{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "a48e9306-f459-4d8a-8608-9bd71a7600ae", "metadata": {}, "source": [ "# 6. Exploring Advanced Features\n", "\n", "This notebook is target at advanced users that want, among other things, access MOA objects directly using the Python API from capymoa. \n", "\n", "* Examples on how to use any MOA Classifier or Regressor from capymoa\n", "* An example of how preprocessing (from MOA) can be used.\n", "* Comparing a SKLearn model against a MOA model\n", "* A variation of **Tutorial 5**: `Creating a new classifier in CapyMOA` which uses MOA learners, thus accessing MOA (Java) objects directly\n", "* How to log experiments using TensorBoard alongside the PyTorch API. This extends **Tutorial 3**: `Using Pytorch with CapyMOA`\n", "* Creating a synthetic stream with concept drifts using the MOA CLI directly\n", "* An example utilising a multi-threaded ensemble\n", "\n", "---\n", "\n", "*More information about CapyMOA can be found in* https://www.capymoa.org\n", "\n", "**last update on 28/07/2024**" ] }, { "cell_type": "markdown", "id": "d2bb536e-4716-48fe-bf9b-05455b9e5a85", "metadata": {}, "source": [ "## 1. Using any MOA learner\n", "\n", "* **CapyMOA gives you access to any MOA classifier or regressor**\n", "\n", "* For some of the MOA learners there are corresponding Python objects (such as the HoeffdingTree or Adaptive Random Forest Classifier). However, MOA has over a hundred learners, and more are added constantly.\n", "\n", "* To allow advanced users to access **any** MOA learner from CapyMOA, we included the ```MOAClassifier``` and ```MOARegressor``` generic wrappers." ] }, { "cell_type": "code", "execution_count": 1, "id": "ded154ef", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:12.975145Z", "iopub.status.busy": "2024-09-23T00:29:12.974567Z", "iopub.status.idle": "2024-09-23T00:29:12.994851Z", "shell.execute_reply": "2024-09-23T00:29:12.993070Z" }, "nbsphinx": "hidden" }, "outputs": [], "source": [ "# This cell is hidden on capymoa.org. See docs/contributing/docs.rst\n", "from util.nbmock import mock_datasets, is_nb_fast\n", "\n", "if is_nb_fast():\n", " mock_datasets()" ] }, { "cell_type": "code", "execution_count": 2, "id": "3d1a9e23-a272-4c01-ab9b-e7f3ec5f7395", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:13.001384Z", "iopub.status.busy": "2024-09-23T00:29:13.000816Z", "iopub.status.idle": "2024-09-23T00:29:15.524383Z", "shell.execute_reply": "2024-09-23T00:29:15.523734Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cumulative accuracy = 83.38629943502825, wall-clock time: 0.6487746238708496\n" ] }, { "data": { "text/html": [ "
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instancesaccuracykappakappa_tkappa_mf1_scoref1_score_0f1_score_1precisionprecision_0precision_1recallrecall_0recall_1
0500.086.071.762808-9.37500068.88888985.88608284.58149887.17948785.93939485.33333386.54545585.83283683.84279587.822878
11000.089.278.45687428.94736878.98832789.44118989.28571489.11290389.40829494.14225984.67433089.47410784.90566094.042553
21500.095.886.82757966.12903283.06451693.43570189.44723697.37827794.26338591.75257796.77419492.62242687.25490297.989950
32000.077.054.896301-47.43589741.32653178.79401575.47974478.34275078.23256064.83516591.62995679.36358890.30612268.421053
42500.086.271.98310925.00000068.63636485.99185284.28246087.70053586.00968584.47488687.54448485.97402684.09090987.857143
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8643500.084.466.15817120.40816362.67942685.19362277.05882488.18181889.430894100.00000078.86178981.33971362.679426100.000000
8744000.077.435.265811-32.94117628.48101374.11711944.33497585.82183287.582418100.00000075.16483564.24050628.481013100.000000
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" ], "text/plain": [ " instances accuracy kappa kappa_t kappa_m f1_score \\\n", "0 500.0 86.0 71.762808 -9.375000 68.888889 85.886082 \n", "1 1000.0 89.2 78.456874 28.947368 78.988327 89.441189 \n", "2 1500.0 95.8 86.827579 66.129032 83.064516 93.435701 \n", "3 2000.0 77.0 54.896301 -47.435897 41.326531 78.794015 \n", "4 2500.0 86.2 71.983109 25.000000 68.636364 85.991852 \n", ".. ... ... ... ... ... ... \n", "86 43500.0 84.4 66.158171 20.408163 62.679426 85.193622 \n", "87 44000.0 77.4 35.265811 -32.941176 28.481013 74.117119 \n", "88 44500.0 72.0 39.008452 -105.882353 36.073059 74.346872 \n", "89 45000.0 77.6 52.642706 -77.777778 45.365854 76.539541 \n", "90 45312.0 76.4 52.842253 -38.823529 47.555556 76.613251 \n", "\n", " f1_score_0 f1_score_1 precision precision_0 precision_1 recall \\\n", "0 84.581498 87.179487 85.939394 85.333333 86.545455 85.832836 \n", "1 89.285714 89.112903 89.408294 94.142259 84.674330 89.474107 \n", "2 89.447236 97.378277 94.263385 91.752577 96.774194 92.622426 \n", "3 75.479744 78.342750 78.232560 64.835165 91.629956 79.363588 \n", "4 