INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Parse a line of text into an MLlib LabeledPoint object. | def parsePoint(line):
"""
Parse a line of text into an MLlib LabeledPoint object.
"""
values = [float(s) for s in line.split(' ')]
if values[0] == -1: # Convert -1 labels to 0 for MLlib
values[0] = 0
return LabeledPoint(values[0], values[1:]) |
Returns f - measure. | def fMeasure(self, label, beta=None):
"""
Returns f-measure.
"""
if beta is None:
return self.call("fMeasure", label)
else:
return self.call("fMeasure", label, beta) |
Returns precision or precision for a given label ( category ) if specified. | def precision(self, label=None):
"""
Returns precision or precision for a given label (category) if specified.
"""
if label is None:
return self.call("precision")
else:
return self.call("precision", float(label)) |
Returns recall or recall for a given label ( category ) if specified. | def recall(self, label=None):
"""
Returns recall or recall for a given label (category) if specified.
"""
if label is None:
return self.call("recall")
else:
return self.call("recall", float(label)) |
Returns f1Measure or f1Measure for a given label ( category ) if specified. | def f1Measure(self, label=None):
"""
Returns f1Measure or f1Measure for a given label (category) if specified.
"""
if label is None:
return self.call("f1Measure")
else:
return self.call("f1Measure", float(label)) |
When converting Spark SQL records to Pandas DataFrame the inferred data type may be wrong. This method gets the corrected data type for Pandas if that type may be inferred uncorrectly. | def _to_corrected_pandas_type(dt):
"""
When converting Spark SQL records to Pandas DataFrame, the inferred data type may be wrong.
This method gets the corrected data type for Pandas if that type may be inferred uncorrectly.
"""
import numpy as np
if type(dt) == ByteType:
return np.int8
... |
Returns the content as an: class: pyspark. RDD of: class: Row. | def rdd(self):
"""Returns the content as an :class:`pyspark.RDD` of :class:`Row`.
"""
if self._lazy_rdd is None:
jrdd = self._jdf.javaToPython()
self._lazy_rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
return self._lazy_rdd |
Converts a: class: DataFrame into a: class: RDD of string. | def toJSON(self, use_unicode=True):
"""Converts a :class:`DataFrame` into a :class:`RDD` of string.
Each row is turned into a JSON document as one element in the returned RDD.
>>> df.toJSON().first()
u'{"age":2,"name":"Alice"}'
"""
rdd = self._jdf.toJSON()
retur... |
Returns the schema of this: class: DataFrame as a: class: pyspark. sql. types. StructType. | def schema(self):
"""Returns the schema of this :class:`DataFrame` as a :class:`pyspark.sql.types.StructType`.
>>> df.schema
StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true)))
"""
if self._schema is None:
try:
self._... |
Prints the ( logical and physical ) plans to the console for debugging purpose. | def explain(self, extended=False):
"""Prints the (logical and physical) plans to the console for debugging purpose.
:param extended: boolean, default ``False``. If ``False``, prints only the physical plan.
>>> df.explain()
== Physical Plan ==
*(1) Scan ExistingRDD[age#0,name#1]... |
Return a new: class: DataFrame containing rows in this: class: DataFrame but not in another: class: DataFrame while preserving duplicates. | def exceptAll(self, other):
"""Return a new :class:`DataFrame` containing rows in this :class:`DataFrame` but
not in another :class:`DataFrame` while preserving duplicates.
This is equivalent to `EXCEPT ALL` in SQL.
>>> df1 = spark.createDataFrame(
... [("a", 1), ("a", ... |
Prints the first n rows to the console. | def show(self, n=20, truncate=True, vertical=False):
"""Prints the first ``n`` rows to the console.
:param n: Number of rows to show.
:param truncate: If set to True, truncate strings longer than 20 chars by default.
If set to a number greater than one, truncates long strings to len... |
Returns a dataframe with html code when you enabled eager evaluation by spark. sql. repl. eagerEval. enabled this only called by REPL you are using support eager evaluation with HTML. | def _repr_html_(self):
"""Returns a dataframe with html code when you enabled eager evaluation
by 'spark.sql.repl.eagerEval.enabled', this only called by REPL you are
using support eager evaluation with HTML.
