mirror of
https://github.com/Doctorado-ML/STree.git
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Show sample_weight use in test2 notebook
Update revision to RC4 Lint Stree grapher
This commit is contained in:
@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -14,7 +14,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -30,7 +30,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -42,7 +42,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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@@ -88,24 +88,40 @@
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"Xtrain = data[0]\n",
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"Xtest = data[1]\n",
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"ytrain = data[2]\n",
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"ytest = data[3]"
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"ytest = data[3]\n",
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"# Set weights inverse to its count class in dataset\n",
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"weights = np.ones(Xtrain.shape[0],) * 1.00244\n",
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"weights[ytrain==1] = 1.99755 "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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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 0.996415770609319\nAccuracy of Train with weights 0.994026284348865\nAccuracy of Tests without weights 0.9665738161559888\nAccuracy of Tests with weights 0.9721448467966574\n"
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}
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],
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"source": [
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"C = 23\n",
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"print(\"Accuracy of Train without weights\", Stree(C=C, random_state=1).fit(Xtrain, ytrain).score(Xtrain, ytrain))\n",
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"print(\"Accuracy of Train with weights\", Stree(C=C, random_state=1).fit(Xtrain, ytrain, sample_weight=weights).score(Xtrain, ytrain))\n",
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"print(\"Accuracy of Tests without weights\", Stree(C=C, random_state=1).fit(Xtrain, ytrain).score(Xtest, ytest))\n",
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"print(\"Accuracy of Tests with weights\", Stree(C=C, random_state=1).fit(Xtrain, ytrain, sample_weight=weights).score(Xtest, ytest))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"outputPrepend"
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]
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},
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"outputs": [
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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): 0.9737\nClassifier's accuracy (test) : 0.9805\nroot\nroot - Down, <cgaf> - Leaf class=1 belief= 0.945736 counts=(array([0, 1]), array([ 7, 122]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.978784 counts=(array([0, 1]), array([692, 15]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9809\nClassifier's accuracy (test) : 0.9805\nroot\nroot - Down, <cgaf> - Leaf class=1 belief= 0.983871 counts=(array([0, 1]), array([ 2, 122]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([1]))\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([2]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.980170 counts=(array([0, 1]), array([692, 14]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9904\nClassifier's accuracy (test) : 0.9777\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([122]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([1]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([8]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.988669 counts=(array([0, 1]), array([698, 8]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9916\nClassifier's accuracy (test) : 0.9721\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([125]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([1]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([5]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.990071 counts=(array([0, 1]), array([698, 7]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9940\nClassifier's accuracy (test) : 0.9749\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([128]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\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([1]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.992867 counts=(array([0, 1]), array([696, 5]))\n\n**************************************************\n0.2412 secs\n"
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}
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],
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"outputs": [],
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"source": [
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"t = time.time()\n",
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"for C in (.001, .01, 1, 5, 17):\n",
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@@ -121,15 +137,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "[[0.97223657 0.02776343]\n [0.96965421 0.03034579]\n [0.96918057 0.03081943]\n [0.94009975 0.05990025]]\n"
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}
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],
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.preprocessing import StandardScaler\n",
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@@ -144,15 +154,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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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([128]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\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([1]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.992867 counts=(array([0, 1]), array([696, 5]))\n"