84.282460 87.700535 86.009685 84.474886 87.544484 85.974026 \n", ".. ... ... ... ... ... ... \n", "86 77.058824 88.181818 89.430894 100.000000 78.861789 81.339713 \n", "87 44.334975 85.821832 87.582418 100.000000 75.164835 64.240506 \n", "88 53.947368 79.885057 81.729270 96.470588 66.987952 68.187653 \n", "89 70.526316 81.935484 77.362637 76.571429 78.153846 75.733774 \n", "90 75.105485 77.566540 76.446493 70.634921 82.258065 76.780738 \n", "\n", " recall_0 recall_1 \n", "0 83.842795 87.822878 \n", "1 84.905660 94.042553 \n", "2 87.254902 97.989950 \n", "3 90.306122 68.421053 \n", "4 84.090909 87.857143 \n", ".. ... ... \n", "86 62.679426 100.000000 \n", "87 28.481013 100.000000 \n", "88 37.442922 98.932384 \n", "89 65.365854 86.101695 \n", "90 80.180180 73.381295 \n", "\n", "[91 rows x 14 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.base import MOAClassifier\n", "from capymoa.datasets import Electricity\n", "\n", "# This is an import from MOA\n", "from moa.classifiers.trees import HoeffdingAdaptiveTree\n", "\n", "stream = Electricity()\n", "\n", "# Creates a wrapper around the HoeffdingAdaptiveTree, which then can be used as any other capymoa classifier\n", "HAT = MOAClassifier(schema=stream.get_schema(), moa_learner=HoeffdingAdaptiveTree)\n", "\n", "results_HAT = prequential_evaluation(stream=stream, learner=HAT, window_size=500)\n", "\n", "print(\n", " f\"Cumulative accuracy = {results_HAT['cumulative'].accuracy()}, wall-clock time: {results_HAT['wallclock']}\"\n", ")\n", "display(results_HAT[\"windowed\"].metrics_per_window())" ] }, { "cell_type": "markdown", "id": "3c102052-1a19-4f30-b3d1-f0163cab6af0", "metadata": {}, "source": [ "### 1.1 Checking the hyperparameters for the MOA CLI\n", "\n", "* MOA objects can be parametrized using the MOA CLI (Command Line Interface)\n", "* Sometimes you may not know the relevent parameters for ```moa_learner```, ```moa_learner.CLI_help()``` presents all the hyperparameters available for the ```moa_learner``` object." ] }, { "cell_type": "code", "execution_count": 3, "id": "3fbca563-e87f-41f2-98f2-dcad2ab65fb6", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:15.526653Z", "iopub.status.busy": "2024-09-23T00:29:15.526425Z", "iopub.status.idle": "2024-09-23T00:29:15.536747Z", "shell.execute_reply": "2024-09-23T00:29:15.536082Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-l treeLearner (default: ARFHoeffdingTree -e 2000000 -g 50 -c 0.01)\n", "Random Forest Tree.\n", "-s ensembleSize (default: 100)\n", "The number of trees.\n", "-o mFeaturesMode (default: Percentage (M * (m / 100)))\n", "Defines how m, defined by mFeaturesPerTreeSize, is interpreted. M represents the total number of features.\n", "-m mFeaturesPerTreeSize (default: 60)\n", "Number of features allowed considered for each split. Negative values corresponds to M - m\n", "-a lambda (default: 6.0)\n", "The lambda parameter for bagging.\n", "-j numberOfJobs (default: 1)\n", "Total number of concurrent jobs used for processing (-1 = as much as possible, 0 = do not use multithreading)\n", "-x driftDetectionMethod (default: ADWINChangeDetector -a 1.0E-3)\n", "Change detector for drifts and its parameters\n", "-p warningDetectionMethod (default: ADWINChangeDetector -a 1.0E-2)\n", "Change detector for warnings (start training bkg learner)\n", "-w disableWeightedVote\n", "Should use weighted voting?\n", "-u disableDriftDetection\n", "Should use drift detection? If disabled then bkg learner is also disabled\n", "-q disableBackgroundLearner\n", "Should use bkg learner? If disabled then reset tree immediately.\n", "\n" ] } ], "source": [ "from moa.classifiers.meta import AdaptiveRandomForest\n", "\n", "arf = MOAClassifier(schema=stream.get_schema(), moa_learner=AdaptiveRandomForest)\n", "\n", "print(arf.CLI_help())" ] }, { "attachments": {}, "cell_type": "markdown", "id": "55d070de-8697-4f98-a11b-eab4e3d5c281", "metadata": {}, "source": [ "## 2. Using preprocessing from MOA (filters)\n", "\n", "We are working on a more user friendly API for preprocessing, this example just show how one can do that using MOA filters from here\n", "\n", "* Here we use ```NormalisationFilter``` filter from MOA to normalize instances in an online fashion.