"""
import cgi
if not self._support_repr_html:
self... |
Returns a checkpointed version of this Dataset. Checkpointing can be used to truncate the logical plan of this DataFrame which is especially useful in iterative algorithms where the plan may grow exponentially. It will be saved to files inside the checkpoint directory set with L { SparkContext. setCheckpointDir () }. | def checkpoint(self, eager=True):
"""Returns a checkpointed version of this Dataset. Checkpointing can be used to truncate the
logical plan of this DataFrame, which is especially useful in iterative algorithms where the
plan may grow exponentially. It will be saved to files inside the checkpoint... |
Returns a locally checkpointed version of this Dataset. Checkpointing can be used to truncate the logical plan of this DataFrame which is especially useful in iterative algorithms where the plan may grow exponentially. Local checkpoints are stored in the executors using the caching subsystem and therefore they are not ... | def localCheckpoint(self, eager=True):
"""Returns a locally checkpointed version of this Dataset. Checkpointing can be used to
truncate the logical plan of this DataFrame, which is especially useful in iterative
algorithms where the plan may grow exponentially. Local checkpoints are stored in th... |
Defines an event time watermark for this: class: DataFrame. A watermark tracks a point in time before which we assume no more late data is going to arrive. | def withWatermark(self, eventTime, delayThreshold):
"""Defines an event time watermark for this :class:`DataFrame`. A watermark tracks a point
in time before which we assume no more late data is going to arrive.
Spark will use this watermark for several purposes:
- To know when a give... |
Specifies some hint on the current DataFrame. | def hint(self, name, *parameters):
"""Specifies some hint on the current DataFrame.
:param name: A name of the hint.
:param parameters: Optional parameters.
:return: :class:`DataFrame`
>>> df.join(df2.hint("broadcast"), "name").show()
+----+---+------+
|name|age... |
Returns all the records as a list of: class: Row. | def collect(self):
"""Returns all the records as a list of :class:`Row`.
>>> df.collect()
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
"""
with SCCallSiteSync(self._sc) as css:
sock_info = self._jdf.collectToPython()
return list(_load_from_socket(sock... |
Returns an iterator that contains all of the rows in this: class: DataFrame. The iterator will consume as much memory as the largest partition in this DataFrame. | def toLocalIterator(self):
"""
Returns an iterator that contains all of the rows in this :class:`DataFrame`.
The iterator will consume as much memory as the largest partition in this DataFrame.
>>> list(df.toLocalIterator())
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
... |
Limits the result count to the number specified. | def limit(self, num):
"""Limits the result count to the number specified.
>>> df.limit(1).collect()
[Row(age=2, name=u'Alice')]
>>> df.limit(0).collect()
[]
"""
jdf = self._jdf.limit(num)
return DataFrame(jdf, self.sql_ctx) |
Sets the storage level to persist the contents of the: class: DataFrame across operations after the first time it is computed. This can only be used to assign a new storage level if the: class: DataFrame does not have a storage level set yet. If no storage level is specified defaults to ( C { MEMORY_AND_DISK } ). | def persist(self, storageLevel=StorageLevel.MEMORY_AND_DISK):
"""Sets the storage level to persist the contents of the :class:`DataFrame` across
operations after the first time it is computed. This can only be used to assign
a new storage level if the :class:`DataFrame` does not have a storage l... |
Get the: class: DataFrame s current storage level. | def storageLevel(self):
"""Get the :class:`DataFrame`'s current storage level.
>>> df.storageLevel
StorageLevel(False, False, False, False, 1)
>>> df.cache().storageLevel
StorageLevel(True, True, False, True, 1)
>>> df2.persist(StorageLevel.DISK_ONLY_2).storageLevel
... |
Marks the: class: DataFrame as non - persistent and remove all blocks for it from memory and disk. | def unpersist(self, blocking=False):
"""Marks the :class:`DataFrame` as non-persistent, and remove all blocks for it from
memory and disk.
.. note:: `blocking` default has changed to False to match Scala in 2.0.
"""
self.is_cached = False
self._jdf.unpersist(blocking)
... |
Returns a new: class: DataFrame that has exactly numPartitions partitions. | def coalesce(self, numPartitions):
"""
Returns a new :class:`DataFrame` that has exactly `numPartitions` partitions.
:param numPartitions: int, to specify the target number of partitions
Similar to coalesce defined on an :class:`RDD`, this operation results in a
narrow dependen... |
Returns a new: class: DataFrame partitioned by the given partitioning expressions. The resulting DataFrame is hash partitioned. | def repartition(self, numPartitions, *cols):
"""
Returns a new :class:`DataFrame` partitioned by the given partitioning expressions. The
resulting DataFrame is hash partitioned.
