Deploy our serving class using as a serverless function¶
-
in the following section we create a new model serving function which wraps our class , and specify model and other resources.
-
the models dict store model names and the assosiated model dir URL (the URL can start with S3:// and other blob store options), the faster way is to use a shared file volume, we use .apply(mount_v3io()) to attach a v3io (iguazio data fabric) volume to our function. By default v3io will mount the current user home into the \User function path.
-
verify the model dir does contain a valid model.bst file
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/0.8.0/src/README.md b/functions/development/churn_server/0.8.0/src/README.md
deleted file mode 100644
index b6a517a5..00000000
--- a/functions/development/churn_server/0.8.0/src/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# churn server
-
-the `churn-server` function was created as part of the **[churn demo](https://github.com/yjb-ds/demo-churn)**. A model server was needed that could combine the static model which answers the binary classification question "is this client churned or not-churned?" and the more dynamic model, which tries to add a time dimension to the prediction by providing an esdtimate of when and with what certainty churn events are likely to occur.
-
-the function `coxph_trainer` will output multiple models within a nested directory structire starting at `models_dest`:
-* the coxph model is stored at `models_dest/cox`
-* the [kaplan-meier](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) model at `models_dest/cox/km`
-
-each one of these pickled models stores all of the meta-data, vector and table estimates, including projections and scenarios
-
-with only slight modification, a more generic version of this server would enable its application in the domains of **[predictive maintenance](https://docs.microsoft.com/en-us/archive/msdn-magazine/2019/may/machine-learning-using-survival-analysis-for-predictive-maintenance)**, **[health](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3227332/)**, **finance** and **insurance** to name a few.
-
-**note**
-
-a small file `encode-data.csv` can be find in the root of this function folder, it is used to test the server.
\ No newline at end of file
diff --git a/functions/development/churn_server/0.8.0/src/churn_server.ipynb b/functions/development/churn_server/0.8.0/src/churn_server.ipynb
deleted file mode 100644
index b8a96277..00000000
--- a/functions/development/churn_server/0.8.0/src/churn_server.ipynb
+++ /dev/null
@@ -1,503 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "# **Churn Server**\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "in the following section we create a new model serving function which wraps our class , and specify model and other resources.\n",
- "Deploying the serving function will provide us an http endpoint that can handle requests in real time.\n",
- "This function is part of the [customer-churn-prediction demo](https://github.com/mlrun/demos/tree/master/customer-churn-prediction). \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` and `xgb_trainer` functions from the function marketplace repository."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Testing the function locally](#Testing-the-function-locally)\n",
- "4. [Testing the function remotely](#Testing-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Following packages are required, make sure to install\n",
- "# !pip install xgboost==1.3.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Setting up models path\n",
- "xgb_model_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/churn_server/xgb_model.pkl'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "# Importing the function from the hub\n",
- "fn = mlrun.import_function(\"hub://churn_server:development\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "# Manually specifying needed packages \n",
- "fn.spec.build.commands = ['pip install lifelines==0.22.8', 'pip install xgboost==1.3.1']\n",
- "\n",
- "# Adding the model \n",
- "fn.add_model(key='xgb_model', model_path=xgb_model_path ,class_name='ChurnModel')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Testing the function locally**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Note that this function is a serving function, hence not needs to run, but deployed. \n",
- "\n",
- "in order to test locally without deploying to server, mlrun provides mocking api that simulate the action."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:19,145 [info] model xgb_model was loaded\n",
- "> 2021-10-14 06:10:19,145 [info] Initializing endpoint records\n",
- "> 2021-10-14 06:10:19,164 [info] Loaded ['xgb_model']\n"
- ]
- }
- ],
- "source": [
- "# When mocking, class has to be present\n",
- "from churn_server import *\n",
- "\n",
- "# Mocking function\n",
- "server = fn.to_mock_server()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
in the following section we create a new model serving function which wraps our class , and specify model and other resources.
-Deploying the serving function will provide us an http endpoint that can handle requests in real time.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer and xgb_trainer functions from the function marketplace repository.
importmlrun
-mlrun.set_environment(project='function-marketplace')
-
-# Importing the function from the hub
-fn=mlrun.import_function("hub://churn_server:development")
-fn.apply(mlrun.auto_mount())
-
-# Manually specifying needed packages
-fn.spec.build.commands=['pip install lifelines==0.22.8','pip install xgboost==1.3.1']
-
-# Adding the model
-fn.add_model(key='xgb_model',model_path=xgb_model_path,class_name='ChurnModel')
-
-
-
-
-
> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB
-
-
-
<mlrun.serving.states.TaskStep at 0x7f8f2306ca90>
-
> 2021-10-14 06:10:20,163 [info] Starting remote function deploy
-2021-10-14 06:10:20 (info) Deploying function
-2021-10-14 06:10:20 (info) Building
-2021-10-14 06:10:20 (info) Staging files and preparing base images
-2021-10-14 06:10:20 (info) Building processor image
-2021-10-14 06:10:21 (info) Build complete
-2021-10-14 06:10:29 (info) Function deploy complete
-> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}
-
-
-
-
-
-
-
importjson
-importrequests
-
-# using requests to predict
-response=requests.put(address+"/v2/models/xgb_model/predict",json=json.dumps(event_data))
-
-# returned data is a string
-y_predict=json.loads(response.text)['outputs']
-accuracy=sum(1forx,yinzip(y_predict,y_test)ifx==y)/len(y_test)
-print(f"model's accuracy : {accuracy}")
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/0.9.0/src/README.md b/functions/development/churn_server/0.9.0/src/README.md
deleted file mode 100644
index b6a517a5..00000000
--- a/functions/development/churn_server/0.9.0/src/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# churn server
-
-the `churn-server` function was created as part of the **[churn demo](https://github.com/yjb-ds/demo-churn)**. A model server was needed that could combine the static model which answers the binary classification question "is this client churned or not-churned?" and the more dynamic model, which tries to add a time dimension to the prediction by providing an esdtimate of when and with what certainty churn events are likely to occur.
-
-the function `coxph_trainer` will output multiple models within a nested directory structire starting at `models_dest`:
-* the coxph model is stored at `models_dest/cox`
-* the [kaplan-meier](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) model at `models_dest/cox/km`
-
-each one of these pickled models stores all of the meta-data, vector and table estimates, including projections and scenarios
-
-with only slight modification, a more generic version of this server would enable its application in the domains of **[predictive maintenance](https://docs.microsoft.com/en-us/archive/msdn-magazine/2019/may/machine-learning-using-survival-analysis-for-predictive-maintenance)**, **[health](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3227332/)**, **finance** and **insurance** to name a few.
-
-**note**
-
-a small file `encode-data.csv` can be find in the root of this function folder, it is used to test the server.
\ No newline at end of file
diff --git a/functions/development/churn_server/0.9.0/src/churn_server.ipynb b/functions/development/churn_server/0.9.0/src/churn_server.ipynb
deleted file mode 100644
index b8a96277..00000000
--- a/functions/development/churn_server/0.9.0/src/churn_server.ipynb
+++ /dev/null
@@ -1,503 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "# **Churn Server**\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "in the following section we create a new model serving function which wraps our class , and specify model and other resources.\n",
- "Deploying the serving function will provide us an http endpoint that can handle requests in real time.\n",
- "This function is part of the [customer-churn-prediction demo](https://github.com/mlrun/demos/tree/master/customer-churn-prediction). \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` and `xgb_trainer` functions from the function marketplace repository."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Testing the function locally](#Testing-the-function-locally)\n",
- "4. [Testing the function remotely](#Testing-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Following packages are required, make sure to install\n",
- "# !pip install xgboost==1.3.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Setting up models path\n",
- "xgb_model_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/churn_server/xgb_model.pkl'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "# Importing the function from the hub\n",
- "fn = mlrun.import_function(\"hub://churn_server:development\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "# Manually specifying needed packages \n",
- "fn.spec.build.commands = ['pip install lifelines==0.22.8', 'pip install xgboost==1.3.1']\n",
- "\n",
- "# Adding the model \n",
- "fn.add_model(key='xgb_model', model_path=xgb_model_path ,class_name='ChurnModel')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Testing the function locally**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Note that this function is a serving function, hence not needs to run, but deployed. \n",
- "\n",
- "in order to test locally without deploying to server, mlrun provides mocking api that simulate the action."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:19,145 [info] model xgb_model was loaded\n",
- "> 2021-10-14 06:10:19,145 [info] Initializing endpoint records\n",
- "> 2021-10-14 06:10:19,164 [info] Loaded ['xgb_model']\n"
- ]
- }
- ],
- "source": [
- "# When mocking, class has to be present\n",
- "from churn_server import *\n",
- "\n",
- "# Mocking function\n",
- "server = fn.to_mock_server()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
in the following section we create a new model serving function which wraps our class , and specify model and other resources.
-Deploying the serving function will provide us an http endpoint that can handle requests in real time.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer and xgb_trainer functions from the function marketplace repository.
importmlrun
-mlrun.set_environment(project='function-marketplace')
-
-# Importing the function from the hub
-fn=mlrun.import_function("hub://churn_server:development")
-fn.apply(mlrun.auto_mount())
-
-# Manually specifying needed packages
-fn.spec.build.commands=['pip install lifelines==0.22.8','pip install xgboost==1.3.1']
-
-# Adding the model
-fn.add_model(key='xgb_model',model_path=xgb_model_path,class_name='ChurnModel')
-
-
-
-
-
> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB
-
-
-
<mlrun.serving.states.TaskStep at 0x7f8f2306ca90>
-
> 2021-10-14 06:10:20,163 [info] Starting remote function deploy
-2021-10-14 06:10:20 (info) Deploying function
-2021-10-14 06:10:20 (info) Building
-2021-10-14 06:10:20 (info) Staging files and preparing base images
-2021-10-14 06:10:20 (info) Building processor image
-2021-10-14 06:10:21 (info) Build complete
-2021-10-14 06:10:29 (info) Function deploy complete
-> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}
-
-
-
-
-
-
-
importjson
-importrequests
-
-# using requests to predict
-response=requests.put(address+"/v2/models/xgb_model/predict",json=json.dumps(event_data))
-
-# returned data is a string
-y_predict=json.loads(response.text)['outputs']
-accuracy=sum(1forx,yinzip(y_predict,y_test)ifx==y)/len(y_test)
-print(f"model's accuracy : {accuracy}")
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/1.0.0/src/README.md b/functions/development/churn_server/1.0.0/src/README.md
deleted file mode 100644
index b6a517a5..00000000
--- a/functions/development/churn_server/1.0.0/src/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# churn server
-
-the `churn-server` function was created as part of the **[churn demo](https://github.com/yjb-ds/demo-churn)**. A model server was needed that could combine the static model which answers the binary classification question "is this client churned or not-churned?" and the more dynamic model, which tries to add a time dimension to the prediction by providing an esdtimate of when and with what certainty churn events are likely to occur.
-
-the function `coxph_trainer` will output multiple models within a nested directory structire starting at `models_dest`:
-* the coxph model is stored at `models_dest/cox`
-* the [kaplan-meier](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) model at `models_dest/cox/km`
-
-each one of these pickled models stores all of the meta-data, vector and table estimates, including projections and scenarios
-
-with only slight modification, a more generic version of this server would enable its application in the domains of **[predictive maintenance](https://docs.microsoft.com/en-us/archive/msdn-magazine/2019/may/machine-learning-using-survival-analysis-for-predictive-maintenance)**, **[health](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3227332/)**, **finance** and **insurance** to name a few.
-
-**note**
-
-a small file `encode-data.csv` can be find in the root of this function folder, it is used to test the server.
\ No newline at end of file
diff --git a/functions/development/churn_server/1.0.0/src/churn_server.ipynb b/functions/development/churn_server/1.0.0/src/churn_server.ipynb
deleted file mode 100644
index b8a96277..00000000
--- a/functions/development/churn_server/1.0.0/src/churn_server.ipynb
+++ /dev/null
@@ -1,503 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "# **Churn Server**\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "in the following section we create a new model serving function which wraps our class , and specify model and other resources.\n",
- "Deploying the serving function will provide us an http endpoint that can handle requests in real time.\n",
- "This function is part of the [customer-churn-prediction demo](https://github.com/mlrun/demos/tree/master/customer-churn-prediction). \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` and `xgb_trainer` functions from the function marketplace repository."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Testing the function locally](#Testing-the-function-locally)\n",
- "4. [Testing the function remotely](#Testing-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Following packages are required, make sure to install\n",
- "# !pip install xgboost==1.3.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Setting up models path\n",
- "xgb_model_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/churn_server/xgb_model.pkl'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "# Importing the function from the hub\n",
- "fn = mlrun.import_function(\"hub://churn_server:development\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "# Manually specifying needed packages \n",
- "fn.spec.build.commands = ['pip install lifelines==0.22.8', 'pip install xgboost==1.3.1']\n",
- "\n",
- "# Adding the model \n",
- "fn.add_model(key='xgb_model', model_path=xgb_model_path ,class_name='ChurnModel')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Testing the function locally**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Note that this function is a serving function, hence not needs to run, but deployed. \n",
- "\n",
- "in order to test locally without deploying to server, mlrun provides mocking api that simulate the action."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:19,145 [info] model xgb_model was loaded\n",
- "> 2021-10-14 06:10:19,145 [info] Initializing endpoint records\n",
- "> 2021-10-14 06:10:19,164 [info] Loaded ['xgb_model']\n"
- ]
- }
- ],
- "source": [
- "# When mocking, class has to be present\n",
- "from churn_server import *\n",
- "\n",
- "# Mocking function\n",
- "server = fn.to_mock_server()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
in the following section we create a new model serving function which wraps our class , and specify model and other resources.