\n", "* MOA filters syntax wraps the whole stream, so we are always composing commands like `Filter(Stream, \n", "* We obtain the MOA CLI from the rbf_100k stream, since it can be mapped to a MOA stream, it is possible to obtain that. Comment out the print statements if you would like to inspect the actual creation strings (perhaps to copy and paste that into MOA?)" ] }, { "cell_type": "code", "execution_count": 4, "id": "ae9bb646-e0d1-4de6-b5a1-cff0f0a1b172", "metadata": { "ExecuteTime": { "end_time": "2024-04-29T11:52:48.998749Z", "start_time": "2024-04-29T11:52:45.889095Z" }, "execution": { "iopub.execute_input": "2024-09-23T00:29:15.538820Z", "iopub.status.busy": "2024-09-23T00:29:15.538644Z", "iopub.status.idle": "2024-09-23T00:29:18.121257Z", "shell.execute_reply": "2024-09-23T00:29:18.120745Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-s (ArffFileStream -f /local/scratch/antonlee/datasets/electricity.arff) -f NormalisationFilter\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy with online normalization: 80.53937146892656\n", "Accuracy without normalization: 82.06656073446328\n" ] } ], "source": [ "from capymoa.stream import Stream\n", "from capymoa.classifier import OnlineBagging\n", "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.datasets import Electricity, get_download_dir\n", "\n", "from moa.streams import FilteredStream\n", "\n", "stream = Electricity()\n", "cli = (\n", " f\"-s (ArffFileStream -f {get_download_dir() / Electricity._filename}) \"\n", " f\" -f NormalisationFilter\"\n", ")\n", "print(cli)\n", "# Create a FilterStream and use the NormalisationFilter\n", "rbf_stream_normalised = Stream(CLI=cli, moa_stream=FilteredStream())\n", "\n", "# print(f'MOA creation string for filtered version: {rbf_stream_normalised.moa_stream.getCLICreationString(rbf_stream_normalised.moa_stream.__class__)}')\n", "\n", "ob_learner_norm = OnlineBagging(\n", " schema=rbf_stream_normalised.get_schema(), ensemble_size=5\n", ")\n", "ob_learner = OnlineBagging(schema=stream.get_schema(), ensemble_size=5)\n", "\n", "ob_results_norm = prequential_evaluation(\n", " stream=rbf_stream_normalised, learner=ob_learner_norm\n", ")\n", "ob_results = prequential_evaluation(stream=stream, learner=ob_learner)\n", "\n", "\n", "print(f\"Accuracy with online normalization: {ob_results_norm['cumulative'].accuracy()}\")\n", "print(f\"Accuracy without normalization: {ob_results['cumulative'].accuracy()}\")" ] }, { "cell_type": "markdown", "id": "f74c58fb-dd90-49f4-8b4f-81a9e36e47ff", "metadata": {}, "source": [ "## 3. Comparing a MOA and SKLearn models\n", "\n", "* This simple example shows how it is simple to compare a MOA and a SKLearn regressors. \n", "* For the sake of this example, we are using the wrappers\n", "* SKClassifier (and SKRegressor) are parametrized directly as part of the object initialization\n", "* MOAClassifier (and MOARegressor) are parametrized through a CLI (a separate parameter)" ] }, { "cell_type": "code", "execution_count": 5, "id": "afe7193c-5bab-4b46-8627-c74b28a3b7c5", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:18.123102Z", "iopub.status.busy": "2024-09-23T00:29:18.122818Z", "iopub.status.idle": "2024-09-23T00:29:20.988442Z", "shell.execute_reply": "2024-09-23T00:29:20.987622Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.base import SKClassifier, MOAClassifier\n", "from capymoa.datasets import CovtypeTiny\n", "from capymoa.evaluation import prequential_evaluation_multiple_learners\n", "from capymoa.evaluation.visualization import plot_windowed_results\n", "\n", "from sklearn.linear_model import SGDClassifier\n", "from moa.classifiers.trees import HoeffdingTree\n", "\n", "covt_tiny = CovtypeTiny()\n", "\n", "sk_sgd = SKClassifier(\n", " schema=covt_tiny.schema,\n", " sklearner=SGDClassifier(loss=\"log_loss\", penalty=\"l1\", alpha=0.001),\n", ")\n", "moa_ht = MOAClassifier(schema=covt_tiny.schema, moa_learner=HoeffdingTree, CLI=\"-g 50\")\n", "\n", "results = prequential_evaluation_multiple_learners(\n", " stream=covt_tiny, learners={\"sk_sgd\": sk_sgd, \"moa_ht\": moa_ht}, window_size=100\n", ")\n", "plot_windowed_results(results[\"sk_sgd\"], results[\"moa_ht\"], metric=\"accuracy\")" ] }, { "cell_type": "markdown", "id": "df198282-7a87-4e03-ba0b-b3de3ccf9163", "metadata": {}, "source": [ "## 4. Creating Python learners with MOA Objects\n", "\n", "* This example follow the example from `06_new_learner` which shows how to create a custom online bagging implementation.\n", "* Here we also create an online bagging implementation, but the base_learner is a MOA class" ] }, { "cell_type": "code", "execution_count": 6, "id": "a0a0906a-c953-4d50-8be8-c1c94e3eac4d", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:20.990976Z", "iopub.status.busy": "2024-09-23T00:29:20.990635Z", "iopub.status.idle": "2024-09-23T00:29:21.000499Z", "shell.execute_reply": "2024-09-23T00:29:20.999858Z" } }, "outputs": [], "source": [ "from capymoa.base import Classifier, MOAClassifier\n", "from moa.classifiers.trees import HoeffdingTree\n", "from collections import Counter\n", "import numpy as np\n", "import random\n", "import math\n", "\n", "\n", "def poisson(lambd, random_generator):\n", " if lambd < 100.0:\n", " product = 1.0\n", " _sum = 1.0\n", " threshold = random_generator.random() * math.exp(lambd)\n", " i = 1\n", " max_val = max(100, 10 * math.ceil(lambd))\n", " while i < max_val and _sum <= threshold:\n", " product *= lambd / i\n", " _sum += product\n", " i += 1\n", " return i - 1\n", " x = lambd + math.sqrt(lambd) * random_generator.gauss(0, 1)\n", " if x < 0.0:\n", " return 0\n", " return int(math.floor(x))\n", "\n", "\n", "class CustomOnlineBagging(Classifier):\n", " def __init__(\n", " self,\n", " schema=None,\n", " random_seed=1,\n", " ensemble_size=5,\n", " moa_base_learner_class=None,\n", " CLI_base_learner=None,\n", " ):\n", " super().