ads.common package
Submodules
ads.common.card_identifier module
credit card patterns refer to https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN) Active and frequent card information American Express: 34, 37 Diners Club (US & Canada): 54,55 Discover Card: 6011, 622126 - 622925, 624000 - 626999, 628200 - 628899, 64, 65 Master Card: 2221-2720, 51–55 Visa: 4
ads.common.auth module
- ads.common.auth.api_keys(oci_config: str = '/home/docs/.oci/config', profile: str = 'DEFAULT', client_kwargs: Optional[dict] = None) dict
Prepares authentication and extra arguments necessary for creating clients for different OCI services using API Keys.
- Parameters
oci_config (str) – OCI authentication config file location. Default is $HOME/.oci/config.
profile (str) – Profile name to select from the config file. The defautl is DEFAULT
client_kwargs (dict) – kwargs that are required to instantiate the Client if we need to override the defaults.
- Returns
Contains keys - config, signer and client_kwargs.
The config contains the config loaded from the configuration loaded from oci_config.
The signer contains the signer object created from the api keys.
client_kwargs contains the client_kwargs that was passed in as input parameter.
- Return type
dict
Examples
>>> from ads.common import auth as authutil >>> from ads.common import oci_client as oc >>> auth = authutil.api_keys(oci_config="/home/datascience/.oci/config", profile="TEST", client_kwargs={"timeout": 6000}) >>> oc.OCIClientFactory(**auth).object_storage # Creates Object storage client with timeout set to 6000 using API Key authentication
- ads.common.auth.default_signer(client_kwargs=None)
Prepares authentication and extra arguments necessary for creating clients for different OCI services based on the default authentication setting for the session. Refer ads.set_auth API for further reference.
- Parameters
client_kwargs (dict) – kwargs that are required to instantiate the Client if we need to override the defaults.
- Returns
Contains keys - config, signer and client_kwargs.
The config contains the config loaded from the configuration loaded from the default location if the default auth mode is API keys, otherwise it is empty dictionary.
The signer contains the signer object created from default auth mode.
client_kwargs contains the client_kwargs that was passed in as input parameter.
- Return type
dict
Examples
>>> from ads.common import auth as authutil >>> from ads.common import oci_client as oc >>> auth = authutil.default_signer() >>> oc.OCIClientFactory(**auth).object_storage # Creates Object storage client
- ads.common.auth.get_signer(oci_config=None, oci_profile=None, **client_kwargs)
- ads.common.auth.resource_principal(client_kwargs=None)
Prepares authentication and extra arguments necessary for creating clients for different OCI services using Resource Principals.
- Parameters
client_kwargs (dict) – kwargs that are required to instantiate the Client if we need to override the defaults.
- Returns
Contains keys - config, signer and client_kwargs.
The config contains and empty dictionary.
The signer contains the signer object created from the resource principal.
client_kwargs contains the client_kwargs that was passed in as input parameter.
- Return type
dict
Examples
>>> from ads.common import auth as authutil >>> from ads.common import oci_client as oc >>> auth = authutil.resource_principal({"timeout": 6000}) >>> oc.OCIClientFactory(**auth).object_storage # Creates Object Storage client with timeout set to 6000 seconds using resource principal authentication
ads.common.data module
- class ads.common.data.ADSData(X=None, y=None, name='', dataset_type=None)
Bases:
object
This class wraps the input dataframe to various models, evaluation, and explanation frameworks. It’s primary purpose is to hold any metadata relevant to these tasks. This can include it’s:
X - the independent variables as some dataframe-like structure,
y - the dependent variable or target column as some array-like structure,
name - a string to name the data for user convenience,
dataset_type - the type of the X value.
As part of this initiative, ADSData knows how to turn itself into an onnxruntime compatible data structure with the method .to_onnxrt(), which takes and onnx session as input.
- Parameters
X (Union[pandas.DataFrame, dask.DataFrame, numpy.ndarray, scipy.sparse.csr.csr_matrix]) – If str, URI for the dataset. The dataset could be read from local or network file system, hdfs, s3 and gcs Should be none if X_train, y_train, X_test, Y_test are provided
y (Union[str, pandas.DataFrame, dask.DataFrame, pandas.Series, dask.Series, numpy.ndarray]) – If str, name of the target in X, otherwise series of labels corresponding to X
name (str, optional) – Name to identify this data
dataset_type (ADSDataset optional) – When this value is available, would be used to evaluate the ads task type
kwargs – Additional keyword arguments that would be passed to the underlying Pandas read API.
- static build(X=None, y=None, name='', dataset_type=None, **kwargs)
Returns an ADSData object built from the (source, target) or (X,y)
- Parameters
X (Union[pandas.DataFrame, dask.DataFrame, numpy.ndarray, scipy.sparse.csr.csr_matrix]) – If str, URI for the dataset. The dataset could be read from local or network file system, hdfs, s3 and gcs Should be none if X_train, y_train, X_test, Y_test are provided
y (Union[str, pandas.DataFrame, dask.DataFrame, pandas.Series, dask.Series, numpy.ndarray]) – If str, name of the target in X, otherwise series of labels corresponding to X
name (str, optional) – Name to identify this data
dataset_type (ADSDataset, optional) – When this value is available, would be used to evaluate the ads task type
kwargs – Additional keyword arguments that would be passed to the underlying Pandas read API.
- Returns
ads_data – A built ADSData object
- Return type
Examples
>>> data = open_csv("my.csv")
>>> data_ads = ADSData(data, 'target').build(data, 'target')
- to_onnxrt(sess, idx_range=None, model=None, impute_values={}, **kwargs)
Returns itself formatted as an input for the onnxruntime session inputs passed in.
- Parameters
sess (Session) – The session object
idx_range (Range) – The range of inputs to convert to onnx
model (SupportedModel) – A model that supports being serialized for the onnx runtime.
kwargs (additional keyword arguments) –
sess_inputs - Pass in the output from onnxruntime.InferenceSession(“model.onnx”).get_inputs()
input_dtypes (list) - If sess_inputs cannot be passed in, pass in the numpy dtypes of each input
input_shapes (list) - If sess_inputs cannot be passed in, pass in the shape of each input
input_names (list) -If sess_inputs cannot be passed in, pass in the name of each input
- Returns
ort – array of inputs formatted for the given session.
- Return type
Array
ads.common.model module
- class ads.common.model.ADSModel(est, target=None, transformer_pipeline=None, client=None, booster=None, classes=None, name=None)
Bases:
object
Construct an ADSModel
- Parameters
est (fitted estimator object) – The estimator can be a standard sklearn estimator, a keras, lightgbm, or xgboost estimator, or any other object that implement methods from (BaseEstimator, RegressorMixin) for regression or (BaseEstimator, ClassifierMixin) for classification.
target (PandasSeries) – The target column you are using in your dataset, this is assigned as the “y” attribute.
transformer_pipeline (TransformerPipeline) – A custom trasnformer pipeline object.
client (Str) – Currently unused.
booster (Str) – Currently unused.
classes (list, optional) – List of target classes. Required for classification problem if the est does not contain classes attribute.
name (str, optional) – Name of the model.
- static convert_dataframe_schema(df, drop=None)
- feature_names(X=None)
- static from_estimator(est, transformers=None, classes=None, name=None)
Build ADSModel from a fitted estimator
- Parameters
est (fitted estimator object) – The estimator can be a standard sklearn estimator or any object that implement methods from (BaseEstimator, RegressorMixin) for regression or (BaseEstimator, ClassifierMixin) for classification.
transformers (a scalar or an iterable of objects implementing transform function, optional) – The transform function would be applied on data before calling predict and predict_proba on estimator.
classes (list, optional) – List of target classes. Required for classification problem if the est does not contain classes attribute.
name (str, optional) – Name of the model.
