{ "cells": [ { "cell_type": "markdown", "id": "measured-liabilities", "metadata": {}, "source": [ "# Saving, Loading Qiskit Machine Learning Models and Continuous Training\n", "\n", "In this tutorial we will show how to save and load Qiskit machine learning models. Ability to save a model is very important, especially when a significant amount of time is invested in training a model on a real hardware. Also, we will show how to resume training of the previously saved model.\n", "\n", "In this tutorial we will cover how to:\n", "\n", "* Generate a simple dataset, split it into training/test datasets and plot them\n", "* Train and save a model\n", "* Load a saved model and resume training\n", "* Evaluate performance of models\n", "* PyTorch hybrid models" ] }, { "cell_type": "markdown", "id": "speaking-glance", "metadata": {}, "source": [ "First off, we start from the required imports. We'll heavily use SciKit-Learn on the data preparation step. In the next cell we also fix a random seed for reproducibility purposes." ] }, { "cell_type": "code", "execution_count": 1, "id": "exposed-cholesterol", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from qiskit.circuit.library import RealAmplitudes\n", "from qiskit.primitives import Sampler\n", "from qiskit_algorithms.optimizers import COBYLA\n", "from qiskit_algorithms.utils import algorithm_globals\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler\n", "\n", "from qiskit_machine_learning.algorithms.classifiers import VQC\n", "\n", "from IPython.display import clear_output\n", "\n", "algorithm_globals.random_seed = 42" ] }, { "cell_type": "markdown", "id": "rural-mileage", "metadata": {}, "source": [ "We will be using two quantum simulators, in particular, two instances of the `Sampler` primitive. We'll start training on the first one, then will resume training on the second one. The approach shown in this tutorial can be used to train a model on a real hardware available on the cloud and then re-use the model for inference on a local simulator." ] }, { "cell_type": "code", "execution_count": 2, "id": "charming-seating", "metadata": {}, "outputs": [], "source": [ "sampler1 = Sampler()\n", "\n", "sampler2 = Sampler()" ] }, { "cell_type": "markdown", "id": "careful-allowance", "metadata": {}, "source": [ "## 1. Prepare a dataset\n", "\n", "Next step is to prepare a dataset. Here, we generate some data in the same way as in other tutorials. The difference is that we apply some transformations to the generated data. We generates `40` samples, each sample has `2` features, so our features is an array of shape `(40, 2)`. Labels are obtained by summing up features by columns and if the sum is more than `1` then this sample is labeled as `1` and `0` otherwise." ] }, { "cell_type": "code", "execution_count": 3, "id": "ceramic-florida", "metadata": {}, "outputs": [], "source": [ "num_samples = 40\n", "num_features = 2\n", "features = 2 * algorithm_globals.random.random([num_samples, num_features]) - 1\n", "labels = 1 * (np.sum(features, axis=1) >= 0) # in { 0, 1}" ] }, { "cell_type": "markdown", "id": "reduced-injury", "metadata": {}, "source": [ "Then, we scale down our features into a range of `[0, 1]` by applying `MinMaxScaler` from SciKit-Learn. Model training convergence is better when this transformation is applied." ] }, { "cell_type": "code", "execution_count": 4, "id": "dirty-director", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(40, 2)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features = MinMaxScaler().fit_transform(features)\n", "features.shape" ] }, { "cell_type": "markdown", "id": "julian-amount", "metadata": {}, "source": [ "Let's take a look at the features of the first `5` samples of our dataset after the transformation." ] }, { "cell_type": "code", "execution_count": 5, "id": "thorough-script", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.79067335, 0.44566143],\n", " [0.88072937, 0.7126244 ],\n", " [0.06741233, 1. ],\n", " [0.7770372 , 0.80422817],\n", " [0.10351936, 0.45754615]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features[0:5, :]" ] }, { "cell_type": "markdown", "id": "racial-aluminum", "metadata": {}, "source": [ "We choose `VQC` or Variational Quantum Classifier as a model we will train. This model, by default, takes one-hot encoded labels, so we have to transform the labels that are in the set of `{0, 1}` into one-hot representation. We employ SciKit-Learn for this transformation as well. Please note that the input array must be reshaped to `(num_samples, 1)` first. The `OneHotEncoder` encoder