diff --git a/dbscan.ipynb b/dbscan.ipynb index 7074bf3..424166f 100644 --- a/dbscan.ipynb +++ b/dbscan.ipynb @@ -2,30 +2,32 @@ "cells": [ { "cell_type": "code", - "execution_count": 55, + "execution_count": 1, "id": "42deea39-24b4-4d66-bbe6-7c925ddc360f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('spark.app.startTime', '1700871052401'),\n", + "[('spark.stage.maxConsecutiveAttempts', '10'),\n", " ('spark.dynamicAllocation.minExecutors', '1'),\n", - " ('spark.stage.maxConsecutiveAttempts', '10'),\n", + " ('spark.eventLog.enabled', 'true'),\n", " ('spark.submit.pyFiles',\n", " '/root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,/root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,/root/.ivy2/jars/com.typesafe_config-1.4.2.jar,/root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,/root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,/root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,/root/.ivy2/jars/com.navigamez_greex-1.0.jar,/root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,/root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,/root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,/root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,/root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,/root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,/root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,/root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,/root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,/root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,/root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,/root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,/root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,/root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,/root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,/root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,/root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,/root/.ivy2/jars/com.google.api_gax-2.20.1.jar,/root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,/root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,/root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,/root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,/root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,/root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,/root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,/root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,/root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,/root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,/root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,/root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,/root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,/root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,/root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,/root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,/root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,/root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,/root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,/root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,/root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n", - " ('spark.eventLog.enabled', 'true'),\n", + " ('spark.history.fs.logDirectory',\n", + " 'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/24b513ac-fceb-45d7-87ce-724ecdca7081/spark-job-history'),\n", " ('spark.dataproc.sql.joinConditionReorder.enabled', 'true'),\n", " ('spark.sql.autoBroadcastJoinThreshold', '191m'),\n", - " ('spark.eventLog.dir',\n", - " 'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/4f3dcfe4-99eb-4d99-bb0e-a5a10f0bc58b/spark-job-history'),\n", " ('spark.kryoserializer.buffer.max', '2000M'),\n", " ('spark.serializer', 'org.apache.spark.serializer.KryoSerializer'),\n", " ('spark.dataproc.sql.local.rank.pushdown.enabled', 'true'),\n", + " ('spark.app.id', 'application_1700888021537_0001'),\n", " ('spark.driver.maxResultSize', '0'),\n", " ('spark.yarn.unmanagedAM.enabled', 'true'),\n", " ('spark.ui.filters',\n", " 'org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter'),\n", + " ('spark.eventLog.dir',\n", + " 'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/24b513ac-fceb-45d7-87ce-724ecdca7081/spark-job-history'),\n", " ('spark.metrics.namespace',\n", " 'app_name:${spark.app.name}.app_id:${spark.app.id}'),\n", " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS',\n", @@ -37,8 +39,6 @@ " 'hub-msca-bdp-dphub-students-test-ridhi-m.c.msca-bdp-student-ap.internal'),\n", " ('spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version', '2'),\n", " ('spark.dynamicAllocation.maxExecutors', '10000'),\n", - " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n", - " 'http://hub-msca-bdp-dphub-students-test-ridhi-m:8088/proxy/application_1700846724434_0009'),\n", " ('spark.yarn.dist.pyFiles',\n", " 'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n", " ('spark.yarn.am.attemptFailuresValidityInterval', '1h'),\n", @@ -48,10 +48,11 @@ " ('spark.executorEnv.OPENBLAS_NUM_THREADS', '1'),\n", " ('spark.yarn.secondary.jars',\n", " 'com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,com.typesafe_config-1.4.2.jar,org.rocksdb_rocksdbjni-6.29.5.jar,com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,com.github.universal-automata_liblevenshtein-3.0.0.jar,com.google.cloud_google-cloud-storage-2.16.0.jar,com.navigamez_greex-1.0.jar,com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,it.unimi.dsi_fastutil-7.0.12.jar,org.projectlombok_lombok-1.16.8.jar,com.google.guava_guava-31.1-jre.jar,com.google.guava_failureaccess-1.0.1.jar,com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,com.google.errorprone_error_prone_annotations-2.16.jar,com.google.j2objc_j2objc-annotations-1.3.jar,com.google.http-client_google-http-client-1.42.3.jar,io.opencensus_opencensus-contrib-http-util-0.31.1.jar,com.google.http-client_google-http-client-jackson2-1.42.3.jar,com.google.http-client_google-http-client-gson-1.42.3.jar,com.google.api-client_google-api-client-2.1.1.jar,commons-codec_commons-codec-1.15.jar,com.google.oauth-client_google-oauth-client-1.34.1.jar,com.google.http-client_google-http-client-apache-v2-1.42.3.jar,com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,com.google.code.gson_gson-2.10.jar,com.google.cloud_google-cloud-core-2.9.0.jar,com.google.auto.value_auto-value-annotations-1.10.1.jar,com.google.cloud_google-cloud-core-http-2.9.0.jar,com.google.http-client_google-http-client-appengine-1.42.3.jar,com.google.api_gax-httpjson-0.105.1.jar,com.google.cloud_google-cloud-core-grpc-2.9.0.jar,io.grpc_grpc-core-1.51.0.jar,com.google.api_gax-2.20.1.jar,com.google.api_gax-grpc-2.20.1.jar,io.grpc_grpc-alts-1.51.0.jar,io.grpc_grpc-grpclb-1.51.0.jar,org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,io.grpc_grpc-protobuf-1.51.0.jar,com.google.auth_google-auth-library-credentials-1.13.0.jar,com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,com.google.api_api-common-2.2.2.jar,javax.annotation_javax.annotation-api-1.3.2.jar,io.opencensus_opencensus-api-0.31.1.jar,io.grpc_grpc-context-1.51.0.jar,com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,com.google.protobuf_protobuf-java-3.21.10.jar,com.google.protobuf_protobuf-java-util-3.21.10.jar