mirror of
https://github.com/Doctorado-ML/STree.git
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#2 Add predict and score support
Add a test in features notebook Show max_features in main.py
This commit is contained in:
@@ -64,7 +64,7 @@
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28) y.shape (1492,)\nFraud: 33.110% 494\nValid: 66.890% 998\n"
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"text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28) y.shape (1492,)\nFraud: 33.244% 496\nValid: 66.756% 996\n"
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}
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],
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"source": [
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@@ -135,7 +135,7 @@
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Accuracy of Train without weights 0.9789272030651341\nAccuracy of Train with weights 0.9952107279693486\nAccuracy of Tests without weights 0.9598214285714286\nAccuracy of Tests with weights 0.9508928571428571\n"
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"text": "Accuracy of Train without weights 0.9808429118773946\nAccuracy of Train with weights 0.9904214559386973\nAccuracy of Tests without weights 0.9441964285714286\nAccuracy of Tests with weights 0.9375\n"
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}
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],
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"source": [
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@@ -162,7 +162,7 @@
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Time: 0.27s\tKernel: linear\tAccuracy_train: 0.9683908045977011\tAccuracy_test: 0.953125\nTime: 0.09s\tKernel: rbf\tAccuracy_train: 0.9875478927203065\tAccuracy_test: 0.9598214285714286\nTime: 0.06s\tKernel: poly\tAccuracy_train: 0.9885057471264368\tAccuracy_test: 0.9464285714285714\n"
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"text": "Time: 0.13s\tKernel: linear\tAccuracy_train: 0.9693486590038314\tAccuracy_test: 0.9598214285714286\nTime: 0.09s\tKernel: rbf\tAccuracy_train: 0.9923371647509579\tAccuracy_test: 0.953125\nTime: 0.09s\tKernel: poly\tAccuracy_train: 0.9913793103448276\tAccuracy_test: 0.9375\n"
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}
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],
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"source": [
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@@ -195,7 +195,7 @@
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9531\nClassifier's accuracy (test) : 0.9621\nroot\nroot - Down, <cgaf> - Leaf class=1 belief= 0.983713 counts=(array([0, 1]), array([ 5, 302]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.940299 counts=(array([0, 1]), array([693, 44]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9569\nClassifier's accuracy (test) : 0.9621\nroot\nroot - Down, <cgaf> - Leaf class=1 belief= 0.990228 counts=(array([0, 1]), array([ 3, 304]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.943012 counts=(array([0, 1]), array([695, 42]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9655\nClassifier's accuracy (test) : 0.9643\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([310]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([5]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.950617 counts=(array([0, 1]), array([693, 36]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9684\nClassifier's accuracy (test) : 0.9598\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([311]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([8]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.954039 counts=(array([0, 1]), array([685, 33]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9751\nClassifier's accuracy (test) : 0.9464\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([304]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([8]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([4]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([3]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([4]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([3]))\nroot - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.963225 counts=(array([0, 1]), array([681, 26]))\n\n**************************************************\n0.6869 secs\n"
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"text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9588\nClassifier's accuracy (test) : 0.9487\nroot feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4438\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0374\nroot - Down - Down, <cgaf> - Leaf class=1 belief= 0.984076 impurity=0.0313 counts=(array([0, 1]), array([ 5, 309]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.947874 impurity=0.0988 counts=(array([0, 1]), array([691, 38]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9588\nClassifier's accuracy (test) : 0.9531\nroot feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4438\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0192\nroot - Down - Down, <cgaf> - Leaf class=1 belief= 0.993506 impurity=0.0129 