:param numPartitions:
can be an int to specify the target number of partitions or a Column.
... |
Returns a sampled subset of this: class: DataFrame. | def sample(self, withReplacement=None, fraction=None, seed=None):
"""Returns a sampled subset of this :class:`DataFrame`.
:param withReplacement: Sample with replacement or not (default False).
:param fraction: Fraction of rows to generate, range [0.0, 1.0].
:param seed: Seed for sampli... |
Returns a stratified sample without replacement based on the fraction given on each stratum. | def sampleBy(self, col, fractions, seed=None):
"""
Returns a stratified sample without replacement based on the
fraction given on each stratum.
:param col: column that defines strata
:param fractions:
sampling fraction for each stratum. If a stratum is not
... |
Randomly splits this: class: DataFrame with the provided weights. | def randomSplit(self, weights, seed=None):
"""Randomly splits this :class:`DataFrame` with the provided weights.
:param weights: list of doubles as weights with which to split the DataFrame. Weights will
be normalized if they don't sum up to 1.0.
:param seed: The seed for sampling.
... |
Returns all column names and their data types as a list. | def dtypes(self):
"""Returns all column names and their data types as a list.
>>> df.dtypes
[('age', 'int'), ('name', 'string')]
"""
return [(str(f.name), f.dataType.simpleString()) for f in self.schema.fields] |
Selects column based on the column name specified as a regex and returns it as: class: Column. | def colRegex(self, colName):
"""
Selects column based on the column name specified as a regex and returns it
as :class:`Column`.
:param colName: string, column name specified as a regex.
>>> df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["Col1", "Col2"])
>... |
Returns a new: class: DataFrame with an alias set. | def alias(self, alias):
"""Returns a new :class:`DataFrame` with an alias set.
:param alias: string, an alias name to be set for the DataFrame.
>>> from pyspark.sql.functions import *
>>> df_as1 = df.alias("df_as1")
>>> df_as2 = df.alias("df_as2")
>>> joined_df = df_as1... |
Returns the cartesian product with another: class: DataFrame. | def crossJoin(self, other):
"""Returns the cartesian product with another :class:`DataFrame`.
:param other: Right side of the cartesian product.
>>> df.select("age", "name").collect()
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
>>> df2.select("name", "height").collect(... |
Joins with another: class: DataFrame using the given join expression. | def join(self, other, on=None, how=None):
"""Joins with another :class:`DataFrame`, using the given join expression.
:param other: Right side of the join
:param on: a string for the join column name, a list of column names,
a join expression (Column), or a list of Columns.
... |
Returns a new: class: DataFrame with each partition sorted by the specified column ( s ). | def sortWithinPartitions(self, *cols, **kwargs):
"""Returns a new :class:`DataFrame` with each partition sorted by the specified column(s).
:param cols: list of :class:`Column` or column names to sort by.
:param ascending: boolean or list of boolean (default True).
Sort ascending vs... |
Return a JVM Seq of Columns from a list of Column or names | def _jseq(self, cols, converter=None):
"""Return a JVM Seq of Columns from a list of Column or names"""
return _to_seq(self.sql_ctx._sc, cols, converter) |
Return a JVM Seq of Columns from a list of Column or column names | def _jcols(self, *cols):
"""Return a JVM Seq of Columns from a list of Column or column names
If `cols` has only one list in it, cols[0] will be used as the list.
"""
if len(cols) == 1 and isinstance(cols[0], list):
cols = cols[0]
return self._jseq(cols, _to_java_col... |
Return a JVM Seq of Columns that describes the sort order | def _sort_cols(self, cols, kwargs):
""" Return a JVM Seq of Columns that describes the sort order
"""
if not cols:
raise ValueError("should sort by at least one column")
if len(cols) == 1 and isinstance(cols[0], list):
cols = cols[0]
jcols = [_to_java_colu... |
Computes basic statistics for numeric and string columns. | def describe(self, *cols):
"""Computes basic statistics for numeric and string columns.
This include count, mean, stddev, min, and max. If no columns are
given, this function computes statistics for all numerical or string columns.
