Examples with RegexFilter cascading.operation.regex.RegexFilter used on opensource projects

Example 1 with RegexFilter

use of cascading.operation.regex.RegexFilter in project Impatient by Cascading.

the class Main method main.

public static void main(String[] args) {
    String docPath = args[0];
    String wcPath = args[1];
    String stopPath = args[2];
    String tfidfPath = args[3];
    String trapPath = args[4];
    String checkPath = args[5];
    Properties properties = new Properties();
    AppProps.setApplicationJarClass(properties, Main.class);
    AppProps.setApplicationName(properties, "Impatient Part 6");
    AppProps.addApplicationTag(properties, "tutorial:impatient");
    AppProps.addApplicationTag(properties, "technology:Cascading");
    FlowConnector flowConnector = new Hadoop2MR1FlowConnector(properties);
    // create source and sink taps
    Tap docTap = new Hfs(new TextDelimited(true, "\t"), docPath);
    Tap wcTap = new Hfs(new TextDelimited(true, "\t"), wcPath);
    Fields stop = new Fields("stop");
    Tap stopTap = new Hfs(new TextDelimited(stop, true, "\t"), stopPath);
    Tap tfidfTap = new Hfs(new TextDelimited(true, "\t"), tfidfPath);
    Tap trapTap = new Hfs(new TextDelimited(true, "\t"), trapPath);
    Tap checkTap = new Hfs(new TextDelimited(true, "\t"), checkPath);
    // use a stream assertion to validate the input data
    Pipe docPipe = new Pipe("token");
    AssertMatches assertMatches = new AssertMatches("doc\\d+\\s.*");
    docPipe = new Each(docPipe, AssertionLevel.STRICT, assertMatches);
    // specify a regex operation to split the "document" text lines into a token stream
    Fields token = new Fields("token");
    Fields text = new Fields("text");
    RegexSplitGenerator splitter = new RegexSplitGenerator(token, "[ \\[\\]\\(\\),.]");
    Fields fieldSelector = new Fields("doc_id", "token");
    docPipe = new Each(docPipe, text, splitter, fieldSelector);
    // define "ScrubFunction" to clean up the token stream
    Fields scrubArguments = new Fields("doc_id", "token");
    docPipe = new Each(docPipe, scrubArguments, new ScrubFunction(scrubArguments), Fields.RESULTS);
    // perform a left join to remove stop words, discarding the rows
    // which joined with stop words, i.e., were non-null after left join
    Pipe stopPipe = new Pipe("stop");
    Pipe tokenPipe = new HashJoin(docPipe, token, stopPipe, stop, new LeftJoin());
    tokenPipe = new Each(tokenPipe, stop, new RegexFilter("^$"));
    tokenPipe = new Retain(tokenPipe, fieldSelector);
    // one branch of the flow tallies the token counts for term frequency (TF)
    Pipe tfPipe = new Pipe("TF", tokenPipe);
    Fields tf_count = new Fields("tf_count");
    tfPipe = new CountBy(tfPipe, new Fields("doc_id", "token"), tf_count);
    Fields tf_token = new Fields("tf_token");
    tfPipe = new Rename(tfPipe, token, tf_token);
    // one branch counts the number of documents (D)
    Fields doc_id = new Fields("doc_id");
    Fields tally = new Fields("tally");
    Fields rhs_join = new Fields("rhs_join");
