# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. """Placeholder docstring""" from __future__ import absolute_import import logging from typing import Union, Optional from sagemaker import image_uris from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase from sagemaker.amazon.common import RecordSerializer, RecordDeserializer from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa from sagemaker.amazon.validation import isin, gt, lt, ge, le from sagemaker.predictor import Predictor from sagemaker.model import Model from sagemaker.session import Session from sagemaker.utils import pop_out_unused_kwarg from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT from sagemaker.workflow.entities import PipelineVariable logger = logging.getLogger(__name__) class LinearLearner(AmazonAlgorithmEstimatorBase): """A supervised learning algorithms used for solving classification or regression problems. For input, you give the model labeled examples (x, y). x is a high-dimensional vector and y is a numeric label. For binary classification problems, the label must be either 0 or 1. For multiclass classification problems, the labels must be from 0 to num_classes - 1. For regression problems, y is a real number. The algorithm learns a linear function, or, for classification problems, a linear threshold function, and maps a vector x to an approximation of the label y. """ repo_name: str = "linear-learner" repo_version: str = "1" DEFAULT_MINI_BATCH_SIZE: int = 1000 binary_classifier_model_selection_criteria: hp = hp( "binary_classifier_model_selection_criteria", isin( "accuracy", "f1", "f_beta", "precision_at_target_recall", "recall_at_target_precision", "cross_entropy_loss", "loss_function", ), data_type=str, ) target_recall: hp = hp("target_recall", (gt(0), lt(1)), "A float in (0,1)", float) target_precision: hp = hp("target_precision", (gt(0), lt(1)), "A float in (0,1)", float) positive_example_weight_mult: hp = hp( "positive_example_weight_mult", (), "A float greater than 0 or 'auto' or 'balanced'", str ) epochs: hp = hp("epochs", gt(0), "An integer greater-than 0", int) predictor_type: hp = hp( "predictor_type", isin("binary_classifier", "regressor", "multiclass_classifier"), 'One of "binary_classifier" or "multiclass_classifier" or "regressor"', str, ) use_bias: hp = hp("use_bias", (), "Either True or False", bool) num_models: hp = hp("num_models", gt(0), "An integer greater-than 0", int) num_calibration_samples: hp = hp( "num_calibration_samples", gt(0), "An integer greater-than 0", int ) init_method: hp = hp( "init_method", isin("uniform", "normal"), 'One of "uniform" or "normal"', str ) init_scale: hp = hp("init_scale", gt(0), "A float greater-than 0", float) init_sigma: hp = hp("init_sigma", gt(0), "A float greater-than 0", float) init_bias: hp = hp("init_bias", (), "A number", float) optimizer: hp = hp( "optimizer", isin("sgd", "adam", "rmsprop", "auto"), 'One of "sgd", "adam", "rmsprop" or "auto', str, ) loss: hp = hp( "loss", isin( "logistic", "squared_loss", "absolute_loss", "hinge_loss", "eps_insensitive_squared_loss", "eps_insensitive_absolute_loss", "quantile_loss", "huber_loss", "softmax_loss", "auto", ), '"logistic", "squared_loss", "absolute_loss", "hinge_loss", "eps_insensitive_squared_loss",' ' "eps_insensitive_absolute_loss", "quantile_loss", "huber_loss", "softmax_loss" or "auto"', str, ) wd: hp = hp("wd", ge(0), "A float greater-than or equal to 0", float) l1: hp = hp("l1", ge(0), "A float greater-than or equal to 0", float) momentum: hp = hp("momentum", (ge(0), lt(1)), "A float in [0,1)", float) learning_rate: hp = hp("learning_rate", gt(0), "A float greater-than 0", float) beta_1: hp = hp("beta_1", (ge(0), lt(1)), "A float in [0,1)", float) beta_2: hp = hp("beta_2", (ge(0), lt(1)), "A float in [0,1)", float) bias_lr_mult: hp = hp("bias_lr_mult", gt(0), "A float greater-than 0", float) bias_wd_mult: hp = hp("bias_wd_mult", ge(0), "A float greater-than or equal to 0", float) use_lr_scheduler: hp = hp("use_lr_scheduler", (), "A boolean", bool) lr_scheduler_step: hp = hp("lr_scheduler_step", gt(0), "An integer greater-than 0", int) lr_scheduler_factor: hp = hp("lr_scheduler_factor", (gt(0), lt(1)), "A float in (0,1)", float) lr_scheduler_minimum_lr: hp = hp( "lr_scheduler_minimum_lr", gt(0), "A float greater-than 0", float ) normalize_data: hp = hp("normalize_data", (), "A boolean", bool) normalize_label: hp = hp("normalize_label", (), "A boolean", bool) unbias_data: hp = hp("unbias_data", (), "A boolean", bool) unbias_label: hp = hp("unbias_label", (), "A boolean", bool) num_point_for_scaler: hp = hp("num_point_for_scaler", gt(0), "An integer greater-than 0", int) margin: hp = hp("margin", ge(0), "A float greater-than or equal to 0", float) quantile: hp = hp("quantile", (gt(0), lt(1)), "A float in (0,1)", float) loss_insensitivity: hp = hp("loss_insensitivity", gt(0), "A float greater-than 0", float) huber_delta: hp = hp("huber_delta", ge(0), "A float greater-than or equal to 0", float) early_stopping_patience: hp = hp( "early_stopping_patience", gt(0), "An integer greater-than 0", int ) early_stopping_tolerance: hp = hp( "early_stopping_tolerance", gt(0), "A float greater-than 0", float ) num_classes: hp = hp("num_classes", (gt(0), le(1000000)), "An integer in [1,1000000]", int) accuracy_top_k: hp = hp( "accuracy_top_k", (gt(0), le(1000000)), "An integer in [1,1000000]", int ) f_beta: hp = hp("f_beta", gt(0), "A float greater-than 0", float) balance_multiclass_weights: hp = hp("balance_multiclass_weights", (), "A boolean", bool) def __init__( self, role: Optional[Union[str, PipelineVariable]] = None, instance_count: Optional[Union[int, PipelineVariable]] = None, instance_type: Optional[Union[str, PipelineVariable]] = None, predictor_type: Optional[str] = None, binary_classifier_model_selection_criteria: Optional[str] = None, target_recall: Optional[float] = None, target_precision: Optional[float] = None, positive_example_weight_mult: Optional[float] = None, epochs: Optional[int] = None, use_bias: Optional[bool] = None, num_models: Optional[int] = None, num_calibration_samples: Optional[int] = None, init_method: Optional[str] = None, init_scale: Optional[float] = None, init_sigma: Optional[float] = None, init_bias: Optional[float] = None, optimizer: Optional[str] = None, loss: Optional[str] = None, wd: Optional[float] = None, l1: Optional[float] = None, momentum: Optional[float] = None, learning_rate: Optional[float] = None, beta_1: Optional[float] = None, beta_2: Optional[float] = None, bias_lr_mult: Optional[float] = None, bias_wd_mult: Optional[float] = None, use_lr_scheduler: Optional[bool] = None, lr_scheduler_step: Optional[int] = None, lr_scheduler_factor: Optional[float] = None, lr_scheduler_minimum_lr: Optional[float] = None, normalize_data: Optional[bool] = None, normalize_label: Optional[bool] = None, unbias_data: Optional[bool] = None, unbias_label: Optional[bool] = None, num_point_for_scaler: Optional[int] = None, margin: Optional[float] = None, quantile: Optional[float] = None, loss_insensitivity: Optional[float] = None, huber_delta: Optional[float] = None, early_stopping_patience: Optional[int] = None, early_stopping_tolerance: Optional[float] = None, num_classes: Optional[int] = None, accuracy_top_k: Optional[int] = None, f_beta: Optional[float] = None, balance_multiclass_weights: Optional[bool] = None, **kwargs ): """An :class:`Estimator` for binary classification and regression. Amazon SageMaker Linear Learner provides a solution for both classification and regression problems, allowing for exploring different training objectives simultaneously and choosing the best solution from a validation set. It allows the user to explore a large number of models and choose the best, which optimizes either continuous objectives such as mean square error, cross entropy loss, absolute error, etc., or discrete objectives suited for classification such as F1 measure, precision@recall, accuracy. The implementation provides a significant speedup over naive hyperparameter optimization techniques and an added convenience, when compared with solutions providing a solution only to continuous objectives. This Estimator may be fit via calls to :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit_ndarray` or :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`. The former allows a LinearLearner model to be fit on a 2-dimensional numpy array. The latter requires Amazon :class:`~sagemaker.amazon.record_pb2.Record` protobuf