.. note:: This function is meant for exploratory data ... |
Computes specified statistics for numeric and string columns. Available statistics are: - count - mean - stddev - min - max - arbitrary approximate percentiles specified as a percentage ( eg 75% ) | def summary(self, *statistics):
"""Computes specified statistics for numeric and string columns. Available statistics are:
- count
- mean
- stddev
- min
- max
- arbitrary approximate percentiles specified as a percentage (eg, 75%)
If no statistics are giv... |
Returns the first n rows. | def head(self, n=None):
"""Returns the first ``n`` rows.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
:param n: int, default 1. Number of rows to return.
:return: If n is greate... |
Projects a set of expressions and returns a new: class: DataFrame. | def select(self, *cols):
"""Projects a set of expressions and returns a new :class:`DataFrame`.
:param cols: list of column names (string) or expressions (:class:`Column`).
If one of the column names is '*', that column is expanded to include all columns
in the current DataFrame... |
Projects a set of SQL expressions and returns a new: class: DataFrame. | def selectExpr(self, *expr):
"""Projects a set of SQL expressions and returns a new :class:`DataFrame`.
This is a variant of :func:`select` that accepts SQL expressions.
>>> df.selectExpr("age * 2", "abs(age)").collect()
[Row((age * 2)=4, abs(age)=2), Row((age * 2)=10, abs(age)=5)]
... |
Filters rows using the given condition. | def filter(self, condition):
"""Filters rows using the given condition.
:func:`where` is an alias for :func:`filter`.
:param condition: a :class:`Column` of :class:`types.BooleanType`
or a string of SQL expression.
>>> df.filter(df.age > 3).collect()
[Row(age=5, na... |
Groups the: class: DataFrame using the specified columns so we can run aggregation on them. See: class: GroupedData for all the available aggregate functions. | def groupBy(self, *cols):
"""Groups the :class:`DataFrame` using the specified columns,
so we can run aggregation on them. See :class:`GroupedData`
for all the available aggregate functions.
:func:`groupby` is an alias for :func:`groupBy`.
:param cols: list of columns to group ... |
Return a new: class: DataFrame containing union of rows in this and another frame. | def union(self, other):
""" Return a new :class:`DataFrame` containing union of rows in this and another frame.
This is equivalent to `UNION ALL` in SQL. To do a SQL-style set union
(that does deduplication of elements), use this function followed by :func:`distinct`.
Also as standard ... |
Returns a new: class: DataFrame containing union of rows in this and another frame. | def unionByName(self, other):
""" Returns a new :class:`DataFrame` containing union of rows in this and another frame.
This is different from both `UNION ALL` and `UNION DISTINCT` in SQL. To do a SQL-style set
union (that does deduplication of elements), use this function followed by :func:`dis... |
Return a new: class: DataFrame containing rows only in both this frame and another frame. | def intersect(self, other):
""" Return a new :class:`DataFrame` containing rows only in
both this frame and another frame.
This is equivalent to `INTERSECT` in SQL.
"""
return DataFrame(self._jdf.intersect(other._jdf), self.sql_ctx) |
Return a new: class: DataFrame containing rows in both this dataframe and other dataframe while preserving duplicates. | def intersectAll(self, other):
""" Return a new :class:`DataFrame` containing rows in both this dataframe and other
dataframe while preserving duplicates.
This is equivalent to `INTERSECT ALL` in SQL.
>>> df1 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3), ("c", 4)], ["C1", "C2"]... |
Return a new: class: DataFrame containing rows in this frame but not in another frame. | def subtract(self, other):
""" Return a new :class:`DataFrame` containing rows in this frame
but not in another frame.
This is equivalent to `EXCEPT DISTINCT` in SQL.
"""
return DataFrame(getattr(self._jdf, "except")(other._jdf), self.sql_ctx) |
Return a new: class: DataFrame with duplicate rows removed optionally only considering certain columns. | def dropDuplicates(self, subset=None):
"""Return a new :class:`DataFrame` with duplicate rows removed,
optionally only considering certain columns.
For a static batch :class:`DataFrame`, it just drops duplicate rows. For a streaming
:class:`DataFrame`, it will keep all data across trigg... |
Returns a new: class: DataFrame omitting rows with null values.: func: DataFrame. dropna and: func: DataFrameNaFunctions. drop are aliases of each other. | def dropna(self, how='any', thresh=None, subset=None):
"""Returns a new :class:`DataFrame` omitting rows with null values.