- Returns
model
- Return type
Examples
>>> model = MyModelClass.train() >>> model_ads = from_estimator(model)
- static get_init_types(df, underlying_model=None)
- is_classifier()
Returns True if ADS believes that the model is a classifier
- Returns
Boolean
- Return type
True if the model is a classifier, False otherwise.
- predict(X)
Runs the models predict function on some data
- Parameters
X (MLData) – A MLData object which holds the examples to be predicted on.
- Returns
Usually a list or PandasSeries of predictions
- Return type
Union[List, pandas.Series], depending on the estimator
- predict_proba(X)
Runs the models predict probabilities function on some data
- Parameters
X (MLData) – A MLData object which holds the examples to be predicted on.
- Returns
Usually a list or PandasSeries of predictions
- Return type
Union[List, pandas.Series], depending on the estimator
- prepare(target_dir=None, data_sample=None, X_sample=None, y_sample=None, include_data_sample=False, force_overwrite=False, fn_artifact_files_included=False, fn_name='model_api', inference_conda_env=None, data_science_env=False, ignore_deployment_error=False, use_case_type=None, inference_python_version=None, imputed_values={}, **kwargs)
Prepare model artifact directory to be published to model catalog
- Parameters
target_dir (str, default: model.name[:12]) – Target directory under which the model artifact files need to be added
data_sample (ADSData) – Note: This format is preferable to X_sample and y_sample. A sample of the test data that will be provided to predict() API of scoring script Used to generate schema_input.json and schema_output.json which defines the input and output formats
X_sample (pandas.DataFrame) – A sample of input data that will be provided to predict() API of scoring script Used to generate schema.json which defines the input formats
y_sample (pandas.Series) – A sample of output data that is expected to be returned by predict() API of scoring script, corresponding to X_sample Used to generate schema_output.json which defines the output formats
force_overwrite (bool, default: False) – If True, overwrites the target directory if exists already
fn_artifact_files_included (bool, default: True) – If True, generates artifacts to export a model as a function without ads dependency
fn_name (str, default: 'model_api') – Required parameter if fn_artifact_files_included parameter is setup.
inference_conda_env (str, default: None) – Conda environment to use within the model deployment service for inferencing
data_science_env (bool, default: False) – If set to True, datascience environment represented by the slug in the training conda environment will be used.
ignore_deployment_error (bool, default: False) – If set to True, the prepare will ignore all the errors that may impact model deployment
use_case_type (str) – The use case type of the model. Use it through UserCaseType class or string provided in UseCaseType. For example, use_case_type=UseCaseType.BINARY_CLASSIFICATION or use_case_type=”binary_classification”. Check with UseCaseType class to see all supported types.
inference_python_version (str, default:None.) – If provided will be added to the generated runtime yaml
**kwargs –
-------- –
max_col_num ((int, optional). Defaults to utils.DATA_SCHEMA_MAX_COL_NUM.) – The maximum column size of the data that allows to auto generate schema.
- Returns
model_artifact
- Return type
an instance of ModelArtifact that can be used to test the generated scoring script
- rename(name)
Changes the name of a model
- Parameters
name (str) – A string which is supplied for naming a model.
- score(X, y_true, score_fn=None)
Scores a model according to a custom score function
- Parameters
X (MLData) – A MLData object which holds the examples to be predicted on.
y_true (MLData) – A MLData object which holds ground truth labels for the examples which are being predicted on.
score_fn (Scorer (callable)) – A callable object that returns a score, usually created with sklearn.metrics.make_scorer().
- Returns
Almost always a scalar score (usually a float).
- Return type
float, depending on the estimator
- show_in_notebook()
Describe the model by showing it’s properties
- summary()
A summary of the ADSModel
- transform(X)
Process some MLData through the selected ADSModel transformers
- Parameters
X (MLData) – A MLData object which holds the examples to be transformed.
- visualize_transforms()
A graph of the ADSModel transformer pipeline. It is only supported in JupyterLabs Notebooks.
ads.common.model_metadata module
- class ads.common.model_metadata.ExtendedEnumMeta(name, bases, namespace, **kwargs)
Bases:
ABCMeta
The helper metaclass to extend functionality of a general class.
- values(cls) list:
Gets the list of class attributes.
- values() list
Gets the list of class attributes.
- Returns
The list of class values.
- Return type
list
- class ads.common.model_metadata.Framework
Bases:
str
- BERT = 'bert'
- CUML = 'cuml'
- EMCEE = 'emcee'
- ENSEMBLE = 'ensemble'
- FLAIR = 'flair'
- GENSIM = 'gensim'
- H20 = 'h2o'
- KERAS = 'keras'
- LIGHT_GBM = 'lightgbm'
- MXNET = 'mxnet'
- NLTK = 'nltk'
- ORACLE_AUTOML = 'oracle_automl'
- OTHER = 'other'
- PROPHET = 'prophet'
- PYMC3 = 'pymc3'
- PYOD = 'pyod'
- PYSTAN = 'pystan'
- PYTORCH = 'pytorch'
- SCIKIT_LEARN = 'scikit-learn'
- SKTIME = 'sktime'
- SPACY = 'spacy'
- STATSMODELS = 'statsmodels'
- TENSORFLOW = 'tensorflow'
- TRANSFORMERS = 'transformers'
- WORD2VEC = 'word2vec'
- XGBOOST = 'xgboost'
- class ads.common.model_metadata.MetadataCustomCategory
Bases:
str
- OTHER = 'Other'
- PERFORMANCE = 'Performance'
- TRAINING_AND_VALIDATION_DATASETS = 'Training and Validation Datasets'
- TRAINING_ENV = 'Training Environment'
- TRAINING_PROFILE = 'Training Profile'
- class ads.common.model_metadata.MetadataCustomKeys
Bases:
str
- CLIENT_LIBRARY = 'ClientLibrary'
- CONDA_ENVIRONMENT = 'CondaEnvironment'
- CONDA_ENVIRONMENT_PATH = 'CondaEnvironmentPath'
- ENVIRONMENT_TYPE = 'EnvironmentType'
- MODEL_ARTIFACTS = 'ModelArtifacts'
- MODEL_SERIALIZATION_FORMAT = 'ModelSerializationFormat'
- SLUG_NAME = 'SlugName'
- TRAINING_DATASET = 'TrainingDataset'
- TRAINING_DATASET_NUMBER_OF_COLS = 'TrainingDatasetNumberOfCols'
- TRAINING_DATASET_NUMBER_OF_ROWS = 'TrainingDatasetNumberOfRows'
- TRAINING_DATASET_SIZE = 'TrainingDatasetSize'
- VALIDATION_DATASET = 'ValidationDataset'
- VALIDATION_DATASET_NUMBER_OF_COLS = 'ValidationDataSetNumberOfCols'
- VALIDATION_DATASET_NUMBER_OF_ROWS = 'ValidationDatasetNumberOfRows'
- VALIDATION_DATASET_SIZE = 'ValidationDatasetSize'
- class ads.common.model_metadata.MetadataCustomPrintColumns
Bases:
str
- CATEGORY = 'Category'
- DESCRIPTION = 'Description'
- KEY = 'Key'
- VALUE = 'Value'
- exception ads.common.model_metadata.MetadataDescriptionTooLong(key: str, length: int)
Bases:
ValueError
Maximum allowed length of metadata description has been exceeded. See https://docs.oracle.com/en-us/iaas/data-science/using/models_saving_catalog.htm for more details.