    Fields n_docs = new Fields("n_docs");
    Pipe dPipe = new Unique("D", tokenPipe, doc_id);
    dPipe = new Each(dPipe, new Insert(tally, 1), Fields.ALL);
    dPipe = new Each(dPipe, new Insert(rhs_join, 1), Fields.ALL);
    dPipe = new SumBy(dPipe, rhs_join, tally, n_docs, long.class);
    // one branch tallies the token counts for document frequency (DF)
    Pipe dfPipe = new Unique("DF", tokenPipe, Fields.ALL);
    Fields df_count = new Fields("df_count");
    dfPipe = new CountBy(dfPipe, token, df_count);
    Fields df_token = new Fields("df_token");
    Fields lhs_join = new Fields("lhs_join");
    dfPipe = new Rename(dfPipe, token, df_token);
    dfPipe = new Each(dfPipe, new Insert(lhs_join, 1), Fields.ALL);
    // example use of a debug, to observe tuple stream; turn off below
    dfPipe = new Each(dfPipe, DebugLevel.VERBOSE, new Debug(true));
    // join to bring together all the components for calculating TF-IDF
    // the D side of the join is smaller, so it goes on the RHS
    Pipe idfPipe = new HashJoin(dfPipe, lhs_join, dPipe, rhs_join);
    // create a checkpoint, to observe the intermediate data in DF stream
    Checkpoint idfCheck = new Checkpoint("checkpoint", idfPipe);
    // the IDF side of the join is smaller, so it goes on the RHS
    Pipe tfidfPipe = new CoGroup(tfPipe, tf_token, idfCheck, df_token);
    // calculate the TF-IDF weights, per token, per document
    Fields tfidf = new Fields("tfidf");
    String expression = "(double) tf_count * Math.log( (double) n_docs / ( 1.0 + df_count ) )";
    ExpressionFunction tfidfExpression = new ExpressionFunction(tfidf, expression, Double.class);
    Fields tfidfArguments = new Fields("tf_count", "df_count", "n_docs");
    tfidfPipe = new Each(tfidfPipe, tfidfArguments, tfidfExpression, Fields.ALL);
    fieldSelector = new Fields("tf_token", "doc_id", "tfidf");
    tfidfPipe = new Retain(tfidfPipe, fieldSelector);
    tfidfPipe = new Rename(tfidfPipe, tf_token, token);
    // keep track of the word counts, which are useful for QA
    Pipe wcPipe = new Pipe("wc", tfPipe);
    Fields count = new Fields("count");
    wcPipe = new SumBy(wcPipe, tf_token, tf_count, count, long.class);
    wcPipe = new Rename(wcPipe, tf_token, token);
    // additionally, sort by count
    wcPipe = new GroupBy(wcPipe, count, count);
    // connect the taps, pipes, traps, checkpoints, etc., into a flow
    FlowDef flowDef = FlowDef.flowDef().setName("tfidf").addSource(docPipe, docTap).addSource(stopPipe, stopTap).addTailSink(tfidfPipe, tfidfTap).addTailSink(wcPipe, wcTap).addTrap(docPipe, trapTap).addCheckpoint(idfCheck, checkTap);
    // set to DebugLevel.VERBOSE for trace, or DebugLevel.NONE in production
    flowDef.setDebugLevel(DebugLevel.VERBOSE);
    // set to AssertionLevel.STRICT for all assertions, or AssertionLevel.NONE in production
    flowDef.setAssertionLevel(AssertionLevel.STRICT);
    // write a DOT file and run the flow
    Flow tfidfFlow = flowConnector.connect(flowDef);
    tfidfFlow.writeDOT("dot/tfidf.dot");
    tfidfFlow.complete();
}