__init__(schema=schema, random_seed=random_seed)\n", "\n", " self.random_generator = random.Random()\n", " self.CLI_base_learner = CLI_base_learner\n", "\n", " self.ensemble_size = ensemble_size\n", " self.moa_base_learner_class = moa_base_learner_class\n", "\n", " # Default base learner if None is specified\n", " if self.moa_base_learner_class is None:\n", " self.moa_base_learner_class = HoeffdingTree\n", "\n", " self.ensemble = []\n", " # Create several instances for the base_learners\n", " for i in range(self.ensemble_size):\n", " self.ensemble.append(\n", " MOAClassifier(\n", " schema=self.schema,\n", " moa_learner=self.moa_base_learner_class(),\n", " CLI=self.CLI_base_learner,\n", " )\n", " )\n", "\n", " def __str__(self):\n", " return \"CustomOnlineBagging\"\n", "\n", " def train(self, instance):\n", " for i in range(self.ensemble_size):\n", " k = poisson(1.0, self.random_generator)\n", " for _ in range(k):\n", " self.ensemble[i].train(instance)\n", "\n", " def predict(self, instance):\n", " predictions = []\n", " for i in range(self.ensemble_size):\n", " predictions.append(self.ensemble[i].predict(instance))\n", " majority_vote = Counter(predictions)\n", " prediction = majority_vote.most_common(1)[0][0]\n", " return prediction\n", "\n", " def predict_proba(self, instance):\n", " probabilities = []\n", " for i in range(self.ensemble_size):\n", " classifier_proba = self.ensemble[i].predict_proba(instance)\n", " classifier_proba = classifier_proba / np.sum(classifier_proba)\n", " probabilities.append(classifier_proba)\n", " avg_proba = np.mean(probabilities, axis=0)\n", " return avg_proba" ] }, { "cell_type": "markdown", "id": "c971ac60-0aa5-4295-be56-bcb6ee1ccb40", "metadata": {}, "source": [ "### 4.1 Testing the custom online bagging\n", "\n", "* We choose to use an HoeffdingAdaptiveTree from MOA as the base learner\n", "* We also specify the CLI commands to configure the base learner" ] }, { "cell_type": "code", "execution_count": 7, "id": "0740765c-35c0-416a-99ca-f8e55f921032", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:21.003376Z", "iopub.status.busy": "2024-09-23T00:29:21.003149Z", "iopub.status.idle": "2024-09-23T00:29:26.177264Z", "shell.execute_reply": "2024-09-23T00:29:26.176754Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 85.89556850282486\n" ] } ], "source": [ "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.datasets import Electricity\n", "from moa.classifiers.trees import HoeffdingAdaptiveTree\n", "\n", "elec_stream = Electricity()\n", "\n", "# Creating a learner: using a hoeffding adaptive tree as the base learner with grace period of 50 (-g 50)\n", "NEW_OB = CustomOnlineBagging(\n", " schema=elec_stream.get_schema(),\n", " ensemble_size=5,\n", " moa_base_learner_class=HoeffdingAdaptiveTree,\n", " CLI_base_learner=\"-g 50\",\n", ")\n", "\n", "results_NEW_OB = prequential_evaluation(\n", " stream=elec_stream, learner=NEW_OB, window_size=4500\n", ")\n", "\n", "print(f\"Accuracy: {results_NEW_OB.cumulative.accuracy()}\")" ] }, { "cell_type": "markdown", "id": "62e3e70a-2422-4b3a-b2bf-b8f96a3efdeb", "metadata": {}, "source": [ "## 5. Using TensorBoard with PyTorch in CapyMOA\n", "\n", "* One can use TensorBoard to visualize logged data in an online fashion\n", "* We go through all the steps below, including installing TensorBoard" ] }, { "cell_type": "markdown", "id": "8fda8006-e0e9-4547-a2c9-8fc43d16ca57", "metadata": {}, "source": [ "### 5.1 Install TensorBoard\n", "Clear any logs from previous runs\n", "\n", "```sh\n", "rm -rf ./runs\n", "```" ] }, { "cell_type": "code", "execution_count": 8, "id": "f11baceb-2c77-4636-8e91-d19aadf7b3b3", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:26.178950Z", "iopub.status.busy": "2024-09-23T00:29:26.178803Z", "iopub.status.idle": "2024-09-23T00:29:27.705372Z", "shell.execute_reply": "2024-09-23T00:29:27.703057Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tensorboard in /local/scratch/antonlee/miniconda3/envs/capymoa/lib/python3.9/site-packages (2.17.1)\r\n", "Requirement already satisfied: 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werkzeug>=1.0.1->tensorboard) (2.1.5)\r\n", "Requirement already satisfied: zipp>=0.5 in /local/scratch/antonlee/miniconda3/envs/capymoa/lib/python3.9/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard) (3.19.2)\r\n" ] } ], "source": [ "!pip install tensorboard" ] }, { "cell_type": "markdown", "id": "b1e64bd6-b0c7-4296-aee7-a29985a9da21", "metadata": {}, "source": [ "### 5.2 PyTorchClassifier\n", "* We define `PyTorchClassifier` and `NeuralNetwork` classes similarly to those from **Tutorial 3**: `Using Pytorch with CapyMOA`" ] }, { "cell_type": "code", "execution_count": 9, "id": "ea9a4d94-7515-424c-a9fd-76c78ddf52d1", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:27.713429Z", "iopub.status.busy": "2024-09-23T00:29:27.712681Z", "iopub.status.idle": "2024-09-23T00:29:27.755315Z", "shell.execute_reply": "2024-09-23T00:29:27.754066Z" } }, "outputs": [], "source": [ "from capymoa.base import Classifier\n", "import torch\n", "from torch import nn\n", "\n", "torch.manual_seed(1)\n", "torch.use_deterministic_algorithms(True)\n", "\n", "# Get cpu device for training.