- exception ads.common.model_metadata.MetadataSizeTooLarge(size: int)
Bases:
ValueError
Maximum allowed size for model metadata has been exceeded. See https://docs.oracle.com/en-us/iaas/data-science/using/models_saving_catalog.htm for more details.
- class ads.common.model_metadata.MetadataTaxonomyKeys
Bases:
str
- ALGORITHM = 'Algorithm'
- ARTIFACT_TEST_RESULT = 'ArtifactTestResults'
- FRAMEWORK = 'Framework'
- FRAMEWORK_VERSION = 'FrameworkVersion'
- HYPERPARAMETERS = 'Hyperparameters'
- USE_CASE_TYPE = 'UseCaseType'
- class ads.common.model_metadata.MetadataTaxonomyPrintColumns
Bases:
str
- KEY = 'Key'
- VALUE = 'Value'
- exception ads.common.model_metadata.MetadataValueTooLong(key: str, length: int)
Bases:
ValueError
Maximum allowed length of metadata value has been exceeded. See https://docs.oracle.com/en-us/iaas/data-science/using/models_saving_catalog.htm for more details.
- class ads.common.model_metadata.ModelCustomMetadata
Bases:
ModelMetadata
Class that represents Model Custom Metadata.
- get(self, key: str) ModelCustomMetadataItem
Returns the model metadata item by provided key.
- reset(self) None
Resets all model metadata items to empty values.
- to_dataframe(self) pd.DataFrame
Returns the model metadata list in a data frame format.
- size(self) int
Returns the size of the model metadata in bytes.
- validate(self) bool
Validates metadata.
- to_dict(self)
Serializes model metadata into a dictionary.
- to_yaml(self)
Serializes model metadata into a YAML.
- add(self, key: str, value: str, description: str = '', category: str = MetadataCustomCategory.OTHER, replace: bool = False) None:
Adds a new model metadata item. Replaces existing one if replace flag is True.
- remove(self, key: str) None
Removes a model metadata item by key.
- clear(self) None
Removes all metadata items.
- isempty(self) bool
Checks if metadata is empty.
- to_json(self)
Serializes model metadata into a JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata to a local file or object storage.
Examples
>>> metadata_custom = ModelCustomMetadata() >>> metadata_custom.add(key="format", value="pickle") >>> metadata_custom.add(key="note", value="important note", description="some description") >>> metadata_custom["format"].description = "some description" >>> metadata_custom.to_dataframe() Key Value Description Category ---------------------------------------------------------------------------- 0 format pickle some description user defined 1 note important note some description user defined >>> metadata_custom metadata: - category: user defined description: some description key: format value: pickle - category: user defined description: some description key: note value: important note >>> metadata_custom.remove("format") >>> metadata_custom metadata: - category: user defined description: some description key: note value: important note >>> metadata_custom.to_dict() {'metadata': [{ 'key': 'note', 'value': 'important note', 'category': 'user defined', 'description': 'some description' }]} >>> metadata_custom.reset() >>> metadata_custom metadata: - category: None description: None key: note value: None >>> metadata_custom.clear() >>> metadata_custom.to_dataframe() Key Value Description Category ----------------------------------------------------------------------------
Initializes custom model metadata.
- add(key: str, value: str, description: str = '', category: str = 'Other', replace: bool = False) None
Adds a new model metadata item. Overrides the existing one if replace flag is True.
- Parameters
key (str) – The metadata item key.
value (str) – The metadata item value.
description (str) – The metadata item description.
category (str) – The metadata item category.
replace (bool) – Overrides the existing metadata item if replace flag is True.
- Returns
Nothing.
- Return type
None
- Raises
TypeError – If provided key is not a string. If provided description not a string.
ValueError – If provided key is empty. If provided value is empty. If provided value cannot be serialized to JSON. If item with provided key is already registered and replace flag is False. If provided category is not supported.
MetadataValueTooLong – If the length of provided value exceeds 255 charracters.
MetadataDescriptionTooLong – If the length of provided description exceeds 255 charracters.
- clear() None
Removes all metadata items.
- Returns
Nothing.
- Return type
None
- isempty() bool
Checks if metadata is empty.
- Returns
True if metadata is empty, False otherwise.
- Return type
bool
- remove(key: str) None
Removes a model metadata item.
- Parameters
key (str) – The key of the metadata item that should be removed.
- Returns
Nothing.
- Return type
None
- set_training_data(path: str, data_size: Optional[str] = None)
Adds training_data path and data size information into model custom metadata.
- Parameters
path (str) – The path where the training_data is stored.
data_size (str) – The size of the training_data.
- Returns
Nothing.
- Return type
None
- set_validation_data(path: str, data_size: Optional[str] = None)
Adds validation_data path and data size information into model custom metadata.
- Parameters
path (str) – The path where the validation_data is stored.
data_size (str) – The size of the validation_data.
- Returns
Nothing.
- Return type
None
- to_dataframe() DataFrame
Returns the model metadata list in a data frame format.
- Returns
The model metadata in a dataframe format.
- Return type
pandas.DataFrame
- class ads.common.model_metadata.ModelCustomMetadataItem(key: str, value: Optional[str] = None, description: Optional[str] = None, category: Optional[str] = None)
Bases:
ModelTaxonomyMetadataItem
Class that represents model custom metadata item.
- key
The model metadata item key.
- Type
str
- value
The model metadata item value.
- Type
str
- description
The model metadata item description.
- Type
str
- category
The model metadata item category.
- Type
str
- reset(self) None
Resets model metadata item.
- to_dict(self) dict
Serializes model metadata item to dictionary.
- to_yaml(self)
Serializes model metadata item to YAML.
- size(self) int
Returns the size of the metadata in bytes.
- update(self, value: str = '', description: str = '', category: str = '') None
Updates metadata item information.
- to_json(self) JSON
Serializes metadata item into a JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata item value to a local file or object storage.
- validate(self) bool
Validates metadata item.
- property category: str
- property description: str
- reset() None
Resets model metadata item.
Resets value, description and category to None.
- Returns
Nothing.
- Return type
None
- update(value: str, description: str, category: str) None
Updates metadata item.
- Parameters
value (str) – The value of model metadata item.
description (str) – The description of model metadata item.
category (str) – The category of model metadata item.
- Returns
Nothing.
- Return type
None
- validate() bool
Validates metadata item.
- Returns
True if validation passed.
- Return type
bool
- Raises
ValueError – If invalid category provided.
MetadataValueTooLong – If value exceeds the length limit.
- class ads.common.model_metadata.ModelMetadata
Bases:
ABC
The base abstract class representing model metadata.
- get(self, key: str) ModelMetadataItem
Returns the model metadata item by provided key.
- reset(self) None
Resets all model metadata items to empty values.
- to_dataframe(self) pd.DataFrame
Returns the model metadata list in a data frame format.
- size(self) int
Returns the size of the model metadata in bytes.
- validate(self) bool
Validates metadata.
- to_dict(self)
Serializes model metadata into a dictionary.
- to_yaml(self)
Serializes model metadata into a YAML.
- to_json(self)
Serializes model metadata into a JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata to a local file or object storage.
Initializes Model Metadata.
- get(key: str) ModelMetadataItem
Returns the model metadata item by provided key.
- Parameters
key (str) – The key of model metadata item.
