pyspark.ml.feature.Word2VecModel(java_model: Optional[JavaObject] = None)¶
Model fitted by Word2Vec.
Methods
clear(param)
Clears a param from the param map if it has been explicitly set.
copy([extra])
Creates a copy of this instance with the same uid and some extra params.
explainParam(param)
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([extra])
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
findSynonyms(word, num)
Find ânumâ number of words closest in similarity to âwordâ.
findSynonymsArray(word, num)
Find ânumâ number of words closest in similarity to âwordâ.
Gets the value of inputCol or its default value.
Gets the value of maxIter or its default value.
Gets the value of maxSentenceLength or its default value.
Gets the value of minCount or its default value.
Gets the value of numPartitions or its default value.
getOrDefault(param)
Gets the value of a param in the user-supplied param map or its default value.
Gets the value of outputCol or its default value.
getParam(paramName)
Gets a param by its name.
getSeed()
Gets the value of seed or its default value.
Gets the value of stepSize or its default value.
Gets the value of vectorSize or its default value.
Returns the vector representation of the words as a dataframe with two fields, word and vector.
Gets the value of windowSize or its default value.
hasDefault(param)
Checks whether a param has a default value.
hasParam(paramName)
Tests whether this instance contains a param with a given (string) name.
isDefined(param)
Checks whether a param is explicitly set by user or has a default value.
isSet(param)
Checks whether a param is explicitly set by user.
load(path)
Reads an ML instance from the input path, a shortcut of read().load(path).
read()
Returns an MLReader instance for this class.
save(path)
Save this ML instance to the given path, a shortcut of âwrite().save(path)â.
set(param, value)
Sets a parameter in the embedded param map.
setInputCol(value)
Sets the value of inputCol.
setOutputCol(value)
Sets the value of outputCol.
transform(dataset[, params])
Transforms the input dataset with optional parameters.
write()
Returns an MLWriter instance for this ML instance.
Attributes
Methods Documentation
clear(param: pyspark.ml.param.Param) → None¶
Clears a param from the param map if it has been explicitly set.
copy(extra: Optional[ParamMap] = None) → JP¶
Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.
Extra parameters to copy to the new instance
JavaParams
Copy of this instance
explainParam(param: Union[str, pyspark.ml.param.Param]) → str¶
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams() → str¶
Returns the documentation of all params with their optionally default values and user-supplied values.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
extra param values
merged param map
findSynonyms(word: Union[str, pyspark.ml.linalg.Vector], num: int) → pyspark.sql.dataframe.DataFrame¶
Find ânumâ number of words closest in similarity to âwordâ. word can be a string or vector representation. Returns a dataframe with two fields word and similarity (which gives the cosine similarity).
findSynonymsArray(word: Union[pyspark.ml.linalg.Vector, str], num: int) → List[Tuple[str, float]]¶
Find ânumâ number of words closest in similarity to âwordâ. word can be a string or vector representation. Returns an array with two fields word and similarity (which gives the cosine similarity).
getInputCol() → str¶
Gets the value of inputCol or its default value.
getMaxIter() → int¶
Gets the value of maxIter or its default value.
getMaxSentenceLength() → int¶
Gets the value of maxSentenceLength or its default value.
getMinCount() → int¶
Gets the value of minCount or its default value.
getNumPartitions() → int¶
Gets the value of numPartitions or its default value.
getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
getOutputCol() → str¶
Gets the value of outputCol or its default value.
getParam(paramName: str) → pyspark.ml.param.Param¶
Gets a param by its name.
getSeed() → int¶
Gets the value of seed or its default value.
getStepSize() → float¶
Gets the value of stepSize or its default value.
getVectorSize() → int¶
Gets the value of vectorSize or its default value.
getVectors() → pyspark.sql.dataframe.DataFrame¶
Returns the vector representation of the words as a dataframe with two fields, word and vector.
getWindowSize() → int¶
Gets the value of windowSize or its default value.
hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶
Checks whether a param has a default value.
hasParam(paramName: str) → bool¶
Tests whether this instance contains a param with a given (string) name.
isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶
Checks whether a param is explicitly set by user or has a default value.
isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶
Checks whether a param is explicitly set by user.
load(path: str) → RL¶
Reads an ML instance from the input path, a shortcut of read().load(path).
read() → pyspark.ml.util.JavaMLReader[RL]¶
Returns an MLReader instance for this class.
save(path: str) → None¶
Save this ML instance to the given path, a shortcut of âwrite().save(path)â.
set(param: pyspark.ml.param.Param, value: Any) → None¶
Sets a parameter in the embedded param map.
setInputCol(value: str) → pyspark.ml.feature.Word2VecModel¶
Sets the value of inputCol.
setOutputCol(value: str) → pyspark.ml.feature.Word2VecModel¶
Sets the value of outputCol.
transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶
Transforms the input dataset with optional parameters.
pyspark.sql.DataFrame
input dataset
an optional param map that overrides embedded params.
pyspark.sql.DataFrame
transformed dataset
write() → pyspark.ml.util.JavaMLWriter¶
Returns an MLWriter instance for this ML instance.
Attributes Documentation
inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')¶
maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')¶
maxSentenceLength: pyspark.ml.param.Param[int] = Param(parent='undefined', name='maxSentenceLength', doc='Maximum length (in words) of each sentence in the input data. Any sentence longer than this threshold will be divided into chunks up to the size.')¶
minCount: pyspark.ml.param.Param[int] = Param(parent='undefined', name='minCount', doc="the minimum number of times a token must appear to be included in the word2vec model's vocabulary")¶
numPartitions: pyspark.ml.param.Param[int] = Param(parent='undefined', name='numPartitions', doc='number of partitions for sentences of words')¶
outputCol = Param(parent='undefined', name='outputCol', doc='output column name.')¶
params¶
Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.
seed = Param(parent='undefined', name='seed', doc='random seed.')¶
stepSize = Param(parent='undefined', name='stepSize', doc='Step size to be used for each iteration of optimization (>= 0).')¶
vectorSize: pyspark.ml.param.Param[int] = Param(parent='undefined', name='vectorSize', doc='the dimension of codes after transforming from words')¶
windowSize: pyspark.ml.param.Param[int] = Param(parent='undefined', name='windowSize', doc='the window size (context words from [-window, window]). Default value is 5')¶
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