\n", "device = \"cpu\"\n", "\n", "\n", "# Define model\n", "class NeuralNetwork(nn.Module):\n", " def __init__(self, input_size=0, number_of_classes=0):\n", " super().__init__()\n", " self.flatten = nn.Flatten()\n", " self.linear_relu_stack = nn.Sequential(\n", " nn.Linear(input_size, 512),\n", " nn.ReLU(),\n", " nn.Linear(512, 512),\n", " nn.ReLU(),\n", " nn.Linear(512, number_of_classes),\n", " )\n", "\n", " def forward(self, x):\n", " x = self.flatten(x)\n", " logits = self.linear_relu_stack(x)\n", " return logits\n", "\n", "\n", "class PyTorchClassifier(Classifier):\n", " def __init__(\n", " self,\n", " schema=None,\n", " random_seed=1,\n", " nn_model: nn.Module = None,\n", " optimizer=None,\n", " loss_fn=nn.CrossEntropyLoss(),\n", " device=(\"cpu\"),\n", " lr=1e-3,\n", " ):\n", " super().__init__(schema, random_seed)\n", " self.model = None\n", " self.optimizer = None\n", " self.loss_fn = loss_fn\n", " self.lr = lr\n", " self.device = device\n", "\n", " torch.manual_seed(random_seed)\n", "\n", " if nn_model is None:\n", " self.set_model(None)\n", " else:\n", " self.model = nn_model.to(device)\n", " if optimizer is None:\n", " if self.model is not None:\n", " self.optimizer = torch.optim.SGD(self.model.parameters(), lr=lr)\n", " else:\n", " self.optimizer = optimizer\n", "\n", " def __str__(self):\n", " return str(self.model)\n", "\n", " def CLI_help(self):\n", " return str(\n", " 'schema=None, random_seed=1, nn_model: nn.Module = None, optimizer=None, loss_fn=nn.CrossEntropyLoss(), device=(\"cpu\"), lr=1e-3'\n", " )\n", "\n", " def set_model(self, instance):\n", " if self.schema is None:\n", " moa_instance = instance.java_instance.getData()\n", " self.model = NeuralNetwork(\n", " input_size=moa_instance.get_num_attributes(),\n", " number_of_classes=moa_instance.get_num_classes(),\n", " ).to(self.device)\n", " elif instance is not None:\n", " self.model = NeuralNetwork(\n", " input_size=self.schema.get_num_attributes(),\n", " number_of_classes=self.schema.get_num_classes(),\n", " ).to(self.device)\n", "\n", " def train(self, instance):\n", " if self.model is None:\n", " self.set_model(instance)\n", "\n", " X = torch.tensor(instance.x, dtype=torch.float32)\n", " y = torch.tensor(instance.y_index, dtype=torch.long)\n", " # set the device and add a dimension to the tensor\n", " X, y = (\n", " torch.unsqueeze(X.to(self.device), 0),\n", " torch.unsqueeze(y.to(self.device), 0),\n", " )\n", "\n", " # Compute prediction error\n", " pred = self.model(X)\n", " loss = self.loss_fn(pred, y)\n", "\n", " # Backpropagation\n", " loss.backward()\n", " self.optimizer.step()\n", " self.optimizer.zero_grad()\n", "\n", " def predict(self, instance):\n", " return np.argmax(self.predict_proba(instance))\n", "\n", " def predict_proba(self, instance):\n", " if self.model is None:\n", " self.set_model(instance)\n", " X = torch.unsqueeze(\n", " torch.tensor(instance.x, dtype=torch.float32).to(self.device), 0\n", " )\n", " # turn off gradient collection\n", " with torch.no_grad():\n", " pred = np.asarray(self.model(X).numpy(), dtype=np.double)\n", " return pred" ] }, { "cell_type": "markdown", "id": "2b166ade-23b3-445c-a382-6f0cb6231d66", "metadata": {}, "source": [ "### 5.3 PyTorchClassifier + the test-then-train loop + TensorBoard\n", "* Here we use instance loop to log relevant log information to TensorBoard\n", "* These information can be viewed while the processing is happening using TensorBoard" ] }, { "cell_type": "code", "execution_count": 10, "id": "9e93527d-26cb-4a0b-a4e4-2f3399724502", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:27.759393Z", "iopub.status.busy": "2024-09-23T00:29:27.759034Z", "iopub.status.idle": "2024-09-23T00:30:14.994165Z", "shell.execute_reply": "2024-09-23T00:30:14.993711Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processed 10000 instances\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processed 20000 instances\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processed 30000 instances\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processed 40000 instances\n" ] } ], "source": [ "from capymoa.evaluation import ClassificationEvaluator\n", "from capymoa.datasets import Electricity\n", "from torch.utils.tensorboard import SummaryWriter\n", "\n", "# Create a SummaryWriter instance.