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Update examples
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‎examples/Counterfactuals.ipynb

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@@ -16,21 +16,8 @@
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"outputs": [],
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"source": [
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"import trustyai\n",
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"import os\n",
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"\n",
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"pwd = os.path.abspath('')\n",
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"\n",
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"trustyai.init(\n",
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" path = [\n",
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" pwd + \"/../dep/org/kie/kogito/explainability-core/1.16.0.Final/*\",\n",
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" pwd + \"/../dep/org/slf4j/slf4j-api/1.7.30/slf4j-api-1.7.30.jar\",\n",
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" pwd + \"/../dep/org/apache/commons/commons-lang3/3.12.0/commons-lang3-3.12.0.jar\",\n",
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" pwd + \"/../dep/org/optaplanner/optaplanner-core/8.16.0.Final/*\",\n",
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" pwd + \"/../dep/org/apache/commons/commons-math3/3.6.1/commons-math3-3.6.1.jar\",\n",
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" pwd + \"/../dep/org/kie/kie-api/8.16.0.Beta/*\",\n",
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" pwd + \"/../dep/io/micrometer/micrometer-core/1.8.1/*\",\n",
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" ]\n",
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")"
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"trustyai.init()"
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]
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},
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{
@@ -145,12 +132,12 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Feature x1 has value 1.0380081180194922\n",
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"Feature x2 has value 9.63798897368338\n",
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"Feature x3 has value 3.5581213895741595\n",
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"Feature x4 has value 5.670566321767616\n",
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"Feature x1 has value 8.383112183880774\n",
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"Feature x2 has value 1.5296907450266672\n",
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"Feature x3 has value 6.324409811461678\n",
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"Feature x4 has value 3.0125007391588077\n",
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"\n",
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"Features sum is 19.904684803044645\n"
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"Features sum is 19.249713479527923\n"
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]
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}
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],
@@ -266,8 +253,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import uuid\n",
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"from trustyai.local.counterfactual import counterfactual_prediction\n",
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"from trustyai.model import counterfactual_prediction\n",
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"\n",
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"prediction = counterfactual_prediction(\n",
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" input_features=features,\n",
@@ -311,12 +297,12 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"java.lang.DoubleEntity{value=1.0380081180194922, rangeMinimum=0.0, rangeMaximum=1000.0, id='x1'}\n",
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"java.lang.DoubleEntity{value=484.86792394543664, rangeMinimum=0.0, rangeMaximum=1000.0, id='x2'}\n",
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"java.lang.DoubleEntity{value=3.5581213895741595, rangeMinimum=0.0, rangeMaximum=1000.0, id='x3'}\n",
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"java.lang.DoubleEntity{value=9.63798897368338, rangeMinimum=0.0, rangeMaximum=1000.0, id='x4'}\n",
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"java.lang.DoubleEntity{value=8.383112183880774, rangeMinimum=0.0, rangeMaximum=1000.0, id='x1'}\n",
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"java.lang.DoubleEntity{value=481.32000081209515, rangeMinimum=0.0, rangeMaximum=1000.0, id='x2'}\n",
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"java.lang.DoubleEntity{value=6.495107736558037, rangeMinimum=0.0, rangeMaximum=1000.0, id='x3'}\n",
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"java.lang.DoubleEntity{value=3.0125007391588077, rangeMinimum=0.0, rangeMaximum=1000.0, id='x4'}\n",
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"\n",
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"Feature sum is 499.1020424267137\n"
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"Feature sum is 499.21072147169275\n"
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]
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}
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],
@@ -409,13 +395,13 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Original x1: 1.0380081180194922\n",
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"Original x4: 5.670566321767616\n",
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"Original x1: 8.383112183880774\n",
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"Original x4: 3.0125007391588077\n",
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"\n",
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"java.lang.DoubleEntity{value=1.0380081180194922, rangeMinimum=1.0380081180194922, rangeMaximum=1.0380081180194922, id='x1'}\n",
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"java.lang.DoubleEntity{value=488.87828084645156, rangeMinimum=0.0, rangeMaximum=1000.0, id='x2'}\n",
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"java.lang.DoubleEntity{value=4.2932049776029935, rangeMinimum=0.0, rangeMaximum=1000.0, id='x3'}\n",
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"java.lang.DoubleEntity{value=5.670566321767616, rangeMinimum=5.670566321767616, rangeMaximum=5.670566321767616, id='x4'}\n"
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"java.lang.DoubleEntity{value=8.383112183880774, rangeMinimum=8.383112183880774, rangeMaximum=8.383112183880774, id='x1'}\n",
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"java.lang.DoubleEntity{value=481.32000081209515, rangeMinimum=0.0, rangeMaximum=1000.0, id='x2'}\n",
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"java.lang.DoubleEntity{value=6.495107736558037, rangeMinimum=0.0, rangeMaximum=1000.0, id='x3'}\n",
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"java.lang.DoubleEntity{value=3.0125007391588077, rangeMinimum=3.0125007391588077, rangeMaximum=3.0125007391588077, id='x4'}\n"
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]
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}
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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": 52,
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"execution_count": 40,
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"outputs": [],
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"source": [
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"from sklearn.datasets import make_blobs\n",
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"execution_count": 41,
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"outputs": [
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"data": {
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"execution_count": 42,
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"outputs": [
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{
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"data": {
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"text/plain": "KNeighborsClassifier(n_neighbors=3)"
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},
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"execution_count": 54,
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"execution_count": 42,
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"metadata": {},
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"output_type": "execute_result"
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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": 56,
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"execution_count": 43,
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"outputs": [
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{
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"data": {
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"execution_count": 44,
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"outputs": [
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{
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"data": {
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"text/plain": "array([[0., 0., 1.]])"
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},
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"execution_count": 57,
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"execution_count": 44,
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"metadata": {},
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"output_type": "execute_result"
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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": 58,
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"execution_count": 45,
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"outputs": [],
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"source": [
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"def knn_classify(inputs):\n",
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"execution_count": 46,
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"outputs": [],
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"source": [
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"from trustyai.model import Model\n",
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"execution_count": 47,
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"outputs": [
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{
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"data": {
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"text/plain": "'Output{value=2, type=number, score=1.0, name='cluster'}'"
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},
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"execution_count": 60,
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"execution_count": 47,
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"metadata": {},
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"output_type": "execute_result"
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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": 61,
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"execution_count": 48,
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"outputs": [],
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"source": [
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"goal = [output(name=\"cluster\", dtype=\"number\", value=1)]"
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"execution_count": 49,
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"outputs": [],
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"source": [
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"prediction = counterfactual_prediction(\n",
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},
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{
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"cell_type": "code",
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"data": {
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"text/plain": "[2.501921601686025, 2.6401536249215436]"
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},
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"execution_count": 50,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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"data": {
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"text/plain": "'Output{value=1, type=number, score=1.0, name='cluster'}'"
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},
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"metadata": {},
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"output_type": "execute_result"
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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": 65,
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"execution_count": 52,
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"outputs": [
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{
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"data": {
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"name": "#%%\n"
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}
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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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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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}
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],
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"metadata": {

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