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null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f316b723d85d422690cd691f1025b4d1": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# Objective\n", "\n", "To illustrate monitoring checks using prediction logs." ], "metadata": { "id": "mdTqr5PGVc6h" } }, { "cell_type": "markdown", "source": [ "# Setup" ], "metadata": { "id": "uYQV07KaH5cb" } }, { "cell_type": "code", "source": [ "!pip install -q datasets==2.18.0" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "y0oyC419HdJD", "outputId": "7ca5ff34-a2cc-403f-c09d-f47e14292c18" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "pwq_WX0EHUTS" }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from datasets import load_dataset\n", "from sklearn.datasets import fetch_openml\n", "from sklearn.model_selection import train_test_split\n", "\n", "from scipy.stats import ttest_ind" ] }, { "cell_type": "code", "source": [ "sns.set_style('white')\n", "sns.set_context('notebook')" ], "metadata": { "id": "zxNj9aoHUaZE" }, "execution_count": 46, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Access Logs" ], "metadata": { "id": "CTUqiIIYH8LL" } }, { "cell_type": "code", "source": [ "dataset = load_dataset(\"pgurazada1/machine-failure-logs\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 301, "referenced_widgets": [ "a601940ed1984a399b94f7934240d164", "459fb433a6ce48818d3885e37d3e5f85", "7be7cb0417a44787b9b3c7ec6091f204", "49c0b466180d474eb3d7744d137b235e", "fc67ff0f18fc47fdba6f4ce38111bf4f", "daecd6d45abf493098206fa8368e0d22", "d521b7fe675b4ca5837481ad311e4432", "c8bd3b54ebc84d53886680c2efb99b35", "1b8d360e855c47328ecd98b857ea4279", "cc57e61efb094198be8e972b5c8c8db4", "9a5ca661d37340698b1fd15bc3086068", "601a6ce0249b4b8283247be4e589c611", "d1c0848ac1cd4143a35d4c998d947b3a", "d02069e29498464eb9d69cb31cb8c12d", "21375cad5a9f4d54bef9bc22ae6c3910", "4e57e4b6203048a68a29d58ca90e6954", "7ae7a9d923364ae998a11b1fbd53e86c", "2cc7d9643a4f4bb88ddbfd8ff03421f6", "66bc01717fe648a39e7a80390e0d1bd1", "28439aa1f693417d9c7f81984af10876", "7494b924a956401f809abd285fa63725", "787cc0568eb9417db9cde019138c24d1", "36565ecdf38e45f4b9f002289c665bb5", "159961c22d9641e087c24dd24edf200c", "1a9e6ec4f5f146acae1d0f7c32be94d3", 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"1850ada5ef4c4a448247744b9d43a8fd", "fa7d909b6b9f4ffe98ca45c15d1f80bc", "f316b723d85d422690cd691f1025b4d1" ] }, "id": "pLiju_40HaA7", "outputId": "b6a63809-4c5b-4d90-a48c-b00857737ec9" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading data: 0%| | 0.00/27.6k [00:00\n", "
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Air temperature [K]Process temperature [K]Rotational speed [rpm]Torque [Nm]Tool wear [min]Typeprediction
209301.7310.2168628.959L0
172303.4311.5156036.4114M0
47296.9307.8140057.1202L0
103300.6309.4158135.7117M0
249300.0309.0145451.72L0
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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"logs_df\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"Air temperature [K]\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.401457890532333,\n \"min\": 296.9,\n \"max\": 303.4,\n \"num_unique_values\": 5,\n \"samples\": [\n 303.4,\n 300.0,\n 296.9\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Process temperature [K]\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.3791301606447408,\n \"min\": 307.8,\n \"max\": 311.5,\n \"num_unique_values\": 5,\n \"samples\": [\n 311.5,\n 309.0,\n 307.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rotational speed [rpm]\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 112,\n \"min\": 1400,\n \"max\": 1686,\n \"num_unique_values\": 5,\n \"samples\": [\n 1560,\n 1454,\n 1400\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Torque [Nm]\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11.882255678110955,\n \"min\": 28.9,\n \"max\": 57.1,\n \"num_unique_values\": 5,\n \"samples\": [\n 36.4,\n 51.7,\n 57.1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Tool wear [min]\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 74,\n \"min\": 2,\n \"max\": 202,\n \"num_unique_values\": 5,\n \"samples\": [\n 114,\n 2,\n 202\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"M\",\n \"L\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"prediction\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "markdown", "source": [ "# Access Training Data" ], "metadata": { "id": "m826gUE9IGvZ" } }, { "cell_type": "code", "source": [ "dataset = fetch_openml(data_id=42890, as_frame=True, parser=\"auto\")\n", "data_df = dataset.data\n", "\n", "target = 'Machine failure'\n", "numeric_features = [\n", " 'Air temperature [K]',\n", " 'Process temperature [K]',\n", " 'Rotational speed [rpm]',\n", " 'Torque [Nm]',\n", " 'Tool wear [min]'\n", "]\n", "categorical_features = ['Type']\n", "\n", "X = data_df[numeric_features + categorical_features]\n", "y = data_df[target]\n", "\n", "Xtrain, Xtest, ytrain, ytest = train_test_split(\n", " X, y,\n", " test_size=0.2,\n", " random_state=42\n", ")" ], "metadata": { "id": "iNVPKMJeIH-A" }, "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Drift" ], "metadata": { "id": "Kcfwr0sJN6Gt" } }, { "cell_type": "markdown", "source": [ "The idea of drift is to check if there is a variation in live (production) data compared to the data that the model saw during training. Drift checks are conducted both on the features (data drift) and the targets (target drift)." ], "metadata": { "id": "YUhJJVlBN9h0" } }, { "cell_type": "markdown", "source": [ "## Model Drift" ], "metadata": { "id": "V72tjtIbH9s7" } }, { "cell_type": "code", "source": [ "ytrain.value_counts()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_3emd1OJHwAI", "outputId": "dfa42e41-c43b-442c-ff25-b8748df7f4b7" }, "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Machine failure\n", "0 7722\n", "1 278\n", "Name: count, dtype: int64" ] }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "code", "source": [ "logs_df.prediction.value_counts()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AMp7kFt9IlXI", "outputId": "16f15e98-64e0-4548-e27e-e8a4d94b3510" }, "execution_count": 9, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "prediction\n", "0 247\n", "1 18\n", "Name: count, dtype: int64" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "code", "source": [ "fig, axs = plt.subplots(2, figsize=(14, 12))\n", "\n", "sns.countplot(\n", " pd.DataFrame(ytrain),\n", " x='Machine failure',\n", " stat='proportion',\n", " ax=axs[0]\n", ").set_title('Distribution in training data')\n", "\n", "sns.countplot(\n", " logs_df,\n", " x='prediction',\n", " stat='proportion',\n", " ax=axs[1]\n", ").set_title('Distribution in production data')\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "rfGkLnUGRQ9f", "outputId": "f34aa229-2d4b-483d-fb5c-d04c2e8f9d52" }, "execution_count": 51, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "p_reference = 278/ytrain.shape[0]\n", "p_predictions = 18/logs_df.shape[0]" ], "metadata": { "id": "rQi4nNnHL-M-" }, "execution_count": 10, "outputs": [] }, { "cell_type": "code", "source": [ "p_reference, p_predictions" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GTrSucsHMKBk", "outputId": "340df9f3-6df9-42c4-fd5b-06f8a2a2fc48" }, "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(0.03475, 0.06792452830188679)" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "markdown", "source": [ "## Data Drift" ], "metadata": { "id": "iYYIygl1NQDo" } }, { "cell_type": "markdown", "source": [ "We illustrate data drift checks using one feature - `Torque`." ], "metadata": { "id": "yTk6zTzvN1BQ" } }, { "cell_type": "code", "source": [ "fig, axs = plt.subplots(2, figsize=(14, 12))\n", "\n", "sns.histplot(\n", " Xtrain,\n", " x='Torque [Nm]',\n", " kde=True,\n", " ax=axs[0]\n", ").set_title('Distribution in training data')\n", "\n", "sns.histplot(\n", " logs_df,\n", " x='Torque [Nm]',\n", " kde=True,\n", " ax=axs[1]\n", ").set_title('Distribution in production data')\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "HpjSlGMfTo1Z", "outputId": "70a11771-3167-4c15-f946-8557423b1d93" }, "execution_count": 53, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "ttest_ind(\n", " Xtrain['Torque [Nm]'],\n", " logs_df['Torque [Nm]']\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Y1OHx34UMMg9", "outputId": "2aacc4c7-6005-4c51-f533-aad61e135cbb" }, "execution_count": 35, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "TtestResult(statistic=-3.330300844334276, pvalue=0.0008713229935694878, df=8263.0)" ] }, "metadata": {}, "execution_count": 35 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "0tFLSKKkT2mw" }, "execution_count": null, "outputs": [] } ] }