serialized data to be stored in S3. To learn more about the Amazon protobuf Record class and how to prepare bulk data in this format, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html After this Estimator is fit, model data is stored in S3. The model may be deployed to an Amazon SageMaker Endpoint by invoking :meth:`~sagemaker.amazon.estimator.EstimatorBase.deploy`. As well as deploying an Endpoint, ``deploy`` returns a :class:`~sagemaker.amazon.linear_learner.LinearLearnerPredictor` object that can be used to make class or regression predictions, using the trained model. LinearLearner Estimators can be configured by setting hyperparameters. The available hyperparameters for LinearLearner are documented below. For further information on the AWS LinearLearner algorithm, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html Args: role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if accessing AWS resource. instance_count (int or PipelineVariable): Number of Amazon EC2 instances to use for training. instance_type (str or PipelineVariable): Type of EC2 instance to use for training, for example, 'ml.c4.xlarge'. predictor_type (str): The type of predictor to learn. Either "binary_classifier" or "multiclass_classifier" or "regressor". binary_classifier_model_selection_criteria (str): One of 'accuracy', 'f1', 'f_beta', 'precision_at_target_recall', 'recall_at_target_precision', 'cross_entropy_loss', 'loss_function' target_recall (float): Target recall. Only applicable if binary_classifier_model_selection_criteria is precision_at_target_recall. target_precision (float): Target precision. Only applicable if binary_classifier_model_selection_criteria is recall_at_target_precision. positive_example_weight_mult (float): The importance weight of positive examples is multiplied by this constant. Useful for skewed datasets. Only applies for classification tasks. epochs (int): The maximum number of passes to make over the training data. use_bias (bool): Whether to include a bias field num_models (int): Number of models to train in parallel. If not set, the number of parallel models to train will be decided by the algorithm itself. One model will be trained according to the given training parameter (regularization, optimizer, loss) and the rest by close by parameters. num_calibration_samples (int): Number of observations to use from validation dataset for doing model calibration (finding the best threshold). init_method (str): Function to use to set the initial model weights. One of "uniform" or "normal" init_scale (float): For "uniform" init, the range of values. init_sigma (float): For "normal" init, the standard-deviation. init_bias (float): Initial weight for bias term optimizer (str): One of 'sgd', 'adam', 'rmsprop' or 'auto' loss (str): One of 'logistic', 'squared_loss', 'absolute_loss', 'hinge_loss', 'eps_insensitive_squared_loss', 'eps_insensitive_absolute_loss', 'quantile_loss', 'huber_loss' or 'softmax_loss' or 'auto'. wd (float): L2 regularization parameter i.e. the weight decay parameter. Use 0 for no L2 regularization. l1 (float): L1 regularization parameter. Use 0 for no L1 regularization. momentum (float): Momentum parameter of sgd optimizer. learning_rate (float): The SGD learning rate beta_1 (float): Exponential decay rate for first moment estimates. Only applies for adam optimizer. beta_2 (float): Exponential decay rate for second moment estimates. Only applies for adam optimizer. bias_lr_mult (float): Allows different learning rate for the bias term. The actual learning rate for the bias is learning rate times bias_lr_mult. bias_wd_mult (float): Allows different regularization for the bias term. The actual L2 regularization weight for the bias is wd times bias_wd_mult. By default there is no regularization on the bias term. use_lr_scheduler (bool): If true, we use a scheduler for the learning rate. lr_scheduler_step (int): The number of steps between decreases of the learning rate. Only applies to learning rate scheduler. lr_scheduler_factor (float): Every lr_scheduler_step the learning rate will decrease by this quantity. Only applies for learning rate scheduler. lr_scheduler_minimum_lr (float): The learning rate will never