- Returns
The model metadata item.
- Return type
- Raises
ValueError – If provided key is empty or metadata item not found.
- property keys: Tuple[str]
Returns all registered metadata keys.
- Returns
The list of metadata keys.
- Return type
Tuple[str]
- reset() None
Resets all model metadata items to empty values.
Resets value, description and category to None for every metadata item.
- size() int
Returns the size of the model metadata in bytes.
- Returns
The size of model metadata in bytes.
- Return type
int
- abstract to_dataframe() DataFrame
Returns the model metadata list in a data frame format.
- Returns
The model metadata in a dataframe format.
- Return type
pandas.DataFrame
- to_dict()
Serializes model metadata into a dictionary.
- Returns
The model metadata in a dictionary representation.
- Return type
Dict
- to_json()
Serializes model metadata into a JSON.
- Returns
The model metadata in a JSON representation.
- Return type
JSON
- to_json_file(file_path: str, storage_options: Optional[dict] = None) None
Saves the metadata to a local file or object storage.
- Parameters
file_path (str) – The file path to store the data. “oci://bucket_name@namespace/folder_name/” “oci://bucket_name@namespace/folder_name/metadata.json” “path/to/local/folder” “path/to/local/folder/metadata.json”
storage_options (dict. Default None) – Parameters passed on to the backend filesystem class. Defaults to options set using DatasetFactory.set_default_storage().
- Returns
Nothing.
- Return type
None
- Raises
ValueError – When file path is empty.:
TypeError – When file path not a string.:
Examples
>>> metadata = ModelTaxonomyMetadataItem() >>> storage_options = {"config": oci.config.from_file(os.path.join("~/.oci", "config"))} >>> storage_options {'log_requests': False, 'additional_user_agent': '', 'pass_phrase': None, 'user': '<user-id>', 'fingerprint': '05:15:2b:b1:46:8a:32:ec:e2:69:5b:32:01:**:**:**)', 'tenancy': '<tenancy-id>', 'region': 'us-ashburn-1', 'key_file': '/home/datascience/.oci/oci_api_key.pem'} >>> metadata.to_json_file(file_path = 'oci://bucket_name@namespace/folder_name/metadata_taxonomy.json', storage_options=storage_options) >>> metadata_item.to_json_file("path/to/local/folder/metadata_taxonomy.json")
- to_yaml()
Serializes model metadata into a YAML.
- Returns
The model metadata in a YAML representation.
- Return type
Yaml
- validate() bool
Validates model metadata.
- Returns
True if metadata is valid.
- Return type
bool
- validate_size() bool
Validates model metadata size.
Validates the size of metadata. Throws an error if the size of the metadata exceeds expected value.
- Returns
True if metadata size is valid.
- Return type
bool
- Raises
MetadataSizeTooLarge – If the size of the metadata exceeds expected value.
- class ads.common.model_metadata.ModelMetadataItem
Bases:
ABC
The base abstract class representing model metadata item.
- to_dict(self) dict
Serializes model metadata item to dictionary.
- to_yaml(self)
Serializes model metadata item to YAML.
- size(self) int
Returns the size of the metadata in bytes.
- to_json(self) JSON
Serializes metadata item to JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata item value to a local file or object storage.
- validate(self) bool
Validates metadata item.
- size() int
Returns the size of the model metadata in bytes.
- Returns
The size of model metadata in bytes.
- Return type
int
- to_dict() dict
Serializes model metadata item to dictionary.
- Returns
The dictionary representation of model metadata item.
- Return type
dict
- to_json()
Serializes metadata item into a JSON.
- Returns
The metadata item in a JSON representation.
- Return type
JSON
- to_json_file(file_path: str, storage_options: Optional[dict] = None) None
Saves the metadata item value to a local file or object storage.
- Parameters
file_path (str) – The file path to store the data. “oci://bucket_name@namespace/folder_name/” “oci://bucket_name@namespace/folder_name/result.json” “path/to/local/folder” “path/to/local/folder/result.json”
storage_options (dict. Default None) – Parameters passed on to the backend filesystem class. Defaults to options set using DatasetFactory.set_default_storage().
- Returns
Nothing.
- Return type
None
- Raises
ValueError – When file path is empty.:
TypeError – When file path not a string.:
Examples
>>> metadata_item = ModelCustomMetadataItem(key="key1", value="value1") >>> storage_options = {"config": oci.config.from_file(os.path.join("~/.oci", "config"))} >>> storage_options {'log_requests': False, 'additional_user_agent': '', 'pass_phrase': None, 'user': '<user-id>', 'fingerprint': '05:15:2b:b1:46:8a:32:ec:e2:69:5b:32:01:**:**:**)', 'tenancy': '<tenency-id>', 'region': 'us-ashburn-1', 'key_file': '/home/datascience/.oci/oci_api_key.pem'} >>> metadata_item.to_json_file(file_path = 'oci://bucket_name@namespace/folder_name/file.json', storage_options=storage_options) >>> metadata_item.to_json_file("path/to/local/folder/file.json")
- to_yaml()
Serializes model metadata item to YAML.
- Returns
The model metadata item in a YAML representation.
- Return type
Yaml
- abstract validate() bool
Validates metadata item.
- Returns
True if validation passed.
- Return type
bool
- class ads.common.model_metadata.ModelProvenanceMetadata(repo: Optional[str] = None, git_branch: Optional[str] = None, git_commit: Optional[str] = None, repository_url: Optional[str] = None, training_script_path: Optional[str] = None, training_id: Optional[str] = None, artifact_dir: Optional[str] = None)
Bases:
object
ModelProvenanceMetadata class.
Examples
>>> provenance_metadata = ModelProvenanceMetadata.fetch_training_code_details() ModelProvenanceMetadata(repo=<git.repo.base.Repo '/home/datascience/.git'>, git_branch='master', git_commit='99ad04c31803f1d4ffcc3bf4afbd6bcf69a06af2', repository_url='file:///home/datascience', "", "") >>> provenance_metadata.assert_path_not_dirty("your_path", ignore=False)
- artifact_dir: str = None
- assert_path_not_dirty(path: str, ignore: bool)
Checks if all the changes in this path has been commited.
- Parameters
path ((str)) – path.
(bool) (ignore) – whether to ignore the changes or not.
- Raises
ChangesNotCommitted – if there are changes not being commited.:
- Returns
Nothing.
- Return type
None
- classmethod fetch_training_code_details(training_script_path: Optional[str] = None, training_id: Optional[str] = None, artifact_dir: Optional[str] = None)
Fetches the training code details: repo, git_branch, git_commit, repository_url, training_script_path and training_id.
- Parameters
training_script_path ((str, optional). Defaults to None.) – Training script path.
training_id ((str, optional). Defaults to None.) – The training OCID for model.
artifact_dir (str) – artifact directory to store the files needed for deployment.
- Returns
A ModelProvenanceMetadata instance.
- Return type
- git_branch: str = None
- git_commit: str = None
- repo: str = None
- repository_url: str = None
- training_id: str = None
- training_script_path: str = None
- class ads.common.model_metadata.ModelTaxonomyMetadata
Bases:
ModelMetadata
Class that represents Model Taxonomy Metadata.
- get(self, key: str) ModelTaxonomyMetadataItem
Returns the model metadata item by provided key.
- reset(self) None
Resets all model metadata items to empty values.
- to_dataframe(self) pd.DataFrame
Returns the model metadata list in a data frame format.
- size(self) int
Returns the size of the model metadata in bytes.
- validate(self) bool
Validates metadata.