counts=(array([0, 1]), array([ 2, 306]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.944218 impurity=0.1053 counts=(array([0, 1]), array([694, 41]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9665\nClassifier's accuracy (test) : 0.9643\nroot feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4438\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0189\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([312]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([3]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.951989 impurity=0.0914 counts=(array([0, 1]), array([694, 35]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9665\nClassifier's accuracy (test) : 0.9621\nroot feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4438\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0250\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([312]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([4]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.951923 impurity=0.0915 counts=(array([0, 1]), array([693, 35]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9703\nClassifier's accuracy (test) : 0.9665\nroot feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4438\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0367\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([315]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([6]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0846\nroot - Up - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.957064 impurity=0.0822 counts=(array([0, 1]), array([691, 31]))\n\n**************************************************\n0.4375 secs\n"
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}
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],
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"source": [
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@@ -227,7 +227,7 @@
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([304]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([8]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([4]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([3]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([4]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([3]))\nroot - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.963225 counts=(array([0, 1]), array([681, 26]))\n"
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"text": "root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4438\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0367\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([315]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([6]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0846\nroot - Up - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.957064 impurity=0.0822 counts=(array([0, 1]), array([691, 31]))\n"
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}
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],
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"source": [
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@@ -244,7 +244,7 @@
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([304]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([8]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([4]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([3]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([4]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([3]))\nroot - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.963225 counts=(array([0, 1]), array([681, 26]))\n"
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"text": "root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4438\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0367\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([315]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([6]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0846\nroot - Up - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.957064 impurity=0.0822 counts=(array([0, 1]), array([691, 31]))\n"
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}
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],
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"source": [
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@@ -268,7 +268,7 @@
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "1 functools.partial(<function check_no_attributes_set_in_init at 0x1254f13b0>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x1254e84d0>, 'Stree')\n3 functools.partial(<function check_fit_score_takes_y at 0x1254e83b0>, 'Stree')\n4 functools.partial(<function check_sample_weights_pandas_series at 0x1254e0cb0>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x1254e0dd0>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x1254e0ef0>, 'Stree')\n7 functools.partial(<function check_sample_weights_shape at 0x1254e2050>, 'Stree')\n8 functools.partial(<function check_sample_weights_invariance at 0x1254e2170>, 'Stree')\n9 functools.partial(<function check_estimators_fit_returns_self at 0x1254eb4d0>, 'Stree')\n10 functools.partial(<function check_estimators_fit_returns_self at 