\n", "writer = SummaryWriter()\n", "## Opening a file again to start from the beginning\n", "stream = Electricity()\n", "\n", "# Creating the evaluator\n", "evaluator = ClassificationEvaluator(schema=stream.get_schema())\n", "\n", "# Creating a learner\n", "simple_pyTorch_classifier = PyTorchClassifier(\n", " schema=stream.get_schema(),\n", " nn_model=NeuralNetwork(\n", " input_size=stream.get_schema().get_num_attributes(),\n", " number_of_classes=stream.get_schema().get_num_classes(),\n", " ).to(device),\n", ")\n", "\n", "i = 0\n", "while stream.has_more_instances():\n", " i += 1\n", " instance = stream.next_instance()\n", "\n", " prediction = simple_pyTorch_classifier.predict(instance)\n", " evaluator.update(instance.y_index, prediction)\n", " simple_pyTorch_classifier.train(instance)\n", "\n", " if i % 1000 == 0:\n", " writer.add_scalar(\"accuracy\", evaluator.accuracy(), i)\n", "\n", " if i % 10000 == 0:\n", " print(f\"Processed {i} instances\")\n", "\n", "writer.add_scalar(\"accuracy\", evaluator.accuracy(), i)\n", "# Call flush() method to make sure that all pending events have been written to disk.\n", "writer.flush()\n", "\n", "# If you do not need the summary writer anymore, call close() method.\n", "writer.close()" ] }, { "cell_type": "markdown", "id": "9da96643-1900-41e8-96aa-6af460194ac6", "metadata": {}, "source": [ "#### 5.4 Run TensorBoard\n", "Now, start TensorBoard, specifying the root log directory you used above. \n", "Argument ``logdir`` points to directory where TensorBoard will look to find \n", "event files that it can display. TensorBoard will recursively walk \n", "the directory structure rooted at ``logdir``, looking for ``.*tfevents.*`` files.\n", "\n", "```sh\n", "tensorboard --logdir=runs\n", "```\n", "Go to the URL it provides\n", "\n", "This dashboard shows how the accuracy change with time. \n", "You can use it to also track training speed, learning rate, and other \n", "scalar values." ] }, { "cell_type": "markdown", "id": "38b1f9ce-c3a1-4944-8cb1-f2a84cd4ff25", "metadata": {}, "source": [ "## 6. Creating a synthetic stream with concept drifts from MOA\n", "\n", "* Demonstrates the flexibility of the API, these level of manipulation of the API is expected from experienced MOA users.\n", "* To use the API like this the user must be familiar with how concept drifts are simulatd in MOA\n", "\n", "EvaluatePrequential -l trees.HoeffdingAdaptiveTree **-s (ConceptDriftStream -s generators.AgrawalGenerator -d (generators.AgrawalGenerator -f 2) -p 5000)** -e (WindowClassificationPerformanceEvaluator **-w 100**) **-i 10000 -f 100**" ] }, { "cell_type": "code", "execution_count": 11, "id": "7b79ac8e-d7ac-48fb-b983-22301d272364", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:30:14.997825Z", "iopub.status.busy": "2024-09-23T00:30:14.997552Z", "iopub.status.idle": "2024-09-23T00:30:15.375085Z", "shell.execute_reply": "2024-09-23T00:30:15.374632Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.stream import Stream\n", "from capymoa.classifier import OnlineBagging\n", "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.evaluation.visualization import plot_windowed_results\n", "from moa.streams import ConceptDriftStream\n", "\n", "# Using the API to generate the data using the ConceptDriftStream and AgrawalGenerator.\n", "# The drift location is based on the number of instances (5000) as well as the drift width (1000, the default value)\n", "stream_sea1drift = Stream(\n", " moa_stream=ConceptDriftStream(),\n", " CLI=\"-s generators.SEAGenerator -d (generators.SEAGenerator -f 2) -p 5000 -w 1000\",\n", ")\n", "\n", "OB = OnlineBagging(schema=stream_sea1drift.get_schema(), ensemble_size=10)\n", "\n", "results_sea1drift_OB = prequential_evaluation(\n", " stream=stream_sea1drift, learner=OB, window_size=100, max_instances=10000\n", ")\n", "\n", "plot_windowed_results(results_sea1drift_OB, metric=\"accuracy\")" ] }, { "cell_type": "markdown", "id": "0f2f9fb6-0994-4f3f-aaf3-c73b09847019", "metadata": {}, "source": [ "## 7. Drift, Multi-threated Ensemble and Results\n", "\n", "* Generate a stream with 3 drifts, 2 abrupt and one gradual. \n", "* Evaluate utilising test-then-train (cumulative) and windowed evaluation.\n", "* Execute a multi-threated version of AdaptiveRandomForest.\n", "* For more on multi-threaded ensembles, see **parallel_ensembles.ipynb** notebook" ] }, { "cell_type": "code", "execution_count": 12, "id": "3142e7e7-7175-40da-a89c-b528d71eb00c", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:30:15.376980Z", "iopub.status.busy": "2024-09-23T00:30:15.376832Z", "iopub.status.idle": "2024-09-23T00:30:25.563962Z", "shell.execute_reply": "2024-09-23T00:30:25.563432Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Cumulative accuracy = 89.346\n", "wallclock = 10.006356000900269 seconds\n" ] }, { "data": { "text/html": [ "