- to_dict(self)
Serializes model metadata into a dictionary.
- to_yaml(self)
Serializes model metadata into a YAML.
- to_json(self)
Serializes model metadata into a JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata to a local file or object storage.
Examples
>>> metadata_taxonomy = ModelTaxonomyMetadata() >>> metadata_taxonomy.to_dataframe() Key Value -------------------------------------------- 0 UseCaseType binary_classification 1 Framework sklearn 2 FrameworkVersion 0.2.2 3 Algorithm algorithm 4 Hyperparameters {}
>>> metadata_taxonomy.reset() >>> metadata_taxonomy.to_dataframe() Key Value -------------------------------------------- 0 UseCaseType None 1 Framework None 2 FrameworkVersion None 3 Algorithm None 4 Hyperparameters None
>>> metadata_taxonomy metadata: - key: UseCaseType category: None description: None value: None
Initializes Model Metadata.
- to_dataframe() DataFrame
Returns the model metadata list in a data frame format.
- Returns
The model metadata in a dataframe format.
- Return type
pandas.DataFrame
- class ads.common.model_metadata.ModelTaxonomyMetadataItem(key: str, value: Optional[str] = None)
Bases:
ModelMetadataItem
Class that represents model taxonomy metadata item.
- key
The model metadata item key.
- Type
str
- value
The model metadata item value.
- Type
str
- reset(self) None
Resets model metadata item.
- to_dict(self) dict
Serializes model metadata item to dictionary.
- to_yaml(self)
Serializes model metadata item to YAML.
- size(self) int
Returns the size of the metadata in bytes.
- update(self, value: str = '') None
Updates metadata item information.
- to_json(self) JSON
Serializes metadata item into a JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata item value to a local file or object storage.
- validate(self) bool
Validates metadata item.
- property key: str
- reset() None
Resets model metadata item.
Resets value to None.
- Returns
Nothing.
- Return type
None
- update(value: str) None
Updates metadata item value.
- Parameters
value (str) – The value of model metadata item.
- Returns
Nothing.
- Return type
None
- validate() bool
Validates metadata item.
- Returns
True if validation passed.
- Return type
bool
- Raises
ValueError – If invalid UseCaseType provided. If invalid Framework provided.
- property value: str
- class ads.common.model_metadata.UseCaseType
Bases:
str
- ANOMALY_DETECTION = 'anomaly_detection'
- BINARY_CLASSIFICATION = 'binary_classification'
- CLUSTERING = 'clustering'
- DIMENSIONALITY_REDUCTION = 'dimensionality_reduction/representation'
- IMAGE_CLASSIFICATION = 'image_classification'
- MULTINOMIAL_CLASSIFICATION = 'multinomial_classification'
- NER = 'ner'
- OBJECT_LOCALIZATION = 'object_localization'
- OTHER = 'other'
- RECOMMENDER = 'recommender'
- REGRESSION = 'regression'
- SENTIMENT_ANALYSIS = 'sentiment_analysis'
- TIME_SERIES_FORECASTING = 'time_series_forecasting'
- TOPIC_MODELING = 'topic_modeling'
ads.common.decorator.runtime_dependency module
The module that provides the decorator helping to add runtime dependencies in functions.
Examples
>>> @runtime_dependency(module="pandas", short_name="pd")
... def test_function()
... print(pd)
>>> @runtime_dependency(module="pandas", object="DataFrame", short_name="df")
... def test_function()
... print(df)
>>> @runtime_dependency(module="pandas", short_name="pd")
... @runtime_dependency(module="pandas", object="DataFrame", short_name="df")
... def test_function()
... print(df)
... print(pd)
>>> @runtime_dependency(module="pandas", object="DataFrame", short_name="df", install_from="ads[optional]")
... def test_function()
... pass
>>> @runtime_dependency(module="pandas", object="DataFrame", short_name="df", err_msg="Custom error message.")
... def test_function()
... pass
- class ads.common.decorator.runtime_dependency.OptionalDependency
Bases:
object
- BDS = 'oracle-ads[bds]'
- BOOSTED = 'oracle-ads[boosted]'
- DATA = 'oracle-ads[data]'
- LABS = 'oracle-ads[labs]'
- MACHINE_LEARNING = 'oracle-ads[machine_learning]'
- MYSQL = 'oracle-ads[mysql]'
- NOTEBOOK = 'oracle-ads[notebook]'
- OPCTL = 'oracle-ads[opctl]'
- TEXT = 'oracle-ads[text]'
- ads.common.decorator.runtime_dependency.runtime_dependency(module: str, short_name: str = '', object: Optional[str] = None, install_from: Optional[str] = None, err_msg: str = '', is_for_notebook_only=False)
The decorator which is helping to add runtime dependencies to functions.
- Parameters
module (str) – The module name to be imported.
short_name ((str, optional). Defaults to empty string.) – The short name for the imported module.
object ((str, optional). Defaults to None.) – The name of the object to be imported. Can be a function or a class, or any variable provided by module.
install_from ((str, optional). Defaults to None.) – The parameter helping to answer from where the required dependency can be installed.
err_msg ((str, optional). Defaults to empty string.) – The custom error message.
is_for_notebook_only ((bool, optional). Defaults to False.) – If the value of this flag is set to True, the dependency will be added only in case when the current environment is a jupyter notebook.
- Raises
ModuleNotFoundError – In case if requested module not found.
ImportError – In case if object cannot be imported from the module.
Examples
>>> @runtime_dependency(module="pandas", short_name="pd") ... def test_function() ... print(pd)
>>> @runtime_dependency(module="pandas", object="DataFrame", short_name="df") ... def test_function() ... print(df)
>>> @runtime_dependency(module="pandas", short_name="pd") ... @runtime_dependency(module="pandas", object="DataFrame", short_name="df") ... def test_function() ... print(df) ... print(pd)
>>> @runtime_dependency(module="pandas", object="DataFrame", short_name="df", install_from="ads[optional]") ... def test_function() ... pass
>>> @runtime_dependency(module="pandas", object="DataFrame", short_name="df", err_msg="Custom error message.") ... def test_function() ... pass
ads.common.decorator.deprecate module
- class ads.common.decorator.deprecate.TARGET_TYPE(value)
Bases:
Enum
An enumeration.
- ATTRIBUTE = 'Attribute'
- CLASS = 'Class'
- METHOD = 'Method'
- ads.common.decorator.deprecate.deprecated(deprecated_in: str, removed_in: Optional[str] = None, details: Optional[str] = None, target_type: Optional[str] = None)
This is a decorator which can be used to mark functions as deprecated. It will result in a warning being emitted when the function is used.
- Parameters
deprecated_in (str) – Version of ADS where this function deprecated.
removed_in (str) – Future version where this function will be removed.
details (str) – More information to be shown.
ads.common.model_introspect module
The module that helps to minimize the number of errors of the model post-deployment process. The model provides a simple testing harness to ensure that model artifacts are thoroughly tested before being saved to the model catalog.
Classes
- ModelIntrospect
Class to introspect model artifacts.
Examples
>>> model_introspect = ModelIntrospect(artifact=model_artifact)
>>> model_introspect()
... Test key Test name Result Message
... ----------------------------------------------------------------------------
... test_key_1 test_name_1 Passed test passed
... test_key_2 test_name_2 Not passed some error occured
>>> model_introspect.status
... Passed
- class ads.common.model_introspect.Introspectable
Bases:
ABC
Base class that represents an introspectable object.