-Deploying the serving function will provide us an http endpoint that can handle requests in real time.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer and xgb_trainer functions from the function marketplace repository.
importmlrun
-mlrun.set_environment(project='function-marketplace')
-
-# Importing the function from the hub
-fn=mlrun.import_function("hub://churn_server:development")
-fn.apply(mlrun.auto_mount())
-
-# Manually specifying needed packages
-fn.spec.build.commands=['pip install lifelines==0.22.8','pip install xgboost==1.3.1']
-
-# Adding the model
-fn.add_model(key='xgb_model',model_path=xgb_model_path,class_name='ChurnModel')
-
-
-
-
-
> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB
-
-
-
<mlrun.serving.states.TaskStep at 0x7f8f2306ca90>
-
> 2021-10-14 06:10:20,163 [info] Starting remote function deploy
-2021-10-14 06:10:20 (info) Deploying function
-2021-10-14 06:10:20 (info) Building
-2021-10-14 06:10:20 (info) Staging files and preparing base images
-2021-10-14 06:10:20 (info) Building processor image
-2021-10-14 06:10:21 (info) Build complete
-2021-10-14 06:10:29 (info) Function deploy complete
-> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}
-
-
-
-
-
-
-
importjson
-importrequests
-
-# using requests to predict
-response=requests.put(address+"/v2/models/xgb_model/predict",json=json.dumps(event_data))
-
-# returned data is a string
-y_predict=json.loads(response.text)['outputs']
-accuracy=sum(1forx,yinzip(y_predict,y_test)ifx==y)/len(y_test)
-print(f"model's accuracy : {accuracy}")
-
-
-
-
-
model's accuracy : 0.7913907284768212
-
-
-
-
-
Back to the top
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/1.0.0/static/function.html b/functions/development/churn_server/1.0.0/static/function.html
deleted file mode 100644
index 171a9472..00000000
--- a/functions/development/churn_server/1.0.0/static/function.html
+++ /dev/null
@@ -1,73 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/1.1.0/src/README.md b/functions/development/churn_server/1.1.0/src/README.md
deleted file mode 100644
index b6a517a5..00000000
--- a/functions/development/churn_server/1.1.0/src/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# churn server
-
-the `churn-server` function was created as part of the **[churn demo](https://github.com/yjb-ds/demo-churn)**. A model server was needed that could combine the static model which answers the binary classification question "is this client churned or not-churned?" and the more dynamic model, which tries to add a time dimension to the prediction by providing an esdtimate of when and with what certainty churn events are likely to occur.
-
-the function `coxph_trainer` will output multiple models within a nested directory structire starting at `models_dest`:
-* the coxph model is stored at `models_dest/cox`
-* the [kaplan-meier](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) model at `models_dest/cox/km`
-
-each one of these pickled models stores all of the meta-data, vector and table estimates, including projections and scenarios
-
-with only slight modification, a more generic version of this server would enable its application in the domains of **[predictive maintenance](https://docs.microsoft.com/en-us/archive/msdn-magazine/2019/may/machine-learning-using-survival-analysis-for-predictive-maintenance)**, **[health](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3227332/)**, **finance** and **insurance** to name a few.
-
-**note**
-
-a small file `encode-data.csv` can be find in the root of this function folder, it is used to test the server.
\ No newline at end of file
diff --git a/functions/development/churn_server/1.1.0/src/churn_server.ipynb b/functions/development/churn_server/1.1.0/src/churn_server.ipynb
deleted file mode 100644
index b8a96277..00000000
--- a/functions/development/churn_server/1.1.0/src/churn_server.ipynb
+++ /dev/null
@@ -1,503 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "# **Churn Server**\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "in the following section we create a new model serving function which wraps our class , and specify model and other resources.\n",
- "Deploying the serving function will provide us an http endpoint that can handle requests in real time.\n",
- "This function is part of the [customer-churn-prediction demo](https://github.com/mlrun/demos/tree/master/customer-churn-prediction). \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` and `xgb_trainer` functions from the function marketplace repository."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Testing the function locally](#Testing-the-function-locally)\n",
- "4. [Testing the function remotely](#Testing-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Following packages are required, make sure to install\n",
- "# !pip install xgboost==1.3.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Setting up models path\n",
- "xgb_model_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/churn_server/xgb_model.pkl'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "# Importing the function from the hub\n",
- "fn = mlrun.import_function(\"hub://churn_server:development\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "# Manually specifying needed packages \n",
- "fn.spec.build.commands = ['pip install lifelines==0.22.8', 'pip install xgboost==1.3.1']\n",
- "\n",
- "# Adding the model \n",
- "fn.add_model(key='xgb_model', model_path=xgb_model_path ,class_name='ChurnModel')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Testing the function locally**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Note that this function is a serving function, hence not needs to run, but deployed. \n",
- "\n",
- "in order to test locally without deploying to server, mlrun provides mocking api that simulate the action."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:19,145 [info] model xgb_model was loaded\n",
- "> 2021-10-14 06:10:19,145 [info] Initializing endpoint records\n",
- "> 2021-10-14 06:10:19,164 [info] Loaded ['xgb_model']\n"
- ]
- }
- ],
- "source": [
- "# When mocking, class has to be present\n",
- "from churn_server import *\n",
- "\n",
- "# Mocking function\n",
- "server = fn.to_mock_server()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "
gender
\n",
- "
senior
\n",
- "
partner
\n",
- "
deps
\n",
- "
tenure
\n",
- "
PhoneService
\n",
- "
MultipleLines
\n",
- "
OnlineSecurity
\n",
- "
OnlineBackup
\n",
- "
DeviceProtection
\n",
- "
...
\n",
- "
PaperlessBilling
\n",
- "
MonthlyCharges
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- "
tenure_map
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- "
ISP_1
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0
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0
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1
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0
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0
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101.90
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85.70
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69.55
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0.0
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- "
"
- ],
- "text/plain": [
- " gender senior partner deps tenure PhoneService MultipleLines \\\n",
- "0 0 0 1 0 27 1 0 \n",
- "1 0 1 0 0 1 1 1 \n",
- "2 1 0 0 0 1 1 0 \n",
- "3 0 0 0 0 53 1 1 \n",
- "4 0 0 0 0 43 1 1 \n",
- "\n",
- " OnlineSecurity OnlineBackup DeviceProtection ... PaperlessBilling \\\n",
- "0 1 0 0 ... 1 \n",
- "1 0 0 0 ... 1 \n",
- "2 0 0 0 ... 1 \n",
- "3 0 1 1 ... 0 \n",
- "4 0 1 1 ... 1 \n",
- "\n",
- " MonthlyCharges tenure_map ISP_1 ISP_2 Contract_1 Contract_2 \\\n",
- "0 101.90 2.0 1 0 1 0 \n",
- "1 85.70 0.0 1 0 0 0 \n",
- "2 69.55 0.0 1 0 0 0 \n",
- "3 105.55 4.0 1 0 0 1 \n",
- "4 104.60 3.0 1 0 0 1 \n",
- "\n",
- " Payment_1 Payment_2 Payment_3 \n",
- "0 1 0 0 \n",
- "1 0 1 0 \n",
- "2 0 1 0 \n",
- "3 0 1 0 \n",
- "4 0 1 0 \n",
- "\n",
- "[5 rows x 23 columns]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "\n",
- "#declaring test_set path\n",
- "test_set_path = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/churn_server/test_set.csv\"\n",
- "\n",
- "# Getting the data\n",
- "x_test = pd.read_csv(test_set_path)\n",
- "y_test = x_test['labels']\n",
- "x_test.drop(['labels'],axis=1,inplace=True)\n",
- "x_test.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "# KFServing protocol event\n",
- "event_data = {\"inputs\": x_test.values.tolist()}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "response = server.test(path='/v2/models/xgb_model/predict',body=event_data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "When mocking to server, returned dict has the following fields : id, model_name, outputs\n"
- ]
- }
- ],
- "source": [
- "print(f'When mocking to server, returned dict has the following fields : {\", \".join([x for x in response.keys()])}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Testing the function remotely**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:20,163 [info] Starting remote function deploy\n",
- "2021-10-14 06:10:20 (info) Deploying function\n",
- "2021-10-14 06:10:20 (info) Building\n",
- "2021-10-14 06:10:20 (info) Staging files and preparing base images\n",
- "2021-10-14 06:10:20 (info) Building processor image\n",
- "2021-10-14 06:10:21 (info) Build complete\n",
- "2021-10-14 06:10:29 (info) Function deploy complete\n",
- "> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}\n"
- ]
- }
- ],
- "source": [
- "address = fn.deploy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "model's accuracy : 0.7913907284768212\n"
- ]
- }
- ],
- "source": [
- "import json\n",
- "import requests\n",
- "\n",
- "# using requests to predict\n",
- "response = requests.put(address + \"/v2/models/xgb_model/predict\", json=json.dumps(event_data))\n",
- "\n",
- "# returned data is a string \n",
- "y_predict = json.loads(response.text)['outputs']\n",
- "accuracy = sum(1 for x,y in zip(y_predict,y_test) if x == y) / len(y_test)\n",
- "print(f\"model's accuracy : {accuracy}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[Back to the top](#Churn-Server)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/functions/development/churn_server/1.1.0/src/churn_server.py b/functions/development/churn_server/1.1.0/src/churn_server.py
deleted file mode 100644
index 55f37f28..00000000
--- a/functions/development/churn_server/1.1.0/src/churn_server.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-import numpy as np
-from cloudpickle import load
-
-
-import mlrun
-
-
-class ChurnModel(mlrun.serving.V2ModelServer):
- def load(self):
- """
- load multiple models in nested folders, churn model only
- """
- clf_model_file, extra_data = self.get_model(".pkl")
- self.model = load(open(str(clf_model_file), "rb"))
- if "cox" in extra_data.keys():
- cox_model_file = extra_data["cox"]
- self.cox_model = load(open(str(cox_model_file), "rb"))
- if "cox/km" in extra_data.keys():
- km_model_file = extra_data["cox/km"]
- self.km_model = load(open(str(km_model_file), "rb"))
-
- def predict(self, body):
- try:
- feats = np.asarray(body["inputs"], dtype=np.float32).reshape(-1, 23)
- result = self.model.predict(feats, validate_features=False)
- return result.tolist()
- except Exception as e:
- raise Exception("Failed to predict %s" % e)
-
-
-from mlrun.runtimes import nuclio_init_hook
-
-
-def init_context(context):
- nuclio_init_hook(context, globals(), "serving_v2")
-
-
-def handler(context, event):
- return context.mlrun_handler(context, event)
diff --git a/functions/development/churn_server/1.1.0/src/function.yaml b/functions/development/churn_server/1.1.0/src/function.yaml
deleted file mode 100644
index 7a73c11a..00000000
--- a/functions/development/churn_server/1.1.0/src/function.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-kind: serving
-metadata:
- name: churn-server
- tag: ''
- hash: 805b4583ab8fa8df90c71d97eef54bbccf8729e8
- project: ''
- labels:
- author: Iguazio
- framework: churn
- categories:
- - model-serving
- - machine-learning
-spec:
- command: ''
- args: []
- image: mlrun/ml-models
- description: churn classification and predictor
- min_replicas: 1
- max_replicas: 4
- env:
- - name: ENABLE_EXPLAINER
- value: 'False'
- base_spec:
- apiVersion: nuclio.io/v1
- kind: Function
- metadata:
- name: churn-server
- labels: {}
- annotations:
- nuclio.io/generated_by: function generated from /User/functions/churn_server/churn_server.py
- spec:
- runtime: python:3.6
- handler: churn_server:handler
- env: []
- volumes: []
- build:
- commands: []
- noBaseImagesPull: true
- functionSourceCode: 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
- source: ''
- function_kind: serving_v2
- default_class: ChurnModel
- build:
- commands:
- - python -m pip install xgboost==1.3.1 lifelines==0.22.8
- code_origin: https://github.com/daniels290813/functions.git#34d1b0d7e26924d931c2df2869425d01df21a23c:/User/functions/churn_server/churn_server.py
- origin_filename: /User/functions/churn_server/churn_server.py
- secret_sources: []
- disable_auto_mount: false
- affinity: null
-verbose: false
diff --git a/functions/development/churn_server/1.1.0/src/item.yaml b/functions/development/churn_server/1.1.0/src/item.yaml
deleted file mode 100644
index 3a3b4b6b..00000000
--- a/functions/development/churn_server/1.1.0/src/item.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-apiVersion: v1
-categories:
-- model-serving
-- machine-learning
-description: churn classification and predictor
-doc: ''
-example: churn_server.ipynb
-generationDate: 2022-08-28:17-25
-hidden: false
-icon: ''
-labels:
- author: Iguazio
- framework: churn
-maintainers: []
-marketplaceType: ''
-mlrunVersion: 1.1.0
-name: churn-server
-platformVersion: 3.5.0
-spec:
- customFields:
- default_class: ChurnModel
- env:
- ENABLE_EXPLAINER: 'False'
- filename: churn_server.py
- handler: handler
- image: mlrun/ml-models
- kind: serving
- requirements:
- - xgboost==1.3.1
- - lifelines==0.22.8
-url: ''
-version: 1.1.0
diff --git a/functions/development/churn_server/1.1.0/src/requirements.txt b/functions/development/churn_server/1.1.0/src/requirements.txt
deleted file mode 100644
index eb8827c5..00000000
--- a/functions/development/churn_server/1.1.0/src/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-wget
-pygit2
\ No newline at end of file
diff --git a/functions/development/churn_server/1.1.0/src/test_churn_server.py b/functions/development/churn_server/1.1.0/src/test_churn_server.py
deleted file mode 100644
index 64d1b849..00000000
--- a/functions/development/churn_server/1.1.0/src/test_churn_server.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-import os
-import wget
-from mlrun import import_function
-import os.path
-from os import path
-import mlrun
-from pygit2 import Repository
-
-
-MODEL_PATH = os.path.join(os.path.abspath("./"), "models")
-MODEL = MODEL_PATH + "model.pt"
-
-
-def set_mlrun_hub_url():
- branch = Repository(".").head.shorthand
- hub_url = "https://raw.githubusercontent.com/mlrun/functions/{}/churn_server/function.yaml".format(
- branch
- )
- mlrun.mlconf.hub_url = hub_url
-
-
-def download_pretrained_model(model_path):
- # Run this to download the pre-trained model to your `models` directory
- import os
-
- model_location = None
- saved_models_directory = model_path
- # Create paths
- os.makedirs(saved_models_directory, exist_ok=1)
- model_filepath = os.path.join(
- saved_models_directory, os.path.basename(model_location)
- )
- wget.download(model_location, model_filepath)
-
-
-def test_local_churn_server():
- # set_mlrun_hub_url()
- # model_path = os.path.join(os.path.abspath("./"), "models")
- # model = model_path + "/model.pt"
- # if not path.exists(model):
- # download_pretrained_model(model_path)
- # fn = import_function("hub://churn_server")
- # fn.add_model("mymodel", model_path=model, class_name="ChurnModel")
- # # create an emulator (mock server) from the function configuration)
- # server = fn.to_mock_server()
- #
- # instances = [
- # "I had a pleasure to work with such dedicated team. Looking forward to \
- # cooperate with each and every one of them again."
