formalizing models

src/models/basic.py

@@ -19,45 +19,43 @@ class model :
         self.cache[key] = value
     def get(self,key):
         return self.cache[key]
-class simple:
+# class simple:
-    class app_status(model):
+#     class app_status(model):
-        """
+#         """
-        This model will perform a simple count of application status
+#         This model will perform a simple count of application status
-        The intent is to quickly inform the user if there's a crash     
+#         The intent is to quickly inform the user if there's a crash     
-        """
+#         """
-        def __init(self,**args):
+#         def __init(self,**args):
-            model.__init__(self,**args)
+#             model.__init__(self,**args)
-        def compute(self):
+#         def compute(self):
-            """
+#             """
-                This function performs the actual counts associated with the status of an application 
+#                 This function performs the actual counts associated with the status of an application 
-            """
+#             """
-            df      = self.data[df.name.str.contains('other',na=False)==False]
+#             df      = self.data[df.name.str.contains('other',na=False)==False]
-            x_crash = df.status.str.contains('X').sum()
+#             x_crash = df.status.str.contains('X').sum()
-            x_idle  = df.status.str.contains('S').sum()
+#             x_idle  = df.status.str.contains('S').sum()
-            x_run   = df.shape[0] - x_crash - x_idle
+#             x_run   = df.shape[0] - x_crash - x_idle
-            odf = pd.DataFrame({"labels":['crash','idle','running'],"counts":[x_crash,x_idle,x_run]})
+#             odf = pd.DataFrame({"labels":['crash','idle','running'],"counts":[x_crash,x_idle,x_run]})
-            self.set("type","doughnut")
+#             self.set("type","doughnut")
-            # self.set("labels",["crash","idle","running"])
+#             # self.set("labels",["crash","idle","running"])
-            # self.set("data",{"data":[x_crash,x_idle,x_run]})
+#             # self.set("data",{"data":[x_crash,x_idle,x_run]})
-            self.set('data',odf)
+#             self.set('data',odf)
-            if x_crash > 0 :
+#             if x_crash > 0 :
-                self.set("analysis"," ".join([x_crash,"applications found out of ",str(df.shape[0]),"monitored" ]))
+#                 self.set("analysis"," ".join([x_crash,"applications found out of ",str(df.shape[0]),"monitored" ]))
-    class app_ranking(model):
+#     class app_resource(model):
-        """
+#         """
-            This model will group the applications that are monitored and the rest of the system to guage resource consumption (CPU,RAM)
+#             This model will group the applications that are monitored and the rest of the system to guage resource consumption (CPU,RAM)
-        """      
+#         """      
-        def __init__(self,**args):
+#         def __init__(self,**args):
-            model.__init__(self,**args)    
+#             model.__init__(self,**args)    
-        def compute(self):
+#         def compute(self):
-            N = self.data.shape[0] - 1
+#             N = self.data.shape[0] - 1
             
-            df = pd.DataFrame(self.data[self.data.name == 'other'].sum()[['cpu','mem']] ) .T
+#             df = pd.DataFrame(self.data[self.data.name == 'other'].sum()[['cpu','mem']] ) .T
-            df = df.append(pd.DataFrame( self.data[self.data.name != 'other'].sum()[['cpu','mem']] ).T)
+#             df = df.append(pd.DataFrame( self.data[self.data.name != 'other'].sum()[['cpu','mem']] ).T)
-            df['labels'] = ['other','monitored']
+#             df['labels'] = ['other','monitored']
-            # other_df = pd.DataFrame(self.data[self.data.name.str.contains('other',na=False)])
+#             # other_df = pd.DataFrame(self.data[self.data.name.str.contains('other',na=False)])
-            # watch_df = pd.DataFrame(self.data[self.data.name.str.contains('other',na=False)==False])
+#             # watch_df = pd.DataFrame(self.data[self.data.name.str.contains('other',na=False)==False])
-            # datasets = [[other_df.cpu.sum(),watch_df.cpu.sum()],[other_df.mem.sum(),watch_df.mem.sum()]]
+#             # datasets = [[other_df.cpu.sum(),watch_df.cpu.sum()],[other_df.mem.sum(),watch_df.mem.sum()]]
-            self.set("data",odf)
+#             self.set("data",df)
-            self.set("type","bar")
+#             self.set("type","bar")
-
-            

src/models/free/apps.py

@@ -0,0 +1,70 @@
+
+"""
+    (c) 2018 Smart Top - Free Models
+    Steve L. Nyemba, steve@the-phi.com
+
+    This file contains free models for smart-top, these are basic models that will serve as data visualization
+    The most we will do here is a basic regression (basis for prediction).
+
+    @TODO: Include process counts in the equation so as to add another variable (good for ml)
+"""
+from models.basic import model
+
+class status(model):
+        """
+        This model will perform a simple count of application status
+        The intent is to quickly inform the user if there's a crash     
+        """
+        def __init(self,**args):
+            model.__init__(self,**args)
+        def compute(self):
+            """
+                This function performs the actual counts associated with the status of an application 
+            """
+            df      = self.data[df.name.str.contains('other',na=False)==False]
+            x_crash = df.status.str.contains('X').sum()
+            x_idle  = df.status.str.contains('S').sum()
+            x_run   = df.shape[0] - x_crash - x_idle
+            odf = pd.DataFrame({"labels":['crash','idle','running'],"counts":[x_crash,x_idle,x_run]})
+            self.set("type","doughnut")
+            # self.set("labels",["crash","idle","running"])
+            # self.set("data",{"data":[x_crash,x_idle,x_run]})
+            self.set('data',odf)
+            if x_crash > 0 :
+                self.set("analysis"," ".join([x_crash,"applications found out of ",str(df.shape[0]),"monitored" ]))
+class resource(model):
+    """
+    This model will group the applications that are monitored and the rest of the system to guage resource consumption (CPU,RAM)
+    The intent of this model is to see how resource intensive the selected applications are relative to the rest of the system
+    """      
+    def __init__(self,**args):
+        model.__init__(self,**args)    
+    def compute(self):
+        N = self.data.shape[0] - 1
+        
+        df = pd.DataFrame(self.data[self.data.name == 'other'].sum()[['cpu','mem']] ) .T
+        df = df.append(pd.DataFrame( self.data[self.data.name != 'other'].sum()[['cpu','mem']] ).T)
+        df['labels'] = ['other','monitored']
+        self.set("data",df)
+        self.set("type","bar")
+
+class trend(model):
+    """
+    This model is designed to display the trends for a given attribute over a period of time
+    Additionally there will be a regression line associated with it
+    """
+    def __init__(self,**args):
+        model.__init__(self,**args)
+        self.attr_name = args['name']
+        self.attr_values= args['values']
+    def compute(self):
+        df = self.data[self.data[self.attr_name].isin(self.attr_values)]
+        cols = ['cpu','mem']
+        pass
+
+
+model = keras.Sequential([keras.layers.Dense(2, activation=tf.nn.relu,input_shape=(x.shape[1],)),keras.layers.Dense(2, activation=tf.nn.relu),keras.layers.Dense(1)])
+
+optimizer = tf.train.RMSPropOptimizer(0.001)
+
+model.compile(loss='mse', optimizer=optimizer,metrics=['mae'])        

src/models/paid/__init__.py

@@ -0,0 +1,10 @@
+"""
+    (c) 2018 Smart-Top, Paid Models
+    Steve L. Nyemba <steve@the-phi.com>
+
+
+    The Paid models are largely based around machine learning and will deliver insights into :
+        - Anomaly detection
+        - Clustering
+        - And Crash Prediction (regression)
+"""