decrease to a value lower than this. Only applies for learning rate scheduler. normalize_data (bool): Normalizes the features before training to have standard deviation of 1.0. normalize_label (bool): Normalizes the regression label to have a standard deviation of 1.0. If set for classification, it will be ignored. unbias_data (bool): If true, features are modified to have mean 0.0. unbias_label (bool): If true, labels are modified to have mean 0.0. num_point_for_scaler (int): The number of data points to use for calculating the normalizing and unbiasing terms. margin (float): the margin for hinge_loss. quantile (float): Quantile for quantile loss. For quantile q, the model will attempt to produce predictions such that true_label < prediction with probability q. loss_insensitivity (float): Parameter for epsilon insensitive loss type. During training and metric evaluation, any error smaller than this is considered to be zero. huber_delta (float): Parameter for Huber loss. During training and metric evaluation, compute L2 loss for errors smaller than delta and L1 loss for errors larger than delta. early_stopping_patience (int): the number of epochs to wait before ending training if no improvement is made. The improvement is training loss if validation data is not provided, or else it is the validation loss or the binary classification model selection criteria like accuracy, f1-score etc. To disable early stopping, set early_stopping_patience to a value larger than epochs. early_stopping_tolerance (float): Relative tolerance to measure an improvement in loss. If the ratio of the improvement in loss divided by the previous best loss is smaller than this value, early stopping will consider the improvement to be zero. num_classes (int): The number of classes for the response variable. Required when predictor_type is multiclass_classifier and ignored otherwise. The classes are assumed to be labeled 0, ..., num_classes - 1. accuracy_top_k (int): The value of k when computing the Top K Accuracy metric for multiclass classification. An example is scored as correct if the model assigns one of the top k scores to the true label. f_beta (float): The value of beta to use when calculating F score metrics for binary or multiclass classification. Also used if binary_classifier_model_selection_criteria is f_beta. balance_multiclass_weights (bool): Whether to use class weights which give each class equal importance in the loss function. Only used when predictor_type is multiclass_classifier. **kwargs: base class keyword argument values. .. tip:: You can find additional parameters for initializing this class at :class:`~sagemaker.estimator.amazon_estimator.AmazonAlgorithmEstimatorBase` and :class:`~sagemaker.estimator.EstimatorBase`. """ super(LinearLearner, self).__init__(role, instance_count, instance_type, **kwargs) self.predictor_type = predictor_type self.binary_classifier_model_selection_criteria = binary_classifier_model_selection_criteria self.target_recall = target_recall self.target_precision = target_precision self.positive_example_weight_mult = positive_example_weight_mult self.epochs = epochs self.use_bias = use_bias self.num_models = num_models self.num_calibration_samples = num_calibration_samples self.init_method = init_method self.init_scale = init_scale self.init_sigma = init_sigma self.init_bias = init_bias self.optimizer = optimizer self.loss = loss self.wd = wd self.l1 = l1 self.momentum = momentum self.learning_rate = learning_rate self.beta_1 = beta_1 self.beta_2 = beta_2 self.bias_lr_mult = bias_lr_mult self.bias_wd_mult = bias_wd_mult self.use_lr_scheduler = use_lr_scheduler self.lr_scheduler_step = lr_scheduler_step self.lr_scheduler_factor = lr_scheduler_factor self.lr_scheduler_minimum_lr = lr_scheduler_minimum_lr self.normalize_data = normalize_data self.normalize_label = normalize_label self.unbias_data = unbias_data self.unbias_label = unbias_label self.num_point_for_scaler = num_point_for_scaler self.margin = margin self.quantile = quantile self.loss_insensitivity = loss_insensitivity self.huber_delta = huber_delta self.early_stopping_patience = early_stopping_patience self.early_stopping_tolerance = early_stopping_tolerance self.num_classes = num_classes