From 5c31c2b2a5cb96aa9690cb5690df428cacc15cf2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Montan=CC=83ana?= <rmontanana@gmail.com>
Date: Tue, 9 Jun 2020 02:12:56 +0200
Subject: [PATCH] #3 update features notebook

---
 notebooks/features.ipynb | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/notebooks/features.ipynb b/notebooks/features.ipynb
index 3f277f9..9eda9b0 100644
--- a/notebooks/features.ipynb
+++ b/notebooks/features.ipynb
@@ -64,7 +64,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28)  y.shape (1492,)\nFraud: 33.177% 495\nValid: 66.823% 997\n"
+     "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"
     }
    ],
    "source": [
@@ -135,7 +135,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Accuracy of Train without weights 0.9722222222222222\nAccuracy of Train with    weights 0.9875478927203065\nAccuracy of Tests without weights 0.9508928571428571\nAccuracy of Tests with    weights 0.9486607142857143\n"
+     "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"
     }
    ],
    "source": [
@@ -162,7 +162,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Time: 0.27s\tKernel: linear\tAccuracy_train: 0.9712643678160919\tAccuracy_test: 0.953125\nTime: 0.08s\tKernel: rbf\tAccuracy_train: 0.9932950191570882\tAccuracy_test: 0.9620535714285714\nTime: 0.05s\tKernel: poly\tAccuracy_train: 0.9923371647509579\tAccuracy_test: 0.9419642857142857\n"
+     "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"
     }
    ],
    "source": [
@@ -195,13 +195,13 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9550\nClassifier's accuracy (test) : 0.9554\nroot\nroot - Down, <cgaf> - Leaf class=1 belief= 0.977636 counts=(array([0, 1]), array([  7, 306]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.945280 counts=(array([0, 1]), array([691,  40]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9569\nClassifier's accuracy (test) : 0.9554\nroot\nroot - Down, <cgaf> - Leaf class=1 belief= 0.983923 counts=(array([0, 1]), array([  5, 306]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.945430 counts=(array([0, 1]), array([693,  40]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9665\nClassifier's accuracy (test) : 0.9576\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([4]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.951989 counts=(array([0, 1]), array([694,  35]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9703\nClassifier's accuracy (test) : 0.9509\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\nroot - Up - Down, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([3]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.957004 counts=(array([0, 1]), array([690,  31]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9799\nClassifier's accuracy (test) : 0.9531\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([310]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([5]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([15]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([9]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([10]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.969653 counts=(array([0, 1]), array([671,  21]))\n\n**************************************************\n0.5032 secs\n"
+     "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"
     }
    ],
    "source": [
     "t = time.time()\n",
     "for C in (.001, .01, 1, 5, 17):\n",
-    "    clf = Stree(C=C, random_state=random_state)\n",
+    "    clf = Stree(C=C, kernel=\"linear\", random_state=random_state)\n",
     "    clf.fit(Xtrain, ytrain)\n",
     "    print(f\"************** C={C} ****************************\")\n",
     "    print(f\"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}\")\n",
@@ -227,7 +227,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([310]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([5]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([15]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([9]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([10]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.969653 counts=(array([0, 1]), array([671,  21]))\n"
+     "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"
     }
    ],
    "source": [
@@ -244,7 +244,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([310]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([5]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([15]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([9]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([10]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.969653 counts=(array([0, 1]), array([671,  21]))\n"
+     "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"
     }
    ],
    "source": [