:func:`DataFrame.dropna` and :func:`DataFrameNaFunctions.drop` are aliases of each other.
:param how: 'any' or 'all'.
If 'any', drop a row if it contains any nu... |
Replace null values alias for na. fill ().: func: DataFrame. fillna and: func: DataFrameNaFunctions. fill are aliases of each other. | def fillna(self, value, subset=None):
"""Replace null values, alias for ``na.fill()``.
:func:`DataFrame.fillna` and :func:`DataFrameNaFunctions.fill` are aliases of each other.
:param value: int, long, float, string, bool or dict.
Value to replace null values with.
If th... |
Returns a new: class: DataFrame replacing a value with another value.: func: DataFrame. replace and: func: DataFrameNaFunctions. replace are aliases of each other. Values to_replace and value must have the same type and can only be numerics booleans or strings. Value can have None. When replacing the new value will be ... | def replace(self, to_replace, value=_NoValue, subset=None):
"""Returns a new :class:`DataFrame` replacing a value with another value.
:func:`DataFrame.replace` and :func:`DataFrameNaFunctions.replace` are
aliases of each other.
Values to_replace and value must have the same type and can ... |
Calculates the approximate quantiles of numerical columns of a DataFrame. | def approxQuantile(self, col, probabilities, relativeError):
"""
Calculates the approximate quantiles of numerical columns of a
DataFrame.
The result of this algorithm has the following deterministic bound:
If the DataFrame has N elements and if we request the quantile at
... |
Calculates the correlation of two columns of a DataFrame as a double value. Currently only supports the Pearson Correlation Coefficient.: func: DataFrame. corr and: func: DataFrameStatFunctions. corr are aliases of each other. | def corr(self, col1, col2, method=None):
"""
Calculates the correlation of two columns of a DataFrame as a double value.
Currently only supports the Pearson Correlation Coefficient.
:func:`DataFrame.corr` and :func:`DataFrameStatFunctions.corr` are aliases of each other.
:param ... |
Calculate the sample covariance for the given columns specified by their names as a double value.: func: DataFrame. cov and: func: DataFrameStatFunctions. cov are aliases. | def cov(self, col1, col2):
"""
Calculate the sample covariance for the given columns, specified by their names, as a
double value. :func:`DataFrame.cov` and :func:`DataFrameStatFunctions.cov` are aliases.
:param col1: The name of the first column
:param col2: The name of the sec... |
Computes a pair - wise frequency table of the given columns. Also known as a contingency table. The number of distinct values for each column should be less than 1e4. At most 1e6 non - zero pair frequencies will be returned. The first column of each row will be the distinct values of col1 and the column names will be t... | def crosstab(self, col1, col2):
"""
Computes a pair-wise frequency table of the given columns. Also known as a contingency
table. The number of distinct values for each column should be less than 1e4. At most 1e6
non-zero pair frequencies will be returned.
The first column of eac... |
Finding frequent items for columns possibly with false positives. Using the frequent element count algorithm described in https:// doi. org/ 10. 1145/ 762471. 762473 proposed by Karp Schenker and Papadimitriou.: func: DataFrame. freqItems and: func: DataFrameStatFunctions. freqItems are aliases. | def freqItems(self, cols, support=None):
"""
Finding frequent items for columns, possibly with false positives. Using the
frequent element count algorithm described in
"https://doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou".
:func:`DataFrame.freqIte... |
Returns a new: class: DataFrame by adding a column or replacing the existing column that has the same name. | def withColumn(self, colName, col):
"""
Returns a new :class:`DataFrame` by adding a column or replacing the
existing column that has the same name.
The column expression must be an expression over this DataFrame; attempting to add
a column from some other dataframe will raise a... |
Returns a new: class: DataFrame by renaming an existing column. This is a no - op if schema doesn t contain the given column name. | def withColumnRenamed(self, existing, new):
"""Returns a new :class:`DataFrame` by renaming an existing column.
This is a no-op if schema doesn't contain the given column name.
:param existing: string, name of the existing column to rename.
:param new: string, new name of the column.
... |
Returns a new: class: DataFrame that drops the specified column. This is a no - op if schema doesn t contain the given column name ( s ). | def drop(self, *cols):
"""Returns a new :class:`DataFrame` that drops the specified column.
This is a no-op if schema doesn't contain the given column name(s).
