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classifier.ipynb.amltmp
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classifier.ipynb.amltmp
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{
"cells": [
{
"cell_type": "markdown",
"source": [],
"metadata": {
"nteract": {
"transient": {
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{
"cell_type": "markdown",
"source": [
"## Step1 : Load the workspace configurations"
],
"metadata": {
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}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
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}
}
},
{
"cell_type": "code",
"source": [
"from azureml.core import Workspace,Dataset,Experiment\n",
"import joblib \n",
"subscription_id = 'cbd01ab5-5792-49a4-a518-b094e967e9a6'\n",
"resource_group = 'aditya.kuchulakanti-rg'\n",
"workspace_name = 'ml-ops-workspace'\n",
"\n",
"workspace = Workspace(subscription_id, resource_group, workspace_name)\n"
],
"outputs": [],
"execution_count": 66,
"metadata": {
"jupyter": {
"source_hidden": false,
"outputs_hidden": false
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{
"cell_type": "markdown",
"source": [
"## Step2: Start with the Experiment"
],
"metadata": {
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"transient": {
"deleting": false
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},
{
"cell_type": "code",
"source": [
"experiment = Experiment(workspace=workspace, name= \"Anyname\")\n",
"dataset= Dataset.get_by_name(workspace=workspace, name=\"Iris-data\")\n",
"\n",
"df=dataset.to_pandas_dataframe()\n",
"df.head()"
],
"outputs": [
{
"output_type": "execute_result",
"execution_count": 67,
"data": {
"text/plain": " Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n0 1 5.1 3.5 1.4 0.2 Iris-setosa\n1 2 4.9 3.0 1.4 0.2 Iris-setosa\n2 3 4.7 3.2 1.3 0.2 Iris-setosa\n3 4 4.6 3.1 1.5 0.2 Iris-setosa\n4 5 5.0 3.6 1.4 0.2 Iris-setosa",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Id</th>\n <th>SepalLengthCm</th>\n <th>SepalWidthCm</th>\n <th>PetalLengthCm</th>\n <th>PetalWidthCm</th>\n <th>Species</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>5.1</td>\n <td>3.5</td>\n <td>1.4</td>\n <td>0.2</td>\n <td>Iris-setosa</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>4.9</td>\n <td>3.0</td>\n <td>1.4</td>\n <td>0.2</td>\n <td>Iris-setosa</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>4.7</td>\n <td>3.2</td>\n <td>1.3</td>\n <td>0.2</td>\n <td>Iris-setosa</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>4.6</td>\n <td>3.1</td>\n <td>1.5</td>\n <td>0.2</td>\n <td>Iris-setosa</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5</td>\n <td>5.0</td>\n <td>3.6</td>\n <td>1.4</td>\n <td>0.2</td>\n <td>Iris-setosa</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
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}
],
"execution_count": 67,
"metadata": {
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},
{
"cell_type": "code",
"source": [
"X=df[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']]\n",
"Y=df['Species']\n",
"\n",
"run = experiment.start_logging()"
],
"outputs": [],
"execution_count": 68,
"metadata": {
"jupyter": {
"source_hidden": false,
"outputs_hidden": false
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"deleting": false
}
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"gather": {
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},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X,Y, test_size=0.09 , shuffle=True)\n",
"\n"
],
"outputs": [],
"execution_count": 69,
"metadata": {
"jupyter": {
"source_hidden": false,
"outputs_hidden": false
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"nteract": {
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"gather": {
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},
{
"cell_type": "code",
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"#step1-initialize the model class\n",
"#clf = DecisionTreeClassifier()\n",
"clf = DecisionTreeClassifier(criterion=\"gini\",max_depth=4)\n",
"\n",
"#step2:train the model \n",
"clf.fit(X_train, y_train)\n"
],
"outputs": [
{
"output_type": "execute_result",
"execution_count": 70,
"data": {
"text/plain": "DecisionTreeClassifier(max_depth=4)",
"text/html": "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=4)</pre></div></div></div></div></div>"
},
"metadata": {}
}
],
"execution_count": 70,
"metadata": {
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"source_hidden": false,
"outputs_hidden": false
},
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"deleting": false
}
},
"gather": {
"logged": 1694834946949
}
}
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import accuracy_score\n",
"#step3 = evaluate the model \n",
"y_pred = clf.predict(X_test)\n",
"acc = accuracy_score(y_test, y_pred)\n",
"run.log(\"accuracy\", acc*100)\n",
"#print(acc*100)\n",
"\n",
"model_name =\"iris_decission_tree.pkl\"\n",
"filepath=\"outputs/\"+model_name\n",
"\n",
"joblib.dump(value=clf,filename=filepath)\n",
"run.upload_file(name=model_name,path_or_stream=filepath)\n",
"run.complete()"
],
"outputs": [],
"execution_count": 71,
"metadata": {
"jupyter": {
"source_hidden": false,
"outputs_hidden": false
},
"nteract": {
"transient": {
"deleting": false
}
},
"gather": {
"logged": 1694834949511
}
}
},
{
"cell_type": "markdown",
"source": [
"## Registering Process "
],
"metadata": {
"nteract": {
"transient": {
"deleting": false
}
}
}
},
{
"cell_type": "code",
"source": [
"from azureml.core import Model\n",
"from azureml.core.resource_configuration import ResourceConfiguration\n",
"import sklearn\n",
"\n",
"model = Model.register(\n",
"workspace=workspace,\n",
"model_name=\"iris\",\n",
"model_path=filepath,\n",
"model_framework=Model.Framework.SCIKITLEARN,\n",
"model_framework_version= sklearn.__version__,\n",
"resource_configuration= ResourceConfiguration(cpu=1, memory_in_gb=0.5)\n",
")\n",
"\n",
"print(\"Name:\", model_name)\n",
"print(\"version:\", model.version)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Registering model iris\nName: iris_decission_tree.pkl\nversion: 1\n"
}
],
"execution_count": 75,
"metadata": {
"jupyter": {
"source_hidden": false,
"outputs_hidden": false
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"nteract": {
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"gather": {
"logged": 1694835155406
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{
"cell_type": "code",
"source": [],
"outputs": [],
"execution_count": null,
"metadata": {
"jupyter": {
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"cell_type": "code",
"source": [],
"outputs": [],
"execution_count": null,
"metadata": {
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],
"metadata": {
"kernelspec": {
"name": "python310-sdkv2",
"language": "python",
"display_name": "Python 3.10 - SDK v2"
},
"language_info": {
"name": "python",
"version": "3.10.11",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
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"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"microsoft": {
"ms_spell_check": {
"ms_spell_check_language": "en"
},
"host": {
"AzureML": {
"notebookHasBeenCompleted": true
}
}
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"kernel_info": {
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"nteract": {
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