- # ]
- # result = server.test("/v2/models/mymodel/infer", {"instances": instances})
- # assert result[0] == 2
- print("we need to download churn model")
diff --git a/functions/development/churn_server/1.1.0/static/churn_server.html b/functions/development/churn_server/1.1.0/static/churn_server.html
deleted file mode 100644
index 3cdcc727..00000000
--- a/functions/development/churn_server/1.1.0/static/churn_server.html
+++ /dev/null
@@ -1,195 +0,0 @@
-
-
-
-
-
-
-
-churn_server.churn_server
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-importnumpyasnp
-fromcloudpickleimportload
-
-
-importmlrun
-
-
-
in the following section we create a new model serving function which wraps our class , and specify model and other resources.
-Deploying the serving function will provide us an http endpoint that can handle requests in real time.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer and xgb_trainer functions from the function marketplace repository.
importmlrun
-mlrun.set_environment(project='function-marketplace')
-
-# Importing the function from the hub
-fn=mlrun.import_function("hub://churn_server:development")
-fn.apply(mlrun.auto_mount())
-
-# Manually specifying needed packages
-fn.spec.build.commands=['pip install lifelines==0.22.8','pip install xgboost==1.3.1']
-
-# Adding the model
-fn.add_model(key='xgb_model',model_path=xgb_model_path,class_name='ChurnModel')
-
-
-
-
-
> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB
-
-
-
<mlrun.serving.states.TaskStep at 0x7f8f2306ca90>
-
> 2021-10-14 06:10:20,163 [info] Starting remote function deploy
-2021-10-14 06:10:20 (info) Deploying function
-2021-10-14 06:10:20 (info) Building
-2021-10-14 06:10:20 (info) Staging files and preparing base images
-2021-10-14 06:10:20 (info) Building processor image
-2021-10-14 06:10:21 (info) Build complete
-2021-10-14 06:10:29 (info) Function deploy complete
-> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}
-
-
-
-
-
-
-
importjson
-importrequests
-
-# using requests to predict
-response=requests.put(address+"/v2/models/xgb_model/predict",json=json.dumps(event_data))
-
-# returned data is a string
-y_predict=json.loads(response.text)['outputs']
-accuracy=sum(1forx,yinzip(y_predict,y_test)ifx==y)/len(y_test)
-print(f"model's accuracy : {accuracy}")
-
-
-
-
-
model's accuracy : 0.7913907284768212
-
-
-
-
-
Back to the top
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/1.1.0/static/function.html b/functions/development/churn_server/1.1.0/static/function.html
deleted file mode 100644
index 171a9472..00000000
--- a/functions/development/churn_server/1.1.0/static/function.html
+++ /dev/null
@@ -1,73 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/1.1.0/static/source.html b/functions/development/churn_server/1.1.0/static/source.html
deleted file mode 100644
index 3937ee1b..00000000
--- a/functions/development/churn_server/1.1.0/static/source.html
+++ /dev/null
@@ -1,77 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-import numpy as np
-from cloudpickle import load
-
-
-import mlrun
-
-
-class ChurnModel(mlrun.serving.V2ModelServer):
- def load(self):
- """
- load multiple models in nested folders, churn model only
- """
- clf_model_file, extra_data = self.get_model(".pkl")
- self.model = load(open(str(clf_model_file), "rb"))
- if "cox" in extra_data.keys():
- cox_model_file = extra_data["cox"]
- self.cox_model = load(open(str(cox_model_file), "rb"))
- if "cox/km" in extra_data.keys():
- km_model_file = extra_data["cox/km"]
- self.km_model = load(open(str(km_model_file), "rb"))
-
- def predict(self, body):
- try:
- feats = np.asarray(body["inputs"], dtype=np.float32).reshape(-1, 23)
- result = self.model.predict(feats, validate_features=False)
- return result.tolist()
- except Exception as e:
- raise Exception("Failed to predict %s" % e)
-
-
-from mlrun.runtimes import nuclio_init_hook
-
-
-def init_context(context):
- nuclio_init_hook(context, globals(), "serving_v2")
-
-
-def handler(context, event):
- return context.mlrun_handler(context, event)
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/1.2.0/src/README.md b/functions/development/churn_server/1.2.0/src/README.md
deleted file mode 100644
index b6a517a5..00000000
--- a/functions/development/churn_server/1.2.0/src/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# churn server
-
-the `churn-server` function was created as part of the **[churn demo](https://github.com/yjb-ds/demo-churn)**. A model server was needed that could combine the static model which answers the binary classification question "is this client churned or not-churned?" and the more dynamic model, which tries to add a time dimension to the prediction by providing an esdtimate of when and with what certainty churn events are likely to occur.
-
-the function `coxph_trainer` will output multiple models within a nested directory structire starting at `models_dest`:
-* the coxph model is stored at `models_dest/cox`
-* the [kaplan-meier](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) model at `models_dest/cox/km`
-
-each one of these pickled models stores all of the meta-data, vector and table estimates, including projections and scenarios
-
-with only slight modification, a more generic version of this server would enable its application in the domains of **[predictive maintenance](https://docs.microsoft.com/en-us/archive/msdn-magazine/2019/may/machine-learning-using-survival-analysis-for-predictive-maintenance)**, **[health](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3227332/)**, **finance** and **insurance** to name a few.
-
-**note**
-
-a small file `encode-data.csv` can be find in the root of this function folder, it is used to test the server.
\ No newline at end of file
diff --git a/functions/development/churn_server/1.2.0/src/churn_server.ipynb b/functions/development/churn_server/1.2.0/src/churn_server.ipynb
deleted file mode 100644
index b8a96277..00000000
--- a/functions/development/churn_server/1.2.0/src/churn_server.ipynb
+++ /dev/null
@@ -1,503 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "# **Churn Server**\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "in the following section we create a new model serving function which wraps our class , and specify model and other resources.\n",
- "Deploying the serving function will provide us an http endpoint that can handle requests in real time.\n",
- "This function is part of the [customer-churn-prediction demo](https://github.com/mlrun/demos/tree/master/customer-churn-prediction). \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` and `xgb_trainer` functions from the function marketplace repository."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Testing the function locally](#Testing-the-function-locally)\n",
- "4. [Testing the function remotely](#Testing-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Following packages are required, make sure to install\n",
- "# !pip install xgboost==1.3.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Setting up models path\n",
- "xgb_model_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/churn_server/xgb_model.pkl'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "# Importing the function from the hub\n",
- "fn = mlrun.import_function(\"hub://churn_server:development\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "# Manually specifying needed packages \n",
- "fn.spec.build.commands = ['pip install lifelines==0.22.8', 'pip install xgboost==1.3.1']\n",
- "\n",
- "# Adding the model \n",
- "fn.add_model(key='xgb_model', model_path=xgb_model_path ,class_name='ChurnModel')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Testing the function locally**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Note that this function is a serving function, hence not needs to run, but deployed. \n",
- "\n",
- "in order to test locally without deploying to server, mlrun provides mocking api that simulate the action."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:19,145 [info] model xgb_model was loaded\n",
- "> 2021-10-14 06:10:19,145 [info] Initializing endpoint records\n",
- "> 2021-10-14 06:10:19,164 [info] Loaded ['xgb_model']\n"
- ]
- }
- ],
- "source": [
- "# When mocking, class has to be present\n",
- "from churn_server import *\n",
- "\n",
- "# Mocking function\n",
- "server = fn.to_mock_server()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "
gender
\n",
- "
senior
\n",
- "
partner
\n",
- "
deps
\n",
- "
tenure
\n",
- "
PhoneService
\n",
- "
MultipleLines
\n",
- "
OnlineSecurity
\n",
- "
OnlineBackup
\n",
- "
DeviceProtection
\n",
- "
...
\n",
- "
PaperlessBilling
\n",
- "
MonthlyCharges
\n",
- "
tenure_map
\n",
- "
ISP_1
\n",
- "
ISP_2
\n",
- "
Contract_1
\n",
- "
Contract_2
\n",
- "
Payment_1
\n",
- "
Payment_2
\n",
- "
Payment_3
\n",
- "
\n",
- " \n",
- " \n",
- "
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
27
\n",
- "
1
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
0
\n",
- "
...
\n",
- "
1
\n",
- "
101.90
\n",
- "
2.0
\n",
- "
1
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
0
\n",
- "
\n",
- "
\n",
- "
1
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
0
\n",
- "
1
\n",
- "
1
\n",
- "
1
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
...
\n",
- "
1
\n",
- "
85.70
\n",
- "
0.0
\n",
- "
1
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
\n",
- "
\n",
- "
2
\n",
- "
1
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
1
\n",
- "
1
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
...
\n",
- "
1
\n",
- "
69.55
\n",
- "
0.0
\n",
- "
1
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
\n",
- "
\n",
- "
3
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
53
\n",
- "
1
\n",
- "
1
\n",
- "
0
\n",
- "
1
\n",
- "
1
\n",
- "
...
\n",
- "
0
\n",
- "
105.55
\n",
- "
4.0
\n",
- "
1
\n",
- "
0
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
\n",
- "
\n",
- "
4
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
0
\n",
- "
43
\n",
- "
1
\n",
- "
1
\n",
- "
0
\n",
- "
1
\n",
- "
1
\n",
- "
...