:param cols: a string name of the column to drop, or a
:class:`Column` to drop, or a list of string name of the columns to ... |
Returns a new class: DataFrame that with new specified column names | def toDF(self, *cols):
"""Returns a new class:`DataFrame` that with new specified column names
:param cols: list of new column names (string)
>>> df.toDF('f1', 'f2').collect()
[Row(f1=2, f2=u'Alice'), Row(f1=5, f2=u'Bob')]
"""
jdf = self._jdf.toDF(self._jseq(cols))
... |
Returns a new class: DataFrame. Concise syntax for chaining custom transformations. | def transform(self, func):
"""Returns a new class:`DataFrame`. Concise syntax for chaining custom transformations.
:param func: a function that takes and returns a class:`DataFrame`.
>>> from pyspark.sql.functions import col
>>> df = spark.createDataFrame([(1, 1.0), (2, 2.0)], ["int", ... |
Returns the contents of this: class: DataFrame as Pandas pandas. DataFrame. | def toPandas(self):
"""
Returns the contents of this :class:`DataFrame` as Pandas ``pandas.DataFrame``.
This is only available if Pandas is installed and available.
.. note:: This method should only be used if the resulting Pandas's DataFrame is expected
to be small, as all... |
Returns all records as a list of ArrowRecordBatches pyarrow must be installed and available on driver and worker Python environments. | def _collectAsArrow(self):
"""
Returns all records as a list of ArrowRecordBatches, pyarrow must be installed
and available on driver and worker Python environments.
.. note:: Experimental.
"""
with SCCallSiteSync(self._sc) as css:
sock_info = self._jdf.colle... |
Returns the: class: StatCounter members as a dict. | def asDict(self, sample=False):
"""Returns the :class:`StatCounter` members as a ``dict``.
>>> sc.parallelize([1., 2., 3., 4.]).stats().asDict()
{'count': 4L,
'max': 4.0,
'mean': 2.5,
'min': 1.0,
'stdev': 1.2909944487358056,
'sum': 10.0,
'va... |
Returns a list of function information via JVM. Sorts wrapped expression infos by name and returns them. | def _list_function_infos(jvm):
"""
Returns a list of function information via JVM. Sorts wrapped expression infos by name
and returns them.
"""
jinfos = jvm.org.apache.spark.sql.api.python.PythonSQLUtils.listBuiltinFunctionInfos()
infos = []
for jinfo in jinfos:
name = jinfo.getName... |
Makes the usage description pretty and returns a formatted string if usage is not an empty string. Otherwise returns None. | def _make_pretty_usage(usage):
"""
Makes the usage description pretty and returns a formatted string if `usage`
is not an empty string. Otherwise, returns None.
"""
if usage is not None and usage.strip() != "":
usage = "\n".join(map(lambda u: u.strip(), usage.split("\n")))
return "%... |
Makes the arguments description pretty and returns a formatted string if arguments starts with the argument prefix. Otherwise returns None. | def _make_pretty_arguments(arguments):
"""
Makes the arguments description pretty and returns a formatted string if `arguments`
starts with the argument prefix. Otherwise, returns None.
Expected input:
Arguments:
* arg0 - ...
...
* arg0 - ...
...... |
Makes the examples description pretty and returns a formatted string if examples starts with the example prefix. Otherwise returns None. | def _make_pretty_examples(examples):
"""
Makes the examples description pretty and returns a formatted string if `examples`
starts with the example prefix. Otherwise, returns None.
Expected input:
Examples:
> SELECT ...;
...
> SELECT ...;
...
Expe... |
Makes the note description pretty and returns a formatted string if note is not an empty string. Otherwise returns None. | def _make_pretty_note(note):
"""
Makes the note description pretty and returns a formatted string if `note` is not
an empty string. Otherwise, returns None.
Expected input:
...
Expected output:
**Note:**
...
"""
if note != "":
note = "\n".join(map(lambda n: n[4:... |
Makes the deprecated description pretty and returns a formatted string if deprecated is not an empty string. Otherwise returns None. | def _make_pretty_deprecated(deprecated):
"""
Makes the deprecated description pretty and returns a formatted string if `deprecated`
is not an empty string. Otherwise, returns None.
Expected input:
...
Expected output:
**Deprecated:**
...
"""
if deprecated != "":
... |
Generates a markdown file after listing the function information. The output file is created in path. | def generate_sql_markdown(jvm, path):
"""
Generates a markdown file after listing the function information. The output file
is created in `path`.