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instancesaccuracykappakappa_tkappa_mf1_scoref1_score_0f1_score_1precisionprecision_0precision_1recallrecall_0recall_1
05000.088.2673.74368774.33318867.09641387.03348082.53496091.15830788.17689787.95180788.40198785.91933877.74663794.092040
110000.088.9075.53051676.57239369.77124287.92872483.97343391.50986789.02085289.36693388.67477086.86306979.19390094.532238
215000.089.3276.50263577.22814571.00977288.41137284.64634891.81232889.48837089.97555089.00118987.35998979.91313894.806840
320000.088.4674.51258575.45725268.34887587.39721083.28020991.18949588.41030088.26781388.55278886.40707578.82611193.988039
425000.089.9677.60538277.86596171.71831088.91092585.16548592.41233489.85456589.55873290.15039887.98689881.18309994.790698
530000.089.1875.89780576.50890169.75964288.08315284.04600491.81419389.11956488.95131189.28781687.07056879.65343894.487699
635000.089.4276.03782976.06334869.04622688.08920183.89649992.12211588.88474687.43654890.33294487.30777080.63194993.983592
740000.089.8677.44706977.87958171.78631188.86888585.09262092.31701889.95286490.21197089.69375787.81072080.52309495.098345
845000.090.1878.29380779.24767573.12534289.32033285.73918192.51181990.48214791.33663489.62766088.18797580.78817795.587772
950000.089.9277.55185177.96239671.76470688.89716785.15026592.37057289.89039689.80733489.97345987.92564680.95238194.898911
\n", "
" ], "text/plain": [ " instances accuracy kappa kappa_t kappa_m f1_score \\\n", "0 5000.0 88.26 73.743687 74.333188 67.096413 87.033480 \n", "1 10000.0 88.90 75.530516 76.572393 69.771242 87.928724 \n", "2 15000.0 89.32 76.502635 77.228145 71.009772 88.411372 \n", "3 20000.0 88.46 74.512585 75.457252 68.348875 87.397210 \n", "4 25000.0 89.96 77.605382 77.865961 71.718310 88.910925 \n", "5 30000.0 89.18 75.897805 76.508901 69.759642 88.083152 \n", "6 35000.0 89.42 76.037829 76.063348 69.046226 88.089201 \n", "7 40000.0 89.86 77.447069 77.879581 71.786311 88.868885 \n", "8 45000.0 90.18 78.293807 79.247675 73.125342 89.320332 \n", "9 50000.0 89.92 77.551851 77.962396 71.764706 88.897167 \n", "\n", " f1_score_0 f1_score_1 precision precision_0 precision_1 recall \\\n", "0 82.534960 91.158307 88.176897 87.951807 88.401987 85.919338 \n", "1 83.973433 91.509867 89.020852 89.366933 88.674770 86.863069 \n", "2 84.646348 91.812328 89.488370 89.975550 89.001189 87.359989 \n", "3 83.280209 91.189495 88.410300 88.267813 88.552788 86.407075 \n", "4 85.165485 92.412334 89.854565 89.558732 90.150398 87.986898 \n", "5 84.046004 91.814193 89.119564 88.951311 89.287816 87.070568 \n", "6 83.896499 92.122115 88.884746 87.436548 90.332944 87.307770 \n", "7 85.092620 92.317018 89.952864 90.211970 89.693757 87.810720 \n", "8 85.739181 92.511819 90.482147 91.336634 89.627660 88.187975 \n", "9 85.150265 92.370572 89.890396 89.807334 89.973459 87.925646 \n", "\n", " recall_0 recall_1 \n", "0 77.746637 94.092040 \n", "1 79.193900 94.532238 \n", "2 79.913138 94.806840 \n", "3 78.826111 93.988039 \n", "4 81.183099 94.790698 \n", "5 79.653438 94.487699 \n", "6 80.631949 93.983592 \n", "7 80.523094 95.098345 \n", "8 80.788177 95.587772 \n", "9 80.952381 94.898911 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.stream.generator import SEA\n", "from capymoa.stream.drift import DriftStream, AbruptDrift, GradualDrift\n", "from capymoa.classifier import AdaptiveRandomForestClassifier\n", "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.evaluation.visualization import plot_windowed_results\n", "\n", "SEA3drifts = DriftStream(\n", " stream=[\n", " SEA(1),\n", " AbruptDrift(10000),\n", " SEA(2),\n", " GradualDrift(start=20000, end=25000),\n", " SEA(3),\n", " AbruptDrift(45000),\n", " SEA(1),\n", " ]\n", ")\n", "\n", "arf = AdaptiveRandomForestClassifier(\n", " schema=SEA3drifts.get_schema(), ensemble_size=100, number_of_jobs=4\n", ")\n", "\n", "results = prequential_evaluation(\n", " stream=SEA3drifts, learner=arf, window_size=5000, max_instances=50000\n", ")\n", "\n", "print(f\"Cumulative accuracy = {results.cumulative.accuracy()}\")\n", "print(f\"wallclock = {results.wallclock()} seconds\")\n", "display(results.windowed.metrics_per_window())\n", "plot_windowed_results(results, metric=\"accuracy\")" ] }, { "cell_type": "markdown", "id": "2ce76f5f-a427-4ef1-a8ba-14dc4b65a4ac", "metadata": {}, "source": [ "## 8. AutoML with AutoClass\n", "\n", "The following example shows how to use the AutoClass algorithm using CapyMOA. \n", "* AutoClass is configured using a json configuration file `configuration_json` and a list of classifiers `base_classifiers`\n", "* AutoClass can also be configured with either a list of strings `base_classifiers` representing the MOA classifiers. This approach is