Add ModelState children classes #576 (#579)

analytics/analytics/models/__init__.py

@@ -1,4 +1,4 @@
-from models.model import Model, ModelCache
+from models.model import Model, ModelCache, ModelState
 from models.drop_model import DropModel
 from models.peak_model import PeakModel
 from models.jump_model import JumpModel

analytics/analytics/models/drop_model.py

@@ -1,14 +1,42 @@
-from models import Model
+from models import Model, ModelState
 
 import scipy.signal
 from scipy.fftpack import fft
 from scipy.signal import argrelextrema
 from scipy.stats import gaussian_kde
-
+from typing import Optional
 import utils
 import numpy as np
 import pandas as pd
 
+class DropModelState(ModelState):
+
+    def __init__(
+        self,
+        confidence: float = 0,
+        drop_height: float = 0,
+        drop_length: float = 0,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.confidence = confidence
+        self.drop_height = drop_height
+        self.drop_length = drop_length
+
+    def to_json(self) -> dict:
+        json = super().to_json()
+        json.update({
+            'confidence': self.confidence,
+            'drop_height': self.drop_height,
+            'drop_length': self.drop_length,
+        })
+        return json
+
+    @staticmethod
+    def from_json(json: Optional[dict] = None):
+        if json is None:
+            json = {}
+        return DropModelState(**json)
 
 class DropModel(Model):
     def __init__(self):
@@ -38,6 +66,9 @@ class DropModel(Model):
         segment_center_index = utils.find_pattern_center(segment, start, 'drop')
         return segment_center_index
 
+    def get_cache(self, cache: Optional[dict] = None) -> DropModelState:
+        return DropModelState.from_json(cache)
+
     def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list, deleted_segments: list, learning_info: dict, id: str) -> None:
         data = utils.cut_dataframe(dataframe)
         data = data['value']

analytics/analytics/models/general_model.py

@@ -1,4 +1,4 @@
-from models import Model
+from models import Model, ModelState
 from typing import Union, List, Generator
 import utils
 import numpy as np
@@ -7,6 +7,7 @@ import scipy.signal
 from scipy.fftpack import fft
 from scipy.signal import argrelextrema
 from scipy.stats.stats import pearsonr
+from typing import Optional
 import math
 from scipy.stats import gaussian_kde
 from scipy.stats import norm
@@ -16,6 +17,20 @@ from analytic_types import AnalyticUnitId
 
 PEARSON_FACTOR = 0.7
 
+class GeneralModelState(ModelState):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def to_json(self) -> dict:
+        return super().to_json()
+
+    @staticmethod
+    def from_json(json: Optional[dict] = None):
+        if json is None:
+            json = {}
+        return GeneralModelState(**json)
+
 class GeneralModel(Model):
 
     def __init__(self):
@@ -41,6 +56,9 @@ class GeneralModel(Model):
         center_ind = start + math.ceil((end - start) / 2)
         return center_ind
 
+    def get_cache(self, cache: Optional[dict] = None) -> GeneralModelState:
+        return GeneralModelState.from_json(cache)
+
     def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list, deleted_segments: list, learning_info: dict, id: AnalyticUnitId) -> None:
         logging.debug('Start method do_fit for analytic unit: {}'.format(id))
         data = utils.cut_dataframe(dataframe)

analytics/analytics/models/jump_model.py

@@ -1,14 +1,43 @@
-from models import Model
+from models import Model, ModelState
 
 import utils
 import numpy as np
 import pandas as pd
 import scipy.signal
 from scipy.fftpack import fft
+from typing import Optional
 import math
 from scipy.signal import argrelextrema
 from scipy.stats import gaussian_kde
 
+class JumpModelState(ModelState):
+
+    def __init__(
+        self,
+        confidence: float = 0,
+        jump_height: float = 0,
+        jump_length: float = 0,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.confidence = confidence
+        self.jump_height = jump_height
+        self.jump_length = jump_length
+
+    def to_json(self) -> dict:
+        json = super().to_json()
+        json.update({
+            'confidence': self.confidence,
+            'jump_height': self.jump_height,
+            'jump_length': self.jump_length,
+        })
+        return json
+
+    @staticmethod
+    def from_json(json: Optional[dict] = None):
+        if json is None:
+            json = {}
+        return JumpModelState(**json)
 
 class JumpModel(Model):
 