- exception ads.common.model_introspect.IntrospectionNotPassed
Bases:
ValueError
- class ads.common.model_introspect.ModelIntrospect(artifact: Introspectable)
Bases:
object
Class to introspect model artifacts.
- Parameters
status (str) – Returns the current status of model introspection. The possible variants: Passed, Not passed, Not tested.
failures (int) – Returns the number of failures of introspection result.
- run(self) None
Invokes model artifacts introspection.
- to_dataframe(self) pd.DataFrame
Serializes model introspection result into a DataFrame.
Examples
>>> model_introspect = ModelIntrospect(artifact=model_artifact) >>> result = model_introspect() ... Test key Test name Result Message ... ---------------------------------------------------------------------------- ... test_key_1 test_name_1 Passed test passed ... test_key_2 test_name_2 Not passed some error occured
Initializes the Model Introspect.
- Parameters
artifact (Introspectable) – The instance of ModelArtifact object.
- Raises
ValueError – If model artifact object not provided.:
TypeError – If provided input paramater not a ModelArtifact instance.:
- property failures: int
Calculates the number of failures.
- Returns
The number of failures.
- Return type
int
- run() DataFrame
Invokes introspection.
- Returns
The introspection result in a DataFrame format.
- Return type
pd.DataFrame
- property status: str
Gets the current status of model introspection.
- to_dataframe() DataFrame
Serializes model introspection result into a DataFrame.
- Returns
The model introspection result in a DataFrame representation.
- Return type
pandas.DataFrame
- class ads.common.model_introspect.PrintItem(key: str = '', case: str = '', result: str = '', message: str = '')
Bases:
object
Class represents the model introspection print item.
- case: str = ''
- key: str = ''
- message: str = ''
- result: str = ''
- to_list() List[str]
Converts instance to a list representation.
- Returns
The instance in a list representation.
- Return type
List[str]
ads.common.model_export_util module
- class ads.common.model_export_util.ONNXTransformer
Bases:
object
This is a transformer to convert X [pandas.Dataframe, pd.Series] data into Onnx readable dtypes and formats. It is Serializable, so it can be reloaded at another time.
Examples
>>> from ads.common.model_export_util import ONNXTransformer >>> onnx_data_transformer = ONNXTransformer() >>> train_transformed = onnx_data_transformer.fit_transform(train.X, {"column_name1": "impute_value1", "column_name2": "impute_value2"}}) >>> test_transformed = onnx_data_transformer.transform(test.X)
- fit(X: Union[DataFrame, Series, ndarray, list], impute_values: Optional[Dict] = None)
Fits the OnnxTransformer on the dataset :param X: The Dataframe for the training data :type X: Union[pandas.DataFrame, pandas.Series, np.ndarray, list]
- Returns
Self – The fitted estimator
- Return type
ads.Model
- fit_transform(X: Union[DataFrame, Series], impute_values: Optional[Dict] = None)
Fits, then transforms the data :param X: The Dataframe for the training data :type X: Union[pandas.DataFrame, pandas.Series]
- Returns
The transformed X data
- Return type
Union[pandas.DataFrame, pandas.Series]
- static load(filename, **kwargs)
Loads the Onnx model to disk :param filename: The filename location for where the model should be loaded :type filename: Str
- Returns
onnx_transformer – The loaded model
- Return type
- save(filename, **kwargs)
Saves the Onnx model to disk :param filename: The filename location for where the model should be saved :type filename: Str
- Returns
filename – The filename where the model was saved
- Return type
Str
- transform(X: Union[DataFrame, Series, ndarray, list])
Transforms the data for the OnnxTransformer.
- Parameters
X (Union[pandas.DataFrame, pandas.Series, np.ndarray, list]) – The Dataframe for the training data
- Returns
The transformed X data
- Return type
Union[pandas.DataFrame, pandas.Series, np.ndarray, list]
- ads.common.model_export_util.prepare_generic_model(model_path: str, fn_artifact_files_included: bool = False, fn_name: str = 'model_api', force_overwrite: bool = False, model: Optional[Any] = None, data_sample: Optional[ADSData] = None, use_case_type=None, X_sample: Optional[Union[list, tuple, Series, ndarray, DataFrame]] = None, y_sample: Optional[Union[list, tuple, Series, ndarray, DataFrame]] = None, **kwargs) ModelArtifact
Generates template files to aid model deployment. The model could be accompanied by other artifacts all of which can be dumped at model_path. Following files are generated: * func.yaml * func.py * requirements.txt * score.py
- Parameters
model_path (str) – Path where the artifacts must be saved. The serialized model object and any other associated files/objects must be saved in the model_path directory
fn_artifact_files_included (bool) – Default is False, if turned off, function artifacts are not generated.
fn_name (str) – Opional parameter to specify the function name
force_overwrite (bool) – Opional parameter to specify if the model_artifact should overwrite the existing model_path (if it exists)
model ((Any, optional). Defaults to None.) – This is an optional model object which is only used to extract taxonomy metadata. Supported models: automl, keras, lightgbm, pytorch, sklearn, tensorflow, and xgboost. If the model is not under supported frameworks, then extracting taxonomy metadata will be skipped. The alternative way is using atifact.populate_metadata(model=model, usecase_type=UseCaseType.REGRESSION).
data_sample (ADSData) – A sample of the test data that will be provided to predict() API of scoring script Used to generate schema_input and schema_output
use_case_type (str) – The use case type of the model
X_sample (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame, dask.dataframe.core.Series, dask.dataframe.core.DataFrame]) – A sample of input data that will be provided to predict() API of scoring script Used to generate input schema.
y_sample (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame, dask.dataframe.core.Series, dask.dataframe.core.DataFrame]) – A sample of output data that is expected to be returned by predict() API of scoring script, corresponding to X_sample Used to generate output schema.
**kwargs –
________ –
data_science_env (bool, default: False) – If set to True, the datascience environment represented by the slug in the training conda environment will be used.
inference_conda_env (str, default: None) – Conda environment to use within the model deployment service for inferencing. For example, oci://bucketname@namespace/path/to/conda/env
ignore_deployment_error (bool, default: False) – If set to True, the prepare method will ignore all the errors that may impact model deployment.
underlying_model (str, default: 'UNKNOWN') – Underlying Model Type, could be “automl”, “sklearn”, “h2o”, “lightgbm”, “xgboost”, “torch”, “mxnet”, “tensorflow”, “keras”, “pyod” and etc.
model_libs (dict, default: {}) – Model required libraries where the key is the library names and the value is the library versions. For example, {numpy: 1.21.1}.
progress (int, default: None) – max number of progress.
inference_python_version (str, default:None.) – If provided will be added to the generated runtime yaml
max_col_num ((int, optional). Defaults to utils.DATA_SCHEMA_MAX_COL_NUM.) – The maximum column size of the data that allows to auto generate schema.
