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    "info": {
        "author": "Mathieu Rodrigue",
        "author_email": "support@mlrequest.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "# Getting started\nmlrequest-python is a Python client for the [mlrequest](https://mlrequest.com) machine learning API. The client allows you to do a few significant things with only a few lines of code.\n\n* Deploy a latency-routed model to 5 data centers around the world, providing < 60ms global response time and automatic failover. No servers required.\n* Train a model with thousands of training examples per second\n* Get thousands of model predictions per second\n* Create online learning models (models that update incrementally, in real-time). Choose from classification, regression, and reinforcement learning model types.\n\nYou will need an API key to get started with mlrequest-python. You can obtain one for free that provides 10,000 model transactions at https://mlrequest.com/signup.html. The free plan is rate limited, for high throughput see our paid plans at https://mlrequest.com/pricing.html. Check out our [documentation](https://docs.mlrequest.com) for more information.\n\n## Install\n```\npip install mlrequest\n```\n## Create a Model\nModels are created automatically by calling one of the model endpoints below.\n\n## Classifier\n```python\nfrom mlrequest import Classifier\nclassifier = Classifier('your-api-key')\n\n# Learn single\ntraining_data = {features: {'feature1': 23.1, 'feature2': 'some-value'}, 'label': 1}\n# Learn batch\ntraining_data = [{features: {'feature1': 23.1, 'feature2': 'some-value'}, 'label': 1}, ...]\n\nr = classifier.learn(training_data=training_data, model_name='my-model', class_count=2)\nr.content # A single response or list of responses\n\n# Predict single\nfeatures = {'feature1': 23.1, 'feature2': 'some-value'}\n# Predict batch\nfeatures = [{'feature1': 23.1, 'feature2': 'some-value'}, ...]\n\nr = classifier.predict(features=features, model_name='my-model', class_count=2)\nr.predict_result # A single predicted class or a list of predicted classes\n```\n\n## Regression\n```python\nfrom mlrequest import Regression\nregression = Regression('your-api-key')\n\n# Learn single\ntraining_data = {features: {'feature1': 23.1, 'feature2': 'some-value'}, 'label': 1.25}\n# Learn batch\ntraining_data = [{features: {'feature1': 23.1, 'feature2': 'some-value'}, 'label': 1.25}, ...]\n\nr = regression.learn(training_data=training_data, model_name='my-model')\nr.content # A single response or list of responses\n\n# Predict single\nfeatures = {'feature1': 23.1, 'feature2': 'some-value'}\n# Predict batch\nfeatures = [{'feature1': 23.1, 'feature2': 'some-value'}, ...]\n\nr = regression.predict(features=features, model_name='my-model')\nr.predict_result # A single predicted value or a list of predicted values\n```\n\n## Reinforcement Learning\n```python\nfrom mlrequest import RL\nrl = RL('your-api-key')\n\n# Predict\n# Note: epsilon, and action_list fields are optional - see the docs at https://docs.mlrequest.com for more information\nfeatures = {'feature1': 23.1, 'feature2': 'some-value'}\n\nr = rl.predict(features=features, model_name='my-model', session_id='some-session-id', negative_reward=0, action_count=2)\nr.predict_result # A list of actions, ordered by rank (choose r.predict_data[0] for the best action)\n\n# Reward - important note: only the first action from predict_data should be rewarded. Other actions can be used but should not be rewarded.\nr = rl.reward(model_name=model_name, session_id='some_session', reward=1)\nr.content # A single response\n```\n\n## Account\n```python\n# Get account information\nr = account.get_details()\nr.content # Account info response\n\n# Delete a model\nr = account.delete_model(model_name='some-model')\nr.content # Delete success response\n```\n\n\n",
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        "name": "mlrequest",
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