diff --git a/Tutorials/Anomaly Detection Tutorial Level Beginner - ANO101.ipynb b/Tutorials/Anomaly Detection Tutorial Level Beginner - ANO101.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1844086adec491f7244404b1fc2802e786e6b51a --- /dev/null +++ b/Tutorials/Anomaly Detection Tutorial Level Beginner - ANO101.ipynb @@ -0,0 +1,17064 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Anomaly Detection Tutorial (ANO101) - Level Beginner" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Date Updated: Feb 25, 2020**\n", + "\n", + "# 1.0 Objective of Tutorial\n", + "Welcome to Anomaly Detection Tutorial (#ANO101). This tutorial assumes that you are new to PyCaret and looking to get started with Anomaly Detection using `pycaret.anomaly` Module.\n", + "\n", + "In this tutorial we will learn:\n", + "\n", + "\n", + "* **Getting Data:** How to import data from PyCaret repository?\n", + "* **Setting up Environment:** How to setup experiment in PyCaret to get started with building anomaly models?\n", + "* **Create Model:** How to create a model and assigns anomaly labels to original dataset for analysis?\n", + "* **Plot Model:** How to analyze model performance using various plots?\n", + "* **Predict Model:** How to assign anomaly labels to new and unseen dataset based on trained model?\n", + "* **Save / Load Model:** How to save / load model for future use?\n", + "\n", + "Read Time : Approx. 25 Minutes\n", + "\n", + "\n", + "## 1.1 Installing PyCaret\n", + "First step to get started with PyCaret is to install pycaret. Installing pycaret is easy and take few minutes only. Follow the instructions below:\n", + "\n", + "#### Installing PyCaret in Local Jupyter Notebook\n", + "`pip install pycaret`
\n", + "\n", + "#### Installing PyCaret on Google Colab or Azure Notebooks\n", + "`!pip install pycaret`\n", + "\n", + "\n", + "## 1.2 Pre-Requisites\n", + "- Python 3.x\n", + "- Latest version of pycaret\n", + "- Internet connection to load data from pycaret's repository\n", + "- Basic Knowledge of Anomaly Detection\n", + "\n", + "## 1.3 For Google colab users:\n", + "If you are running this notebook on Google colab, below code of cells must be run at top of the notebook to display interactive visuals.
\n", + "
\n", + "`from pycaret.utils import enable_colab`
\n", + "`enable_colab()`\n", + "\n", + "## 1.4 See also:\n", + "- __[Anomaly Detectiom Tutorial (ANO102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/Tutorials/Anomaly%20Detection%20Tutorial%20(ANO102)%20-%20Level%20Intermediate.ipynb)__\n", + "- __[Anomaly Detection Tutorial (ANO103) - Level Expert](https://github.com/pycaret/pycaret/blob/master/Tutorials/Anomaly%20Detection%20Tutorial%20(ANO103)%20-%20Level%20Expert.ipynb)__\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2.0 What is Anomaly Detection?\n", + "\n", + "Anomaly Detection is the task of identifying of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. There are three broad categories of anomaly detection techniques exist:\n", + "\n", + "- **Unsupervised anomaly detection:** Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set.
\n", + "
\n", + "- **Supervised anomaly detection:** This technique requires a data set that has been labeled as \"normal\" and \"abnormal\" and involves training a classifier.
\n", + "
\n", + "- **Semi-supervised anomaly detection:** This technique construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the learnt model.