,com.google.api.grpc_proto-google-common-protos-2.11.0.jar,org.threeten_threetenbp-1.6.4.jar,com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,com.fasterxml.jackson.core_jackson-core-2.14.1.jar,com.google.code.findbugs_jsr305-3.0.2.jar,io.grpc_grpc-api-1.51.0.jar,io.grpc_grpc-auth-1.51.0.jar,io.grpc_grpc-stub-1.51.0.jar,org.checkerframework_checker-qual-3.28.0.jar,com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,io.grpc_grpc-protobuf-lite-1.51.0.jar,com.google.android_annotations-4.1.1.4.jar,org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,io.grpc_grpc-netty-shaded-1.51.0.jar,io.perfmark_perfmark-api-0.26.0.jar,io.grpc_grpc-googleapis-1.51.0.jar,io.grpc_grpc-xds-1.51.0.jar,io.opencensus_opencensus-proto-0.2.0.jar,io.grpc_grpc-services-1.51.0.jar,com.google.re2j_re2j-1.6.jar,dk.brics.automaton_automaton-1.11-8.jar,org.slf4j_slf4j-api-1.7.16.jar'),\n", + " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n", + " 'http://hub-msca-bdp-dphub-students-test-ridhi-m:8088/proxy/application_1700888021537_0001'),\n", " ('spark.repl.local.jars',\n", " 'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n", " ('spark.sql.cbo.enabled', 'true'),\n", - " ('spark.driver.port', '42885'),\n", " ('spark.executorEnv.PYTHONPATH',\n", " '/usr/lib/spark/python/lib/py4j-0.10.9-src.zip:/usr/lib/spark/python/:{{PWD}}/pyspark.zip{{PWD}}/py4j-0.10.9-src.zip{{PWD}}/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar{{PWD}}/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar{{PWD}}/com.typesafe_config-1.4.2.jar{{PWD}}/org.rocksdb_rocksdbjni-6.29.5.jar{{PWD}}/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar{{PWD}}/com.github.universal-automata_liblevenshtein-3.0.0.jar{{PWD}}/com.google.cloud_google-cloud-storage-2.16.0.jar{{PWD}}/com.navigamez_greex-1.0.jar{{PWD}}/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar{{PWD}}/it.unimi.dsi_fastutil-7.0.12.jar{{PWD}}/org.projectlombok_lombok-1.16.8.jar{{PWD}}/com.google.guava_guava-31.1-jre.jar{{PWD}}/com.google.guava_failureaccess-1.0.1.jar{{PWD}}/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar{{PWD}}/com.google.errorprone_error_prone_annotations-2.16.jar{{PWD}}/com.google.j2objc_j2objc-annotations-1.3.jar{{PWD}}/com.google.http-client_google-http-client-1.42.3.jar{{PWD}}/io.opencensus_opencensus-contrib-http-util-0.31.1.jar{{PWD}}/com.google.http-client_google-http-client-jackson2-1.42.3.jar{{PWD}}/com.google.http-client_google-http-client-gson-1.42.3.jar{{PWD}}/com.google.api-client_google-api-client-2.1.1.jar{{PWD}}/commons-codec_commons-codec-1.15.jar{{PWD}}/com.google.oauth-client_google-oauth-client-1.34.1.jar{{PWD}}/com.google.http-client_google-http-client-apache-v2-1.42.3.jar{{PWD}}/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar{{PWD}}/com.google.code.gson_gson-2.10.jar{{PWD}}/com.google.cloud_google-cloud-core-2.9.0.jar{{PWD}}/com.google.auto.value_auto-value-annotations-1.10.1.jar{{PWD}}/com.google.cloud_google-cloud-core-http-2.9.0.jar{{PWD}}/com.google.http-client_google-http-client-appengine-1.42.3.jar{{PWD}}/com.google.api_gax-httpjson-0.105.1.jar{{PWD}}/com.google.cloud_google-cloud-core-grpc-2.9.0.jar{{PWD}}/io.grpc_grpc-core-1.51.0.jar{{PWD}}/com.google.api_gax-2.20.1.jar{{PWD}}/com.google.api_gax-grpc-2.20.1.jar{{PWD}}/io.grpc_grpc-alts-1.51.0.jar{{PWD}}/io.grpc_grpc-grpclb-1.51.0.jar{{PWD}}/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar{{PWD}}/io.grpc_grpc-protobuf-1.51.0.jar{{PWD}}/com.google.auth_google-auth-library-credentials-1.13.0.jar{{PWD}}/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar{{PWD}}/com.google.api_api-common-2.2.2.jar{{PWD}}/javax.annotation_javax.annotation-api-1.3.2.jar{{PWD}}/io.opencensus_opencensus-api-0.31.1.jar{{PWD}}/io.grpc_grpc-context-1.51.0.jar{{PWD}}/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar{{PWD}}/com.google.protobuf_protobuf-java-3.21.10.jar{{PWD}}/com.google.protobuf_protobuf-java-util-3.21.10.jar{{PWD}}/com.google.api.grpc_proto-google-common-protos-2.11.0.jar{{PWD}}/org.threeten_threetenbp-1.6.4.jar{{PWD}}/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar{{PWD}}/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar{{PWD}}/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar{{PWD}}/com.fasterxml.jackson.core_jackson-core-2.14.1.jar{{PWD}}/com.google.code.findbugs_jsr305-3.0.2.jar{{PWD}}/io.grpc_grpc-api-1.51.0.jar{{PWD}}/io.grpc_grpc-auth-1.51.0.jar{{PWD}}/io.grpc_grpc-stub-1.51.0.jar{{PWD}}/org.checkerframework_checker-qual-3.28.0.jar{{PWD}}/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar{{PWD}}/io.grpc_grpc-protobuf-lite-1.51.0.jar{{PWD}}/com.google.android_annotations-4.1.1.4.jar{{PWD}}/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar{{PWD}}/io.grpc_grpc-netty-shaded-1.51.0.jar{{PWD}}/io.perfmark_perfmark-api-0.26.0.jar{{PWD}}/io.grpc_grpc-googleapis-1.51.0.jar{{PWD}}/io.grpc_grpc-xds-1.51.0.jar{{PWD}}/io.opencensus_opencensus-proto-0.2.0.jar{{PWD}}/io.grpc_grpc-services-1.51.0.jar{{PWD}}/com.google.re2j_re2j-1.6.jar{{PWD}}/dk.brics.automaton_automaton-1.11-8.jar{{PWD}}/org.slf4j_slf4j-api-1.7.16.jar'),\n", " ('spark.yarn.dist.jars',\n", @@ -65,25 +66,21 @@ " ('spark.jars.packages',\n", " 'com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0,graphframes:graphframes:0.8.2-spark3.1-s_2.12'),\n", " ('spark.executor.instances', '2'),\n", - " ('spark.ui.proxyBase', '/proxy/application_1700846724434_0009'),\n", " ('spark.dataproc.listeners',\n", " 'com.google.cloud.spark.performance.DataprocMetricsListener'),\n", - " ('spark.history.fs.logDirectory',\n", - " 'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/4f3dcfe4-99eb-4d99-bb0e-a5a10f0bc58b/spark-job-history'),\n", " ('spark.driver.memory', '8g'),\n", " ('spark.serializer.objectStreamReset', '100'),\n", " ('spark.executor.memory', '8g'),\n", " ('spark.submit.deployMode', 'client'),\n", " ('spark.executor.cores', '8'),\n", - " ('spark.driver.appUIAddress',\n", - " 'http://hub-msca-bdp-dphub-students-test-ridhi-m.c.msca-bdp-student-ap.internal:42969'),\n", + " ('spark.app.startTime', '1700888229434'),\n", " ('spark.sql.cbo.joinReorder.enabled', 'true'),\n", - " ('spark.app.id', 'application_1700846724434_0009'),\n", " ('spark.shuffle.service.enabled', 'true'),\n", " ('spark.scheduler.mode', 'FAIR'),\n", " ('spark.sql.adaptive.enabled', 'true'),\n", " ('spark.yarn.jars', 'local:/usr/lib/spark/jars/*'),\n", " ('spark.scheduler.minRegisteredResourcesRatio', '0.0'),\n", + " ('spark.driver.port', '37707'),\n", " ('spark.master', 'yarn'),\n", " ('spark.ui.port', '0'),\n", " ('spark.rpc.message.maxSize', '512'),\n", @@ -91,12 +88,15 @@ " ('spark.task.maxFailures', '10'),\n", " ('spark.yarn.isPython', 'true'),\n", " ('spark.dynamicAllocation.enabled', 'true'),\n", + " ('spark.ui.proxyBase', '/proxy/application_1700888021537_0001'),\n", + " ('spark.driver.appUIAddress',\n", + " 'http://hub-msca-bdp-dphub-students-test-ridhi-m.c.msca-bdp-student-ap.internal:34369'),\n", " ('spark.yarn.historyServer.address',\n", " 'hub-msca-bdp-dphub-students-test-ridhi-m:18080'),\n", " ('spark.ui.showConsoleProgress', 'true')]" ] }, - "execution_count": 55, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -119,7 +119,18 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 2, + "id": "ecbe9e6d-3697-4765-9f11-26f25c69d3ce", + "metadata": {}, + "outputs": [], + "source": [ + "import geopy\n", + "import dbscan" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "id": "b6c5fdcd-e1af-4e2a-b177-7acc4e3dbec6", "metadata": {}, "outputs": [ @@ -150,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "8d149ef9-cefd-4c23-82e0-8f23618102e8", "metadata": {}, "outputs": [ @@ -174,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "acae673f-ccc0-4b14-828e-bbaac88d357b", "metadata": {}, "outputs": [ @@ -198,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "33165f38-fe04-4100-b615-8419b2d12578", "metadata": {}, "outputs": [], @@ -217,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "1df809f1-d2ba-444d-b33e-82cb0ff8feb0", "metadata": {}, "outputs": [ @@ -225,14 +236,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Partitions: 31\n" + "Partitions: 16\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Stage 17:=============================================> (25 + 6) / 31]\r" + "[Stage 17:======================================> (11 + 5) / 16]\r" ] }, { @@ -242,37 +253,22 @@ "+-----------+-----+\n", "|partitionId|count|\n", "+-----------+-----+\n", - "| 14| 1762|\n", - "| 13| 1907|\n", - "| 12| 1970|\n", - "| 11| 2021|\n", - "| 10| 2100|\n", - "| 9| 2182|\n", - "| 8| 2263|\n", - "| 7| 2373|\n", - "| 6| 2506|\n", - "| 5| 2695|\n", - "| 30| 3846|\n", - "| 4| 4487|\n", - "| 3| 4701|\n", - "| 2| 5057|\n", - "| 1| 5664|\n", - "| 0| 5953|\n", - "| 29| 7868|\n", - "| 28| 8062|\n", - "| 27| 8256|\n", - "| 26| 8484|\n", - "| 25| 9493|\n", - "| 24|10490|\n", - "| 23|10618|\n", - "| 22|10800|\n", - "| 21|11035|\n", - "| 20|15695|\n", - "| 19|18407|\n", - "| 18|18610|\n", - "| 17|18886|\n", - "| 16|19071|\n", - "| 15|19369|\n", + "| 7| 3242|\n", + "| 6| 3731|\n", + "| 5| 3982|\n", + "| 4| 4298|\n", + "| 3| 4702|\n", + "| 2| 7344|\n", + "| 1| 9349|\n", + "| 0|10993|\n", + "| 15|12512|\n", + "| 14|16357|\n", + "| 13|18177|\n", + "| 12|20071|\n", + "| 11|20717|\n", + "| 10|33168|\n", + "| 8|38440|\n", + "| 9|39548|\n", "+-----------+-----+\n", "\n" ] @@ -292,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "be9e59b7-4ed1-4f5e-ab22-aab8491c06be", "metadata": {}, "outputs": [ @@ -300,14 +296,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Partitions: 32\n" + "Partitions: 21\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Stage 20:======================================================> (31 + 1) / 32]\r" + "[Stage 22:================================================> (25 + 4) / 29]\r" ] }, { @@ -338,18 +334,8 @@ "| 28|11462|\n", "| 27|11731|\n", "| 0|11912|\n", - "| 26|24062|\n", - "| 25|29470|\n", - "| 24|29783|\n", - "| 23|30064|\n", - "| 22|30327|\n", - "| 21|41156|\n", - "| 20|53078|\n", - "| 19|53357|\n", - "| 18|53848|\n", - "| 17|54237|\n", - "| 16|54823|\n", "+-----------+-----+\n", + "only showing top 21 rows\n", "\n" ] }, @@ -377,9 +363,17 @@ "after = after.repartition(50)" ] }, + { + "cell_type": "markdown", + "id": "180a1850-dcf4-4e70-a5ff-347e5fe5fbd3", + "metadata": {}, + "source": [ + "# Feature Engineering" + ] + }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 12, "id": "b419e72b-6458-4dd5-942f-0bde079c11e5", "metadata": {}, "outputs": [], @@ -392,20 +386,18 @@ "from sklearn import metrics\n", "from pyspark.sql.functions import col, radians, acos, sin, cos, lit\n", "import time\n", - "from pyspark.ml.feature import VectorAssembler\n" - ] - }, - { - "cell_type": "markdown", - "id": "180a1850-dcf4-4e70-a5ff-347e5fe5fbd3", - "metadata": {}, - "source": [ - "# Feature Engineering" + "from pyspark.ml.feature import VectorAssembler\n", + "import geopandas as gpd\n", + "from pyspark.ml.feature import VectorAssembler\n", + "from pyspark.ml.clustering import