Expected output:
### NAME
USAGE
**Arguments:**
ARGUMENTS
**Examples:**
```
EXAMPLES
```
**Note:**
NOTE
**... |
Predict values for a single data point or an RDD of points using the model trained. | def predict(self, x):
"""
Predict values for a single data point or an RDD of points
using the model trained.
"""
if isinstance(x, RDD):
return x.map(lambda v: self.predict(v))
x = _convert_to_vector(x)
if self.numClasses == 2:
margin = se... |
Save this model to the given path. | def save(self, sc, path):
"""
Save this model to the given path.
"""
java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel(
_py2java(sc, self._coeff), self.intercept, self.numFeatures, self.numClasses)
java_model.save(sc._jsc.sc(), path) |
Train a logistic regression model on the given data. | def train(cls, data, iterations=100, initialWeights=None, regParam=0.0, regType="l2",
intercept=False, corrections=10, tolerance=1e-6, validateData=True, numClasses=2):
"""
Train a logistic regression model on the given data.
:param data:
The training data, an RDD of Lab... |
Predict values for a single data point or an RDD of points using the model trained. | def predict(self, x):
"""
Predict values for a single data point or an RDD of points
using the model trained.
"""
if isinstance(x, RDD):
return x.map(lambda v: self.predict(v))
x = _convert_to_vector(x)
margin = self.weights.dot(x) + self.intercept
... |
Save this model to the given path. | def save(self, sc, path):
"""
Save this model to the given path.
"""
java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel(
_py2java(sc, self._coeff), self.intercept)
java_model.save(sc._jsc.sc(), path) |
Load a model from the given path. | def load(cls, sc, path):
"""
Load a model from the given path.
"""
java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel.load(
sc._jsc.sc(), path)
weights = _java2py(sc, java_model.weights())
intercept = java_model.intercept()
threshold =... |
Train a Naive Bayes model given an RDD of ( label features ) vectors. | def train(cls, data, lambda_=1.0):
"""
Train a Naive Bayes model given an RDD of (label, features)
vectors.
This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which
can handle all kinds of discrete data. For example, by
converting documents into TF-IDF vectors, i... |
Push item onto heap maintaining the heap invariant. | def heappush(heap, item):
"""Push item onto heap, maintaining the heap invariant."""
heap.append(item)
_siftdown(heap, 0, len(heap)-1) |
Pop the smallest item off the heap maintaining the heap invariant. | def heappop(heap):
"""Pop the smallest item off the heap, maintaining the heap invariant."""
lastelt = heap.pop() # raises appropriate IndexError if heap is empty
if heap:
returnitem = heap[0]
heap[0] = lastelt
_siftup(heap, 0)
return returnitem
return lastelt |
Pop and return the current smallest value and add the new item. | def heapreplace(heap, item):
"""Pop and return the current smallest value, and add the new item.
This is more efficient than heappop() followed by heappush(), and can be
more appropriate when using a fixed-size heap. Note that the value
returned may be larger than item! That constrains reasonable use... |
Fast version of a heappush followed by a heappop. | def heappushpop(heap, item):
"""Fast version of a heappush followed by a heappop."""
if heap and heap[0] < item:
item, heap[0] = heap[0], item
_siftup(heap, 0)
return item |
Transform list into a heap in - place in O ( len ( x )) time. | def heapify(x):
"""Transform list into a heap, in-place, in O(len(x)) time."""
n = len(x)
# Transform bottom-up. The largest index there's any point to looking at
# is the largest with a child index in-range, so must have 2*i + 1 < n,
# or i < (n-1)/2. If n is even = 2*j, this is (2*j-1)/2 = j-1/2... |
Maxheap version of a heappop. | def _heappop_max(heap):
"""Maxheap version of a heappop."""
lastelt = heap.pop() # raises appropriate IndexError if heap is empty
if heap:
returnitem = heap[0]
heap[0] = lastelt
_siftup_max(heap, 0)
return returnitem
return lastelt |
Maxheap version of a heappop followed by a heappush. | def _heapreplace_max(heap, item):
"""Maxheap version of a heappop followed by a heappush."""
returnitem = heap[0] # raises appropriate IndexError if heap is empty
heap[0] = item
_siftup_max(heap, 0)
return returnitem |
Transform list into a maxheap in - place in O ( len ( x )) time. | def _heapify_max(x):
"""Transform list into a maxheap, in-place, in O(len(x)) time."""