\n", + "\n", + "`pycaret.anomaly` module supports the unsupervised and supervised anomaly detection technique. In this tutorial we will only cover unsupervised anomaly detection technique.\n", + "\n", + "__[Learn More about Anomaly Detection](https://en.wikipedia.org/wiki/Anomaly_detection)__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3.0 Overview of Anomaly Detection Module in PyCaret\n", + "PyCaret's anomaly detection module (`pycaret.anomaly`) is a an unsupervised machine learning module which performs the task of identifying rare items, events or observations which raise suspicions by differing significantly from the majority of the data.\n", + "\n", + "PyCaret anomaly detection module provides several pre-processing features that can be configured when initializing the setup through `setup()` function. It has over 12 algorithms and few plots to analyze the results of anomaly detection. PyCaret's anomaly detection module also implements a unique function `tune_model()` that allows you to tune the hyperparameters of anomaly detection model to optimize the supervised learning objective such as `AUC` for classification or `R2` for regression." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4.0 Dataset for the Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this tutorial we will use a dataset from UCI called **Mice Protein Expression**. The data set consists of the expression levels of 77 proteins/protein modifications that produced detectable signals in the nuclear fraction of cortex. The dataset contains a total of 1080 measurements per protein. Each measurement can be considered as an independent sample/mouse. __[Click Here](https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression)__ to read more about the dataset.\n", + "\n", + "\n", + "#### Dataset Acknowledgement:\n", + "Clara Higuera Department of Software Engineering and Artificial Intelligence, Faculty of Informatics and the Department of Biochemistry and Molecular Biology, Faculty of Chemistry, University Complutense, Madrid, Spain.\n", + "Email: clarahiguera@ucm.es\n", + "\n", + "Katheleen J. Gardiner, creator and owner of the protein expression data, is currently with the Linda Crnic Institute for Down Syndrome, Department of Pediatrics, Department of Biochemistry and Molecular Genetics, Human Medical Genetics and Genomics, and Neuroscience Programs, University of Colorado, School of Medicine, Aurora, Colorado, USA.\n", + "Email: katheleen.gardiner@ucdenver.edu\n", + "\n", + "Krzysztof J. Cios is currently with the Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, USA, and IITiS Polish Academy of Sciences, Poland.\n", + "Email: kcios@vcu.edu\n", + "\n", + "The original dataset and data dictionary can be __[found here.](https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression)__ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.0 Getting the Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can download the data from the original source __[found here](https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression)__ and load it using pandas __[(Learn How)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)__ or you can use PyCaret's data respository to load the data using `get_data()` function (This will require internet connection)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MouseIDDYRK1A_NITSN1_NBDNF_NNR1_NNR2A_NpAKT_NpBRAF_NpCAMKII_NpCREB_N...pCFOS_NSYP_NH3AcK18_NEGR1_NH3MeK4_NCaNA_NGenotypeTreatmentBehaviorclass
0309_10.5036440.7471930.4301752.8163295.9901520.2188300.1775652.3737440.232224...0.1083360.4270990.1147830.1317900.1281861.675652ControlMemantineC/Sc-CS-m
1309_20.5146170.6890640.4117702.7895145.6850380.2116360.1728172.2921500.226972...0.1043150.4415810.1119740.1351030.1311191.743610ControlMemantineC/Sc-CS-m
2309_30.5091830.7302470.4183092.6872015.6220590.2090110.1757222.2833370.230247...0.1062190.4357770.1118830.1333620.1274311.926427ControlMemantineC/Sc-CS-m
3309_40.4421070.6170760.3586262.4669474.9795030.2228860.1764632.1523010.207004...0.1112620.3916910.1304050.1474440.1469011.700563ControlMemantineC/Sc-CS-m
4309_50.4349400.6174300.3588022.3657854.7186790.2131060.1736272.1340140.192158...0.1106940.4341540.1184810.1403140.1483801.839730ControlMemantineC/Sc-CS-m
