/bolsonaro/models/model_parameters.py
index a3286ed..2009190 100644
--- a/code/bolsonaro/models/model_parameters.py
+++ b/code/bolsonaro/models/model_parameters.py
@@ -7,6 +7,20 @@ class ModelParameters(object):
def __init__(self, extracted_forest_size, normalize_D, subsets_used,
normalize_weights, seed, hyperparameters, extraction_strategy):
+ """Init of ModelParameters.
+
+ Args:
+ extracted_forest_size (list): list of all the extracted forest
+ size.
+ normalize_D (bool): true normalize the distribution, false no
+ subsets_used (list): which dataset use for randomForest and for OMP
+ 'train', 'dev' or 'train+dev' and combination of two of this.
+ normalize_weights (bool): if we normalize the weights or no.
+ seed (int): the seed used for the randomization.
+ hyperparameters (dict): dict of the hyperparameters of RandomForest
+ in scikit-learn.
+ extraction_strategy (str): either 'none', 'random', 'omp'
+ """
self._extracted_forest_size = extracted_forest_size
self._normalize_D = normalize_D
self._subsets_used = subsets_used
diff --git a/tests/test_bolsonaro.py b/tests/test_bolsonaro.py
new file mode 100644
index 0000000..e282f20
--- /dev/null
+++ b/tests/test_bolsonaro.py
@@ -0,0 +1,58 @@
+import numpy as np
+
+from bolsonaro.models.model_parameters import ModelParameters
+from bolsonaro.models.omp_forest_classifier import OmpForestBinaryClassifier, OmpForestMulticlassClassifier
+from bolsonaro.models.omp_forest_regressor import OmpForestRegressor
+
+
+def test_binary_classif_omp():
+
+ model_parameters = ModelParameters(
+ 1, False, ['train+dev', 'train+dev'], False, 1,
+ {'n_estimators': 100}, 'omp'
+ )
+
+ omp_forest = OmpForestBinaryClassifier(model_parameters)
+ X_train = [[1, 0], [0, 1]]
+ y_train = [-1, 1]
+
+ omp_forest.fit(X_train, y_train, X_train, y_train)
+
+ results = omp_forest.predict(X_train)
+
+ assert isinstance(results, np.ndarray)
+
+
+def test_regression_omp():
+
+ model_parameters = ModelParameters(
+ 1, False, ['train+dev', 'train+dev'], False, 1,
+ {'n_estimators': 100}, 'omp'
+ )
+
+ omp_forest = OmpForestRegressor(model_parameters)
+ X_train = [[1, 0], [0, 1]]
+ y_train = [-1, 1]
+
+ omp_forest.fit(X_train, y_train, X_train, y_train)
+
+ results = omp_forest.predict(X_train)
+
+ assert isinstance(results, np.ndarray)
+
+def test_multiclassif_omp():
+
+ model_parameters = ModelParameters(
+ 1, False, ['train+dev', 'train+dev'], False, 1,
+ {'n_estimators': 100}, 'omp'
+ )
+
+ omp_forest = OmpForestMulticlassClassifier(model_parameters)
+ X_train = [[1, 0], [0, 1]]
+ y_train = [-1, 1]
+
+ omp_forest.fit(X_train, y_train, X_train, y_train)
+
+ results = omp_forest.predict(X_train)
+
+ assert isinstance(results, np.ndarray)
--
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