0x1254eb4d0>, 'Stree', readonly_memmap=True)\n11 functools.partial(<function check_complex_data at 0x1254e2320>, 'Stree')\n12 functools.partial(<function check_dtype_object at 0x1254e2290>, 'Stree')\n13 functools.partial(<function check_estimators_empty_data_messages at 0x1254e85f0>, 'Stree')\n14 functools.partial(<function check_pipeline_consistency at 0x1254e8290>, 'Stree')\n15 functools.partial(<function check_estimators_nan_inf at 0x1254e8710>, 'Stree')\n16 functools.partial(<function check_estimators_overwrite_params at 0x1254f1290>, 'Stree')\n17 functools.partial(<function check_estimator_sparse_data at 0x1254e0b90>, 'Stree')\n18 functools.partial(<function check_estimators_pickle at 0x1254e8950>, 'Stree')\n19 functools.partial(<function check_classifier_data_not_an_array at 0x1254f15f0>, 'Stree')\n20 functools.partial(<function check_classifiers_one_label at 0x1254eb050>, 'Stree')\n21 functools.partial(<function check_classifiers_classes at 0x1254eba70>, 'Stree')\n22 functools.partial(<function check_estimators_partial_fit_n_features at 0x1254e8a70>, 'Stree')\n23 functools.partial(<function check_classifiers_train at 0x1254eb170>, 'Stree')\n24 functools.partial(<function check_classifiers_train at 0x1254eb170>, 'Stree', readonly_memmap=True)\n25 functools.partial(<function check_classifiers_train at 0x1254eb170>, 'Stree', readonly_memmap=True, X_dtype='float32')\n26 functools.partial(<function check_classifiers_regression_target at 0x1254f40e0>, 'Stree')\n27 functools.partial(<function check_supervised_y_no_nan at 0x1254da9e0>, 'Stree')\n28 functools.partial(<function check_supervised_y_2d at 0x1254eb710>, 'Stree')\n29 functools.partial(<function check_estimators_unfitted at 0x1254eb5f0>, 'Stree')\n30 functools.partial(<function check_non_transformer_estimators_n_iter at 0x1254f1c20>, 'Stree')\n31 functools.partial(<function check_decision_proba_consistency at 0x1254f4200>, 'Stree')\n32 functools.partial(<function check_fit2d_predict1d at 0x1254e2830>, 'Stree')\n33 functools.partial(<function check_methods_subset_invariance at 0x1254e29e0>, 'Stree')\n34 functools.partial(<function check_fit2d_1sample at 0x1254e2b00>, 'Stree')\n35 functools.partial(<function check_fit2d_1feature at 0x1254e2c20>, 'Stree')\n36 functools.partial(<function check_fit1d at 0x1254e2d40>, 'Stree')\n37 functools.partial(<function check_get_params_invariance at 0x1254f1e60>, 'Stree')\n38 functools.partial(<function check_set_params at 0x1254f1f80>, 'Stree')\n39 functools.partial(<function check_dict_unchanged at 0x1254e2440>, 'Stree')\n40 functools.partial(<function check_dont_overwrite_parameters at 0x1254e2710>, 'Stree')\n41 functools.partial(<function check_fit_idempotent at 0x1254f43b0>, 'Stree')\n42 functools.partial(<function check_n_features_in at 0x1254f4440>, 'Stree')\n43 functools.partial(<function check_requires_y_none at 0x1254f44d0>, 'Stree')\n"
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"text": "1 functools.partial(<function check_no_attributes_set_in_init at 0x12735b3b0>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x1273514d0>, 'Stree')\n3 functools.partial(<function check_fit_score_takes_y at 0x1273513b0>, 'Stree')\n4 functools.partial(<function check_sample_weights_pandas_series at 0x12734acb0>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x12734add0>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x12734aef0>, 'Stree')\n7 functools.partial(<function check_sample_weights_shape at 0x12734d050>, 'Stree')\n8 functools.partial(<function check_sample_weights_invariance at 0x12734d170>, 'Stree')\n9 functools.partial(<function check_estimators_fit_returns_self at 0x1273564d0>, 'Stree')\n10 functools.partial(<function check_estimators_fit_returns_self at 0x1273564d0>, 'Stree', readonly_memmap=True)\n11 functools.partial(<function check_complex_data at 0x12734d320>, 'Stree')\n12 functools.partial(<function check_dtype_object at 0x12734d290>, 'Stree')\n13 functools.partial(<function check_estimators_empty_data_messages at 0x1273515f0>, 'Stree')\n14 functools.partial(<function check_pipeline_consistency at 0x127351290>, 'Stree')\n15 functools.partial(<function check_estimators_nan_inf at 0x127351710>, 'Stree')\n16 functools.partial(<function check_estimators_overwrite_params at 0x12735b290>, 'Stree')\n17 functools.partial(<function check_estimator_sparse_data at 0x12734ab90>, 'Stree')\n18 functools.partial(<function check_estimators_pickle at 0x127351950>, 'Stree')\n19 functools.partial(<function check_classifier_data_not_an_array at 0x12735b5f0>, 'Stree')\n20 functools.partial(<function check_classifiers_one_label at 0x127356050>, 'Stree')\n21 functools.partial(<function check_classifiers_classes at 0x127356a70>, 'Stree')\n22 functools.partial(<function check_estimators_partial_fit_n_features at 0x127351a70>, 'Stree')\n23 functools.partial(<function check_classifiers_train at 0x127356170>, 'Stree')\n24 functools.partial(<function check_classifiers_train at 0x127356170>, 'Stree', readonly_memmap=True)\n25 functools.partial(<function check_classifiers_train at 0x127356170>, 'Stree', readonly_memmap=True, X_dtype='float32')\n26 