n = len(x)
for i in reversed(range(n//2)):
_siftup_max(x, i) |
Maxheap variant of _siftdown | def _siftdown_max(heap, startpos, pos):
'Maxheap variant of _siftdown'
newitem = heap[pos]
# Follow the path to the root, moving parents down until finding a place
# newitem fits.
while pos > startpos:
parentpos = (pos - 1) >> 1
parent = heap[parentpos]
if parent < newitem:
... |
Maxheap variant of _siftup | def _siftup_max(heap, pos):
'Maxheap variant of _siftup'
endpos = len(heap)
startpos = pos
newitem = heap[pos]
# Bubble up the larger child until hitting a leaf.
childpos = 2*pos + 1 # leftmost child position
while childpos < endpos:
# Set childpos to index of larger child.
... |
Merge multiple sorted inputs into a single sorted output. | def merge(iterables, key=None, reverse=False):
'''Merge multiple sorted inputs into a single sorted output.
Similar to sorted(itertools.chain(*iterables)) but returns a generator,
does not pull the data into memory all at once, and assumes that each of
the input streams is already sorted (smallest to l... |
Find the n smallest elements in a dataset. | def nsmallest(n, iterable, key=None):
"""Find the n smallest elements in a dataset.
Equivalent to: sorted(iterable, key=key)[:n]
"""
# Short-cut for n==1 is to use min()
if n == 1:
it = iter(iterable)
sentinel = object()
if key is None:
result = min(it, default... |
Find the n largest elements in a dataset. | def nlargest(n, iterable, key=None):
"""Find the n largest elements in a dataset.
Equivalent to: sorted(iterable, key=key, reverse=True)[:n]
"""
# Short-cut for n==1 is to use max()
if n == 1:
it = iter(iterable)
sentinel = object()
if key is None:
result = max... |
Compute the correlation matrix with specified method using dataset. | def corr(dataset, column, method="pearson"):
"""
Compute the correlation matrix with specified method using dataset.
:param dataset:
A Dataset or a DataFrame.
:param column:
The name of the column of vectors for which the correlation coefficient needs
to be... |
Given a list of metrics provides a builder that it turns computes metrics from a column. | def metrics(*metrics):
"""
Given a list of metrics, provides a builder that it turns computes metrics from a column.
See the documentation of [[Summarizer]] for an example.
The following metrics are accepted (case sensitive):
- mean: a vector that contains the coefficient-wise... |
Returns an aggregate object that contains the summary of the column with the requested metrics. | def summary(self, featuresCol, weightCol=None):
"""
Returns an aggregate object that contains the summary of the column with the requested
metrics.
:param featuresCol:
a column that contains features Vector object.
:param weightCol:
a column that contains weigh... |
Compute the correlation ( matrix ) for the input RDD ( s ) using the specified method. Methods currently supported: I { pearson ( default ) spearman }. | def corr(x, y=None, method=None):
"""
Compute the correlation (matrix) for the input RDD(s) using the
specified method.
Methods currently supported: I{pearson (default), spearman}.
If a single RDD of Vectors is passed in, a correlation matrix
comparing the columns in the... |
Creates a list of callables which can be called from different threads to fit and evaluate an estimator in parallel. Each callable returns an ( index metric ) pair. | def _parallelFitTasks(est, train, eva, validation, epm, collectSubModel):
"""
Creates a list of callables which can be called from different threads to fit and evaluate
an estimator in parallel. Each callable returns an `(index, metric)` pair.
:param est: Estimator, the estimator to be fit.
:param ... |
Sets the given parameters in this grid to fixed values. Accepts either a parameter dictionary or a list of ( parameter value ) pairs. | def baseOn(self, *args):
"""
Sets the given parameters in this grid to fixed values.
Accepts either a parameter dictionary or a list of (parameter, value) pairs.
"""
if isinstance(args[0], dict):
self.baseOn(*args[0].items())
else:
for (param, valu... |
Builds and returns all combinations of parameters specified by the param grid. | def build(self):
"""
Builds and returns all combinations of parameters specified
by the param grid.
"""
keys = self._param_grid.keys()
grid_values = self._param_grid.values()
def to_key_value_pairs(keys, values):
return [(key, key.typeConverter(value)... |
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