@@ -39,6 +68,9 @@ class JumpModel(Model):
         segment_center_index = utils.find_pattern_center(segment, start, 'jump')
         return segment_center_index
 
+    def get_cache(self, cache: Optional[dict] = None) -> JumpModelState:
+        return JumpModelState.from_json(cache)
+
     def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list, deleted_segments: list, learning_info: dict, id: str) -> None:
         data = utils.cut_dataframe(dataframe)
         data = data['value']

analytics/analytics/models/model.py

@@ -101,6 +101,10 @@ class Model(ABC):
     def get_model_type(self) -> (str, bool):
         pass
 
+    @abstractmethod
+    def get_cache(self, cache: Optional[dict] = None) -> ModelState:
+        pass
+
     def fit(self, dataframe: pd.DataFrame, segments: list, id: AnalyticUnitId, cache: Optional[ModelCache]) -> ModelCache:
         logging.debug('Start method fit for analytic unit {}'.format(id))
         data = dataframe['value']

analytics/analytics/models/peak_model.py

@@ -1,9 +1,9 @@
-from models import Model
+from models import Model, ModelState
 
 import scipy.signal
 from scipy.fftpack import fft
 from scipy.signal import argrelextrema
-
+from typing import Optional, List
 import utils
 import numpy as np
 import pandas as pd
@@ -11,6 +11,35 @@ import pandas as pd
 SMOOTHING_COEFF = 2400
 EXP_SMOOTHING_FACTOR = 0.01
 
+class PeakModelState(ModelState):
+
+    def __init__(
+        self,
+        confidence: float = 0,
+        height_max: float = 0,
+        height_min: float = 0,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.confidence = confidence
+        self.height_max = height_max
+        self.height_min = height_min
+
+    def to_json(self) -> dict:
+        json = super().to_json()
+        json.update({
+            'confidence': self.confidence,
+            'height_max': self.height_max,
+            'height_min': self.height_min,
+        })
+        return json
+
+    @staticmethod
+    def from_json(json: Optional[dict] = None):
+        if json is None:
+            json = {}
+        return PeakModelState(**json)
+
 class PeakModel(Model):
 
     def __init__(self):
@@ -39,6 +68,9 @@ class PeakModel(Model):
         segment = data[start: end]
         return segment.idxmax()
 
+    def get_cache(self, cache: Optional[dict] = None) -> PeakModelState:
+        return PeakModelState.from_json(cache)
+
     def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list, deleted_segments: list, learning_info: dict, id: str) -> None:
         data = utils.cut_dataframe(dataframe)
         data = data['value']

analytics/analytics/models/trough_model.py

@@ -1,9 +1,9 @@
-from models import Model
+from models import Model, ModelState
 
 import scipy.signal
 from scipy.fftpack import fft
 from scipy.signal import argrelextrema
-
+from typing import Optional
 import utils
 import numpy as np
 import pandas as pd
@@ -11,6 +11,35 @@ import pandas as pd
 SMOOTHING_COEFF = 2400
 EXP_SMOOTHING_FACTOR = 0.01
 
+class TroughModelState(ModelState):
+
+    def __init__(
+        self,
+        confidence: float = 0,
+        height_max: float = 0,
+        height_min: float = 0,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.confidence = confidence
+        self.height_max = height_max
+        self.height_min = height_min
+
+    def to_json(self) -> dict:
+        json = super().to_json()
+        json.update({
+            'confidence': self.confidence,
+            'height_max': self.height_max,
+            'height_min': self.height_min,
+        })
+        return json
+
+    @staticmethod
+    def from_json(json: Optional[dict] = None):
+        if json is None:
+            json = {}
+        return TroughModelState(**json)
+
 class TroughModel(Model):
 
     def __init__(self):
@@ -39,6 +68,9 @@ class TroughModel(Model):
         segment = data[start: end]
         return segment.idxmin()
 
+    def get_cache(self, cache: Optional[dict] = None) -> TroughModelState:
+        return TroughModelState.from_json(cache)
+
     def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list, deleted_segments: list, learning_info: dict, id: str) -> None:
         data = utils.cut_dataframe(dataframe)
         data = data['value']