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}
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],
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"outputs": [],
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"source": [
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"#check iterator\n",
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"for i in list(clf):\n",
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@@ -161,15 +165,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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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([128]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([2]))\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([1]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.992867 counts=(array([0, 1]), array([696, 5]))\n"
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}
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],
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"outputs": [],
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"source": [
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"#check iterator again\n",
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"for i in clf:\n",
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@@ -178,7 +176,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -189,15 +187,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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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 0x12a2f1200>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x12a2e7320>, 'Stree')\n3 functools.partial(<function check_fit_score_takes_y at 0x12a2e7200>, 'Stree')\n4 functools.partial(<function check_sample_weights_pandas_series at 0x12a2d7b00>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x12a2d7c20>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x12a2d7d40>, 'Stree')\n7 functools.partial(<function check_sample_weights_shape at 0x12a2d7e60>, 'Stree')\n8 functools.partial(<function check_sample_weights_invariance at 0x12a2d7f80>, 'Stree')\n9 functools.partial(<function check_estimators_fit_returns_self at 0x12a2ec320>, 'Stree')\n10 functools.partial(<function check_estimators_fit_returns_self at 0x12a2ec320>, 'Stree', readonly_memmap=True)\n11 functools.partial(<function check_complex_data at 0x12a2e2170>, 'Stree')\n12 functools.partial(<function check_dtype_object at 0x12a2e20e0>, 'Stree')\n13 functools.partial(<function check_estimators_empty_data_messages at 0x12a2e7440>, 'Stree')\n14 functools.partial(<function check_pipeline_consistency at 0x12a2e70e0>, 'Stree')\n15 functools.partial(<function check_estimators_nan_inf at 0x12a2e7560>, 'Stree')\n16 functools.partial(<function check_estimators_overwrite_params at 0x12a2f10e0>, 'Stree')\n17 functools.partial(<function check_estimator_sparse_data at 0x12a2d79e0>, 'Stree')\n18 functools.partial(<function check_estimators_pickle at 0x12a2e77a0>, 'Stree')\n19 functools.partial(<function check_classifier_data_not_an_array at 0x12a2f1440>, 'Stree')\n20 functools.partial(<function check_classifiers_one_label at 0x12a2e7e60>, 'Stree')\n21 functools.partial(<function check_classifiers_classes at 0x12a2ec8c0>, 'Stree')\n22 functools.partial(<function check_estimators_partial_fit_n_features at 0x12a2e78c0>, 'Stree')\n23 functools.partial(<function check_classifiers_train at 0x12a2e7f80>, 'Stree')\n24 functools.partial(<function check_classifiers_train at 0x12a2e7f80>, 'Stree', readonly_memmap=True)\n25 functools.partial(<function check_classifiers_train at 0x12a2e7f80>, 'Stree', readonly_memmap=True, X_dtype='float32')\n26 functools.partial(<function check_classifiers_regression_target at 0x12a2f1ef0>, 'Stree')\n27 functools.partial(<function check_supervised_y_no_nan at 0x12a2d8b00>, 'Stree')\n28 functools.partial(<function check_supervised_y_2d at 0x12a2ec560>, 'Stree')\n29 functools.partial(<function check_estimators_unfitted at 0x12a2ec440>, 'Stree')\n30 functools.partial(<function check_non_transformer_estimators_n_iter at 0x12a2f1a70>, 'Stree')\n31 functools.partial(<function check_decision_proba_consistency at 0x12a2f6050>, 'Stree')\n32 functools.partial(<function check_fit2d_predict1d at 0x12a2e2680>, 'Stree')\n33 functools.partial(<function check_methods_subset_invariance at 0x12a2e2830>, 'Stree')\n34 functools.partial(<function check_fit2d_1sample at 0x12a2e2950>, 'Stree')\n35 functools.partial(<function check_fit2d_1feature at 0x12a2e2a70>, 'Stree')\n36 functools.partial(<function check_fit1d at 0x12a2e2b90>, 'Stree')\n37 functools.partial(<function check_get_params_invariance at 0x12a2f1cb0>, 'Stree')\n38 functools.partial(<function check_set_params at 0x12a2f1dd0>, 'Stree')\n39 functools.partial(<function check_dict_unchanged at 0x12a2e2290>, 'Stree')\n40 functools.partial(<function check_dont_overwrite_parameters at 0x12a2e2560>, 'Stree')\n41 functools.partial(<function check_fit_idempotent at 0x12a2f6200>, 'Stree')\n42 functools.partial(<function check_n_features_in at 0x12a2f6290>, 'Stree')\n43 functools.partial(<function check_requires_y_none at 0x12a2f6320>, 'Stree')\n"
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}
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],
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"outputs": [],
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"source": [
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"# Make checks one by one\n",
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"c = 0\n",
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