Example 2 with RegexFilter

use of cascading.operation.regex.RegexFilter in project ambrose by twitter.

the class Main method main.

public static void main(String[] args) {
    String docPath = "src/test/data/rain.txt";
    String wcPath = "output/out";
    String stopPath = "src/test/data/en.stop";
    String tfidfPath = "output/out2";
    Properties properties = new Properties();
    AppProps.setApplicationJarClass(properties, Main.class);
    HadoopFlowConnector flowConnector = new HadoopFlowConnector(properties);
    // create source and sink taps
    Tap docTap = new Hfs(new TextDelimited(true, "\t"), docPath);
    Tap wcTap = new Hfs(new TextDelimited(true, "\t"), wcPath);
    Fields stop = new Fields("stop");
    Tap stopTap = new Hfs(new TextDelimited(stop, true, "\t"), stopPath);
    Tap tfidfTap = new Hfs(new TextDelimited(true, "\t"), tfidfPath);
    // specify a regex operation to split the "document" text lines into a token stream
    Fields token = new Fields("token");
    Fields text = new Fields("text");
    RegexSplitGenerator splitter = new RegexSplitGenerator(token, "[ \\[\\]\\(\\),.]");
    Fields fieldSelector = new Fields("doc_id", "token");
    Pipe docPipe = new Each("token", text, splitter, fieldSelector);
    // define "ScrubFunction" to clean up the token stream
    Fields scrubArguments = new Fields("doc_id", "token");
    docPipe = new Each(docPipe, scrubArguments, new ScrubFunction(scrubArguments), Fields.RESULTS);
    // perform a left join to remove stop words, discarding the rows
    // which joined with stop words, i.e., were non-null after left join
    Pipe stopPipe = new Pipe("stop");
    Pipe tokenPipe = new HashJoin(docPipe, token, stopPipe, stop, new LeftJoin());
    tokenPipe = new Each(tokenPipe, stop, new RegexFilter("^$"));
    tokenPipe = new Retain(tokenPipe, fieldSelector);
    // one branch of the flow tallies the token counts for term frequency (TF)
    Pipe tfPipe = new Pipe("TF", tokenPipe);
    Fields tf_count = new Fields("tf_count");
    tfPipe = new CountBy(tfPipe, new Fields("doc_id", "token"), tf_count);
    Fields tf_token = new Fields("tf_token");
    tfPipe = new Rename(tfPipe, token, tf_token);
    // one branch counts the number of documents (D)
    Fields doc_id = new Fields("doc_id");
    Fields tally = new Fields("tally");
    Fields rhs_join = new Fields("rhs_join");
    Fields n_docs = new Fields("n_docs");
    Pipe dPipe = new Unique("D", tokenPipe, doc_id);
    dPipe = new Each(dPipe, new Insert(tally, 1), Fields.ALL);
    dPipe = new Each(dPipe, new Insert(rhs_join, 1), Fields.ALL);
    dPipe = new SumBy(dPipe, rhs_join, tally, n_docs, long.class);
    // one branch tallies the token counts for document frequency (DF)
    Pipe dfPipe = new Unique("DF", tokenPipe, Fields.ALL);
    Fields df_count = new Fields("df_count");
    dfPipe = new CountBy(dfPipe, token, df_count);
    Fields df_token = new Fields("df_token");
    Fields lhs_join = new Fields("lhs_join");
    dfPipe = new Rename(dfPipe, token, df_token);
    dfPipe = new Each(dfPipe, new Insert(lhs_join, 1), Fields.ALL);
    // join to bring together all the components for calculating TF-IDF
    // the D side of the join is smaller, so it goes on the RHS
    Pipe idfPipe = new HashJoin(dfPipe, lhs_join, dPipe, rhs_join);
    // the IDF side of the join is smaller, so it goes on the RHS
    Pipe tfidfPipe = new CoGroup(tfPipe, tf_token, idfPipe, df_token);
    // calculate the TF-IDF weights, per token, per document
    Fields tfidf = new Fields("tfidf");
    String expression = "(double) tf_count * Math.log( (double) n_docs / ( 1.0 + df_count ) )";
    ExpressionFunction tfidfExpression = new ExpressionFunction(tfidf, expression, Double.class);
    Fields tfidfArguments = new Fields("tf_count", "df_count", "n_docs");
    tfidfPipe = new Each(tfidfPipe, tfidfArguments, tfidfExpression, Fields.ALL);
    fieldSelector = new Fields("tf_token", "doc_id", "tfidf");
    tfidfPipe = new Retain(tfidfPipe, fieldSelector);
    tfidfPipe = new Rename(tfidfPipe, tf_token, token);
    // keep track of the word counts, which are useful for QA
    Pipe wcPipe = new Pipe("wc", tfPipe);
    Fields count = new Fields("count");
    wcPipe = new SumBy(wcPipe, tf_token, tf_count, count, long.class);
    wcPipe = new Rename(wcPipe, tf_token, token);
    // additionally, sort by count
    wcPipe = new GroupBy(wcPipe, count, count);
    // connect the taps, pipes, etc., into a flow
    FlowDef flowDef = FlowDef.flowDef().setName("tfidf").addSource(docPipe, docTap).addSource(stopPipe, stopTap).addTailSink(tfidfPipe, tfidfTap).addTailSink(wcPipe, wcTap);
    // run ambrose and cascading
    Flow tfidfFlow = flowConnector.connect(flowDef);
    EmbeddedAmbroseCascadingNotifier server = new EmbeddedAmbroseCascadingNotifier();
    tfidfFlow.addListener(server);
    tfidfFlow.addStepListener(server);
    tfidfFlow.complete();
}