Merge pull request #310 from lscheinkman/RES-561

RES-561: Fix pandas deprecation warnings.

nab/detectors/relative_entropy/relative_entropy_detector.py

@@ -57,7 +57,7 @@ def __init__(self, *args, **kwargs):
     self.util = []
 
     # Number of bins into which util is to be quantized
-    self.N_bins = 5.0
+    self.N_bins = 5
 
     # Window size
     self.W = 52

nab/detectors/skyline/algorithms.py

@@ -3,10 +3,11 @@
 the best combination of algorithms for NAB is included below.
 """
 
-import pandas
-import numpy as np
 from datetime import datetime, timedelta
 
+import numpy as np
+import pandas
+
 
 
 def tail_avg(timeseries):
@@ -23,6 +24,7 @@ def tail_avg(timeseries):
     return timeseries[-1][1]
 
 
+
 def median_absolute_deviation(timeseries):
   """
   A timeseries is anomalous if the deviation of its latest datapoint with
@@ -34,13 +36,12 @@ def median_absolute_deviation(timeseries):
   demedianed = np.abs(series - median)
   median_deviation = demedianed.median()
 
-
   # The test statistic is infinite when the median is zero,
   # so it becomes super sensitive. We play it safe and skip when this happens.
   if median_deviation == 0:
     return False
 
-  test_statistic = demedianed.iget(-1) / median_deviation
+  test_statistic = demedianed.iloc[-1] / median_deviation
 
   # Completely arbitary...triggers if the median deviation is
   # 6 times bigger than the median
@@ -50,6 +51,7 @@ def median_absolute_deviation(timeseries):
     return False
 
 
+
 # The method below is excluded because it is computationally inefficient
 # def grubbs(timeseries):
 #     """
@@ -91,6 +93,7 @@ def first_hour_average(timeseries):
   return abs(t - mean) > 3 * stdDev
 
 
+
 def stddev_from_average(timeseries):
   """
   A timeseries is anomalous if the absolute value of the average of the latest
@@ -106,6 +109,7 @@ def stddev_from_average(timeseries):
   return abs(t - mean) > 3 * stdDev
 
 
+
 def stddev_from_moving_average(timeseries):
   """
   A timeseries is anomalous if the absolute value of the average of the latest
@@ -114,10 +118,11 @@ def stddev_from_moving_average(timeseries):
   respect to the short term trends.
   """
   series = pandas.Series([x[1] for x in timeseries])
-  expAverage = pandas.stats.moments.ewma(series, com=50)
-  stdDev = pandas.stats.moments.ewmstd(series, com=50)
+  expAverage = series.ewm(ignore_na=False, min_periods=0, adjust=True, com=50).mean()
+  stdDev = series.ewm(ignore_na=False, min_periods=0, adjust=True, com=50).std(bias=False)
+
+  return abs(series.iloc[-1] - expAverage.iloc[-1]) > 3 * stdDev.iloc[-1]
 
-  return abs(series.iget(-1) - expAverage.iget(-1)) > 3 * stdDev.iget(-1)
 
 
 def mean_subtraction_cumulation(timeseries):
@@ -130,9 +135,9 @@ def mean_subtraction_cumulation(timeseries):
   series = pandas.Series([x[1] if x[1] else 0 for x in timeseries])
   series = series - series[0:len(series) - 1].mean()
   stdDev = series[0:len(series) - 1].std()
-  expAverage = pandas.stats.moments.ewma(series, com=15)
 
-  return abs(series.iget(-1)) > 3 * stdDev
+  return abs(series.iloc[-1]) > 3 * stdDev
+
 
 
 def least_squares(timeseries):
@@ -142,7 +147,7 @@ def least_squares(timeseries):
   """
 
   x = np.array(
-    [(t[0] - datetime(1970,1,1)).total_seconds() for t in timeseries])
+    [(t[0] - datetime(1970, 1, 1)).total_seconds() for t in timeseries])
   y = np.array([t[1] for t in timeseries])
   A = np.vstack([x, np.ones(len(x))]).T
   results = np.linalg.lstsq(A, y)