only enticing for people that are very familiar with MOA. \n", "* In the example below, we also compare it against using the base classifiers individually\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "1b658289-9802-469f-b607-fc9f1ae12118", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:30:25.565685Z", "iopub.status.busy": "2024-09-23T00:30:25.565537Z", "iopub.status.idle": "2024-09-23T00:31:37.785055Z", "shell.execute_reply": "2024-09-23T00:31:37.784289Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[HT] Cumulative accuracy = 53.396, wall-clock time: 0.25939011573791504\n", "[HAT] Cumulative accuracy = 57.676, wall-clock time: 0.3388500213623047\n", "[KNN] Cumulative accuracy = 86.956, wall-clock time: 2.3922243118286133\n", "[AUTOCLASS] Cumulative accuracy = 86.268, wall-clock time: 68.99542450904846\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.datasets import RBFm_100k\n", "from capymoa.automl import AutoClass\n", "from capymoa.classifier import HoeffdingTree, HoeffdingAdaptiveTree, KNN\n", "from capymoa.evaluation.visualization import plot_windowed_results\n", "\n", "rbf_100k = RBFm_100k()\n", "\n", "max_instances = 25000\n", "window_size = 2500\n", "\n", "ht = HoeffdingTree(schema=rbf_100k.get_schema())\n", "hat = HoeffdingAdaptiveTree(schema=rbf_100k.get_schema())\n", "knn = KNN(schema=rbf_100k.get_schema())\n", "autoclass = AutoClass(\n", " schema=rbf_100k.get_schema(),\n", " configuration_json=\"./settings_autoclass.json\",\n", " base_classifiers=[KNN, HoeffdingAdaptiveTree, HoeffdingTree],\n", ")\n", "\n", "results_ht = prequential_evaluation(\n", " stream=rbf_100k, learner=ht, window_size=window_size, max_instances=max_instances\n", ")\n", "results_hat = prequential_evaluation(\n", " stream=rbf_100k, learner=hat, window_size=window_size, max_instances=max_instances\n", ")\n", "results_knn = prequential_evaluation(\n", " stream=rbf_100k, learner=knn, window_size=window_size, max_instances=max_instances\n", ")\n", "results_autoclass = prequential_evaluation(\n", " stream=rbf_100k,\n", " learner=autoclass,\n", " window_size=window_size,\n", " max_instances=max_instances,\n", ")\n", "\n", "print(\n", " f\"[HT] Cumulative accuracy = {results_ht.accuracy()}, wall-clock time: {results_ht.wallclock()}\"\n", ")\n", "print(\n", " f\"[HAT] Cumulative accuracy = {results_hat.accuracy()}, wall-clock time: {results_hat.wallclock()}\"\n", ")\n", "print(\n", " f\"[KNN] Cumulative accuracy = {results_knn.accuracy()}, wall-clock time: {results_knn.wallclock()}\"\n", ")\n", "print(\n", " f\"[AUTOCLASS] Cumulative accuracy = {results_autoclass.accuracy()}, wall-clock time: {results_autoclass.wallclock()}\"\n", ")\n", "plot_windowed_results(\n", " results_ht, results_knn, results_hat, results_autoclass, metric=\"accuracy\"\n", ")" ] }, { "cell_type": "markdown", "id": "dbeb8d3b-9527-49dc-8550-e3ceb0f09965", "metadata": {}, "source": [ "### 8.1 AutoClass alternative syntax\n", "\n", "Another way to configure the learners is by using a list of strings `base_classifiers` representing the MOA classifiers" ] }, { "cell_type": "code", "execution_count": 14, "id": "77ef4a93-7e3b-4429-8715-78f00ea47dda", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:31:37.787261Z", "iopub.status.busy": "2024-09-23T00:31:37.787098Z", "iopub.status.idle": "2024-09-23T00:31:38.147333Z", "shell.execute_reply": "2024-09-23T00:31:38.146850Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.automl import AutoClass\n", "from capymoa.datasets import RBFm_100k\n", "from capymoa.classifier import KNN, HoeffdingTree, HoeffdingAdaptiveTree, OnlineBagging\n", "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.evaluation.visualization import plot_windowed_results\n", "\n", "rbf_100k = RBFm_100k()\n", "\n", "autoclass = AutoClass(\n", " schema=rbf_100k.get_schema(),\n", " configuration_json=\"./settings_autoclass.json\",\n", " base_classifiers=[KNN, HoeffdingTree, HoeffdingAdaptiveTree],\n", ")\n", "\n", "autoclass_MOAStrings = AutoClass(\n", " schema=rbf_100k.get_schema(),\n", " configuration_json=\"./settings_autoclass.json\",\n", " base_classifiers=[\"lazy.kNN\", \"trees.HoeffdingTree\", \"trees.HoeffdingAdaptiveTree\"],\n", ")\n", "\n", "results_autoClass = prequential_evaluation(\n", " stream=rbf_100k, learner=autoclass, window_size=100, max_instances=500\n", ")\n", "results_autoclass_MOAStrings = prequential_evaluation(\n", " stream=rbf_100k, learner=autoclass_MOAStrings, window_size=100, max_instances=500\n", ")\n", "\n", "results_autoclass_MOAStrings.learner = \"AutoClass_MOAStrings\"\n", "\n", "plot_windowed_results(\n", " results_autoClass, results_autoclass_MOAStrings, metric=\"accuracy\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "c0b04d99-1142-4f07-b7a1-7de6bba3843f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.19" } }, "nbformat": 4, "nbformat_minor": 5 }