-
Notifications
You must be signed in to change notification settings - Fork 46
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
* Version correction TS * TF INTEGRATION + PMML Correction - TF object - improvement of PMML - load_model support for PMML and TF - more precise docstring * Update test_tools.py
- Loading branch information
Showing
8 changed files
with
231 additions
and
11 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,152 @@ | ||
| """ | ||
| Copyright (c) 2018-2023 Open Text or one of its | ||
| affiliates. Licensed under the Apache License, | ||
| Version 2.0 (the "License"); You may not use this | ||
| file except in compliance with the License. | ||
| You may obtain a copy of the License at: | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in | ||
| writing, software distributed under the License 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. | ||
| """ | ||
| from typing import Literal, Optional | ||
|
|
||
| from verticapy._typing import SQLColumns, SQLRelation | ||
| from verticapy._utils._gen import gen_name | ||
| from verticapy._utils._sql._collect import save_verticapy_logs | ||
| from verticapy._utils._sql._format import format_type, quote_ident | ||
| from verticapy._utils._sql._vertica_version import check_minimum_version | ||
|
|
||
| from verticapy.core.vdataframe.base import vDataFrame | ||
|
|
||
| from verticapy.machine_learning.vertica.base import VerticaModel | ||
|
|
||
|
|
||
| class TensorFlowModel(VerticaModel): | ||
| """ | ||
| Creates a TensorFlow object. | ||
| .. versionadded:: 10.0.0 | ||
| Parameters | ||
| ---------- | ||
| name: str, optional | ||
| Name of the model. The model must be stored in | ||
| the database. If it is not the case, you can use | ||
| :py:mod:`verticapy.machine_learning.vertica.import_models` | ||
| to import your TensorFlow model. | ||
| """ | ||
|
|
||
| # Properties. | ||
|
|
||
| @property | ||
| def _vertica_fit_sql(self) -> None: | ||
| return None | ||
|
|
||
| @property | ||
| def _vertica_predict_sql(self) -> Literal["PREDICT_TENSORFLOW_SCALAR"]: | ||
| return "PREDICT_TENSORFLOW_SCALAR" | ||
|
|
||
| @property | ||
| def _vertica_predict_transform_sql(self) -> Literal["PREDICT_TENSORFLOW"]: | ||
| return "PREDICT_TENSORFLOW" | ||
|
|
||
| @property | ||
| def _model_category(self) -> Literal["INTEGRATION"]: | ||
| return "INTEGRATION" | ||
|
|
||
| @property | ||
| def _model_subcategory(self) -> Literal["TENSORFLOW"]: | ||
| return "TENSORFLOW" | ||
|
|
||
| @property | ||
| def _model_type(self) -> Literal["TensorFlowModel"]: | ||
| return "TensorFlowModel" | ||
|
|
||
| # System & Special Methods. | ||
|
|
||
| @check_minimum_version | ||
| @save_verticapy_logs | ||
| def __init__( | ||
| self, | ||
| name: str, | ||
| ) -> None: | ||
| super().__init__(name, False) | ||
| self.parameters = {} | ||
| attr = self.get_attributes() | ||
| if "input_desc" in attr: | ||
| self.X = self.get_attributes("input_desc")["op_name"] | ||
| if "output_desc" in attr: | ||
| self.y = self.get_attributes("output_desc")["op_name"][0] | ||
|
|
||
| # Prediction / Transformation Methods. | ||
|
|
||
| def predict( | ||
| self, | ||
| vdf: SQLRelation, | ||
| X: Optional[SQLColumns] = None, | ||
| name: Optional[str] = None, | ||
| inplace: bool = True, | ||
| ) -> vDataFrame: | ||
| """ | ||
| Predicts using the input relation. | ||
| Parameters | ||
| ---------- | ||
| vdf: SQLRelation | ||
| Object used to run the prediction. You can | ||
| also specify a customized relation, but you | ||
| must enclose it with an alias. For example, | ||
| "(SELECT 1) x" is valid, whereas "(SELECT 1)" | ||
| and "SELECT 1" are invalid. | ||
| X: SQLColumns | ||
| List of the columns used to deploy the models. | ||
| name: str, optional | ||
| Name of the added vDataColumn. If empty, a name | ||
| is generated. | ||
| .. note:: | ||
| This parameter is only used when the input | ||
| 'X' is a complex data type, otherwise it is | ||
| ignored. | ||
| inplace: bool, optional | ||
| If set to True, the prediction is added to the | ||
| vDataFrame. | ||
| Returns | ||
| ------- | ||
| vDataFrame | ||
| the input object. | ||
| """ | ||
| X = format_type(X, dtype=list, na_out=self.X) | ||
| X = quote_ident(X) | ||
| if isinstance(vdf, str): | ||
| vdf = vDataFrame(vdf) | ||
| inplace = True | ||
| if not name: | ||
| name = gen_name([self._model_type, self.model_name]) | ||
| if len(X) == 1 and vdf[X[0]].category() == "complex": | ||
| if inplace: | ||
| return vdf.eval(name, self.deploySQL(X=X)) | ||
| else: | ||
| return vdf.copy().eval(name, self.deploySQL(X=X)) | ||
| else: | ||
| columns = vdf.get_columns() | ||
| n = len(columns) | ||
| sql = f""" | ||
| SELECT | ||
| {self._vertica_predict_transform_sql}({', '.join(columns + X)} | ||
| USING PARAMETERS | ||
| model_name = '{self.model_name}', | ||
| num_passthru_cols = '{n}') | ||
| OVER(PARTITION BEST) FROM {vdf}""" | ||
| if inplace: | ||
| return vdf.__init__(sql) | ||
| else: | ||
| return vDataFrame(sql) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters