Adding box plot. Feature filter and refactoring files

Author: harsh97

ads/feature_store/feature_stat.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+# -*- coding: utf-8; -*-
+# Copyright (c) 2023 Oracle and/or its affiliates.
+# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
+from abc import abstractmethod
+
+from ads.common.decorator.runtime_dependency import OptionalDependency
+
+try:
+    from plotly.graph_objs import Figure
+except ModuleNotFoundError:
+    raise ModuleNotFoundError(
+        f"The `plotly` module was not found. Please run `pip install "
+        f"{OptionalDependency.FEATURE_STORE}`."
+    )
+
+
+class AbsFeatureStat:
+    @abstractmethod
+    def add_to_figure(self, fig: Figure, xaxis: int, yaxis: int):
+        pass
+
+    @classmethod
+    @abstractmethod
+    def from_json(cls, json_dict: dict):
+        pass
+
+    @staticmethod
+    def get_x_y_str_axes(xaxis: int, yaxis: int) -> ():
+        return (
+            ("xaxis" + str(xaxis + 1)),
+            ("yaxis" + str(yaxis + 1)),
+            ("x" + str(xaxis + 1)),
+            ("y" + str(yaxis + 1)),
+        )

ads/feature_store/statistics/__init__.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+# -*- coding: utf-8; -*-
+# Copyright (c) 2023 Oracle and/or its affiliates.
+# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
+
+from ads.common.decorator.runtime_dependency import OptionalDependency
+from ads.feature_store.statistics.charts.abstract_feature_stat import AbsFeatureStat
+from ads.feature_store.statistics.generic_feature_value import GenericFeatureValue
+
+try:
+    from plotly.graph_objs import Figure
+except ModuleNotFoundError:
+    raise ModuleNotFoundError(
+        f"The `plotly` module was not found. Please run `pip install "
+        f"{OptionalDependency.FEATURE_STORE}`."
+    )
+
+
+class BoxPlot(AbsFeatureStat):
+    CONST_MIN = "Min"
+    CONST_MAX = "Max"
+    CONST_QUARTILES = "Quartiles"
+    CONST_SD = "StandardDeviation"
+    CONST_MEAN = "Mean"
+    CONST_BOX_PLOT_TITLE = "Box Plot"
+
+    class Quartiles:
+        CONST_Q1 = "q1"
+        CONST_Q2 = "q2"
+        CONST_Q3 = "q3"
+
+        def __init__(self, q1: float, q2: float, q3: float):
+            self.q1 = q1
+            self.q2 = q2
+            self.q3 = q3
+
+        @classmethod
+        def from_json(cls, json_dict: dict) -> "BoxPlot.Quartiles":
+            if json_dict is not None:
+                return cls(
+                    json_dict.get(cls.CONST_Q1),
+                    json_dict.get(cls.CONST_Q2),
+                    json_dict.get(cls.CONST_Q3),
+                )
+            else:
+                return None
+
+    def __init__(
+        self,
+        mean: float,
+        median: float,
+        sd: float,
+        q1: float,
+        q3: float,
+        min: float,
+        max: float,
+    ):
+        self.mean = mean
+        self.median = median
+        self.q1 = q1
+        self.q3 = q3
+        self.sd = sd
+        self.min = min
+        self.max = max
+
+    def add_to_figure(self, fig: Figure, xaxis: int, yaxis: int):
+        xaxis_str, yaxis_str, x_str, y_str = self.get_x_y_str_axes(xaxis, yaxis)
+        fig.add_box(
+            mean=[self.mean],
+            median=[self.median],
+            q1=[self.q1],
+            q3=[self.q3],
+            sd=[self.sd],
+            upperfence=[self.max],
+            lowerfence=[self.min],
+            xaxis=x_str,
+            yaxis=y_str,
+        )
+        fig.layout.annotations[xaxis].text = self.CONST_BOX_PLOT_TITLE
+        fig.layout[yaxis_str]["title"] = "Values"
+
+    @classmethod
+    def from_json(cls, json_dict: dict) -> "BoxPlot":
+        if type(json_dict) is dict and json_dict.get(cls.CONST_QUARTILES) is not None:
+            quartiles = cls.Quartiles.from_json(json_dict.get(cls.CONST_QUARTILES))
+            return cls(
+                mean=GenericFeatureValue.from_json(json_dict.get(cls.CONST_MEAN)).val,
+                median=quartiles.q2,
+                sd=GenericFeatureValue.from_json(json_dict.get(cls.CONST_SD)).val,
+                q1=quartiles.q1,
+                q3=quartiles.q3,
+                min=GenericFeatureValue.from_json(json_dict.get(cls.CONST_MIN)).val,
+                max=GenericFeatureValue.from_json(json_dict.get(cls.CONST_MAX)).val,
+            )
+        else:
+            return None