-
Notifications
You must be signed in to change notification settings - Fork 102
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Convert file format from DOS to Unix
- Loading branch information
Showing
1 changed file
with
101 additions
and
101 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,101 +1,101 @@ | ||
| from pyspark.sql import SparkSession | ||
| from pyspark.sql.types import * | ||
| from pyspark.ml.feature import StringIndexer | ||
| from pyspark.ml import Pipeline | ||
| from sklearn.ensemble import RandomForestClassifier | ||
| from sklearn.metrics import roc_auc_score, average_precision_score | ||
| import numpy as np | ||
| import pandas as pd | ||
| import pickle | ||
| import cdsw | ||
| import os | ||
| import time | ||
|
|
||
| spark = SparkSession.builder \ | ||
| .appName("Predictive Maintenance") \ | ||
| .getOrCreate() | ||
|
|
||
| # read 21 colunms large file from HDFS | ||
| schemaData = StructType([StructField("0", DoubleType(), True), | ||
| StructField("1", DoubleType(), True), | ||
| StructField("2", DoubleType(), True), | ||
| StructField("3", DoubleType(), True), | ||
| StructField("4", DoubleType(), True), | ||
| StructField("5", DoubleType(), True), | ||
| StructField("6", DoubleType(), True), | ||
| StructField("7", DoubleType(), True), | ||
| StructField("8", DoubleType(), True), | ||
| StructField("9", DoubleType(), True), | ||
| StructField("10", DoubleType(), True), | ||
| StructField("11", DoubleType(), True), | ||
| StructField("12", IntegerType(), True)]) | ||
|
|
||
| iot_data = spark.read.schema(schemaData).csv('/user/' | ||
| + os.environ['HADOOP_USER_NAME'] | ||
| + '/historical_iot.txt') | ||
|
|
||
|
|
||
| # Create Pipeline | ||
| label_indexer = StringIndexer(inputCol = '12', outputCol = 'label') | ||
| plan_indexer = StringIndexer(inputCol = '1', outputCol = '1_indexed') | ||
| pipeline = Pipeline(stages=[plan_indexer, label_indexer]) | ||
| indexed_data = pipeline.fit(iot_data).transform(iot_data) | ||
| (train_data, test_data) = indexed_data.randomSplit([0.7, 0.3]) | ||
|
|
||
| pdTrain = train_data.toPandas() | ||
| pdTest = test_data.toPandas() | ||
|
|
||
| # 12 features | ||
| features = ["1_indexed", | ||
| "0", | ||
| "2", | ||
| "3", | ||
| "4", | ||
| "5", | ||
| "6", | ||
| "7", | ||
| "8", | ||
| "9", | ||
| "10", | ||
| "11"] | ||
|
|
||
| param_numTrees = int(sys.argv[1]) | ||
| param_maxDepth = int(sys.argv[2]) | ||
| param_impurity = 'gini' | ||
|
|
||
| randF=RandomForestClassifier(n_jobs=10, | ||
| n_estimators=param_numTrees, | ||
| max_depth=param_maxDepth, | ||
| criterion = param_impurity, | ||
| random_state=0) | ||
|
|
||
| cdsw.track_metric("numTrees",param_numTrees) | ||
| cdsw.track_metric("maxDepth",param_maxDepth) | ||
| cdsw.track_metric("impurity",param_impurity) | ||
|
|
||
| # Fit and Predict | ||
| randF.fit(pdTrain[features], pdTrain['label']) | ||
| predictions=randF.predict(pdTest[features]) | ||
|
|
||
| #temp = randF.predict_proba(pdTest[features]) | ||
|
|
||
| pd.crosstab(pdTest['label'], predictions, rownames=['Actual'], colnames=['Prediction']) | ||
|
|
||
| list(zip(pdTrain[features], randF.feature_importances_)) | ||
|
|
||
|
|
||
| y_true = pdTest['label'] | ||
| y_scores = predictions | ||
| auroc = roc_auc_score(y_true, y_scores) | ||
| ap = average_precision_score (y_true, y_scores) | ||
| print(auroc, ap) | ||
|
|
||
| cdsw.track_metric("auroc", auroc) | ||
| cdsw.track_metric("ap", ap) | ||
|
|
||