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" + ], + "text/plain": [ + " MouseID DYRK1A_N ITSN1_N BDNF_N NR1_N NR2A_N pAKT_N \\\n", + "0 309_1 0.503644 0.747193 0.430175 2.816329 5.990152 0.218830 \n", + "1 309_2 0.514617 0.689064 0.411770 2.789514 5.685038 0.211636 \n", + "2 309_3 0.509183 0.730247 0.418309 2.687201 5.622059 0.209011 \n", + "3 309_4 0.442107 0.617076 0.358626 2.466947 4.979503 0.222886 \n", + "4 309_5 0.434940 0.617430 0.358802 2.365785 4.718679 0.213106 \n", + "\n", + " pBRAF_N pCAMKII_N pCREB_N ... pCFOS_N SYP_N H3AcK18_N \\\n", + "0 0.177565 2.373744 0.232224 ... 0.108336 0.427099 0.114783 \n", + "1 0.172817 2.292150 0.226972 ... 0.104315 0.441581 0.111974 \n", + "2 0.175722 2.283337 0.230247 ... 0.106219 0.435777 0.111883 \n", + "3 0.176463 2.152301 0.207004 ... 0.111262 0.391691 0.130405 \n", + "4 0.173627 2.134014 0.192158 ... 0.110694 0.434154 0.118481 \n", + "\n", + " EGR1_N H3MeK4_N CaNA_N Genotype Treatment Behavior class \n", + "0 0.131790 0.128186 1.675652 Control Memantine C/S c-CS-m \n", + "1 0.135103 0.131119 1.743610 Control Memantine C/S c-CS-m \n", + "2 0.133362 0.127431 1.926427 Control Memantine C/S c-CS-m \n", + "3 0.147444 0.146901 1.700563 Control Memantine C/S c-CS-m \n", + "4 0.140314 0.148380 1.839730 Control Memantine C/S c-CS-m \n", + "\n", + "[5 rows x 82 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pycaret.datasets import get_data\n", + "dataset = get_data('mice')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1080, 82)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check the shape of data\n", + "dataset.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to demonstrate the `predict_model()` function on unseen data, a sample of 5% (54 samples) are taken out from original dataset to be used for predictions at the end of experiment. This should not be confused with train/test split. This particular split is performed to simulate real life scenario. Another way to think about this is that these 54 samples are not available at the time when this experiment was performed." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data for Modeling: (1026, 82)\n", + "Unseen Data For Predictions: (54, 82)\n" + ] + } + ], + "source": [ + "data = dataset.sample(frac=0.95, random_state=786).reset_index(drop=True)\n", + "data_unseen = dataset.drop(data.index).reset_index(drop=True)\n", + "\n", + "print('Data for Modeling: ' + str(data.shape))\n", + "print('Unseen Data For Predictions: ' + str(data_unseen.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.0 Setting up Environment in PyCaret" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`setup()` function initializes the environment in pycaret and creates the transformation pipeline to prepare the data for modeling and deployment. `setup()` must be called before executing any other function in pycaret. It takes only one mandatory parameter: pandas dataframe. All other parameters are optional and are used to customize pre-processing pipeline (we will see them in later tutorials).\n", + "\n", + "When `setup()` is executed PyCaret's inference algorithm will automatically infer the data types for all features based on certain properties. Although, most of the times the data type is inferred correctly but it's not always the case. Therefore, after `setup()` is executed, PyCaret displays a table containing features and their inferred data types. At which stage, you can inspect and press `enter` to continue if all data types are correctly infered or type `quit` to end the experiment. Identifying data types correctly is of fundamental importance in PyCaret as it automatically performs few pre-processing tasks which are imperative to perform any machine learning experiment. These pre-processing tasks are performed differently for each data type. As such, it is very important that data types are correctly configured.\n", + "\n", + "In later tutorials we will learn how to overwrite PyCaret's infered data type using `numeric_features` and `categorical_features` parameter in `setup()`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from pycaret.anomaly import *" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Setup Succesfully Completed!\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Description Value
0session_id 123
1Original Data (1026, 82)
2Missing Values True
3Numeric Features 77
4Categorical Features 5
5Ordinal Features False
6High Cardinality Features False
7Transformed Data (1026, 91)
8Numeric Imputer mean
9Categorical Imputer constant
10Normalize True
11Normalize Method zscore
12Transformation False
13Transformation Method None
14PCA False
15PCA Method None
16PCA components None
17Ignore Low Variance False
18Combine Rare Levels False
19Rare Level Threshold None
20Numeric Binning False
21Remove Multicollinearity False
22Multicollinearity Threshold None
23Group Features False
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "exp_ano101 = setup(data, normalize = True, \n", + " ignore_features = ['MouseID'],\n", + " session_id = 123)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the setup is succesfully executed it prints the information grid that contains few important information. Much of the information is related to pre-processing pipeline which is constructed when `setup()` is executed. Much of these features are out of scope for the purpose of this tutorial. However, few important things to note at this stage are:\n", + "\n", + "- **session_id :** A pseduo-random number distributed as a seed in all functions for later reproducibility. If no `session_id` is passed, a random number is automatically generated that is distributed to all functions. In this experiment session_id is set as `123` for later reproducibility.