does not work with 1D arrays and our labels is a 1D array. In this case a user must decide either an array has only one feature(our case!) or has one sample. Also, by default the encoder returns sparse arrays, but for dataset plotting it is easier to have dense arrays, so we set `sparse` to `False`. " ] }, { "cell_type": "code", "execution_count": 6, "id": "understood-ukraine", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(40, 2)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = OneHotEncoder(sparse_output=False).fit_transform(labels.reshape(-1, 1))\n", "labels.shape" ] }, { "cell_type": "markdown", "id": "statewide-symbol", "metadata": {}, "source": [ "Let's take a look at the labels of the first `5` labels of the dataset. The labels should be one-hot encoded." ] }, { "cell_type": "code", "execution_count": 7, "id": "german-agreement", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0., 1.],\n", " [0., 1.],\n", " [0., 1.],\n", " [0., 1.],\n", " [1., 0.]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[0:5, :]" ] }, { "cell_type": "markdown", "id": "aquatic-toner", "metadata": {}, "source": [ "Now we split our dataset into two parts: a training dataset and a test one. As a rule of thumb, 80% of a full dataset should go into a training part and 20% into a test one. Our training dataset has `30` samples. The test dataset should be used only once, when a model is trained to verify how well the model behaves on unseen data. We employ `train_test_split` from SciKit-Learn." ] }, { "cell_type": "code", "execution_count": 8, "id": "about-ordinary", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(30, 2)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_features, test_features, train_labels, test_labels = train_test_split(\n", " features, labels, train_size=30, random_state=algorithm_globals.random_seed\n", ")\n", "train_features.shape" ] }, { "cell_type": "markdown", "id": "critical-angel", "metadata": {}, "source": [ "Now it is time to see how our dataset looks like. Let's plot it." ] }, { "cell_type": "code", "execution_count": 9, "id": "fifty-scottish", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_dataset():\n", " plt.scatter(\n", " train_features[np.where(train_labels[:, 0] == 0), 0],\n", " train_features[np.where(train_labels[:, 0] == 0), 1],\n", " marker=\"o\",\n", " color=\"b\",\n", " label=\"Label 0 train\",\n", " )\n", " plt.scatter(\n", " train_features[np.where(train_labels[:, 0] == 1), 0],\n", " train_features[np.where(train_labels[:, 0] == 1), 1],\n", " marker=\"o\",\n", " color=\"g\",\n", " label=\"Label 1 train\",\n", " )\n", "\n", " plt.scatter(\n", " test_features[np.where(test_labels[:, 0] == 0), 0],\n", " test_features[np.where(test_labels[:, 0] == 0), 1],\n", " marker=\"o\",\n", " facecolors=\"w\",\n", " edgecolors=\"b\",\n", " label=\"Label 0 test\",\n", " )\n", " plt.scatter(\n", " test_features[np.where(test_labels[:, 0] == 1), 0],\n", " test_features[np.where(test_labels[:, 0] == 1), 1],\n", " marker=\"o\",\n", " facecolors=\"w\",\n", " edgecolors=\"g\",\n", " label=\"Label 1 test\",\n", " )\n", "\n", " plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\", borderaxespad=0.0)\n", " plt.plot([1, 0], [0, 1], \"--\", color=\"black\")\n", "\n", "\n", "plot_dataset()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "regulation-depression", "metadata": {}, "source": [ "On the plot above we see:\n", "\n", "* Solid blue dots are the samples from the training dataset labeled as `0`\n", "* Empty blue dots are the samples from the test dataset labeled as `0`\n", "* Solid green dots are the samples from the training dataset labeled as `1`\n", "* Empty green dots are the samples from the test dataset labeled as `1`\n", "\n", "We'll train our model using solid dots and verify it using empty dots." ] }, { "cell_type": "markdown", "id": "egyptian-campaign", "metadata": {}, "source": [ "## 2. Train a model and save it\n", "\n", "We'll train our model in two steps. On the first step we train our model in `20` iterations." ] }, { "cell_type": "code", "execution_count": 10, "id": "brief-lending", "metadata": {}, "outputs": [], "source": [ "maxiter = 20" ] }, { "cell_type": "markdown", "id": "crude-franklin", "metadata": {}, "source": [ "Create an empty array for callback to store values of the objective function." ] }, { "cell_type": "code", "execution_count": 11, "id": "integrated-palestinian", "metadata": {}, "outputs": [], "source": [ "objective_values = []" ] }, { "cell_type": "markdown", "id": "legendary-sherman", "metadata": {}, "source": [ "We re-use