Examples
>>> import cloudpickle >>> import os >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.datasets import make_classification >>> import ads >>> from ads.common.model_export_util import prepare_generic_model >>> import yaml >>> import oci >>> >>> ads.set_auth('api_key', oci_config_location=oci.config.DEFAULT_LOCATION, profile='DEFAULT') >>> model_artifact_location = os.path.expanduser('~/myusecase/model/') >>> inference_conda_env="oci://my-bucket@namespace/conda_environments/cpu/Data Exploration and Manipulation for CPU Python 3.7/2.0/dataexpl_p37_cpu_v2" >>> inference_python_version = "3.7" >>> if not os.path.exists(model_artifact_location): ... os.makedirs(model_artifact_location) >>> X, y = make_classification(n_samples=100, n_features=20, n_classes=2) >>> lrmodel = LogisticRegression().fit(X, y) >>> with open(os.path.join(model_artifact_location, 'model.pkl'), "wb") as mfile: ... cloudpickle.dump(lrmodel, mfile) >>> modelartifact = prepare_generic_model( ... model_artifact_location, ... model = lrmodel, ... force_overwrite=True, ... inference_conda_env=inference_conda_env, ... ignore_deployment_error=True, ... inference_python_version=inference_python_version ... ) >>> modelartifact.reload() # Call reload to update the ModelArtifact object with the generated score.py >>> assert len(modelartifact.predict(X[:5])['prediction']) == 5 #Test the generated score.py works. This may require customization. >>> with open(os.path.join(model_artifact_location, "runtime.yaml")) as rf: ... content = yaml.load(rf, Loader=yaml.FullLoader) ... assert content['MODEL_DEPLOYMENT']['INFERENCE_CONDA_ENV']['INFERENCE_ENV_PATH'] == inference_conda_env ... assert content['MODEL_DEPLOYMENT']['INFERENCE_CONDA_ENV']['INFERENCE_PYTHON_VERSION'] == inference_python_version >>> # Save Model to model artifact >>> ocimodel = modelartifact.save( ... project_id="oci1......", # OCID of the project to which the model to be associated ... compartment_id="oci1......", # OCID of the compartment where the model will reside ... display_name="LRModel_01", ... description="My Logistic Regression Model", ... ignore_pending_changes=True, ... timeout=100, ... ignore_introspection=True, ... ) >>> print(f"The OCID of the model is: {ocimodel.id}")
- Returns
model_artifact – A generic model artifact
- Return type
ads.model_artifact.model_artifact
- ads.common.model_export_util.serialize_model(model=None, target_dir=None, X=None, y=None, model_type=None, **kwargs)
- Parameters
model (ads.Model) – A model to be serialized
target_dir (str, optional) – directory to output the serialized model
X (Union[pandas.DataFrame, pandas.Series]) – The X data
y (Union[list, pandas.DataFrame, pandas.Series]) – Tbe Y data
model_type (str, optional) – A string corresponding to the model type
- Returns
model_kwargs – A dictionary of model kwargs for the serialized model
- Return type
Dict
ads.common.function.fn_util module
- ads.common.function.fn_util.generate_fn_artifacts(path: str, fn_name: Optional[str] = None, fn_attributes=None, artifact_type_generic=False, **kwargs)
- Generates artifacts for fn (https://fnproject.io) at the provided path -
func.py
func.yaml
requirements.txt if not there. If exists appends fdk to the file.
score.py
- Parameters
path (str) – Target folder where the artifacts are placed.
fn_attributes (dict) – dictionary specifying all the function attributes as described in https://github.com/fnproject/docs/blob/master/fn/develop/func-file.md
artifact_type_generic (bool) – default is False. This attribute decides which template to pick for score.py. If True, it is assumed that the code to load is provided by the user.
- ads.common.function.fn_util.get_function_config() dict
Returns dictionary loaded from func_conf.yaml
- ads.common.function.fn_util.prepare_fn_attributes(func_name: str, schema_version=20180708, version=None, python_runtime=None, entry_point=None, memory=None) dict
Workaround for collections.namedtuples. The defaults are not supported.
- ads.common.function.fn_util.write_score(path, **kwargs)
ads.common.utils module
- exception ads.common.utils.FileOverwriteError
Bases:
Exception
- class ads.common.utils.JsonConverter(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)
Bases:
JSONEncoder
Constructor for JSONEncoder, with sensible defaults.
If skipkeys is false, then it is a TypeError to attempt encoding of keys that are not str, int, float or None. If skipkeys is True, such items are simply skipped.
If ensure_ascii is true, the output is guaranteed to be str objects with all incoming non-ASCII characters escaped. If ensure_ascii is false, the output can contain non-ASCII characters.
If check_circular is true, then lists, dicts, and custom encoded objects will be checked for circular references during encoding to prevent an infinite recursion (which would cause an OverflowError). Otherwise, no such check takes place.
If allow_nan is true, then NaN, Infinity, and -Infinity will be encoded as such. This behavior is not JSON specification compliant, but is consistent with most JavaScript based encoders and decoders. Otherwise, it will be a ValueError to encode such floats.
If sort_keys is true, then the output of dictionaries will be sorted by key; this is useful for regression tests to ensure that JSON serializations can be compared on a day-to-day basis.
If indent is a non-negative integer, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0 will only insert newlines. None is the most compact representation.
If specified, separators should be an (item_separator, key_separator) tuple. The default is (’, ‘, ‘: ‘) if indent is
None
and (‘,’, ‘: ‘) otherwise. To get the most compact JSON representation, you should specify (‘,’, ‘:’) to eliminate whitespace.If specified, default is a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a
TypeError
.
- ads.common.utils.copy_from_uri(uri: str, to_path: str, unpack: Optional[bool] = False, force_overwrite: Optional[bool] = False, auth: Optional[Dict] = None) None
Copies file(s) to local path. Can be a folder, archived folder or a separate file. The source files can be located in a local folder or in OCI Object Storage.
- Parameters
uri (str) – The URI of the source file or directory, which can be local path or OCI object storage URI.
to_path (str) – The local destination path. If this is a directory, the source files will be placed under it.
unpack ((bool, optional). Defaults to False.) – Indicate if zip or tar.gz file specified by the uri should be unpacked. This option has no effect on other files.
force_overwrite ((bool, optional). Defaults to False.) – Whether to overwrite existing files or not.
auth ((Dict, optional). Defaults to None.) – The default authetication is set using ads.set_auth API. If you need to override the default, use the ads.common.auth.api_keys or ads.common.auth.resource_principal to create appropriate authentication signer and kwargs required to instantiate IdentityClient object.
- Returns
Nothing
- Return type
None
- Raises
ValueError – If destination path is already exist and force_overwrite is set to False.
- ads.common.utils.download_from_web(url: str, to_path: str) None
Downloads a single file from http/https/ftp.
- Parameters
url (str) – The URL of the source file.
to_path (path-like object) – Local destination path.
- Returns
Nothing
- Return type
None
- ads.common.utils.ellipsis_strings(raw, n=24)
takes a sequence (<string>, list(<string>), tuple(<string>), pd.Series(<string>) and Ellipsis’ize them at position n
- ads.common.utils.extract_lib_dependencies_from_model(model) dict
Extract a dictionary of library dependencies for a model
- Parameters
model –
- Returns
Dict
- Return type
A dictionary of library dependencies.
- ads.common.utils.first_not_none(itr)
returns the first non-none result from an iterable, similar to any() but return value not true/false
- ads.common.utils.flatten(d, parent_key='')
Flattens nested dictionaries to a single layer dictionary
- Parameters
d (dict) – The dictionary that needs to be flattened
parent_key (str) – Keys in the dictionary that are nested
- Returns
a_dict – a single layer dictionary
- Return type
dict
- ads.common.utils.generate_requirement_file(requirements: dict, file_path: str, file_name: str = 'requirements.txt')
Generate requirements file at file_path.