\n", + "
\n", + "- **Missing Values :** When there are missing values in original data it will show as True. Notice that `Missing Values` in the information grid above is `True` as the data contains missing values which are automatically imputed using `mean` for numeric features and `constant` for categorical features. The method of imputation can be changed using `numeric_imputation` and `categorical_imputation` parameter in `setup()`.
\n", + "
\n", + "- **Original Data :** Displays the original shape of dataset. In this experiment (1026, 82) means 1026 samples and 82 features.
\n", + "
\n", + "- **Transformed Data :** Displays the shape of transformed dataset. Notice that shape original dataset (1026, 82) is transformed into (1026, 91). The number of features has increased due to encoding of categorical features in the dataset.
\n", + "
\n", + "- **Numeric Features :** Number of features inferred as numeric. In this dataset, 77 out of 82 features are inferred as numeric.
\n", + "
\n", + "- **Categorical Features :** Number of features inferred as categorical. In this dataset, 5 out of 82 features are inferred as categorical. Also notice, we have ignored one categorical feature i.e. `MouseID` using `ignore_feature` parameter.
\n", + "\n", + "Notice that how few tasks such as missing value imputation and categorical encoding that are imperative to perform modeling are automatically handled. Most of the other parameters in `setup()` are optional and used for customizing pre-processing pipeline. These parameters are out of scope for this tutorial but as you progress to intermediate and expert level, we will cover them in much detail." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7.0 Create a Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating a anomaly detection model in PyCaret is simple and similar to how you would have created a model in supervised modules of pycaret. A anomaly detection model is created using `create_model()` function which takes one mandatory parameter i.e. name of model as a string. This function returns a trained model object. See an example below:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "iforest = create_model('iforest')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IForest(behaviour='new', bootstrap=False, contamination=0.05,\n", + " max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\n", + " random_state=123, verbose=0)\n" + ] + } + ], + "source": [ + "print(iforest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have created Isolation Forest model using `create_model()`. Notice the `contanimation` parameter is set `0.05` which is a default value when you donot pass `fraction` parameter in `create_model()`. `fraction` parameter determines the proportion of outliers in the dataset. In below example, we will create `One Class Support Vector Machine` model with `0.025` fraction." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "svm = create_model('svm', fraction = 0.025)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OCSVM(cache_size=200, coef0=0.0, contamination=0.025, degree=3, gamma='auto',\n", + " kernel='rbf', max_iter=-1, nu=0.5, shrinking=True, tol=0.001,\n", + " verbose=False)\n" + ] + } + ], + "source": [ + "print(svm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just be replacing `iforest` with `svm` inside `create_model()` we have now created `OCSVM` anomaly detection model. There are 12 models available ready-to-use in `pycaret.anomaly` module. To see the complete list, please see docstring. If you would like to read more about different models, their limitations and usecase, you may __[click