\n",
- "
1
\n",
- "
104.60
\n",
- "
3.0
\n",
- "
1
\n",
- "
0
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
1
\n",
- "
0
\n",
- "
\n",
- " \n",
- "
\n",
- "
5 rows × 23 columns
\n",
- "
"
- ],
- "text/plain": [
- " gender senior partner deps tenure PhoneService MultipleLines \\\n",
- "0 0 0 1 0 27 1 0 \n",
- "1 0 1 0 0 1 1 1 \n",
- "2 1 0 0 0 1 1 0 \n",
- "3 0 0 0 0 53 1 1 \n",
- "4 0 0 0 0 43 1 1 \n",
- "\n",
- " OnlineSecurity OnlineBackup DeviceProtection ... PaperlessBilling \\\n",
- "0 1 0 0 ... 1 \n",
- "1 0 0 0 ... 1 \n",
- "2 0 0 0 ... 1 \n",
- "3 0 1 1 ... 0 \n",
- "4 0 1 1 ... 1 \n",
- "\n",
- " MonthlyCharges tenure_map ISP_1 ISP_2 Contract_1 Contract_2 \\\n",
- "0 101.90 2.0 1 0 1 0 \n",
- "1 85.70 0.0 1 0 0 0 \n",
- "2 69.55 0.0 1 0 0 0 \n",
- "3 105.55 4.0 1 0 0 1 \n",
- "4 104.60 3.0 1 0 0 1 \n",
- "\n",
- " Payment_1 Payment_2 Payment_3 \n",
- "0 1 0 0 \n",
- "1 0 1 0 \n",
- "2 0 1 0 \n",
- "3 0 1 0 \n",
- "4 0 1 0 \n",
- "\n",
- "[5 rows x 23 columns]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "\n",
- "#declaring test_set path\n",
- "test_set_path = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/churn_server/test_set.csv\"\n",
- "\n",
- "# Getting the data\n",
- "x_test = pd.read_csv(test_set_path)\n",
- "y_test = x_test['labels']\n",
- "x_test.drop(['labels'],axis=1,inplace=True)\n",
- "x_test.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "# KFServing protocol event\n",
- "event_data = {\"inputs\": x_test.values.tolist()}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "response = server.test(path='/v2/models/xgb_model/predict',body=event_data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "When mocking to server, returned dict has the following fields : id, model_name, outputs\n"
- ]
- }
- ],
- "source": [
- "print(f'When mocking to server, returned dict has the following fields : {\", \".join([x for x in response.keys()])}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Testing the function remotely**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:20,163 [info] Starting remote function deploy\n",
- "2021-10-14 06:10:20 (info) Deploying function\n",
- "2021-10-14 06:10:20 (info) Building\n",
- "2021-10-14 06:10:20 (info) Staging files and preparing base images\n",
- "2021-10-14 06:10:20 (info) Building processor image\n",
- "2021-10-14 06:10:21 (info) Build complete\n",
- "2021-10-14 06:10:29 (info) Function deploy complete\n",
- "> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}\n"
- ]
- }
- ],
- "source": [
- "address = fn.deploy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "model's accuracy : 0.7913907284768212\n"
- ]
- }
- ],
- "source": [
- "import json\n",
- "import requests\n",
- "\n",
- "# using requests to predict\n",
- "response = requests.put(address + \"/v2/models/xgb_model/predict\", json=json.dumps(event_data))\n",
- "\n",
- "# returned data is a string \n",
- "y_predict = json.loads(response.text)['outputs']\n",
- "accuracy = sum(1 for x,y in zip(y_predict,y_test) if x == y) / len(y_test)\n",
- "print(f\"model's accuracy : {accuracy}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[Back to the top](#Churn-Server)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/functions/development/churn_server/1.2.0/src/churn_server.py b/functions/development/churn_server/1.2.0/src/churn_server.py
deleted file mode 100644
index def2850d..00000000
--- a/functions/development/churn_server/1.2.0/src/churn_server.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-import numpy as np
-from cloudpickle import load
-
-
-import mlrun
-
-
-class ChurnModel(mlrun.serving.V2ModelServer):
- def load(self):
- """
- load multiple models in nested folders, churn model only
- """
- clf_model_file, extra_data = self.get_model(".pkl")
- self.model = load(open(str(clf_model_file), "rb"))
- if "cox" in extra_data.keys():
- cox_model_file = extra_data["cox"]
- self.cox_model = load(open(str(cox_model_file), "rb"))
- if "cox/km" in extra_data.keys():
- km_model_file = extra_data["cox/km"]
- self.km_model = load(open(str(km_model_file), "rb"))
-
- def predict(self, body):
- try:
- feats = np.asarray(body["inputs"], dtype=np.float32).reshape(-1, 23)
- result = self.model.predict(feats, validate_features=False)
- return result.tolist()
- except Exception as e:
- raise Exception("Failed to predict %s" % e)
-
diff --git a/functions/development/churn_server/1.2.0/src/function.yaml b/functions/development/churn_server/1.2.0/src/function.yaml
deleted file mode 100644
index 14f6c8ce..00000000
--- a/functions/development/churn_server/1.2.0/src/function.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-kind: serving
-metadata:
- name: churn-server
- tag: ''
- hash: 805b4583ab8fa8df90c71d97eef54bbccf8729e8
- project: ''
- labels:
- author: Iguazio
- framework: churn
- categories:
- - model-serving
- - machine-learning
-spec:
- command: ''
- args: []
- image: mlrun/ml-models
- description: churn classification and predictor
- min_replicas: 1
- max_replicas: 4
- env:
- - name: ENABLE_EXPLAINER
- value: 'False'
- base_spec:
- apiVersion: nuclio.io/v1
- kind: Function
- metadata:
- name: churn-server
- labels: {}
- annotations:
- nuclio.io/generated_by: function generated from /User/functions/churn_server/churn_server.py
- spec:
- runtime: python:3.9
- handler: churn_server:handler
- env: []
- volumes: []
- build:
- commands: []
- noBaseImagesPull: true
- functionSourceCode: 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
- source: ''
- function_kind: serving_v2
- default_class: ChurnModel
- build:
- commands:
- - python -m pip install xgboost==1.3.1 lifelines==0.22.8
- code_origin: https://github.com/daniels290813/functions.git#34d1b0d7e26924d931c2df2869425d01df21a23c:/User/functions/churn_server/churn_server.py
- origin_filename: /User/functions/churn_server/churn_server.py
- secret_sources: []
- disable_auto_mount: false
- affinity: null
-verbose: false
diff --git a/functions/development/churn_server/1.2.0/src/item.yaml b/functions/development/churn_server/1.2.0/src/item.yaml
deleted file mode 100644
index 09ba9b71..00000000
--- a/functions/development/churn_server/1.2.0/src/item.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-apiVersion: v1
-categories:
-- model-serving
-- machine-learning
-description: churn classification and predictor
-doc: ''
-example: churn_server.ipynb
-generationDate: 2022-08-28:17-25
-hidden: false
-icon: ''
-labels:
- author: Iguazio
- framework: churn
-maintainers: []
-marketplaceType: ''
-mlrunVersion: 1.1.0
-name: churn-server
-platformVersion: 3.5.0
-spec:
- customFields:
- default_class: ChurnModel
- env:
- ENABLE_EXPLAINER: 'False'
- filename: churn_server.py
- handler: handler
- image: mlrun/ml-models
- kind: serving
- requirements:
- - xgboost==1.3.1
- - lifelines==0.22.8
-url: ''
-version: 1.2.0
diff --git a/functions/development/churn_server/1.2.0/src/requirements.txt b/functions/development/churn_server/1.2.0/src/requirements.txt
deleted file mode 100644
index eb8827c5..00000000
--- a/functions/development/churn_server/1.2.0/src/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-wget
-pygit2
\ No newline at end of file
diff --git a/functions/development/churn_server/1.2.0/src/test_churn_server.py b/functions/development/churn_server/1.2.0/src/test_churn_server.py
deleted file mode 100644
index 64d1b849..00000000
--- a/functions/development/churn_server/1.2.0/src/test_churn_server.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-import os
-import wget
-from mlrun import import_function
-import os.path
-from os import path
-import mlrun
-from pygit2 import Repository
-
-
-MODEL_PATH = os.path.join(os.path.abspath("./"), "models")
-MODEL = MODEL_PATH + "model.pt"
-
-
-def set_mlrun_hub_url():
- branch = Repository(".").head.shorthand
- hub_url = "https://raw.githubusercontent.com/mlrun/functions/{}/churn_server/function.yaml".format(
- branch
- )
- mlrun.mlconf.hub_url = hub_url
-
-
-def download_pretrained_model(model_path):
- # Run this to download the pre-trained model to your `models` directory
- import os
-
- model_location = None
- saved_models_directory = model_path
- # Create paths
- os.makedirs(saved_models_directory, exist_ok=1)
- model_filepath = os.path.join(
- saved_models_directory, os.path.basename(model_location)
- )
- wget.download(model_location, model_filepath)
-
-
-def test_local_churn_server():
- # set_mlrun_hub_url()
- # model_path = os.path.join(os.path.abspath("./"), "models")
- # model = model_path + "/model.pt"
- # if not path.exists(model):
- # download_pretrained_model(model_path)
- # fn = import_function("hub://churn_server")
- # fn.add_model("mymodel", model_path=model, class_name="ChurnModel")
- # # create an emulator (mock server) from the function configuration)
- # server = fn.to_mock_server()
- #
- # instances = [
- # "I had a pleasure to work with such dedicated team. Looking forward to \
- # cooperate with each and every one of them again."
- # ]
- # result = server.test("/v2/models/mymodel/infer", {"instances": instances})
- # assert result[0] == 2
- print("we need to download churn model")
diff --git a/functions/development/churn_server/1.2.0/static/churn_server.html b/functions/development/churn_server/1.2.0/static/churn_server.html
deleted file mode 100644
index 51e99534..00000000
--- a/functions/development/churn_server/1.2.0/static/churn_server.html
+++ /dev/null
@@ -1,185 +0,0 @@
-
-
-
-
-
-
-
-churn_server.churn_server
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-importnumpyasnp
-fromcloudpickleimportload
-
-
-importmlrun
-
-
-
in the following section we create a new model serving function which wraps our class , and specify model and other resources.
-Deploying the serving function will provide us an http endpoint that can handle requests in real time.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer and xgb_trainer functions from the function marketplace repository.
importmlrun
-mlrun.set_environment(project='function-marketplace')
-
-# Importing the function from the hub
-fn=mlrun.import_function("hub://churn_server:development")
-fn.apply(mlrun.auto_mount())
-
-# Manually specifying needed packages
-fn.spec.build.commands=['pip install lifelines==0.22.8','pip install xgboost==1.3.1']
-
-# Adding the model
-fn.add_model(key='xgb_model',model_path=xgb_model_path,class_name='ChurnModel')
-
-
-
-
-
> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB
-
-
-
<mlrun.serving.states.TaskStep at 0x7f8f2306ca90>
-
> 2021-10-14 06:10:20,163 [info] Starting remote function deploy
-2021-10-14 06:10:20 (info) Deploying function
-2021-10-14 06:10:20 (info) Building
-2021-10-14 06:10:20 (info) Staging files and preparing base images
-2021-10-14 06:10:20 (info) Building processor image
-2021-10-14 06:10:21 (info) Build complete
-2021-10-14 06:10:29 (info) Function deploy complete
-> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}
-
-
-
-
-
-
-
importjson
-importrequests
-
-# using requests to predict
-response=requests.put(address+"/v2/models/xgb_model/predict",json=json.dumps(event_data))
-
-# returned data is a string
-y_predict=json.loads(response.text)['outputs']
-accuracy=sum(1forx,yinzip(y_predict,y_test)ifx==y)/len(y_test)
-print(f"model's accuracy : {accuracy}")
-
-
-
-
-
model's accuracy : 0.7913907284768212
-
-
-
-
-
Back to the top
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/1.2.0/static/function.html b/functions/development/churn_server/1.2.0/static/function.html
deleted file mode 100644
index 74fb1bd8..00000000
--- a/functions/development/churn_server/1.2.0/static/function.html
+++ /dev/null
@@ -1,73 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/1.2.0/static/source.html b/functions/development/churn_server/1.2.0/static/source.html
deleted file mode 100644
index 00d05b83..00000000
--- a/functions/development/churn_server/1.2.0/static/source.html
+++ /dev/null
@@ -1,67 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-import numpy as np
-from cloudpickle import load
-
-
-import mlrun
-
-
-class ChurnModel(mlrun.serving.V2ModelServer):
- def load(self):
- """
- load multiple models in nested folders, churn model only
- """
- clf_model_file, extra_data = self.get_model(".pkl")
- self.model = load(open(str(clf_model_file), "rb"))
- if "cox" in extra_data.keys():
- cox_model_file = extra_data["cox"]
- self.cox_model = load(open(str(cox_model_file), "rb"))
- if "cox/km" in extra_data.keys():
- km_model_file = extra_data["cox/km"]
- self.km_model = load(open(str(km_model_file), "rb"))
-
- def predict(self, body):
- try:
- feats = np.asarray(body["inputs"], dtype=np.float32).reshape(-1, 23)
- result = self.model.predict(feats, validate_features=False)
- return result.tolist()
- except Exception as e:
- raise Exception("Failed to predict %s" % e)
-
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/latest/src/README.md b/functions/development/churn_server/latest/src/README.md
deleted file mode 100644
index b6a517a5..00000000
--- a/functions/development/churn_server/latest/src/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# churn server
-
-the `churn-server` function was created as part of the **[churn demo](https://github.com/yjb-ds/demo-churn)**. A model server was needed that could combine the static model which answers the binary classification question "is this client churned or not-churned?" and the more dynamic model, which tries to add a time dimension to the prediction by providing an esdtimate of when and with what certainty churn events are likely to occur.
-
-the function `coxph_trainer` will output multiple models within a nested directory structire starting at `models_dest`:
-* the coxph model is stored at `models_dest/cox`
-* the [kaplan-meier](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) model at `models_dest/cox/km`
-
-each one of these pickled models stores all of the meta-data, vector and table estimates, including projections and scenarios
-
-with only slight modification, a more generic version of this server would enable its application in the domains of **[predictive maintenance](https://docs.microsoft.com/en-us/archive/msdn-magazine/2019/may/machine-learning-using-survival-analysis-for-predictive-maintenance)**, **[health](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3227332/)**, **finance** and **insurance** to name a few.
-
-**note**
-
-a small file `encode-data.csv` can be find in the root of this function folder, it is used to test the server.
\ No newline at end of file
diff --git a/functions/development/churn_server/latest/src/churn_server.ipynb b/functions/development/churn_server/latest/src/churn_server.ipynb
deleted file mode 100644
index b8a96277..00000000
--- a/functions/development/churn_server/latest/src/churn_server.ipynb
+++ /dev/null
@@ -1,503 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "# **Churn Server**\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "in the following section we create a new model serving function which wraps our class , and specify model and other resources.\n",
- "Deploying the serving function will provide us an http endpoint that can handle requests in real time.\n",
- "This function is part of the [customer-churn-prediction demo](https://github.com/mlrun/demos/tree/master/customer-churn-prediction). \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` and `xgb_trainer` functions from the function marketplace repository."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Testing the function locally](#Testing-the-function-locally)\n",
- "4. [Testing the function remotely](#Testing-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Following packages are required, make sure to install\n",
- "# !pip install xgboost==1.3.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Setting up models path\n",
- "xgb_model_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/churn_server/xgb_model.pkl'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "# Importing the function from the hub\n",
- "fn = mlrun.import_function(\"hub://churn_server:development\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "# Manually specifying needed packages \n",
- "fn.spec.build.commands = ['pip install lifelines==0.22.8', 'pip install xgboost==1.3.1']\n",
- "\n",
- "# Adding the model \n",
- "fn.add_model(key='xgb_model', model_path=xgb_model_path ,class_name='ChurnModel')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Testing the function locally**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Note that this function is a serving function, hence not needs to run, but deployed. \n",
- "\n",
- "in order to test locally without deploying to server, mlrun provides mocking api that simulate the action."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:19,145 [info] model xgb_model was loaded\n",
- "> 2021-10-14 06:10:19,145 [info] Initializing endpoint records\n",
- "> 2021-10-14 06:10:19,164 [info] Loaded ['xgb_model']\n"
- ]
- }
- ],
- "source": [
- "# When mocking, class has to be present\n",
- "from churn_server import *\n",
- "\n",
- "# Mocking function\n",
- "server = fn.to_mock_server()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "
gender
\n",
- "
senior
\n",
- "
partner
\n",
- "
deps
\n",
- "
tenure
\n",
- "
PhoneService
\n",
- "
MultipleLines
\n",
- "
OnlineSecurity
\n",
- "
OnlineBackup
\n",
- "
DeviceProtection
\n",
- "
...
