def main(inputPath):
inputFileName = ntpath.basename(inputPath)
dataFrame = getDataFrame(inputPath)
model = createPredictionModel(dataFrame)
outputFrame = runDataThroughModel(model, dataFrame)
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
outputFrame.to_csv(os.path.join(OUTPUT_DIR, "prediction_" + inputFileName),
index=False)
def main(inputPath):
inputFileName = ntpath.basename(inputPath)
dataFrame = getDataFrame(inputPath)
model = createAnomalyDetectionModel(dataFrame)
outputFrame = runDataThroughModel(model, dataFrame)
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
outputFrame.to_csv(
os.path.join(OUTPUT_DIR, "anomaly_" + inputFileName),
index=False
)
def main(inputPath): # data/nyc_taxi.csv
inputFileName = ntpath.basename(inputPath) # nyc_taxi.csv
dataFrame = getDataFrame(inputPath)
# timestamp value
# 0 2014-07-01 00:00:00 10844
# 1 2014-07-01 00:30:00 8127
# 2 2014-07-01 01:00:00 6210
model = createPredictionModel(dataFrame)
outputFrame = runDataThroughModel(model, dataFrame)
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
outputFrame.to_csv(
os.path.join(OUTPUT_DIR, "prediction2_" + inputFileName),
index=False
)