KMeans\n", + "from pyspark.ml import Pipeline\n", + "from pyspark.ml.feature import StandardScaler\n", + "from pyspark.ml.evaluation import ClusteringEvaluator" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 10, "id": "87108e84-9d70-4174-ba1b-b60ce5af2ba0", "metadata": {}, "outputs": [], @@ -426,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 11, "id": "13d8d93a-5637-4871-b9c7-3a44b9ab2a32", "metadata": {}, "outputs": [], @@ -436,30 +428,15 @@ "\n", "\n", "# KMeans for pickup features\n", - "kmeans_pickup = KMeans(k=15, seed=1, featuresCol='pickup_features', predictionCol='pickup_cluster')\n", + "kmeans_pickup = KMeans(seed=1, featuresCol='pickup_features', predictionCol='pickup_cluster')\n", "\n", "# KMeans for dropoff features\n", - "kmeans_dropoff = KMeans(k=15, seed=1, featuresCol='dropoff_features', predictionCol='dropoff_cluster')\n" + "kmeans_dropoff = KMeans(seed=1, featuresCol='dropoff_features', predictionCol='dropoff_cluster')\n" ] }, { "cell_type": "code", - "execution_count": 47, - "id": "ab24b4ea-eda5-4079-9c29-9008ffad73a6", - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas as gpd\n", - "from pyspark.ml.feature import VectorAssembler\n", - "from pyspark.ml.clustering import KMeans\n", - "from pyspark.ml import Pipeline\n", - "from pyspark.ml.feature import StandardScaler\n", - "from pyspark.ml.evaluation import ClusteringEvaluator" - ] - }, - { - "cell_type": "code", - "execution_count": 51, + "execution_count": 13, "id": "912fc66b-af5f-400a-bff9-7aab44302897", "metadata": {}, "outputs": [], @@ -472,20 +449,10 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 14, "id": "325dfded-9703-4997-828c-c86d4a493e57", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23/11/25 03:16:27 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", - "23/11/25 03:16:27 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", - " \r" - ] - } - ], + "outputs": [], "source": [ "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n", "from pyspark.ml.evaluation import ClusteringEvaluator\n", @@ -519,10 +486,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "f5b59f80-c0d6-4bac-9481-644a39a95f16", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "23/11/25 05:04:38 WARN com.github.fommil.netlib.BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS\n", + "23/11/25 05:04:38 WARN com.github.fommil.netlib.BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS\n", + " \r" + ] + } + ], "source": [ "# Fit the model\n", "cvModel_p = crossval_p.fit(before) \n", @@ -539,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 16, "id": "dfadfe31-66fd-4df6-b574-d3c64e1798c9", "metadata": {}, "outputs": [ @@ -591,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 17, "id": "d50ddb32-27b2-4a39-a193-f30730c2db94", "metadata": {}, "outputs": [ @@ -619,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 18, "id": "afb8a6e4-cb35-4f33-903c-d56ed9fdceb2", "metadata": {}, "outputs": [ @@ -671,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 19, "id": "14783651-c521-4cd9-80c6-a28aff637b9d", "metadata": {}, "outputs": [ @@ -714,7 +691,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 20, "id": "36ad1d50-2b2f-4853-99d0-f1dbef25ad7e", "metadata": {}, "outputs": [ @@ -725,11 +702,11 @@ "+--------------+---------------+\n", "|pickup_cluster|dropoff_cluster|\n", "+--------------+---------------+\n", - "| 1| 12|\n", - "| 11| 13|\n", - "| 5| 11|\n", + "| 1| 3|\n", + "| 6| 11|\n", + "| 5| 13|\n", + "| 5| 12|\n", "| 5| 1|\n", - "| 5| 4|\n", "+--------------+---------------+\n", "only showing top 5 rows\n", "\n" @@ -742,28 +719,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "dcb98e61-f37f-4cab-ae9e-547c68899ad4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], "source": [ "before_pd = before.select('pickup_lon','pickup_lat', 'pickup_cluster').toPandas()\n" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 22, "id": "cbb0387d-9e6f-4ecc-bf42-7ec5a5cb2f9d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 5, 14, 1, 11, 12, 2, 4, 13, 10, 0, 8, 6, 9, 3, 7],\n", + "array([10, 14, 1, 3, 9, 11, 6, 8, 13, 12, 2, 5, 4, 0, 7],\n", " dtype=int32)" ] }, - "execution_count": 72, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -774,23 +759,23 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 23, "id": "ba09b6be-72c4-42b7-bc6c-50e887008a31", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 103, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -810,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 24, "id": "91b07045-a3b7-45a1-8d59-1a5ea3044dcb", "metadata": {}, "outputs": [ @@ -824,11 +809,11 @@ { "data": { "text/plain": [ - "array([ 1, 11, 5, 10, 2, 13, 6, 14, 4, 3, 12, 0, 8, 9, 7],\n", + "array([ 1, 6, 5, 11, 14, 4, 12, 3, 9, 2, 10, 13, 8, 0, 7],\n", " dtype=int32)" ] }, - "execution_count": 104, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -840,23 +825,23 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 25, "id": "b354b23f-c152-4af6-9059-fc27bbe30a4c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 105, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -872,30 +857,181 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 30, "id": "dbde458f-1123-4196-ae9f-4ff9eb508170", "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'geopy'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[106], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgeopy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdistance\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m great_circle\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mshapely\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeometry\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MultiPoint\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_centermost_point\u001b[39m(cluster):\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'geopy'" + "name": "stdout", + "output_type": "stream", + "text": [ + " center_lon center_lat\n", + "0 -87.587479 41.805912\n", + "1 -87.601285 41.790469\n", + "2 -87.598697 41.783225\n", + "3 -87.589607 41.797965\n", + "4 -87.596183 41.808916\n", + "5 -87.594925 41.778877\n", + "6 -87.582630 41.783034\n", + "7 -87.604209 41.813201\n", + "8 -87.594015 41.790567\n", + "9 -87.603746 41.797827\n", + "10 -87.592311 41.794090\n", + "11 -87.585303 41.801227\n", + "12 -87.603559 41.783171\n", + "13 -87.590431 41.790448\n", + "14 -87.598945 41.797971\n", + " center_lon center_lat\n", + "0 -87.587479 41.805912\n", + "1 -87.601285 41.790469\n", + "2 -87.598697 41.783225\n", + "3 -87.589607 41.797965\n", + "4 -87.596183 41.808916\n", + "5 -87.594925 41.778877\n", + "6 -87.582630 41.783034\n", + "7 -87.604209 41.813201\n", + "8 -87.594015 41.790567\n", + "9 -87.603746 41.797827\n", + "10 -87.592311 41.794090\n", + "11 -87.585303 41.801227\n", + "12 -87.603559 41.783171\n", + "13 -87.590431 41.790448\n", + "14 -87.598945 41.797971\n" ] } ], "source": [ "from geopy.distance import great_circle\n", - "from shapely.geometry import MultiPoint\n", + "from shapely.geometry import Point, MultiPoint\n", + "import pandas as pd\n", "\n", + "\n", + "# Define the get_centermost_point function\n", "def get_centermost_point(cluster):\n", " centroid = (MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)\n", " centermost_point = min(cluster, key=lambda point: great_circle(point, centroid).m)\n", - " return