here](https://pyod.readthedocs.io/en/latest/pyod.html)__ to read more about it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 8.0 Assign a Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have created a model, we would like to assign the anomaly labels to our dataset (1080 samples) to analyze the results. We will achieve this by using `assign_model()` function. See an example below:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " MouseID DYRK1A_N ITSN1_N BDNF_N NR1_N NR2A_N pAKT_N \\\n", + "0 3501_12 0.344930 0.626194 0.383583 2.534561 4.097317 0.303547 \n", + "1 3520_5 0.630001 0.839187 0.357777 2.651229 4.261675 0.253184 \n", + "2 3414_13 0.555122 0.726229 0.278319 2.097249 2.897553 0.222222 \n", + "3 3488_8 0.275849 0.430764 0.285166 2.265254 3.250091 0.189258 \n", + "4 3501_7 0.304788 0.617299 0.335164 2.638236 4.876609 0.280590 \n", + "\n", + " pBRAF_N pCAMKII_N pCREB_N ... H3AcK18_N EGR1_N H3MeK4_N \\\n", + "0 0.222829 4.592769 0.239427 ... 0.252700 0.218868 0.249187 \n", + "1 0.185257 3.816673 0.204940 ... 0.155008 0.153219 NaN \n", + "2 0.174356 1.867880 0.203379 ... 0.136109 0.155530 0.185484 \n", + "3 0.157837 2.917611 0.202594 ... 0.127944 0.207671 0.175357 \n", + "4 0.199417 4.835421 0.236314 ... 0.245277 0.202171 0.240372 \n", + "\n", + " CaNA_N Genotype Treatment Behavior class Label Score \n", + "0 1.139493 Ts65Dn Memantine S/C t-SC-m 0 -0.003593 \n", + "1 1.642886 Control Memantine C/S c-CS-m 0 -0.073378 \n", + "2 1.657670 Ts65Dn Memantine C/S t-CS-m 0 -0.062198 \n", + "3 0.893598 Control Saline S/C c-SC-s 0 -0.078707 \n", + "4 0.795637 Ts65Dn Memantine S/C t-SC-m 0 -0.070426 \n", + "\n", + "[5 rows x 84 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iforest_results = assign_model(iforest)\n", + "iforest_results.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that two columns `Label` and `Score` are added towards the end. 0 stands for inliers and 1 for outliers/anomalies. Score is the values computed by the algorithm. Outliers are assigned with larger anomaly scores. Notice that `iforest_results` also includes `MouseID` feature that we have dropped during `setup()`. It wasn't used for the model and is only appended to the dataset when you use `assign_model()`. In next section we will see how to analyze the results of anomaly detection using `plot_model()`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 9.0 Plot a Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`plot_model()` function can be used to analyze the anomaly detection model over different aspects. This function takes a trained model object and returns a plot. See examples below:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.1 T-distributed Stochastic Neighbor Embedding (t-SNE)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + "3501_12" + ], + [ + "3520_5" + ], + [ + "3414_13" + ], + [ + "3488_8" + ], + [ + "3501_7" + ], + [ + "3419_14" + ], + [ + "3522_14" + ], + [ + "3499_11" + ], + [ + "361_7" + ], + [ + "365_8" + ], + [ + "3491_11" + ], + [ + "3429_12" + ], + [ + "3421_5" + ], + [ + "3490_14" + ], + [ + "3484_11" + ], + [ + "3522_4" + ], + [ + "3503_3" + ], + [ + "3415_6" + ], + [ + "3503_10" + ], + [ + "3522_12" + ], + [ + "3477_15" + ], + [ + "311_11" + ], + [ + "293_10" + ], + [ + "3423_3" + ], + 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" + ], + "text/plain": [ + " MouseID DYRK1A_N ITSN1_N BDNF_N NR1_N NR2A_N pAKT_N \\\n", + "0 3517_7 0.361782 0.565987 0.376145 2.774771 4.450250 0.262906 \n", + "1 3517_8 0.329361 0.503216 0.326145 2.487893 3.925842 0.231177 \n", + "2 3517_9 0.328314 0.500465 0.363265 2.409370 3.796988 0.226250 \n", + "3 3517_10 0.377490 0.528331 0.379560 2.689263 3.978008 0.266753 \n", + "4 3517_11 0.367813 0.478322 0.346371 2.356975 3.561499 0.252121 \n", + "\n", + " pBRAF_N pCAMKII_N pCREB_N ... H3AcK18_N EGR1_N H3MeK4_N CaNA_N \\\n", + "0 0.238968 6.041007 0.229808 ... 