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Python feature.Tokenizer方法代码示例

本文整理汇总了Python中pyspark.ml.feature.Tokenizer方法的典型用法代码示例。如果您正苦于以下问题:Python feature.Tokenizer方法的具体用法?Python feature.Tokenizer怎么用?Python feature.Tokenizer使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在pyspark.ml.feature的用法示例。


在下文中一共展示了feature.Tokenizer方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: build_sparkml_operator_name_map

# 需要导入模块: from pyspark.ml import feature [as 别名]
# 或者: from pyspark.ml.feature import Tokenizer [as 别名]
def build_sparkml_operator_name_map():
    res = {k: "pyspark.ml.feature." + k.__name__ for k in [
        Binarizer, BucketedRandomProjectionLSHModel, Bucketizer,
        ChiSqSelectorModel, CountVectorizerModel, DCT, ElementwiseProduct, HashingTF, IDFModel, ImputerModel,
        IndexToString, MaxAbsScalerModel, MinHashLSHModel, MinMaxScalerModel, NGram, Normalizer, OneHotEncoderModel,
        PCAModel, PolynomialExpansion, QuantileDiscretizer, RegexTokenizer,
        StandardScalerModel, StopWordsRemover, StringIndexerModel, Tokenizer, VectorAssembler, VectorIndexerModel,
        VectorSlicer, Word2VecModel
    ]}
    res.update({k: "pyspark.ml.classification." + k.__name__ for k in [
        LinearSVCModel, LogisticRegressionModel, DecisionTreeClassificationModel, GBTClassificationModel,
        RandomForestClassificationModel, NaiveBayesModel, MultilayerPerceptronClassificationModel, OneVsRestModel
    ]})
    res.update({k: "pyspark.ml.regression." + k.__name__ for k in [
        AFTSurvivalRegressionModel, DecisionTreeRegressionModel, GBTRegressionModel, GBTRegressionModel,
        GeneralizedLinearRegressionModel, IsotonicRegressionModel, LinearRegressionModel, RandomForestRegressionModel
    ]})
    return res 
开发者ID:onnx,项目名称:onnxmltools,代码行数:20,代码来源:ops_names.py

示例2: test_tokenizer

# 需要导入模块: from pyspark.ml import feature [as 别名]
# 或者: from pyspark.ml.feature import Tokenizer [as 别名]
def test_tokenizer(self):
        data = self.spark.createDataFrame([("a b c",)], ["text"])
        model = Tokenizer(inputCol='text', outputCol='words')
        predicted = model.transform(data)

        model_onnx = convert_sparkml(model, 'Sparkml Tokenizer', [
            ('text', StringTensorType([1, 1]))
        ])
        self.assertTrue(model_onnx is not None)
        # run the model
        expected = predicted.toPandas().words.apply(pandas.Series).values
        data_np = data.toPandas().text.values.reshape([1, 1])
        paths = save_data_models(data_np, expected, model, model_onnx, basename="SparkmlTokenizer")
        onnx_model_path = paths[3]
        output, output_shapes = run_onnx_model(['prediction'], data_np, onnx_model_path)
        compare_results(expected, output, decimal=5) 
开发者ID:onnx,项目名称:onnxmltools,代码行数:18,代码来源:test_tokenizer.py

示例3: main

# 需要导入模块: from pyspark.ml import feature [as 别名]
# 或者: from pyspark.ml.feature import Tokenizer [as 别名]
def main():
    # Read training data as a DataFrame
    sqlCt = SQLContext(sc)
    trainDF = sqlCt.read.parquet(training_input)
    testDF = sqlCt.read.parquet(testing_input)

    tokenizer = Tokenizer(inputCol="text", outputCol="words")
    evaluator = BinaryClassificationEvaluator()

    # no parameter tuning
    hashingTF_notuning = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features", numFeatures=1000)
    lr_notuning = LogisticRegression(maxIter=20, regParam=0.1)
    pipeline_notuning = Pipeline(stages=[tokenizer, hashingTF_notuning, lr_notuning])
    model_notuning = pipeline_notuning.fit(trainDF)

    prediction_notuning = model_notuning.transform(testDF)
    notuning_output = evaluator.evaluate(prediction_notuning)

    # for cross validation
    hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
    lr = LogisticRegression(maxIter=20)

    paramGrid = ParamGridBuilder()\
        .addGrid(hashingTF.numFeatures, [1000, 5000, 10000])\
        .addGrid(lr.regParam, [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\
        .build()

    pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
    cv = CrossValidator(estimator=pipeline, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=2)
    cvModel = cv.fit(trainDF)

    # Make predictions on test documents. cvModel uses the best model found.
    best_prediction = cvModel.transform(testDF)
    best_output = evaluator.evaluate(best_prediction)

    s = str(notuning_output) + '\n' + str(best_output)
    output_data = sc.parallelize([s])
    output_data.saveAsTextFile(output) 
开发者ID:hanhanwu,项目名称:Hanhan-Spark-Python,代码行数:40,代码来源:spark_ml_pipline.py

示例4: test_cv_lasso_with_mllib_featurization

# 需要导入模块: from pyspark.ml import feature [as 别名]
# 或者: from pyspark.ml.feature import Tokenizer [as 别名]
def test_cv_lasso_with_mllib_featurization(self):
        data = [('hi there', 0.0),
                ('what is up', 1.0),
                ('huh', 1.0),
                ('now is the time', 5.0),
                ('for what', 0.0),
                ('the spark was there', 5.0),
                ('and so', 3.0),
                ('were many socks', 0.0),
                ('really', 1.0),
                ('too cool', 2.0)]
        data = self.sql.createDataFrame(data, ["review", "rating"])

        # Feature extraction using MLlib
        tokenizer = Tokenizer(inputCol="review", outputCol="words")
        hashingTF = HashingTF(inputCol="words", outputCol="features", numFeatures=20000)
        pipeline = Pipeline(stages=[tokenizer, hashingTF])
        data = pipeline.fit(data).transform(data)

        df = self.converter.toPandas(data.select(data.features.alias("review"), "rating"))

        pipeline = SKL_Pipeline([
            ('lasso', SKL_Lasso())
        ])
        parameters = {
            'lasso__alpha': (0.001, 0.005, 0.01)
        }

        grid_search = GridSearchCV(self.sc, pipeline, parameters)
        skl_gs = grid_search.fit(df.review.values, df.rating.values)
        assert len(skl_gs.cv_results_['params']) == len(parameters['lasso__alpha']) 
开发者ID:databricks,项目名称:spark-sklearn,代码行数:33,代码来源:test_search_2.py


注:本文中的pyspark.ml.feature.Tokenizer方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。