- Parameters
requirements (dict) – Key is the library name and value is the version
file_path (str) – Directory to save requirements.txt
file_name (str) – Opional parameter to specify the file name
- ads.common.utils.get_base_modules(model)
Get the base modules from an ADS model
- ads.common.utils.get_bootstrap_styles()
Returns HTML bootstrap style information
- ads.common.utils.get_compute_accelerator_ncores()
- ads.common.utils.get_cpu_count()
Returns the number of CPUs available on this machine
- ads.common.utils.get_dataframe_styles(max_width=75)
Styles used for dataframe, example usage:
df.style .set_table_styles(utils.get_dataframe_styles()) .set_table_attributes(‘class=table’) .render())
- Returns
styles – A list of dataframe table styler styles.
- Return type
array
- ads.common.utils.get_files(directory: str)
List out all the file names under this directory.
- Parameters
directory (str) – The directory to list out all the files from.
- Returns
List of the files in the directory.
- Return type
List
- ads.common.utils.get_oci_config()
Returns the OCI config location, and the OCI config profile.
- ads.common.utils.get_progress_bar(max_progress, description='Initializing')
this will return an instance of ProgressBar, sensitive to the runtime environment
- ads.common.utils.get_sqlalchemy_engine(connection_url, *args, **kwargs)
The SqlAlchemny docs say to use a single engine per connection_url, this class will take care of that.
- Parameters
connection_url (string) – The URL to connect to
- Returns
engine – The engine from which SqlAlchemny commands can be ran on
- Return type
SqlAlchemny engine
- ads.common.utils.highlight_text(text)
Returns text with html highlights. :param text: The text to be highlighted. :type text: String
- Returns
ht – The text with html highlight information.
- Return type
- ads.common.utils.horizontal_scrollable_div(html)
Wrap html with the necessary html to make horizontal scrolling possible.
Examples
display(HTML(utils.horizontal_scrollable_div(my_html)))
- Parameters
html (str) – Your HTML to wrap.
- Returns
Wrapped HTML.
- Return type
type
- ads.common.utils.inject_and_copy_kwargs(kwargs, **args)
Takes in a dictionary and returns a copy with the args injected
Examples
>>> foo(arg1, args, utils.inject_and_copy_kwargs(kwargs, arg3=12, arg4=42))
- Parameters
kwargs (dict) – The original kwargs.
**args (type) – A series of arguments, foo=42, bar=12 etc
- Returns
d – new dictionary object that you can use in place of kwargs
- Return type
dict
- ads.common.utils.is_data_too_wide(data: Union[list, tuple, Series, ndarray, DataFrame], max_col_num: int) bool
Returns true if the data has too many columns.
- Parameters
data (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame]) – A sample of data that will be used to generate schema.
max_col_num (int.) – The maximum column size of the data that allows to auto generate schema.
- ads.common.utils.is_debug_mode()
Returns true if ADS is in debug mode.
- ads.common.utils.is_documentation_mode()
Returns true if ADS is in documentation mode.
- ads.common.utils.is_notebook()
Returns true if the environment is a jupyter notebook.
- ads.common.utils.is_resource_principal_mode()
Returns true if ADS is in resource principal mode.
- ads.common.utils.is_same_class(obj, cls)
checks to see if object is the same class as cls
- ads.common.utils.is_test()
Returns true if ADS is in test mode.
- class ads.common.utils.ml_task_types(value)
Bases:
Enum
An enumeration.
- BINARY_CLASSIFICATION = 2
- BINARY_TEXT_CLASSIFICATION = 4
- MULTI_CLASS_CLASSIFICATION = 3
- MULTI_CLASS_TEXT_CLASSIFICATION = 5
- REGRESSION = 1
- UNSUPPORTED = 6
- ads.common.utils.numeric_pandas_dtypes()
Returns a list of the “numeric” pandas data types
- ads.common.utils.oci_config_file()
Returns the OCI config file location
- ads.common.utils.oci_config_location()
Returns oci configuration file location.
- ads.common.utils.oci_config_profile()
Returns the OCI config profile location.
- ads.common.utils.oci_key_location()
Returns the OCI key location
- ads.common.utils.oci_key_profile()
Returns key profile value specified in oci configuration file.
- ads.common.utils.print_user_message(msg, display_type='tip', see_also_links=None, title='Tip')
This method is deprecated and will be removed in future releases. Prints in html formatted block one of tip|info|warn type.
- Parameters
msg (str or list) – The actual message to display. display_type is “module’, msg can be a list of [module name, module package name], i.e. [“automl”, “ads[ml]”]
display_type (str (default 'tip')) – The type of user message.
see_also_links (list of tuples in the form of [('display_name', 'url')]) –
title (str (default 'tip')) – The title of user message.
- ads.common.utils.random_valid_ocid(prefix='ocid1.dataflowapplication.oc1.iad')
Generates a random valid ocid.
- Parameters
prefix (str) – A prefix, corresponding to a region location.
- Returns
ocid – a valid ocid with the given prefix.
- Return type
str
- ads.common.utils.replace_spaces(lst)
Replace all spaces with underscores for strings in the list.
Requires that the list contains strings for each element.
lst: list of strings
- ads.common.utils.set_oci_config(oci_config_location, oci_config_profile)
- Parameters
oci_config_location – location of the config file, for example, ~/.oci/config
oci_config_profile – The profile to load from the config file. Defaults to “DEFAULT”
- ads.common.utils.split_data(X, y, random_state=42, test_size=0.3)
Splits data using Sklearn based on the input type of the data.
- Parameters
X (a Pandas Dataframe) – The data points.
y (a Pandas Dataframe) – The labels.
random_state (int) – A random state for reproducability.
test_size (int) – The number of elements that should be included in the test dataset.
- ads.common.utils.to_dataframe(data: Union[list, tuple, Series, ndarray, DataFrame])
Convert to pandas DataFrame.
- Parameters
data (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame]) – Convert data to pandas DataFrame.
- Returns
pandas DataFrame.
- Return type
pd.DataFrame
- ads.common.utils.truncate_series_top_n(series, n=24)
take a series which can be interpreted as a dict, index=key, this function sorts by the values and takes the top-n values, and returns a new series
- ads.common.utils.wrap_lines(li, heading='')
Wraps the elements of iterable into multi line string of fixed width
Module contents
ads.common.model_metadata_mixin module
- class ads.common.model_metadata_mixin.MetadataMixin
Bases:
object
MetadataMixin class which populates the custom metadata, taxonomy metadata, input/output schema and provenance metadata.
- populate_metadata(use_case_type: Optional[str] = None, data_sample: Optional[ADSData] = None, X_sample: Optional[Union[list, tuple, Series, ndarray, DataFrame]] = None, y_sample: Optional[Union[list, tuple, Series, ndarray, DataFrame]] = None, training_script_path: Optional[str] = None, training_id: Optional[str] = None, ignore_pending_changes: bool = True, max_col_num: int = 2000)
Populates input schema and output schema. If the schema exceeds the limit of 32kb, save as json files to the artifact directory.
- Parameters
use_case_type ((str, optional). Defaults to None.) – The use case type of the model.
data_sample ((ADSData, optional). Defaults to None.) – A sample of the data that will be used to generate intput_schema and output_schema.
X_sample (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame]. Defaults to None.) – A sample of input data that will be used to generate input schema.
y_sample (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame]. Defaults to None.) – A sample of output data that will be used to generate output schema.
training_script_path (str. Defaults to None.) – Training script path.
training_id ((str, optional). Defaults to None.) – The training model OCID.
ignore_pending_changes (bool. Defaults to False.) – Ignore the pending changes in git.
max_col_num ((int, optional). Defaults to utils.DATA_SCHEMA_MAX_COL_NUM.) – The maximum number of columns allowed in auto generated schema.
- Returns
Nothing.
- Return type
None