0.208611 NaN 0.270820 0.782176 \n", + "1 0.195611 5.337306 0.210556 ... 0.211478 NaN 0.255582 0.882451 \n", + "2 0.187024 5.330917 0.203198 ... 0.201974 NaN 0.241815 0.881029 \n", + "3 0.229495 5.441138 0.227684 ... 0.262620 NaN 0.262620 0.781328 \n", + "4 0.226437 4.868520 0.204288 ... 0.264670 NaN 0.247165 0.783654 \n", + "\n", + " Genotype Treatment Behavior class Label Score \n", + "0 Ts65Dn Saline S/C t-SC-s 1 0.011522 \n", + "1 Ts65Dn Saline S/C t-SC-s 0 -0.056883 \n", + "2 Ts65Dn Saline S/C t-SC-s 0 -0.067360 \n", + "3 Ts65Dn Saline S/C t-SC-s 1 0.048812 \n", + "4 Ts65Dn Saline S/C t-SC-s 0 -0.036365 \n", + "\n", + "[5 rows x 84 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unseen_predictions = predict_model(iforest, data=data_unseen)\n", + "unseen_predictions.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Label` column indicate the outlier (1 = outlier, 0 = inlier). `Score` is the values computed by the algorithm. Outliers are assigned with larger anomaly scores. You can also use `predict_model()` function to label the training data. See example below:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MouseIDDYRK1A_NITSN1_NBDNF_NNR1_NNR2A_NpAKT_NpBRAF_NpCAMKII_NpCREB_N...H3AcK18_NEGR1_NH3MeK4_NCaNA_NGenotypeTreatmentBehaviorclassLabelScore
03501_120.3449300.6261940.3835832.5345614.0973170.3035470.2228294.5927690.239427...0.2527000.2188680.2491871.139493Ts65DnMemantineS/Ct-SC-m0-0.003593
13520_50.6300010.8391870.3577772.6512294.2616750.2531840.1852573.8166730.204940...0.1550080.153219NaN1.642886ControlMemantineC/Sc-CS-m0-0.073378
23414_130.5551220.7262290.2783192.0972492.8975530.2222220.1743561.8678800.203379...0.1361090.1555300.1854841.657670Ts65DnMemantineC/St-CS-m0-0.062198
33488_80.2758490.4307640.2851662.2652543.2500910.1892580.1578372.9176110.202594...0.1279440.2076710.1753570.893598ControlSalineS/Cc-SC-s0-0.078707
43501_70.3047880.6172990.3351642.6382364.8766090.2805900.1994174.8354210.236314...0.2452770.2021710.2403720.795637Ts65DnMemantineS/Ct-SC-m0-0.070426
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5 rows × 84 columns

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" + ], + "text/plain": [ + " MouseID DYRK1A_N ITSN1_N BDNF_N NR1_N NR2A_N pAKT_N \\\n", + "0 3501_12 0.344930 0.626194 0.383583 2.534561 4.097317 0.303547 \n", + "1 3520_5 0.630001 0.839187 0.357777 2.651229 4.261675 0.253184 \n", + "2 3414_13 0.555122 0.726229 0.278319 2.097249 2.897553 0.222222 \n", + "3 3488_8 0.275849 0.430764 0.285166 2.265254 3.250091 0.189258 \n", + "4 3501_7 0.304788 0.617299 0.335164 2.638236 4.876609 0.280590 \n", + "\n", + " pBRAF_N pCAMKII_N pCREB_N ... H3AcK18_N EGR1_N H3MeK4_N \\\n", + "0 0.222829 4.592769 0.239427 ... 0.252700 0.218868 0.249187 \n", + "1 0.185257 3.816673 0.204940 ... 0.155008 0.153219 NaN \n", + "2 0.174356 1.867880 0.203379 ... 0.136109 0.155530 0.185484 \n", + "3 0.157837 2.917611 0.202594 ... 0.127944 0.207671 0.175357 \n", + "4 0.199417 4.835421 0.236314 ... 0.245277 0.202171 0.240372 \n", + "\n", + " CaNA_N Genotype Treatment Behavior class Label Score \n", + "0 1.139493 Ts65Dn Memantine S/C t-SC-m 0 -0.003593 \n", + "1 1.642886 Control Memantine C/S c-CS-m 0 -0.073378 \n", + "2 1.657670 Ts65Dn Memantine C/S t-CS-m 0 -0.062198 \n", + "3 0.893598 Control Saline S/C c-SC-s 0 -0.078707 \n", + "4 0.795637 Ts65Dn Memantine S/C t-SC-m 0 -0.070426 \n", + "\n", + "[5 rows x 84 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_predictions = predict_model(iforest, data = data)\n", + "data_predictions.