a callback function from the Neural Network Classifier & Regressor tutorial to plot iteration versus objective function value with some minor tweaks to plot objective values at each step." ] }, { "cell_type": "code", "execution_count": 12, "id": "periodic-apparel", "metadata": {}, "outputs": [], "source": [ "# callback function that draws a live plot when the .fit() method is called\n", "def callback_graph(_, objective_value):\n", " clear_output(wait=True)\n", " objective_values.append(objective_value)\n", "\n", " plt.title(\"Objective function value against iteration\")\n", " plt.xlabel(\"Iteration\")\n", " plt.ylabel(\"Objective function value\")\n", "\n", " stage1_len = np.min((len(objective_values), maxiter))\n", " stage1_x = np.linspace(1, stage1_len, stage1_len)\n", " stage1_y = objective_values[:stage1_len]\n", "\n", " stage2_len = np.max((0, len(objective_values) - maxiter))\n", " stage2_x = np.linspace(maxiter, maxiter + stage2_len - 1, stage2_len)\n", " stage2_y = objective_values[maxiter : maxiter + stage2_len]\n", "\n", " plt.plot(stage1_x, stage1_y, color=\"orange\")\n", " plt.plot(stage2_x, stage2_y, color=\"purple\")\n", " plt.show()\n", "\n", "\n", "plt.rcParams[\"figure.figsize\"] = (12, 6)" ] }, { "cell_type": "markdown", "id": "institutional-cyprus", "metadata": {}, "source": [ "As mentioned above we train a `VQC` model and set `COBYLA` as an optimizer with a chosen value of the `maxiter` parameter. Then we evaluate performance of the model to see how well it was trained. Then we save this model for a file. On the second step we load this model and will continue to work with it.\n", "\n", "Here, we manually construct an ansatz to fix an initial point where to start optimization from." ] }, { "cell_type": "code", "execution_count": 13, "id": "electronic-impact", "metadata": {}, "outputs": [], "source": [ "original_optimizer = COBYLA(maxiter=maxiter)\n", "\n", "ansatz = RealAmplitudes(num_features)\n", "initial_point = np.asarray([0.5] * ansatz.num_parameters)" ] }, { "cell_type": "markdown", "id": "separated-classroom", "metadata": {}, "source": [ "We create a model and set a sampler to the first sampler we created earlier." ] }, { "cell_type": "code", "execution_count": 14, "id": "revolutionary-freeze", "metadata": {}, "outputs": [], "source": [ "original_classifier = VQC(\n", " ansatz=ansatz, optimizer=original_optimizer, callback=callback_graph, sampler=sampler1\n", ")" ] }, { "cell_type": "markdown", "id": "minute-mexican", "metadata": {}, "source": [ "Now it is time to train the model." ] }, { "cell_type": "code", "execution_count": 15, "id": "suited-appointment", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "original_classifier.fit(train_features, train_labels)" ] }, { "cell_type": "markdown", "id": "revised-torture", "metadata": {}, "source": [ "Let's see how well our model performs after the first step of training." ] }, { "cell_type": "code", "execution_count": 16, "id": "greek-memphis", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score 0.8333333333333334\n", "Test score 0.8\n" ] } ], "source": [ "print(\"Train score\", original_classifier.score(train_features, train_labels))\n", "print(\"Test score \", original_classifier.score(test_features, test_labels))" ] }, { "cell_type": "markdown", "id": "rental-moses", "metadata": {}, "source": [ "Next, we save the model. You may choose any file name you want. Please note that the `save` method does not append an extension if it is not specified in the file name." ] }, { "cell_type": "code", "execution_count": 17, "id": "broadband-interview", "metadata": {}, "outputs": [], "source": [ "original_classifier.save(\"vqc_classifier.model\")" ] }, { "cell_type": "markdown", "id": "sitting-thread", "metadata": {}, "source": [ "## 3. Load a model and continue training\n", "\n", "To load a model a user have to call a class method `load` of the corresponding model class. In our case it is `VQC`. We pass the same file name we used in the previous section where we saved our model." ] }, { "cell_type": "code", "execution_count": 18, "id": "steady-europe", "metadata": {}, "outputs": [], "source": [ "loaded_classifier = VQC.load(\"vqc_classifier.model\")" ] }, { "cell_type": "markdown", "id": "reverse-shaft", "metadata": {}, "source": [ "Next, we want to alter the model in a way it can be trained further and on another simulator. To do so, we set the `warm_start` property. When it is set to `True` and `fit()` is called again the model uses weights from previous fit to start a new fit. We also set the `sampler` property of the