@@ -268,7 +268,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "1 functools.partial(<function check_no_attributes_set_in_init at 0x124d443b0>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x124d3b4d0>, 'Stree')\n3 functools.partial(<function check_fit_score_takes_y at 0x124d3b3b0>, 'Stree')\n4 functools.partial(<function check_sample_weights_pandas_series at 0x124d33cb0>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x124d33dd0>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x124d33ef0>, 'Stree')\n7 functools.partial(<function check_sample_weights_shape at 0x124d35050>, 'Stree')\n8 functools.partial(<function check_sample_weights_invariance at 0x124d35170>, 'Stree')\n9 functools.partial(<function check_estimators_fit_returns_self at 0x124d3e4d0>, 'Stree')\n10 functools.partial(<function check_estimators_fit_returns_self at 0x124d3e4d0>, 'Stree', readonly_memmap=True)\n11 functools.partial(<function check_complex_data at 0x124d35320>, 'Stree')\n12 functools.partial(<function check_dtype_object at 0x124d35290>, 'Stree')\n13 functools.partial(<function check_estimators_empty_data_messages at 0x124d3b5f0>, 'Stree')\n14 functools.partial(<function check_pipeline_consistency at 0x124d3b290>, 'Stree')\n15 functools.partial(<function check_estimators_nan_inf at 0x124d3b710>, 'Stree')\n16 functools.partial(<function check_estimators_overwrite_params at 0x124d44290>, 'Stree')\n17 functools.partial(<function check_estimator_sparse_data at 0x124d33b90>, 'Stree')\n18 functools.partial(<function check_estimators_pickle at 0x124d3b950>, 'Stree')\n19 functools.partial(<function check_classifier_data_not_an_array at 0x124d445f0>, 'Stree')\n20 functools.partial(<function check_classifiers_one_label at 0x124d3e050>, 'Stree')\n21 functools.partial(<function check_classifiers_classes at 0x124d3ea70>, 'Stree')\n22 functools.partial(<function check_estimators_partial_fit_n_features at 0x124d3ba70>, 'Stree')\n23 functools.partial(<function check_classifiers_train at 0x124d3e170>, 'Stree')\n24 functools.partial(<function check_classifiers_train at 0x124d3e170>, 'Stree', readonly_memmap=True)\n25 functools.partial(<function check_classifiers_train at 0x124d3e170>, 'Stree', readonly_memmap=True, X_dtype='float32')\n26 functools.partial(<function check_classifiers_regression_target at 0x124d480e0>, 'Stree')\n27 functools.partial(<function check_supervised_y_no_nan at 0x124d2d9e0>, 'Stree')\n28 functools.partial(<function check_supervised_y_2d at 0x124d3e710>, 'Stree')\n29 functools.partial(<function check_estimators_unfitted at 0x124d3e5f0>, 'Stree')\n30 functools.partial(<function check_non_transformer_estimators_n_iter at 0x124d44c20>, 'Stree')\n31 functools.partial(<function check_decision_proba_consistency at 0x124d48200>, 'Stree')\n32 functools.partial(<function check_fit2d_predict1d at 0x124d35830>, 'Stree')\n33 functools.partial(<function check_methods_subset_invariance at 0x124d359e0>, 'Stree')\n34 functools.partial(<function check_fit2d_1sample at 0x124d35b00>, 'Stree')\n35 functools.partial(<function check_fit2d_1feature at 0x124d35c20>, 'Stree')\n36 functools.partial(<function check_fit1d at 0x124d35d40>, 'Stree')\n37 functools.partial(<function check_get_params_invariance at 0x124d44e60>, 'Stree')\n38 functools.partial(<function check_set_params at 0x124d44f80>, 'Stree')\n39 functools.partial(<function check_dict_unchanged at 0x124d35440>, 'Stree')\n40 functools.partial(<function check_dont_overwrite_parameters at 0x124d35710>, 'Stree')\n41 functools.partial(<function check_fit_idempotent at 0x124d483b0>, 'Stree')\n42 functools.partial(<function check_n_features_in at 0x124d48440>, 'Stree')\n43 functools.partial(<function check_requires_y_none at 0x124d484d0>, 'Stree')\n"
+     "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"
     }
    ],
    "source": [
@@ -306,7 +306,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "== Not Weighted ===\nSVC train score ..: 0.9530651340996169\nSTree train score : 0.960727969348659\nSVC test score ...: 0.9620535714285714\nSTree test score .: 0.9642857142857143\n==== Weighted =====\nSVC train score ..: 0.960727969348659\nSTree train score : 0.960727969348659\nSVC test score ...: 0.953125\nSTree test score .: 0.9553571428571429\n*SVC test score ..: 0.9397723008352139\n*STree test score : 0.9431162390279932\n"
+     "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"
     }
    ],
    "source": [
@@ -338,7 +338,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "root\nroot - Down, <cgaf> - Leaf class=1 belief= 0.978056 counts=(array([0, 1]), array([  7, 312]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.953103 counts=(array([0, 1]), array([691,  34]))\n\n"
+     "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"
     }
    ],
    "source": [