tuple(centermost_point)" + " return tuple(centermost_point)\n", + "\n", + "before_pickup_centers = before_pd.groupby('pickup_cluster')\\\n", + " .apply(lambda cluster: get_centermost_point(list(zip(cluster['pickup_lon'], cluster['pickup_lat']))))\n", + "\n", + "before_pickup_centers = pd.DataFrame(before_pickup_centers.tolist(), columns=['center_lon', 'center_lat'])\n", + "\n", + "\n", + "after_pickup_centers = before_pd.groupby('pickup_cluster')\\\n", + " .apply(lambda cluster: get_centermost_point(list(zip(cluster['pickup_lon'], cluster['pickup_lat']))))\n", + "\n", + "after_pickup_centers = pd.DataFrame(after_pickup_centers.tolist(), columns=['center_lon', 'center_lat'])\n", + "\n", + "\n", + "print(before_pickup_centers)\n", + "print(after_pickup_centers)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "de26371d-101f-418b-abc8-70f32b371ac0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "before_drop = before.select('dropoff_lon','dropoff_lat', 'dropoff_cluster').toPandas()\n", + "after_drop = after.select('dropoff_lon','dropoff_lat', 'dropoff_cluster').toPandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3685f0c6-5ac2-464f-a5e4-c0b05d30d5a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " center_lon center_lat\n", + "0 -87.589607 41.797965\n", + "1 -87.582366 41.797153\n", + "2 -87.603746 41.797827\n", + "3 -87.594925 41.778877\n", + "4 -87.596183 41.808916\n", + "5 -87.601285 41.790469\n", + "6 -87.587479 41.805912\n", + "7 -87.608427 41.783101\n", + "8 -87.598945 41.797971\n", + "9 -87.594015 41.790567\n", + "10 -87.582630 41.783034\n", + "11 -87.592311 41.794090\n", + "12 -87.590431 41.790448\n", + "13 -87.585303 41.801227\n", + "14 -87.598697 41.783225\n", + " center_lon center_lat\n", + "0 -87.587479 41.805912\n", + "1 -87.601285 41.790469\n", + "2 -87.594925 41.778877\n", + "3 -87.592311 41.794090\n", + "4 -87.603746 41.797827\n", + "5 -87.583144 41.790506\n", + "6 -87.598697 41.783225\n", + "7 -87.579948 41.776164\n", + "8 -87.582366 41.797153\n", + "9 -87.594015 41.790567\n", + "10 -87.585303 41.801227\n", + "11 -87.594266 41.801671\n", + "12 -87.589607 41.797965\n", + "13 -87.590431 41.790448\n", + "14 -87.596183 41.808916\n" + ] + } + ], + "source": [ + "before_dropoff_centers = before_drop.groupby('dropoff_cluster')\\\n", + " .apply(lambda cluster: get_centermost_point(list(zip(cluster['dropoff_lon'], cluster['dropoff_lat']))))\n", + "\n", + "before_dropoff_centers = pd.DataFrame(before_dropoff_centers.tolist(), columns=['center_lon', 'center_lat'])\n", + "\n", + "\n", + "after_dropoff_centers = after_drop.groupby('dropoff_cluster')\\\n", + " .apply(lambda cluster: get_centermost_point(list(zip(cluster['dropoff_lon'], cluster['dropoff_lat']))))\n", + "\n", + "after_dropoff_centers = pd.DataFrame(after_dropoff_centers.tolist(), columns=['center_lon', 'center_lat'])\n", + "\n", + "\n", + "print(before_dropoff_centers)\n", + "print(after_dropoff_centers)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27dda7d4-b591-4f34-94e5-b4a1d77601fa", + "metadata": {}, + "outputs": [], + "source": [ + "from geopy.geocoders import GoogleV3\n", + "\n", + "# Replace 'your_api_key' with your actual Google Maps API key\n", + "api_key = 'your_api_key'\n", + "geolocator = GoogleV3(api_key=api_key)\n", + "\n", + "def get_address(lat, lon):\n", + " location = geolocator.reverse((lat, lon), language='en')\n", + " return location.address\n", + "\n", + "# Example usage\n", + "latitude = 37.7749 # Replace with your latitude\n", + "longitude = -122.4194 # Replace with your longitude\n", + "\n", + "address = get_address(latitude, longitude)\n", + "print(\"Address:\", address)\n" ] }, {