\n",
- "
PaperlessBilling
\n",
- "
MonthlyCharges
\n",
- "
tenure_map
\n",
- "
ISP_1
\n",
- "
ISP_2
\n",
- "
Contract_1
\n",
- "
Contract_2
\n",
- "
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- "
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- "
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- "
101.90
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5 rows × 23 columns
\n",
- "
"
- ],
- "text/plain": [
- " gender senior partner deps tenure PhoneService MultipleLines \\\n",
- "0 0 0 1 0 27 1 0 \n",
- "1 0 1 0 0 1 1 1 \n",
- "2 1 0 0 0 1 1 0 \n",
- "3 0 0 0 0 53 1 1 \n",
- "4 0 0 0 0 43 1 1 \n",
- "\n",
- " OnlineSecurity OnlineBackup DeviceProtection ... PaperlessBilling \\\n",
- "0 1 0 0 ... 1 \n",
- "1 0 0 0 ... 1 \n",
- "2 0 0 0 ... 1 \n",
- "3 0 1 1 ... 0 \n",
- "4 0 1 1 ... 1 \n",
- "\n",
- " MonthlyCharges tenure_map ISP_1 ISP_2 Contract_1 Contract_2 \\\n",
- "0 101.90 2.0 1 0 1 0 \n",
- "1 85.70 0.0 1 0 0 0 \n",
- "2 69.55 0.0 1 0 0 0 \n",
- "3 105.55 4.0 1 0 0 1 \n",
- "4 104.60 3.0 1 0 0 1 \n",
- "\n",
- " Payment_1 Payment_2 Payment_3 \n",
- "0 1 0 0 \n",
- "1 0 1 0 \n",
- "2 0 1 0 \n",
- "3 0 1 0 \n",
- "4 0 1 0 \n",
- "\n",
- "[5 rows x 23 columns]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "\n",
- "#declaring test_set path\n",
- "test_set_path = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/churn_server/test_set.csv\"\n",
- "\n",
- "# Getting the data\n",
- "x_test = pd.read_csv(test_set_path)\n",
- "y_test = x_test['labels']\n",
- "x_test.drop(['labels'],axis=1,inplace=True)\n",
- "x_test.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "# KFServing protocol event\n",
- "event_data = {\"inputs\": x_test.values.tolist()}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "response = server.test(path='/v2/models/xgb_model/predict',body=event_data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "When mocking to server, returned dict has the following fields : id, model_name, outputs\n"
- ]
- }
- ],
- "source": [
- "print(f'When mocking to server, returned dict has the following fields : {\", \".join([x for x in response.keys()])}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Testing the function remotely**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-14 06:10:20,163 [info] Starting remote function deploy\n",
- "2021-10-14 06:10:20 (info) Deploying function\n",
- "2021-10-14 06:10:20 (info) Building\n",
- "2021-10-14 06:10:20 (info) Staging files and preparing base images\n",
- "2021-10-14 06:10:20 (info) Building processor image\n",
- "2021-10-14 06:10:21 (info) Build complete\n",
- "2021-10-14 06:10:29 (info) Function deploy complete\n",
- "> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}\n"
- ]
- }
- ],
- "source": [
- "address = fn.deploy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "model's accuracy : 0.7913907284768212\n"
- ]
- }
- ],
- "source": [
- "import json\n",
- "import requests\n",
- "\n",
- "# using requests to predict\n",
- "response = requests.put(address + \"/v2/models/xgb_model/predict\", json=json.dumps(event_data))\n",
- "\n",
- "# returned data is a string \n",
- "y_predict = json.loads(response.text)['outputs']\n",
- "accuracy = sum(1 for x,y in zip(y_predict,y_test) if x == y) / len(y_test)\n",
- "print(f\"model's accuracy : {accuracy}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[Back to the top](#Churn-Server)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/functions/development/churn_server/latest/src/churn_server.py b/functions/development/churn_server/latest/src/churn_server.py
deleted file mode 100644
index def2850d..00000000
--- a/functions/development/churn_server/latest/src/churn_server.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-import numpy as np
-from cloudpickle import load
-
-
-import mlrun
-
-
-class ChurnModel(mlrun.serving.V2ModelServer):
- def load(self):
- """
- load multiple models in nested folders, churn model only
- """
- clf_model_file, extra_data = self.get_model(".pkl")
- self.model = load(open(str(clf_model_file), "rb"))
- if "cox" in extra_data.keys():
- cox_model_file = extra_data["cox"]
- self.cox_model = load(open(str(cox_model_file), "rb"))
- if "cox/km" in extra_data.keys():
- km_model_file = extra_data["cox/km"]
- self.km_model = load(open(str(km_model_file), "rb"))
-
- def predict(self, body):
- try:
- feats = np.asarray(body["inputs"], dtype=np.float32).reshape(-1, 23)
- result = self.model.predict(feats, validate_features=False)
- return result.tolist()
- except Exception as e:
- raise Exception("Failed to predict %s" % e)
-
diff --git a/functions/development/churn_server/latest/src/function.yaml b/functions/development/churn_server/latest/src/function.yaml
deleted file mode 100644
index 14f6c8ce..00000000
--- a/functions/development/churn_server/latest/src/function.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-kind: serving
-metadata:
- name: churn-server
- tag: ''
- hash: 805b4583ab8fa8df90c71d97eef54bbccf8729e8
- project: ''
- labels:
- author: Iguazio
- framework: churn
- categories:
- - model-serving
- - machine-learning
-spec:
- command: ''
- args: []
- image: mlrun/ml-models
- description: churn classification and predictor
- min_replicas: 1
- max_replicas: 4
- env:
- - name: ENABLE_EXPLAINER
- value: 'False'
- base_spec:
- apiVersion: nuclio.io/v1
- kind: Function
- metadata:
- name: churn-server
- labels: {}
- annotations:
- nuclio.io/generated_by: function generated from /User/functions/churn_server/churn_server.py
- spec:
- runtime: python:3.9
- handler: churn_server:handler
- env: []
- volumes: []
- build:
- commands: []
- noBaseImagesPull: true
- functionSourceCode: IyBDb3B5cmlnaHQgMjAxOSBJZ3VhemlvCiMKIyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKIyB5b3UgbWF5IG5vdCB1c2UgdGhpcyBmaWxlIGV4Y2VwdCBpbiBjb21wbGlhbmNlIHdpdGggdGhlIExpY2Vuc2UuCiMgWW91IG1heSBvYnRhaW4gYSBjb3B5IG9mIHRoZSBMaWNlbnNlIGF0CiMKIyAgICAgaHR0cDovL3d3dy5hcGFjaGUub3JnL2xpY2Vuc2VzL0xJQ0VOU0UtMi4wCiMKIyBVbmxlc3MgcmVxdWlyZWQgYnkgYXBwbGljYWJsZSBsYXcgb3IgYWdyZWVkIHRvIGluIHdyaXRpbmcsIHNvZnR3YXJlCiMgZGlzdHJpYnV0ZWQgdW5kZXIgdGhlIExpY2Vuc2UgaXMgZGlzdHJpYnV0ZWQgb24gYW4gIkFTIElTIiBCQVNJUywKIyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KIyBTZWUgdGhlIExpY2Vuc2UgZm9yIHRoZSBzcGVjaWZpYyBsYW5ndWFnZSBnb3Zlcm5pbmcgcGVybWlzc2lvbnMgYW5kCiMgbGltaXRhdGlvbnMgdW5kZXIgdGhlIExpY2Vuc2UuCiMKIyBHZW5lcmF0ZWQgYnkgbnVjbGlvLmV4cG9ydC5OdWNsaW9FeHBvcnRlcgoKaW1wb3J0IG51bXB5IGFzIG5wCmZyb20gY2xvdWRwaWNrbGUgaW1wb3J0IGxvYWQKCgppbXBvcnQgbWxydW4KCgpjbGFzcyBDaHVybk1vZGVsKG1scnVuLnNlcnZpbmcuVjJNb2RlbFNlcnZlcik6CiAgICBkZWYgbG9hZChzZWxmKToKICAgICAgICAiIiIKICAgICAgICBsb2FkIG11bHRpcGxlIG1vZGVscyBpbiBuZXN0ZWQgZm9sZGVycywgY2h1cm4gbW9kZWwgb25seQogICAgICAgICIiIgogICAgICAgIGNsZl9tb2RlbF9maWxlLCBleHRyYV9kYXRhID0gc2VsZi5nZXRfbW9kZWwoIi5wa2wiKQogICAgICAgIHNlbGYubW9kZWwgPSBsb2FkKG9wZW4oc3RyKGNsZl9tb2RlbF9maWxlKSwgInJiIikpCiAgICAgICAgaWYgImNveCIgaW4gZXh0cmFfZGF0YS5rZXlzKCk6CiAgICAgICAgICAgIGNveF9tb2RlbF9maWxlID0gZXh0cmFfZGF0YVsiY294Il0KICAgICAgICAgICAgc2VsZi5jb3hfbW9kZWwgPSBsb2FkKG9wZW4oc3RyKGNveF9tb2RlbF9maWxlKSwgInJiIikpCiAgICAgICAgICAgIGlmICJjb3gva20iIGluIGV4dHJhX2RhdGEua2V5cygpOgogICAgICAgICAgICAgICAga21fbW9kZWxfZmlsZSA9IGV4dHJhX2RhdGFbImNveC9rbSJdCiAgICAgICAgICAgICAgICBzZWxmLmttX21vZGVsID0gbG9hZChvcGVuKHN0cihrbV9tb2RlbF9maWxlKSwgInJiIikpCgogICAgZGVmIHByZWRpY3Qoc2VsZiwgYm9keSk6CiAgICAgICAgdHJ5OgogICAgICAgICAgICBmZWF0cyA9IG5wLmFzYXJyYXkoYm9keVsiaW5wdXRzIl0sIGR0eXBlPW5wLmZsb2F0MzIpLnJlc2hhcGUoLTEsIDIzKQogICAgICAgICAgICByZXN1bHQgPSBzZWxmLm1vZGVsLnByZWRpY3QoZmVhdHMsIHZhbGlkYXRlX2ZlYXR1cmVzPUZhbHNlKQogICAgICAgICAgICByZXR1cm4gcmVzdWx0LnRvbGlzdCgpCiAgICAgICAgZXhjZXB0IEV4Y2VwdGlvbiBhcyBlOgogICAgICAgICAgICByYWlzZSBFeGNlcHRpb24oIkZhaWxlZCB0byBwcmVkaWN0ICVzIiAlIGUpCgoKZnJvbSBtbHJ1bi5ydW50aW1lcyBpbXBvcnQgbnVjbGlvX2luaXRfaG9vawpkZWYgaW5pdF9jb250ZXh0KGNvbnRleHQpOgogICAgbnVjbGlvX2luaXRfaG9vayhjb250ZXh0LCBnbG9iYWxzKCksICdzZXJ2aW5nX3YyJykKCmRlZiBoYW5kbGVyKGNvbnRleHQsIGV2ZW50KToKICAgIHJldHVybiBjb250ZXh0Lm1scnVuX2hhbmRsZXIoY29udGV4dCwgZXZlbnQpCg==
- source: ''
- function_kind: serving_v2
- default_class: ChurnModel
- build:
- commands:
- - python -m pip install xgboost==1.3.1 lifelines==0.22.8
- code_origin: https://github.com/daniels290813/functions.git#34d1b0d7e26924d931c2df2869425d01df21a23c:/User/functions/churn_server/churn_server.py
- origin_filename: /User/functions/churn_server/churn_server.py
- secret_sources: []
- disable_auto_mount: false
- affinity: null
-verbose: false
diff --git a/functions/development/churn_server/latest/src/item.yaml b/functions/development/churn_server/latest/src/item.yaml
deleted file mode 100644
index 09ba9b71..00000000
--- a/functions/development/churn_server/latest/src/item.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-apiVersion: v1
-categories:
-- model-serving
-- machine-learning
-description: churn classification and predictor
-doc: ''
-example: churn_server.ipynb
-generationDate: 2022-08-28:17-25
-hidden: false
-icon: ''
-labels:
- author: Iguazio
- framework: churn
-maintainers: []
-marketplaceType: ''
-mlrunVersion: 1.1.0
-name: churn-server
-platformVersion: 3.5.0
-spec:
- customFields:
- default_class: ChurnModel
- env:
- ENABLE_EXPLAINER: 'False'
- filename: churn_server.py
- handler: handler
- image: mlrun/ml-models
- kind: serving
- requirements:
- - xgboost==1.3.1
- - lifelines==0.22.8
-url: ''
-version: 1.2.0
diff --git a/functions/development/churn_server/latest/src/requirements.txt b/functions/development/churn_server/latest/src/requirements.txt
deleted file mode 100644
index eb8827c5..00000000
--- a/functions/development/churn_server/latest/src/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-wget
-pygit2
\ No newline at end of file
diff --git a/functions/development/churn_server/latest/src/test_churn_server.py b/functions/development/churn_server/latest/src/test_churn_server.py
deleted file mode 100644
index 64d1b849..00000000
--- a/functions/development/churn_server/latest/src/test_churn_server.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-import os
-import wget
-from mlrun import import_function
-import os.path
-from os import path
-import mlrun
-from pygit2 import Repository
-
-
-MODEL_PATH = os.path.join(os.path.abspath("./"), "models")
-MODEL = MODEL_PATH + "model.pt"
-
-
-def set_mlrun_hub_url():
- branch = Repository(".").head.shorthand
- hub_url = "https://raw.githubusercontent.com/mlrun/functions/{}/churn_server/function.yaml".format(
- branch
- )
- mlrun.mlconf.hub_url = hub_url
-
-
-def download_pretrained_model(model_path):
- # Run this to download the pre-trained model to your `models` directory
- import os
-
- model_location = None
- saved_models_directory = model_path
- # Create paths
- os.makedirs(saved_models_directory, exist_ok=1)
- model_filepath = os.path.join(
- saved_models_directory, os.path.basename(model_location)
- )
- wget.download(model_location, model_filepath)
-
-
-def test_local_churn_server():
- # set_mlrun_hub_url()
- # model_path = os.path.join(os.path.abspath("./"), "models")
- # model = model_path + "/model.pt"
- # if not path.exists(model):
- # download_pretrained_model(model_path)
- # fn = import_function("hub://churn_server")
- # fn.add_model("mymodel", model_path=model, class_name="ChurnModel")
- # # create an emulator (mock server) from the function configuration)
- # server = fn.to_mock_server()
- #
- # instances = [
- # "I had a pleasure to work with such dedicated team. Looking forward to \
- # cooperate with each and every one of them again."