| pickle.dump(randF, open("iot_model.pkl","wb")) | ||
|
|
||
| cdsw.track_file("iot_model.pkl") | ||
|
|
||
| time.sleep(15) | ||
| print("Slept for 15 seconds.") | ||
| from pyspark.sql import SparkSession | ||
| from pyspark.sql.types import * | ||
| from pyspark.ml.feature import StringIndexer | ||
| from pyspark.ml import Pipeline | ||
| from sklearn.ensemble import RandomForestClassifier | ||
| from sklearn.metrics import roc_auc_score, average_precision_score | ||
| import numpy as np | ||
| import pandas as pd | ||
| import pickle | ||
| import cdsw | ||
| import os | ||
| import time | ||
|
|
||
| spark = SparkSession.builder \ | ||
| .appName("Predictive Maintenance") \ | ||
| .getOrCreate() | ||
|
|
||
| # read 21 colunms large file from HDFS | ||
| schemaData = StructType([StructField("0", DoubleType(), True), | ||
| StructField("1", DoubleType(), True), | ||
| StructField("2", DoubleType(), True), | ||
| StructField("3", DoubleType(), True), | ||
| StructField("4", DoubleType(), True), | ||
| StructField("5", DoubleType(), True), | ||
| StructField("6", DoubleType(), True), | ||
| StructField("7", DoubleType(), True), | ||
| StructField("8", DoubleType(), True), | ||
| StructField("9", DoubleType(), True), | ||
| StructField("10", DoubleType(), True), | ||
| StructField("11", DoubleType(), True), | ||
| StructField("12", IntegerType(), True)]) | ||
|
|
||
| iot_data = spark.read.schema(schemaData).csv('/user/' | ||
| + os.environ['HADOOP_USER_NAME'] | ||
| + '/historical_iot.txt') | ||
|
|
||
|
|
||
| # Create Pipeline | ||
| label_indexer = StringIndexer(inputCol = '12', outputCol = 'label') | ||
| plan_indexer = StringIndexer(inputCol = '1', outputCol = '1_indexed') | ||
| pipeline = Pipeline(stages=[plan_indexer, label_indexer]) | ||
| indexed_data = pipeline.fit(iot_data).transform(iot_data) | ||
| (train_data, test_data) = indexed_data.randomSplit([0.7, 0.3]) | ||
|
|
||
| pdTrain = train_data.toPandas() | ||
| pdTest = test_data.toPandas() | ||
|
|
||
| # 12 features | ||
| features = ["1_indexed", | ||
| "0", | ||
| "2", | ||
| "3", | ||
| "4", | ||
| "5", | ||
| "6", | ||
| "7", | ||
| "8", | ||
| "9", | ||
| "10", | ||
| "11"] | ||
|
|
||
| param_numTrees = int(sys.argv[1]) | ||
| param_maxDepth = int(sys.argv[2]) | ||
| param_impurity = 'gini' | ||
|
|
||
| randF=RandomForestClassifier(n_jobs=10, | ||
| n_estimators=param_numTrees, | ||
| max_depth=param_maxDepth, | ||
| criterion = param_impurity, | ||
| random_state=0) | ||
|
|
||
| cdsw.track_metric("numTrees",param_numTrees) | ||
| cdsw.track_metric("maxDepth",param_maxDepth) | ||
| cdsw.track_metric("impurity",param_impurity) | ||
|
|
||
| # Fit and Predict | ||
| randF.fit(pdTrain[features], pdTrain['label']) | ||
| predictions=randF.predict(pdTest[features]) | ||
|
|
||
| #temp = randF.predict_proba(pdTest[features]) | ||
|
|
||
| pd.crosstab(pdTest['label'], predictions, rownames=['Actual'], colnames=['Prediction']) | ||
|
|
||
| list(zip(pdTrain[features], randF.feature_importances_)) | ||
|
|
||
|
|
||
| y_true = pdTest['label'] | ||
| y_scores = predictions | ||
| auroc = roc_auc_score(y_true, y_scores) | ||
| ap = average_precision_score (y_true, y_scores) | ||
| print(auroc, ap) | ||
|
|
||
| cdsw.track_metric("auroc", auroc) | ||
| cdsw.track_metric("ap", ap) | ||
|
|
||
| pickle.dump(randF, open("iot_model.pkl","wb")) | ||
|
|
||
| cdsw.track_file("iot_model.pkl") | ||
|
|
||
| time.sleep(15) | ||
| print("Slept for 15 seconds.") |