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 11.0 Saving the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now finished the experiment by using our `iforest` model to predict outlier labels on unseen data. This brings us to the end our experiment but one question is still to be asked. What happens when you have more new data to predict? Do you have to go through the entire experiment again? The answer is No, you don't need to rerun the entire experiment and reconstruct the pipeline to generate predictions on new data. PyCaret inbuilt function `save_model()` allows you to save the model along with entire transformation pipeline for later use." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transformation Pipeline and Model Succesfully Saved\n" + ] + } + ], + "source": [ + "save_model(iforest,'Final IForest Model 08Feb2020')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 12.0 Loading the saved model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To load a saved model on a future date in the same or different environment, we would use the PyCaret's `load_model()` function and then easily apply the saved model on new unseen data for prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transformation Pipeline and Model Sucessfully Loaded\n" + ] + } + ], + "source": [ + "saved_iforest = load_model('Final IForest Model 08Feb2020')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the model is loaded in the environment, you can simply use it to predict on any new data using the same `predict_model()` function . Below we have applied the loaded model to predict the same `data_unseen` that we have used in section 10 above." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "new_prediction = predict_model(saved_iforest, data=data_unseen)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MouseIDDYRK1A_NITSN1_NBDNF_NNR1_NNR2A_NpAKT_NpBRAF_NpCAMKII_NpCREB_N...H3AcK18_NEGR1_NH3MeK4_NCaNA_NGenotypeTreatmentBehaviorclassLabelScore
03517_70.3617820.5659870.3761452.7747714.4502500.2629060.2389686.0410070.229808...0.208611NaN0.2708200.782176Ts65DnSalineS/Ct-SC-s10.011522
13517_80.3293610.5032160.3261452.4878933.9258420.2311770.1956115.3373060.210556...0.211478NaN0.2555820.882451Ts65DnSalineS/Ct-SC-s0-0.056883
23517_90.3283140.5004650.3632652.4093703.7969880.2262500.1870245.3309170.203198...0.201974NaN0.2418150.881029Ts65DnSalineS/Ct-SC-s0-0.067360
33517_100.3774900.5283310.3795602.6892633.9780080.2667530.2294955.4411380.227684...0.262620NaN0.2626200.781328Ts65DnSalineS/Ct-SC-s10.048812
43517_110.3678130.4783220.3463712.3569753.5614990.2521210.2264374.8685200.204288...0.264670NaN0.2471650.783654Ts65DnSalineS/Ct-SC-s0-0.036365
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5 rows × 84 columns

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" + ], + "text/plain": [ + " MouseID DYRK1A_N ITSN1_N BDNF_N NR1_N NR2A_N pAKT_N \\\n", + "0 3517_7 0.361782 0.565987 0.376145 2.774771 4.450250 0.262906 \n", + "1 3517_8 0.329361 0.503216 0.326145 2.487893 3.925842 0.231177 \n", + "2 3517_9 0.328314 0.500465 0.363265 2.409370 3.796988 0.226250 \n", + "3 3517_10 0.377490 0.528331 0.379560 2.689263 3.978008 0.266753 \n", + "4 3517_11 0.367813 0.478322 0.346371 2.356975 3.561499 0.252121 \n", + "\n", + " pBRAF_N pCAMKII_N pCREB_N ... H3AcK18_N EGR1_N H3MeK4_N CaNA_N \\\n", + "0 0.238968 6.041007 0.229808 ... 0.208611 NaN 0.270820 0.782176 \n", + "1 0.195611 5.337306 0.210556 ... 0.211478 NaN 0.255582 0.882451 \n", + "2 0.187024 5.330917 0.203198 ... 0.201974 NaN 0.241815 0.881029 \n", + "3 0.229495 5.441138 0.227684 ... 