- # ]
- # result = server.test("/v2/models/mymodel/infer", {"instances": instances})
- # assert result[0] == 2
- print("we need to download churn model")
diff --git a/functions/development/churn_server/latest/static/churn_server.html b/functions/development/churn_server/latest/static/churn_server.html
deleted file mode 100644
index 51e99534..00000000
--- a/functions/development/churn_server/latest/static/churn_server.html
+++ /dev/null
@@ -1,185 +0,0 @@
-
-
-
-
-
-
-
-churn_server.churn_server
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-importnumpyasnp
-fromcloudpickleimportload
-
-
-importmlrun
-
-
-
in the following section we create a new model serving function which wraps our class , and specify model and other resources.
-Deploying the serving function will provide us an http endpoint that can handle requests in real time.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer and xgb_trainer functions from the function marketplace repository.
importmlrun
-mlrun.set_environment(project='function-marketplace')
-
-# Importing the function from the hub
-fn=mlrun.import_function("hub://churn_server:development")
-fn.apply(mlrun.auto_mount())
-
-# Manually specifying needed packages
-fn.spec.build.commands=['pip install lifelines==0.22.8','pip install xgboost==1.3.1']
-
-# Adding the model
-fn.add_model(key='xgb_model',model_path=xgb_model_path,class_name='ChurnModel')
-
-
-
-
-
> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB
-
-
-
<mlrun.serving.states.TaskStep at 0x7f8f2306ca90>
-
> 2021-10-14 06:10:20,163 [info] Starting remote function deploy
-2021-10-14 06:10:20 (info) Deploying function
-2021-10-14 06:10:20 (info) Building
-2021-10-14 06:10:20 (info) Staging files and preparing base images
-2021-10-14 06:10:20 (info) Building processor image
-2021-10-14 06:10:21 (info) Build complete
-2021-10-14 06:10:29 (info) Function deploy complete
-> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}
-
-
-
-
-
-
-
importjson
-importrequests
-
-# using requests to predict
-response=requests.put(address+"/v2/models/xgb_model/predict",json=json.dumps(event_data))
-
-# returned data is a string
-y_predict=json.loads(response.text)['outputs']
-accuracy=sum(1forx,yinzip(y_predict,y_test)ifx==y)/len(y_test)
-print(f"model's accuracy : {accuracy}")
-
-
-
-
-
model's accuracy : 0.7913907284768212
-
-
-
-
-
Back to the top
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/latest/static/function.html b/functions/development/churn_server/latest/static/function.html
deleted file mode 100644
index 74fb1bd8..00000000
--- a/functions/development/churn_server/latest/static/function.html
+++ /dev/null
@@ -1,73 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/churn_server/latest/static/source.html b/functions/development/churn_server/latest/static/source.html
deleted file mode 100644
index 00d05b83..00000000
--- a/functions/development/churn_server/latest/static/source.html
+++ /dev/null
@@ -1,67 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-import numpy as np
-from cloudpickle import load
-
-
-import mlrun
-
-
-class ChurnModel(mlrun.serving.V2ModelServer):
- def load(self):
- """
- load multiple models in nested folders, churn model only
- """
- clf_model_file, extra_data = self.get_model(".pkl")
- self.model = load(open(str(clf_model_file), "rb"))
- if "cox" in extra_data.keys():
- cox_model_file = extra_data["cox"]
- self.cox_model = load(open(str(cox_model_file), "rb"))
- if "cox/km" in extra_data.keys():
- km_model_file = extra_data["cox/km"]
- self.km_model = load(open(str(km_model_file), "rb"))
-
- def predict(self, body):
- try:
- feats = np.asarray(body["inputs"], dtype=np.float32).reshape(-1, 23)
- result = self.model.predict(feats, validate_features=False)
- return result.tolist()
- except Exception as e:
- raise Exception("Failed to predict %s" % e)
-
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/0.0.1/src/coxph_test.ipynb b/functions/development/coxph_test/0.0.1/src/coxph_test.ipynb
deleted file mode 100644
index 42e28bf9..00000000
--- a/functions/development/coxph_test/0.0.1/src/coxph_test.ipynb
+++ /dev/null
@@ -1,440 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# CoxPH tests"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "# nuclio: ignore\n",
- "import nuclio"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.simplefilter(action=\"ignore\", category=FutureWarning)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import pandas as pd\n",
- "from mlrun.datastore import DataItem\n",
- "from mlrun.artifacts import get_model\n",
- "from cloudpickle import load\n",
- "from mlrun.mlutils.models import eval_class_model\n",
- "\n",
- "def cox_test(\n",
- " context,\n",
- " models_path: DataItem, \n",
- " test_set: DataItem,\n",
- " label_column: str,\n",
- " plots_dest: str = \"plots\",\n",
- " model_evaluator = None\n",
- ") -> None:\n",
- " \"\"\"Test one or more classifier models against held-out dataset\n",
- " \n",
- " Using held-out test features, evaluates the peformance of the estimated model\n",
- " \n",
- " Can be part of a kubeflow pipeline as a test step that is run post EDA and \n",
- " training/validation cycles\n",
- " \n",
- " :param context: the function context\n",
- " :param model_file: model artifact to be tested\n",
- " :param test_set: test features and labels\n",
- " :param label_column: column name for ground truth labels\n",
- " :param score_method: for multiclass classification\n",
- " :param plots_dest: dir for test plots\n",
- " :param model_evaluator: WIP: specific method to generate eval, passed in as string\n",
- " or available in this folder\n",
- " \"\"\" \n",
- " xtest = test_set.as_df()\n",
- " ytest = xtest.pop(label_column)\n",
- " \n",
- " model_file, model_obj, _ = get_model(models_path.url, suffix='.pkl')\n",
- " model_obj = load(open(str(model_file), \"rb\"))\n",
- "\n",
- " try:\n",
- " # there could be different eval_models, type of model (xgboost, tfv1, tfv2...)\n",
- " if not model_evaluator:\n",
- " # binary and multiclass\n",
- " eval_metrics = eval_class_model(context, xtest, ytest, model_obj)\n",
- "\n",
- " # just do this inside log_model?\n",
- " model_plots = eval_metrics.pop(\"plots\")\n",
- " model_tables = eval_metrics.pop(\"tables\")\n",
- " for plot in model_plots:\n",
- " context.log_artifact(plot, local_path=f\"{plots_dest}/{plot.key}.html\")\n",
- " for tbl in model_tables:\n",
- " context.log_artifact(tbl, local_path=f\"{plots_dest}/{plot.key}.csv\")\n",
- "\n",
- " context.log_results(eval_metrics)\n",
- " except:\n",
- " #dummy log:\n",
- " context.log_dataset(\"cox-test-summary\", df=model_obj.summary, index=True, format=\"csv\")\n",
- " context.logger.info(\"cox tester not implemented\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "# nuclio: end-code"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "task_params = {\n",
- " \"name\" : \"tasks cox test\",\n",
- " \"params\": {\n",
- " \"label_column\" : \"labels\",\n",
- " \"plots_dest\" : \"churn/test/plots\"}}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "DATA_URL = \"https://raw.githubusercontent.com/yjb-ds/testdata/master/demos/churn/churn-tests.csv\""
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Run Locally"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[mlrun] 2020-06-14 13:09:54,707 starting run tasks cox test uid=6284f5f5e6d14a969ba81addcfaf200f -> http://mlrun-api:8080\n",
- "[mlrun] 2020-06-14 13:09:55,249 log artifact cox-test-summary at /User/artifacts/cox-test-summary.csv, size: 3395, db: Y\n",
- "[mlrun] 2020-06-14 13:09:55,250 cox tester not implemented\n",
- "\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "
importos
-importpandasaspd
-frommlrun.datastoreimportDataItem
-frommlrun.artifactsimportget_model
-fromcloudpickleimportload
-frommlrun.mlutils.modelsimporteval_class_model
-
-defcox_test(
- context,
- models_path:DataItem,
- test_set:DataItem,
- label_column:str,
- plots_dest:str="plots",
- model_evaluator=None
-)->None:
- """Test one or more classifier models against held-out dataset
-
- Using held-out test features, evaluates the peformance of the estimated model
-
- Can be part of a kubeflow pipeline as a test step that is run post EDA and
- training/validation cycles
-
- :param context: the function context
- :param model_file: model artifact to be tested
- :param test_set: test features and labels
- :param label_column: column name for ground truth labels
- :param score_method: for multiclass classification
- :param plots_dest: dir for test plots
- :param model_evaluator: WIP: specific method to generate eval, passed in as string
- or available in this folder
- """
- xtest=test_set.as_df()
- ytest=xtest.pop(label_column)
-
- model_file,model_obj,_=get_model(models_path.url,suffix='.pkl')
- model_obj=load(open(str(model_file),"rb"))
-
- try:
- # there could be different eval_models, type of model (xgboost, tfv1, tfv2...)
- ifnotmodel_evaluator:
- # binary and multiclass
- eval_metrics=eval_class_model(context,xtest,ytest,model_obj)
-
- # just do this inside log_model?
- model_plots=eval_metrics.pop("plots")
- model_tables=eval_metrics.pop("tables")
- forplotinmodel_plots:
- context.log_artifact(plot,local_path=f"{plots_dest}/{plot.key}.html")
- fortblinmodel_tables:
- context.log_artifact(tbl,local_path=f"{plots_dest}/{plot.key}.csv")
-
- context.log_results(eval_metrics)
- except:
- #dummy log:
- context.log_dataset("cox-test-summary",df=model_obj.summary,index=True,format="csv")
- context.logger.info("cox tester not implemented")
-
to track results use .show() or .logs() or in CLI:
-!mlrun get run 6284f5f5e6d14a969ba81addcfaf200f --project default , !mlrun logs 6284f5f5e6d14a969ba81addcfaf200f --project default
-[mlrun] 2020-06-14 13:09:55,307 run executed, status=completed
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/0.0.1/static/source.html b/functions/development/coxph_test/0.0.1/static/source.html
deleted file mode 100644
index 6af29faf..00000000
--- a/functions/development/coxph_test/0.0.1/static/source.html
+++ /dev/null
@@ -1,83 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-# Generated by nuclio.export.NuclioExporter
-
-import warnings
-
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-import os
-import pandas as pd
-from mlrun.datastore import DataItem
-from mlrun.artifacts import get_model
-from cloudpickle import load
-from mlrun.mlutils.models import eval_class_model
-
-
-def cox_test(
- context,
- models_path: DataItem,
- test_set: DataItem,
- label_column: str,
- plots_dest: str = "plots",
- model_evaluator=None,
-) -> None:
- """Test one or more classifier models against held-out dataset
-
- Using held-out test features, evaluates the peformance of the estimated model
-
- Can be part of a kubeflow pipeline as a test step that is run post EDA and
- training/validation cycles
-
- :param context: the function context
- :param model_file: model artifact to be tested
- :param test_set: test features and labels
- :param label_column: column name for ground truth labels
- :param score_method: for multiclass classification
- :param plots_dest: dir for test plots
- :param model_evaluator: WIP: specific method to generate eval, passed in as string
- or available in this folder
- """
- xtest = test_set.as_df()
- ytest = xtest.pop(label_column)
-
- model_file, model_obj, _ = get_model(models_path.url, suffix=".pkl")
- model_obj = load(open(str(model_file), "rb"))
-
- try:
- if not model_evaluator:
- eval_metrics = eval_class_model(context, xtest, ytest, model_obj)
-
- model_plots = eval_metrics.pop("plots")
- model_tables = eval_metrics.pop("tables")
- for plot in model_plots:
- context.log_artifact(plot, local_path=f"{plots_dest}/{plot.key}.html")
- for tbl in model_tables:
- context.log_artifact(tbl, local_path=f"{plots_dest}/{plot.key}.csv")
-
- context.log_results(eval_metrics)
- except:
- context.log_dataset(
- "cox-test-summary", df=model_obj.summary, index=True, format="csv"
- )
- context.logger.info("cox tester not implemented")
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/0.8.0/src/coxph_test.ipynb b/functions/development/coxph_test/0.8.0/src/coxph_test.ipynb
deleted file mode 100644
index 0ee0b29c..00000000
--- a/functions/development/coxph_test/0.8.0/src/coxph_test.ipynb
+++ /dev/null
@@ -1,969 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# **CoxPH test**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features, and evaluates the peformance of the estimated model. \n",
- "Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles. \n",
- "This function is part of the [customer-churn-prediction](https://github.com/mlrun/demos/tree/master/customer-churn-prediction) demo. \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` function from the function marketplace repository"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Running the function locally](#Running-the-function-locally)\n",
- "4. [Running the function remotely](#Running-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "test_set = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_test/test_set.csv\"\n",
- "models_path = \"https://s3.wasabisys.com/iguazio/models/function-marketplace-models/coxph_test/cx-model.pkl\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-17 13:38:44,758 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "fn = mlrun.import_function(\"hub://coxph_test\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "fn.spec.build.image=\"mlrun/ml-models\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Running the function locally**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-17 13:38:45,149 [info] starting run tasks_coxph_test uid=be4bd195e5c146a69ecdee3b6a631569 DB=http://mlrun-api:8080\n",
- "> 2021-10-17 13:38:49,428 [info] cox tester not implemented\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "
This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features, and evaluates the peformance of the estimated model.
-Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer function from the function marketplace repository
fn.deploy(with_mlrun=False,# mlrun is included in our image (mlrun/ml-models) therefore no mlrun installation is needed.
- skip_deployed=True)# because no new packages or upgrade is required, we can use the original image and not build another one.
-
-coxph_run=fn.run(name='tasks_coxph_test',
- params={"label_column":"labels",
- "plots_dest":"plots/xgb_test"},
- inputs={"test_set":test_set,
- "models_path":models_path},
- local=False)
-
-
-
-
-
> 2021-10-17 13:38:49,644 [info] starting run tasks_coxph_test uid=c28d05f0261b4c60956eee528bf68e96 DB=http://mlrun-api:8080
-> 2021-10-17 13:38:49,776 [info] Job is running in the background, pod: tasks-coxph-test-hfj9b
-> 2021-10-17 13:38:59,015 [info] cox tester not implemented
-> 2021-10-17 13:38:59,049 [info] run executed, status=completed
-final state: completed
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/0.8.0/static/source.html b/functions/development/coxph_test/0.8.0/static/source.html
deleted file mode 100644
index 6af29faf..00000000
--- a/functions/development/coxph_test/0.8.0/static/source.html
+++ /dev/null
@@ -1,83 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-# Generated by nuclio.export.NuclioExporter
-
-import warnings
-
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-import os
-import pandas as pd
-from mlrun.datastore import DataItem
-from mlrun.artifacts import get_model
-from cloudpickle import load
-from mlrun.mlutils.models import eval_class_model
-
-
-def cox_test(
- context,
- models_path: DataItem,
- test_set: DataItem,
- label_column: str,
- plots_dest: str = "plots",
- model_evaluator=None,
-) -> None:
- """Test one or more classifier models against held-out dataset
-
- Using held-out test features, evaluates the peformance of the estimated model
-
- Can be part of a kubeflow pipeline as a test step that is run post EDA and
- training/validation cycles
-
- :param context: the function context
- :param model_file: model artifact to be tested
- :param test_set: test features and labels
- :param label_column: column name for ground truth labels
- :param score_method: for multiclass classification
- :param plots_dest: dir for test plots
- :param model_evaluator: WIP: specific method to generate eval, passed in as string
- or available in this folder
- """
- xtest = test_set.as_df()
- ytest = xtest.pop(label_column)
-
- model_file, model_obj, _ = get_model(models_path.url, suffix=".pkl")
- model_obj = load(open(str(model_file), "rb"))
-
- try:
- if not model_evaluator:
- eval_metrics = eval_class_model(context, xtest, ytest, model_obj)
-
- model_plots = eval_metrics.pop("plots")
- model_tables = eval_metrics.pop("tables")
- for plot in model_plots:
- context.log_artifact(plot, local_path=f"{plots_dest}/{plot.key}.html")
- for tbl in model_tables:
- context.log_artifact(tbl, local_path=f"{plots_dest}/{plot.key}.csv")
-
- context.log_results(eval_metrics)
- except:
- context.log_dataset(
- "cox-test-summary", df=model_obj.summary, index=True, format="csv"
- )
- context.logger.info("cox tester not implemented")
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/0.9.0/src/coxph_test.ipynb b/functions/development/coxph_test/0.9.0/src/coxph_test.ipynb
deleted file mode 100644
index 0ee0b29c..00000000
--- a/functions/development/coxph_test/0.9.0/src/coxph_test.ipynb
+++ /dev/null
@@ -1,969 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# **CoxPH test**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features, and evaluates the peformance of the estimated model. \n",
- "Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles. \n",
- "This function is part of the [customer-churn-prediction](https://github.com/mlrun/demos/tree/master/customer-churn-prediction) demo. \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` function from the function marketplace repository"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Running the function locally](#Running-the-function-locally)\n",
- "4. [Running the function remotely](#Running-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "test_set = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_test/test_set.csv\"\n",
- "models_path = \"https://s3.wasabisys.com/iguazio/models/function-marketplace-models/coxph_test/cx-model.pkl\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-17 13:38:44,758 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "fn = mlrun.import_function(\"hub://coxph_test\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "fn.spec.build.image=\"mlrun/ml-models\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Running the function locally**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-17 13:38:45,149 [info] starting run tasks_coxph_test uid=be4bd195e5c146a69ecdee3b6a631569 DB=http://mlrun-api:8080\n",
- "> 2021-10-17 13:38:49,428 [info] cox tester not implemented\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "
This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features, and evaluates the peformance of the estimated model.
-Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer function from the function marketplace repository
fn.deploy(with_mlrun=False,# mlrun is included in our image (mlrun/ml-models) therefore no mlrun installation is needed.
- skip_deployed=True)# because no new packages or upgrade is required, we can use the original image and not build another one.
-
-coxph_run=fn.run(name='tasks_coxph_test',
- params={"label_column":"labels",
- "plots_dest":"plots/xgb_test"},
- inputs={"test_set":test_set,
- "models_path":models_path},
- local=False)
-
-
-
-
-
> 2021-10-17 13:38:49,644 [info] starting run tasks_coxph_test uid=c28d05f0261b4c60956eee528bf68e96 DB=http://mlrun-api:8080
-> 2021-10-17 13:38:49,776 [info] Job is running in the background, pod: tasks-coxph-test-hfj9b
-> 2021-10-17 13:38:59,015 [info] cox tester not implemented
-> 2021-10-17 13:38:59,049 [info] run executed, status=completed
-final state: completed
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/0.9.0/static/source.html b/functions/development/coxph_test/0.9.0/static/source.html
deleted file mode 100644
index 6af29faf..00000000
--- a/functions/development/coxph_test/0.9.0/static/source.html
+++ /dev/null
@@ -1,83 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-# Generated by nuclio.export.NuclioExporter
-
-import warnings
-
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-import os
-import pandas as pd
-from mlrun.datastore import DataItem
-from mlrun.artifacts import get_model
-from cloudpickle import load
-from mlrun.mlutils.models import eval_class_model
-
-
-def cox_test(
- context,
- models_path: DataItem,
- test_set: DataItem,
- label_column: str,
- plots_dest: str = "plots",
- model_evaluator=None,
-) -> None:
- """Test one or more classifier models against held-out dataset
-
- Using held-out test features, evaluates the peformance of the estimated model
-
- Can be part of a kubeflow pipeline as a test step that is run post EDA and
- training/validation cycles
-
- :param context: the function context
- :param model_file: model artifact to be tested
- :param test_set: test features and labels
- :param label_column: column name for ground truth labels
- :param score_method: for multiclass classification
- :param plots_dest: dir for test plots
- :param model_evaluator: WIP: specific method to generate eval, passed in as string
- or available in this folder
- """
- xtest = test_set.as_df()
- ytest = xtest.pop(label_column)
-
- model_file, model_obj, _ = get_model(models_path.url, suffix=".pkl")
- model_obj = load(open(str(model_file), "rb"))
-
- try:
- if not model_evaluator:
- eval_metrics = eval_class_model(context, xtest, ytest, model_obj)
-
- model_plots = eval_metrics.pop("plots")
- model_tables = eval_metrics.pop("tables")
- for plot in model_plots:
- context.log_artifact(plot, local_path=f"{plots_dest}/{plot.key}.html")
- for tbl in model_tables:
- context.log_artifact(tbl, local_path=f"{plots_dest}/{plot.key}.csv")
-
- context.log_results(eval_metrics)
- except:
- context.log_dataset(
- "cox-test-summary", df=model_obj.summary, index=True, format="csv"
- )
- context.logger.info("cox tester not implemented")
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/1.0.0/src/coxph_test.ipynb b/functions/development/coxph_test/1.0.0/src/coxph_test.ipynb
deleted file mode 100644
index 0ee0b29c..00000000
--- a/functions/development/coxph_test/1.0.0/src/coxph_test.ipynb
+++ /dev/null
@@ -1,969 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# **CoxPH test**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features, and evaluates the peformance of the estimated model. \n",
- "Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles. \n",
- "This function is part of the [customer-churn-prediction](https://github.com/mlrun/demos/tree/master/customer-churn-prediction) demo. \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` function from the function marketplace repository"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Running the function locally](#Running-the-function-locally)\n",
- "4. [Running the function remotely](#Running-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "test_set = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_test/test_set.csv\"\n",
- "models_path = \"https://s3.wasabisys.com/iguazio/models/function-marketplace-models/coxph_test/cx-model.pkl\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-17 13:38:44,758 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "fn = mlrun.import_function(\"hub://coxph_test\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "fn.spec.build.image=\"mlrun/ml-models\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Running the function locally**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-17 13:38:45,149 [info] starting run tasks_coxph_test uid=be4bd195e5c146a69ecdee3b6a631569 DB=http://mlrun-api:8080\n",
- "> 2021-10-17 13:38:49,428 [info] cox tester not implemented\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "
This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features, and evaluates the peformance of the estimated model.
-Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer function from the function marketplace repository
fn.deploy(with_mlrun=False,# mlrun is included in our image (mlrun/ml-models) therefore no mlrun installation is needed.
- skip_deployed=True)# because no new packages or upgrade is required, we can use the original image and not build another one.
-
-coxph_run=fn.run(name='tasks_coxph_test',
- params={"label_column":"labels",
- "plots_dest":"plots/xgb_test"},
- inputs={"test_set":test_set,
- "models_path":models_path},
- local=False)
-
-
-
-
-
> 2021-10-17 13:38:49,644 [info] starting run tasks_coxph_test uid=c28d05f0261b4c60956eee528bf68e96 DB=http://mlrun-api:8080
-> 2021-10-17 13:38:49,776 [info] Job is running in the background, pod: tasks-coxph-test-hfj9b
-> 2021-10-17 13:38:59,015 [info] cox tester not implemented
-> 2021-10-17 13:38:59,049 [info] run executed, status=completed
-final state: completed
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/1.0.0/static/source.html b/functions/development/coxph_test/1.0.0/static/source.html
deleted file mode 100644
index 6af29faf..00000000
--- a/functions/development/coxph_test/1.0.0/static/source.html
+++ /dev/null
@@ -1,83 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-# Generated by nuclio.export.NuclioExporter
-
-import warnings
-
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-import os
-import pandas as pd
-from mlrun.datastore import DataItem
-from mlrun.artifacts import get_model
-from cloudpickle import load
-from mlrun.mlutils.models import eval_class_model
-
-
-def cox_test(
- context,
- models_path: DataItem,
- test_set: DataItem,
- label_column: str,
- plots_dest: str = "plots",
- model_evaluator=None,
-) -> None:
- """Test one or more classifier models against held-out dataset
-
- Using held-out test features, evaluates the peformance of the estimated model
-
- Can be part of a kubeflow pipeline as a test step that is run post EDA and
- training/validation cycles
-
- :param context: the function context
- :param model_file: model artifact to be tested
- :param test_set: test features and labels
- :param label_column: column name for ground truth labels
- :param score_method: for multiclass classification
- :param plots_dest: dir for test plots
- :param model_evaluator: WIP: specific method to generate eval, passed in as string
- or available in this folder
- """
- xtest = test_set.as_df()
- ytest = xtest.pop(label_column)
-
- model_file, model_obj, _ = get_model(models_path.url, suffix=".pkl")
- model_obj = load(open(str(model_file), "rb"))
-
- try:
- if not model_evaluator:
- eval_metrics = eval_class_model(context, xtest, ytest, model_obj)
-
- model_plots = eval_metrics.pop("plots")
- model_tables = eval_metrics.pop("tables")
- for plot in model_plots:
- context.log_artifact(plot, local_path=f"{plots_dest}/{plot.key}.html")
- for tbl in model_tables:
- context.log_artifact(tbl, local_path=f"{plots_dest}/{plot.key}.csv")
-
- context.log_results(eval_metrics)
- except:
- context.log_dataset(
- "cox-test-summary", df=model_obj.summary, index=True, format="csv"
- )
- context.logger.info("cox tester not implemented")
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/1.1.0/src/coxph_test.ipynb b/functions/development/coxph_test/1.1.0/src/coxph_test.ipynb
deleted file mode 100644
index 0ee0b29c..00000000
--- a/functions/development/coxph_test/1.1.0/src/coxph_test.ipynb
+++ /dev/null
@@ -1,969 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# **CoxPH test**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features, and evaluates the peformance of the estimated model. \n",
- "Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles. \n",
- "This function is part of the [customer-churn-prediction](https://github.com/mlrun/demos/tree/master/customer-churn-prediction) demo. \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` function from the function marketplace repository"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Running the function locally](#Running-the-function-locally)\n",
- "4. [Running the function remotely](#Running-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "test_set = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_test/test_set.csv\"\n",
- "models_path = \"https://s3.wasabisys.com/iguazio/models/function-marketplace-models/coxph_test/cx-model.pkl\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-17 13:38:44,758 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "fn = mlrun.import_function(\"hub://coxph_test\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "fn.spec.build.image=\"mlrun/ml-models\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Running the function locally**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-17 13:38:45,149 [info] starting run tasks_coxph_test uid=be4bd195e5c146a69ecdee3b6a631569 DB=http://mlrun-api:8080\n",
- "> 2021-10-17 13:38:49,428 [info] cox tester not implemented\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-importwarnings
-
-warnings.simplefilter(action="ignore",category=FutureWarning)
-
-importos
-importpandasaspd
-frommlrun.datastoreimportDataItem
-frommlrun.artifactsimportget_model
-fromcloudpickleimportload
-frommlrun.mlutils.modelsimporteval_class_model
-
-
-
[docs]defcox_test(
- context,
- models_path:DataItem,
- test_set:DataItem,
- label_column:str,
- plots_dest:str="plots",
- model_evaluator=None,
-)->None:
-"""Test one or more classifier models against held-out dataset
-
- Using held-out test features, evaluates the peformance of the estimated model
-
- Can be part of a kubeflow pipeline as a test step that is run post EDA and
- training/validation cycles
-
- :param context: the function context
- :param model_file: model artifact to be tested
- :param test_set: test features and labels
- :param label_column: column name for ground truth labels
- :param score_method: for multiclass classification
- :param plots_dest: dir for test plots
- :param model_evaluator: WIP: specific method to generate eval, passed in as string
- or available in this folder
- """
- xtest=test_set.as_df()
- ytest=xtest.pop(label_column)
-
- model_file,model_obj,_=get_model(models_path.url,suffix=".pkl")
- model_obj=load(open(str(model_file),"rb"))
-
- try:
- ifnotmodel_evaluator:
- eval_metrics=eval_class_model(context,xtest,ytest,model_obj)
-
- model_plots=eval_metrics.pop("plots")
- model_tables=eval_metrics.pop("tables")
- forplotinmodel_plots:
- context.log_artifact(plot,local_path=f"{plots_dest}/{plot.key}.html")
- fortblinmodel_tables:
- context.log_artifact(tbl,local_path=f"{plots_dest}/{plot.key}.csv")
-
- context.log_results(eval_metrics)
- except:
- context.log_dataset(
- "cox-test-summary",df=model_obj.summary,index=True,format="csv"
- )
- context.logger.info("cox tester not implemented")
This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features, and evaluates the peformance of the estimated model.
-Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer function from the function marketplace repository
fn.deploy(with_mlrun=False,# mlrun is included in our image (mlrun/ml-models) therefore no mlrun installation is needed.
- skip_deployed=True)# because no new packages or upgrade is required, we can use the original image and not build another one.
-
-coxph_run=fn.run(name='tasks_coxph_test',
- params={"label_column":"labels",
- "plots_dest":"plots/xgb_test"},
- inputs={"test_set":test_set,
- "models_path":models_path},
- local=False)
-
-
-
-
-
> 2021-10-17 13:38:49,644 [info] starting run tasks_coxph_test uid=c28d05f0261b4c60956eee528bf68e96 DB=http://mlrun-api:8080
-> 2021-10-17 13:38:49,776 [info] Job is running in the background, pod: tasks-coxph-test-hfj9b
-> 2021-10-17 13:38:59,015 [info] cox tester not implemented
-> 2021-10-17 13:38:59,049 [info] run executed, status=completed
-final state: completed
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/1.1.0/static/source.html b/functions/development/coxph_test/1.1.0/static/source.html
deleted file mode 100644
index 7fc415f5..00000000
--- a/functions/development/coxph_test/1.1.0/static/source.html
+++ /dev/null
@@ -1,97 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-import warnings
-
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-import os
-import pandas as pd
-from mlrun.datastore import DataItem
-from mlrun.artifacts import get_model
-from cloudpickle import load
-from mlrun.mlutils.models import eval_class_model
-
-
-def cox_test(
- context,
- models_path: DataItem,
- test_set: DataItem,
- label_column: str,
- plots_dest: str = "plots",
- model_evaluator=None,
-) -> None:
- """Test one or more classifier models against held-out dataset
-
- Using held-out test features, evaluates the peformance of the estimated model
-
- Can be part of a kubeflow pipeline as a test step that is run post EDA and
- training/validation cycles
-
- :param context: the function context
- :param model_file: model artifact to be tested
- :param test_set: test features and labels
- :param label_column: column name for ground truth labels
- :param score_method: for multiclass classification
- :param plots_dest: dir for test plots
- :param model_evaluator: WIP: specific method to generate eval, passed in as string
- or available in this folder
- """
- xtest = test_set.as_df()
- ytest = xtest.pop(label_column)
-
- model_file, model_obj, _ = get_model(models_path.url, suffix=".pkl")
- model_obj = load(open(str(model_file), "rb"))
-
- try:
- if not model_evaluator:
- eval_metrics = eval_class_model(context, xtest, ytest, model_obj)
-
- model_plots = eval_metrics.pop("plots")
- model_tables = eval_metrics.pop("tables")
- for plot in model_plots:
- context.log_artifact(plot, local_path=f"{plots_dest}/{plot.key}.html")
- for tbl in model_tables:
- context.log_artifact(tbl, local_path=f"{plots_dest}/{plot.key}.csv")
-
- context.log_results(eval_metrics)
- except:
- context.log_dataset(
- "cox-test-summary", df=model_obj.summary, index=True, format="csv"
- )
- context.logger.info("cox tester not implemented")
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/latest/src/coxph_test.ipynb b/functions/development/coxph_test/latest/src/coxph_test.ipynb
deleted file mode 100644
index 0ee0b29c..00000000
--- a/functions/development/coxph_test/latest/src/coxph_test.ipynb
+++ /dev/null
@@ -1,969 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# **CoxPH test**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features, and evaluates the peformance of the estimated model. \n",
- "Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles. \n",
- "This function is part of the [customer-churn-prediction](https://github.com/mlrun/demos/tree/master/customer-churn-prediction) demo. \n",
- "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` function from the function marketplace repository"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Steps**\n",
- "1. [Setup function parameters](#Setup-function-parameters)\n",
- "2. [Importing the function](#Importing-the-function)\n",
- "3. [Running the function locally](#Running-the-function-locally)\n",
- "4. [Running the function remotely](#Running-the-function-remotely)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Setup function parameters**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "test_set = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_test/test_set.csv\"\n",
- "models_path = \"https://s3.wasabisys.com/iguazio/models/function-marketplace-models/coxph_test/cx-model.pkl\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Importing the function**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-17 13:38:44,758 [info] loaded project function-marketplace from MLRun DB\n"
- ]
- }
- ],
- "source": [
- "import mlrun\n",
- "mlrun.set_environment(project='function-marketplace')\n",
- "\n",
- "fn = mlrun.import_function(\"hub://coxph_test\")\n",
- "fn.apply(mlrun.auto_mount())\n",
- "\n",
- "fn.spec.build.image=\"mlrun/ml-models\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### **Running the function locally**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2021-10-17 13:38:45,149 [info] starting run tasks_coxph_test uid=be4bd195e5c146a69ecdee3b6a631569 DB=http://mlrun-api:8080\n",
- "> 2021-10-17 13:38:49,428 [info] cox tester not implemented\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-importwarnings
-
-warnings.simplefilter(action="ignore",category=FutureWarning)
-
-importos
-importpandasaspd
-frommlrun.datastoreimportDataItem
-frommlrun.artifactsimportget_model
-fromcloudpickleimportload
-frommlrun.mlutils.modelsimporteval_class_model
-
-
-
[docs]defcox_test(
- context,
- models_path:DataItem,
- test_set:DataItem,
- label_column:str,
- plots_dest:str="plots",
- model_evaluator=None,
-)->None:
-"""Test one or more classifier models against held-out dataset
-
- Using held-out test features, evaluates the peformance of the estimated model
-
- Can be part of a kubeflow pipeline as a test step that is run post EDA and
- training/validation cycles
-
- :param context: the function context
- :param model_file: model artifact to be tested
- :param test_set: test features and labels
- :param label_column: column name for ground truth labels
- :param score_method: for multiclass classification
- :param plots_dest: dir for test plots
- :param model_evaluator: WIP: specific method to generate eval, passed in as string
- or available in this folder
- """
- xtest=test_set.as_df()
- ytest=xtest.pop(label_column)
-
- model_file,model_obj,_=get_model(models_path.url,suffix=".pkl")
- model_obj=load(open(str(model_file),"rb"))
-
- try:
- ifnotmodel_evaluator:
- eval_metrics=eval_class_model(context,xtest,ytest,model_obj)
-
- model_plots=eval_metrics.pop("plots")
- model_tables=eval_metrics.pop("tables")
- forplotinmodel_plots:
- context.log_artifact(plot,local_path=f"{plots_dest}/{plot.key}.html")
- fortblinmodel_tables:
- context.log_artifact(tbl,local_path=f"{plots_dest}/{plot.key}.csv")
-
- context.log_results(eval_metrics)
- except:
- context.log_dataset(
- "cox-test-summary",df=model_obj.summary,index=True,format="csv"
- )
- context.logger.info("cox tester not implemented")
This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features, and evaluates the peformance of the estimated model.
-Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles.
-This function is part of the customer-churn-prediction demo.
-To see how the model is trained or how the data-set is generated, check out coxph_trainer function from the function marketplace repository
fn.deploy(with_mlrun=False,# mlrun is included in our image (mlrun/ml-models) therefore no mlrun installation is needed.
- skip_deployed=True)# because no new packages or upgrade is required, we can use the original image and not build another one.
-
-coxph_run=fn.run(name='tasks_coxph_test',
- params={"label_column":"labels",
- "plots_dest":"plots/xgb_test"},
- inputs={"test_set":test_set,
- "models_path":models_path},
- local=False)
-
-
-
-
-
> 2021-10-17 13:38:49,644 [info] starting run tasks_coxph_test uid=c28d05f0261b4c60956eee528bf68e96 DB=http://mlrun-api:8080
-> 2021-10-17 13:38:49,776 [info] Job is running in the background, pod: tasks-coxph-test-hfj9b
-> 2021-10-17 13:38:59,015 [info] cox tester not implemented
-> 2021-10-17 13:38:59,049 [info] run executed, status=completed
-final state: completed
-
-
-
\ No newline at end of file
diff --git a/functions/development/coxph_test/latest/static/source.html b/functions/development/coxph_test/latest/static/source.html
deleted file mode 100644
index 7fc415f5..00000000
--- a/functions/development/coxph_test/latest/static/source.html
+++ /dev/null
@@ -1,97 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- Source
-
-
-
-
-
-
-# Copyright 2019 Iguazio
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Generated by nuclio.export.NuclioExporter
-
-import warnings
-
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-import os
-import pandas as pd
-from mlrun.datastore import DataItem
-from mlrun.artifacts import get_model
-from cloudpickle import load
-from mlrun.mlutils.models import eval_class_model
-
-
-def cox_test(
- context,
- models_path: DataItem,
- test_set: DataItem,
- label_column: str,
- plots_dest: str = "plots",
- model_evaluator=None,
-) -> None:
- """Test one or more classifier models against held-out dataset
-
- Using held-out test features, evaluates the peformance of the estimated model
-
- Can be part of a kubeflow pipeline as a test step that is run post EDA and
- training/validation cycles
-
- :param context: the function context
- :param model_file: model artifact to be tested
- :param test_set: test features and labels
- :param label_column: column name for ground truth labels
- :param score_method: for multiclass classification
- :param plots_dest: dir for test plots
- :param model_evaluator: WIP: specific method to generate eval, passed in as string
- or available in this folder
- """
- xtest = test_set.as_df()
- ytest = xtest.pop(label_column)
-
- model_file, model_obj, _ = get_model(models_path.url, suffix=".pkl")
- model_obj = load(open(str(model_file), "rb"))
-
- try:
- if not model_evaluator:
- eval_metrics = eval_class_model(context, xtest, ytest, model_obj)
-
- model_plots = eval_metrics.pop("plots")
- model_tables = eval_metrics.pop("tables")
- for plot in model_plots:
- context.log_artifact(plot, local_path=f"{plots_dest}/{plot.key}.html")
- for tbl in model_tables:
- context.log_artifact(tbl, local_path=f"{plots_dest}/{plot.key}.csv")
-
- context.log_results(eval_metrics)
- except:
- context.log_dataset(
- "cox-test-summary", df=model_obj.summary, index=True, format="csv"
- )
- context.logger.info("cox tester not implemented")
-
-
-
-
-
\ No newline at end of file
diff --git a/functions/development/tags.json b/functions/development/tags.json
index e2157e25..968e5617 100644
--- a/functions/development/tags.json
+++ b/functions/development/tags.json
@@ -1 +1 @@
-{"kind": ["serving", "nuclio:serving", "job"], "categories": ["deep-learning", "model-testing", "data-generation", "model-training", "machine-learning", "huggingface", "utils", "data-analysis", "data-preparation", "data-validation", "NLP", "audio", "genai", "pytorch", "etl", "model-serving", "monitoring"]}
\ No newline at end of file
+{"categories": ["model-testing", "utils", "genai", "machine-learning", "model-training", "data-analysis", "pytorch", "data-generation", "data-preparation", "huggingface", "audio", "model-serving", "deep-learning", "etl", "monitoring", "NLP", "data-validation"], "kind": ["nuclio:serving", "job", "serving"]}
\ No newline at end of file