self.accuracy_top_k = accuracy_top_k self.f_beta = f_beta self.balance_multiclass_weights = balance_multiclass_weights if self.predictor_type == "multiclass_classifier" and ( num_classes is None or int(num_classes) < 3 ): raise ValueError( "For predictor_type 'multiclass_classifier', 'num_classes' should be set to a " "value greater than 2." ) def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs): """Return a :class:`~sagemaker.amazon.LinearLearnerModel`. It references the latest s3 model data produced by this Estimator. Args: vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on the model. Default: use subnets and security groups from this Estimator. * 'Subnets' (list[str]): List of subnet ids. * 'SecurityGroupIds' (list[str]): List of security group ids. **kwargs: Additional kwargs passed to the LinearLearnerModel constructor. """ return LinearLearnerModel( self.model_data, self.role, self.sagemaker_session, vpc_config=self.get_vpc_config(vpc_config_override), **kwargs ) def _prepare_for_training(self, records, mini_batch_size=None, job_name=None): """Placeholder docstring""" num_records = None if isinstance(records, list): for record in records: if record.channel == "train": num_records = record.num_records break if num_records is None: raise ValueError("Must provide train channel.") else: num_records = records.num_records # mini_batch_size can't be greater than number of records or training job fails mini_batch_size = mini_batch_size or self._get_default_mini_batch_size(num_records) super(LinearLearner, self)._prepare_for_training( records, mini_batch_size=mini_batch_size, job_name=job_name ) class LinearLearnerPredictor(Predictor): """Performs binary-classification or regression prediction from input vectors. The implementation of :meth:`~sagemaker.predictor.Predictor.predict` in this `Predictor` requires a numpy ``ndarray`` as input. The array should contain the same number of columns as the feature-dimension of the data used to fit the model this Predictor performs inference on. :func:`predict` returns a list of :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default recordio-protobuf ``deserializer`` is used), one for each row in the input ``ndarray``. The prediction is stored in the ``"predicted_label"`` key of the ``Record.label`` field. """ def __init__( self, endpoint_name, sagemaker_session=None, serializer=RecordSerializer(), deserializer=RecordDeserializer(), ): """Initialization for LinearLearnerPredictor. Args: endpoint_name (str): Name of the Amazon SageMaker endpoint to which requests are sent. sagemaker_session (sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain. serializer (sagemaker.serializers.BaseSerializer): Optional. Default serializes input data to x-recordio-protobuf format. deserializer (sagemaker.deserializers.BaseDeserializer): Optional. Default parses responses from x-recordio-protobuf format. """ super(LinearLearnerPredictor, self).__init__( endpoint_name, sagemaker_session, serializer=serializer, deserializer=deserializer, ) class LinearLearnerModel(Model): """Reference LinearLearner s3 model data. Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and returns a :class:`LinearLearnerPredictor` """ def __init__( self, model_data: Union[str, PipelineVariable], role: Optional[str] = None, sagemaker_session: Optional[Session] = None, **kwargs ): """Initialization for LinearLearnerModel. Args: model_data (str or PipelineVariable): The S3 location of a SageMaker model data ``.tar.gz`` file. role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource. sagemaker_session (sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one using the default AWS configuration chain. **kwargs: Keyword arguments passed to the ``FrameworkModel`` initializer. """ sagemaker_session = sagemaker_session or Session() image_uri = image_uris.retrieve( LinearLearner.repo_name, sagemaker_session.boto_region_name, version=LinearLearner.repo_version, ) pop_out_unused_kwarg("predictor_cls", kwargs, LinearLearnerPredictor.__name__) pop_out_unused_kwarg("image_uri", kwargs, image_uri) super(LinearLearnerModel, self).__init__( image_uri, model_data, role, predictor_cls=LinearLearnerPredictor, sagemaker_session=sagemaker_session, **kwargs )