0.262620 NaN 0.262620 0.781328 \n", + "4 0.226437 4.868520 0.204288 ... 0.264670 NaN 0.247165 0.783654 \n", + "\n", + " Genotype Treatment Behavior class Label Score \n", + "0 Ts65Dn Saline S/C t-SC-s 1 0.011522 \n", + "1 Ts65Dn Saline S/C t-SC-s 0 -0.056883 \n", + "2 Ts65Dn Saline S/C t-SC-s 0 -0.067360 \n", + "3 Ts65Dn Saline S/C t-SC-s 1 0.048812 \n", + "4 Ts65Dn Saline S/C t-SC-s 0 -0.036365 \n", + "\n", + "[5 rows x 84 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_prediction.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that results of `unseen_predictions` and `new_prediction` are identical." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 16.0 Wrap-up / Next Steps?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What we have covered in this tutorial is the entire machine learning pipeline from data ingestion, pre-processing, training the anomaly detector, prediction on unseen data and saving the model for later use. We have completed all this in less than 10 commands which are naturally constructed and very intuitive to remember such as `create_model()`, `assign_model()`, `plot_model()`. Re-creating the entire experiment without PyCaret would have taken well over 100 lines of code in most of the libraries.\n", + "\n", + "In this tutorial, we have only covered basics of `pycaret.anomaly`. In the following tutorials, we will go deeper into advance pre-processing techniques that allows you to fully customize your machine learning pipeline that are must to know for any data scientist. \n", + "\n", + "See you at the next tutorial. Follow the link to __[Anomaly Detection Tutorial (ANO102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/Tutorials/Anomaly%20Detection%20Tutorial%20(ANO102)%20-%20Level%20Intermediate.ipynb)__" + ] + } + ], + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Tutorials/Anomaly Detection Tutorial Level Expert - ANO103.ipynb b/Tutorials/Anomaly Detection Tutorial Level Expert - ANO103.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6e363e9ad603f8ecd6274b969a45aea43f7949e8 --- /dev/null +++ b/Tutorials/Anomaly Detection Tutorial Level Expert - ANO103.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Anomaly Detection Tutorial (ANO103) - Level Expert" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Date Updated: Feb 25, 2020**\n", + "\n", + "# Work in progress\n", + "We are currently working on this tutorial. Please check back soon! \n", + "\n", + "### In the mean time, you can see: \n", + "- __[Anomaly Detection Tutorial (ANO101) - Level Beginner](https://github.com/pycaret/pycaret/blob/master/Tutorials/Anomaly%20Detection%20Tutorial%20(ANO101)%20-%20Level%20Beginner.ipynb)__" + ] + } + ], + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Tutorials/Anomaly Detection Tutorial Level Intermediate - ANO102.ipynb b/Tutorials/Anomaly Detection Tutorial Level Intermediate - ANO102.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..debfba9c66bbfab115e21b7d6b5136ae0a0013c3 --- /dev/null +++ b/Tutorials/Anomaly Detection Tutorial Level Intermediate - ANO102.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Anomaly Detection Tutorial (ANO102) - Level Intermediate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Date Updated: Feb 25, 2020**\n", + "\n", + "# Work in progress\n", + "We are currently working on this tutorial. Please check back soon! \n", + "\n", + "### In the mean time, you can see: \n", + "- __[Anomaly Detection Tutorial (ANO101) - Level Beginner](https://github.com/pycaret/pycaret/blob/master/Tutorials/Anomaly%20Detection%20Tutorial%20(ANO101)%20-%20Level%20Beginner.ipynb)__" + ] + } + ], + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}