underlying network to the second instance of the `Sampler` primitive we created in the beginning of the tutorial. Finally, we create and set a new optimizer with `maxiter` is set to `80`, so the total number of iterations is `100`." ] }, { "cell_type": "code", "execution_count": 19, "id": "accessible-cowboy", "metadata": {}, "outputs": [], "source": [ "loaded_classifier.warm_start = True\n", "loaded_classifier.neural_network.sampler = sampler2\n", "loaded_classifier.optimizer = COBYLA(maxiter=80)" ] }, { "cell_type": "markdown", "id": "revised-bruce", "metadata": {}, "source": [ "Now we continue training our model from the state we finished in the previous section." ] }, { "cell_type": "code", "execution_count": 20, "id": "metric-cyprus", "metadata": { "nbsphinx-thumbnail": { "output-index": 0 } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loaded_classifier.fit(train_features, train_labels)" ] }, { "cell_type": "code", "execution_count": 21, "id": "bronze-spread", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score 0.9\n", "Test score 0.8\n" ] } ], "source": [ "print(\"Train score\", loaded_classifier.score(train_features, train_labels))\n", "print(\"Test score\", loaded_classifier.score(test_features, test_labels))" ] }, { "cell_type": "markdown", "id": "apparent-bloom", "metadata": {}, "source": [ "Let's see which data points were misclassified. First, we call `predict` to infer predicted values from the training and test features." ] }, { "cell_type": "code", "execution_count": 22, "id": "catholic-norway", "metadata": {}, "outputs": [], "source": [ "train_predicts = loaded_classifier.predict(train_features)\n", "test_predicts = loaded_classifier.predict(test_features)" ] }, { "cell_type": "markdown", "id": "guided-croatia", "metadata": {}, "source": [ "Plot the whole dataset and the highlight the points that were classified incorrectly." ] }, { "cell_type": "code", "execution_count": 23, "id": "tested-handling", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# return plot to default figsize\n", "plt.rcParams[\"figure.figsize\"] = (6, 4)\n", "\n", "plot_dataset()\n", "\n", "# plot misclassified data points\n", "plt.scatter(\n", " train_features[np.all(train_labels != train_predicts, axis=1), 0],\n", " train_features[np.all(train_labels != train_predicts, axis=1), 1],\n", " s=200,\n", " facecolors=\"none\",\n", " edgecolors=\"r\",\n", " linewidths=2,\n", ")\n", "plt.scatter(\n", " test_features[np.all(test_labels != test_predicts, axis=1), 0],\n", " test_features[np.all(test_labels != test_predicts, axis=1), 1],\n", " s=200,\n", " facecolors=\"none\",\n", " edgecolors=\"r\",\n", " linewidths=2,\n", ")" ] }, { "cell_type": "markdown", "id": "genuine-preference", "metadata": {}, "source": [ "So, if you have a large dataset or a large model you can train it in multiple steps as shown in this tutorial." ] }, { "cell_type": "markdown", "id": "acknowledged-freight", "metadata": {}, "source": [ "## 4. PyTorch hybrid models\n", "\n", "To save and load hybrid models, when using the TorchConnector, follow the PyTorch recommendations of saving and loading the models. For more details please refer to the [PyTorch Connector tutorial](05_torch_connector.ipynb) where a short snippet shows how to do it.\n", "\n", "Take a look at this pseudo-like code to get the idea:\n", "```python\n", "# create a QNN and a hybrid model\n", "qnn = create_qnn()\n", "model = Net(qnn)\n", "# ... train the model ...\n", "\n", "# save the model\n", "torch.save(model.state_dict(), \"model.pt\")\n", "\n", "# create a new model\n", "new_qnn = create_qnn()\n", "loaded_model = Net(new_qnn)\n", "loaded_model.load_state_dict(torch.load(\"model.pt\"))\n", "```" ] }, { "cell_type": "code", "execution_count": 24, "id": "persistent-combine", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.25.0
qiskit-aer0.13.0
qiskit-machine-learning0.7.0
System information
Python version3.8.13
Python compilerClang 12.0.0
Python builddefault, Oct 19 2022 17:54:22
OSDarwin
CPUs10
Memory (Gb)64.0
Mon Jun 12 11:51:03 2023 IST
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

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This code is licensed under the Apache License, Version 2.0. You may
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Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import qiskit.tools.jupyter\n", "\n", "%qiskit_version_table\n", "%qiskit_copyright" ] } ], "metadata": { "celltoolbar": "Tags", "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": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 5 }