upgrade to py37 and fix tensorflow function conflicts

primitive_tests/test.sh

@@ -1,9 +1,8 @@
 #!/bin/bash
 
-#modules="data_processing timeseries_processing feature_analysis detection_algorithms reinforcement"
+modules="data_processing timeseries_processing feature_analysis detection_algorithm reinforcement"
 #modules="data_processing timeseries_processing"
-modules="detection_algorithm"
-#test_scripts=$(ls primitive_tests | grep -v -f tested_file.txt)
+#modules="detection_algorithm"
 
 for module in $modules
 do

setup.py

@@ -35,13 +35,14 @@ setup(
                  ]
     },
     install_requires=[
-        'tamu_d3m',
-        'tamu_axolotl',
-        'Jinja2',
+        #'tamu_d3m',
+        #'tamu_axolotl',
+        #'Jinja2',
         'numpy==1.18.2',
         'combo',
         'simplejson==3.12.0',
-        'scikit-learn==0.22.0',
+        #'scikit-learn==0.22.0',
+        'scikit-learn',
 	'statsmodels==0.11.1',
         'PyWavelets>=1.1.1',
         'pillow==7.1.2',

tods/detection_algorithm/DeepLog.py

@@ -7,10 +7,10 @@ import sklearn
 import numpy
 import typing
 import numpy as np
-from keras.models import Sequential
-from keras.layers import Dense, Dropout , LSTM
-from keras.regularizers import l2
-from keras.losses import mean_squared_error
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout , LSTM
+from tensorflow.keras.regularizers import l2
+from tensorflow.keras.losses import mean_squared_error
 from sklearn.preprocessing import StandardScaler
 from sklearn.utils import check_array
 from sklearn.utils.validation import check_is_fitted

tods/detection_algorithm/LSTMODetect.py

@@ -196,7 +196,7 @@ class LSTMODetectorPrimitive(UnsupervisedOutlierDetectorBase[Inputs, Outputs, Pa
         "python_path": "d3m.primitives.tods.detection_algorithm.LSTMODetector",
         "source": {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu',
         'uris': ['https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/Junjie/anomaly-primitives/anomaly_primitives/LSTMOD.py']},
-        "algorithm_types": [metadata_base.PrimitiveAlgorithmType.ISOLATION_FOREST, ], # up to update
+        "algorithm_types": [metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE ], # up to update
         "primitive_family": metadata_base.PrimitiveFamily.ANOMALY_DETECTION,
         "version": "0.0.1",
         "hyperparams_to_tune": ['contamination', 'train_contamination', 'min_attack_time',

tods/detection_algorithm/Telemanom.py

@@ -9,11 +9,11 @@ import typing
 import pandas as pd
 
 
-from keras.models import Sequential, load_model
-from keras.callbacks import History, EarlyStopping, Callback
-from keras.layers.recurrent import LSTM
-from keras.layers.core import Dense, Activation, Dropout
-from keras.layers import Flatten
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.callbacks import History, EarlyStopping, Callback
+from tensorflow.keras.layers import LSTM
+from tensorflow.keras.layers import Dense, Activation, Dropout
+from tensorflow.keras.layers import Flatten
 
 from d3m import container, utils
 from d3m.base import utils as base_ut

tods/detection_algorithm/core/LSTMOD.py

@@ -11,8 +11,8 @@ from .CollectiveBase import CollectiveBaseDetector
 
 # from tod.utility import get_sub_matrices
 
-from keras.layers import Dense, LSTM
-from keras.models import Sequential
+from tensorflow.keras.layers import Dense, LSTM
+from tensorflow.keras.models import Sequential
 
 class LSTMOutlierDetector(CollectiveBaseDetector):
 

tods/detection_algorithm/core/utils/modeling.py

@@ -1,8 +1,8 @@
-from keras.models import Sequential, load_model
-from keras.callbacks import History, EarlyStopping, Callback
-from keras.layers.recurrent import LSTM
-from keras.layers.core import Dense, Activation, Dropout
-from keras.layers import Flatten
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.callbacks import History, EarlyStopping, Callback
+from tensorflow.keras.layers import LSTM
+from tensorflow.keras.layers import Dense, Activation, Dropout
+from tensorflow.keras.layers import Flatten
 import numpy as np
 import os
 import logging

tods/reinforcement/RuleBasedFilter.py

@@ -115,8 +115,8 @@ class RuleBasedFilter(transformer.TransformerPrimitiveBase[Inputs, Outputs, Hype
          "python_path": "d3m.primitives.tods.reinforcement.rule_filter",
          "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', 
          'uris': ['https://gitlab.com/lhenry15/tods.git', ]},
-         "algorithm_types": [metadata_base.PrimitiveAlgorithmType.RULE_BASED_FILTER,],
-         "primitive_family": metadata_base.PrimitiveFamily.REINFORCEMENT,
+         "algorithm_types": [metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,],
+         "primitive_family": metadata_base.PrimitiveFamily.ANOMALY_DETECTION,
          "id": "42744c37-8879-4785-9f18-6de9d612ea93",
          "hyperparams_to_tune": ['rule',],
          "version": "0.0.1",

tods/tests/run_tests.py

@@ -4,11 +4,11 @@ import sys
 import unittest
 
 runner = unittest.TextTestRunner(verbosity=1)
-tests = unittest.TestLoader().discover('./')
-if not runner.run(tests).wasSuccessful():
-    sys.exit(1)
+#tests = unittest.TestLoader().discover('./')
+#if not runner.run(tests).wasSuccessful():
+#    sys.exit(1)
 
-#for each in ['data_processing', 'timeseries_processing', 'feature_analysis', 'detection_algorithm']:
-#    tests = unittest.TestLoader().discover(each)
-#    if not runner.run(tests).wasSuccessful():
-#        sys.exit(1)
+for each in ['data_processing', 'timeseries_processing', 'feature_analysis', 'detection_algorithm']:
+    tests = unittest.TestLoader().discover(each)
+    if not runner.run(tests).wasSuccessful():
+        sys.exit(1)