functools.partial(<function check_classifiers_regression_target at 0x12735f0e0>, 'Stree')\n27 functools.partial(<function check_supervised_y_no_nan at 0x1273449e0>, 'Stree')\n28 functools.partial(<function check_supervised_y_2d at 0x127356710>, 'Stree')\n29 functools.partial(<function check_estimators_unfitted at 0x1273565f0>, 'Stree')\n30 functools.partial(<function check_non_transformer_estimators_n_iter at 0x12735bc20>, 'Stree')\n31 functools.partial(<function check_decision_proba_consistency at 0x12735f200>, 'Stree')\n32 functools.partial(<function check_fit2d_predict1d at 0x12734d830>, 'Stree')\n33 functools.partial(<function check_methods_subset_invariance at 0x12734d9e0>, 'Stree')\n34 functools.partial(<function check_fit2d_1sample at 0x12734db00>, 'Stree')\n35 functools.partial(<function check_fit2d_1feature at 0x12734dc20>, 'Stree')\n36 functools.partial(<function check_fit1d at 0x12734dd40>, 'Stree')\n37 functools.partial(<function check_get_params_invariance at 0x12735be60>, 'Stree')\n38 functools.partial(<function check_set_params at 0x12735bf80>, 'Stree')\n39 functools.partial(<function check_dict_unchanged at 0x12734d440>, 'Stree')\n40 functools.partial(<function check_dont_overwrite_parameters at 0x12734d710>, 'Stree')\n41 functools.partial(<function check_fit_idempotent at 0x12735f3b0>, 'Stree')\n42 functools.partial(<function check_n_features_in at 0x12735f440>, 'Stree')\n43 functools.partial(<function check_requires_y_none at 0x12735f4d0>, 'Stree')\n"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -306,7 +306,7 @@
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": "== Not Weighted ===\nSVC train score ..: 0.9521072796934866\nSTree train score : 0.9578544061302682\nSVC test score ...: 0.9553571428571429\nSTree test score .: 0.9575892857142857\n==== Weighted =====\nSVC train score ..: 0.9616858237547893\nSTree train score : 0.9616858237547893\nSVC test score ...: 0.9642857142857143\nSTree test score .: 0.9598214285714286\n*SVC test score ..: 0.951413553411694\n*STree test score : 0.9480517444389333\n"
|
||||
"text": "== Not Weighted ===\nSVC train score ..: 0.9578544061302682\nSTree train score : 0.960727969348659\nSVC test score ...: 0.9508928571428571\nSTree test score .: 0.9553571428571429\n==== Weighted =====\nSVC train score ..: 0.9636015325670498\nSTree train score : 0.9626436781609196\nSVC test score ...: 0.9553571428571429\nSTree test score .: 0.9553571428571429\n*SVC test score ..: 0.9447820728419238\n*STree test score : 0.9447820728419238\n"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -338,12 +338,49 @@
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": "root\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1 belief= 0.969325 counts=(array([0, 1]), array([ 10, 316]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([1]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.958159 counts=(array([0, 1]), array([687, 30]))\n\n"
|
||||
"text": "root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4438\nroot - Down, <cgaf> - Leaf class=1 belief= 0.978261 impurity=0.0425 counts=(array([0, 1]), array([ 7, 315]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.955679 impurity=0.0847 counts=(array([0, 1]), array([690, 32]))\n\n"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(clf)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Test max_features"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": "****************************************\nmax_features None = 28\nTrain score : 0.9664750957854407\nTest score .: 0.9642857142857143\nTook 0.09 seconds\n****************************************\nmax_features auto = 5\nTrain score : 0.9511494252873564\nTest score .: 0.9441964285714286\nTook 0.37 seconds\n****************************************\nmax_features log2 = 4\nTrain score : 0.935823754789272\nTest score .: 0.9330357142857143\nTook 0.10 seconds\n****************************************\nmax_features 7 = 7\nTrain score : 0.9568965517241379\nTest score .: 0.9397321428571429\nTook 3.36 seconds\n****************************************\nmax_features 0.5 = 14\nTrain score : 0.960727969348659\nTest score .: 0.9486607142857143\nTook 112.42 seconds\n****************************************\nmax_features 0.1 = 2\nTrain score : 0.8793103448275862\nTest score .: 0.8839285714285714\nTook 0.06 seconds\n****************************************\nmax_features 0.7 = 19\nTrain score : 0.9655172413793104\nTest score .: 0.9553571428571429\nTook 10.59 seconds\n"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for max_features in [None, \"auto\", \"log2\", 7, .5, .1, .7]:\n",
|
||||
" now = time.time()\n",
|
||||
" print(\"*\"*40)\n",
|
||||
" clf = Stree(random_state=random_state, max_features=max_features)\n",
|
||||
" clf.fit(Xtrain, ytrain)\n",
|
||||
" print(f\"max_features {max_features} = {clf.max_features_}\")\n",
|
||||
" print(\"Train score :\", clf.score(Xtrain, ytrain))\n",
|
||||
" print(\"Test score .:\", clf.score(Xtest, ytest))\n",
|
||||
" print(f\"Took {time.time() - now:.2f} seconds\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Reference in New Issue
Block a user