feat(drift): added drift mvp

Features:
- reference covariate drift
- reference label drift
- sample covariate drift
- sample label drift
- sample concept drift

requirements.txt

@@ -1,3 +1,4 @@
 pandas==1.2.*
 pydantic==1.8.2
 scikit-learn==0.24.2
+matplotlib==3.4.2

src/ydata_quality/core/engine.py

@@ -7,15 +7,17 @@
 import pandas as pd
 from ydata_quality.core import QualityWarning
 from ydata_quality.core.warnings import Priority
-
+from ydata_quality.utils.modelling import infer_dtypes
 
 class QualityEngine(ABC):
     "Main class for running and storing data quality analysis."
 
-    def __init__(self, df: pd.DataFrame):
+    def __init__(self, df: pd.DataFrame, label: str = None, dtypes: dict = None):
         self._df = df
         self._warnings = set()
         self._tests = []
+        self._label = label
+        self._dtypes = dtypes
 
     @property
     def df(self):
@@ -27,6 +29,44 @@ def warnings(self):
         "Storage of all detected data quality warnings."
         return self._warnings
 
+
+    @property
+    def label(self):
+        "Property that returns the label under inspection."
+        return self._label
+
+    @label.setter
+    def label(self, label: str):
+        if not isinstance(label, str):
+            raise ValueError("Property 'label' should be a string.")
+        assert label in self.df.columns, "Given label should exist as a DataFrame column."
+        self._label = label
+
+    @property
+    def dtypes(self):
+        "Infered dtypes for the dataset."
+        if self._dtypes is None:
+            self._dtypes = infer_dtypes(self.df)
+        return self._dtypes
+
+    @dtypes.setter
+    def dtypes(self, dtypes: dict):
+        if not isinstance(dtypes, dict):
+            raise ValueError("Property 'dtypes' should be a dictionary.")
+        assert all(col in self.df.columns for col in dtypes), "All dtypes keys \
+            must be columns in the dataset."
+        supported_dtypes = ['numerical', 'categorical']
+        assert all(dtype in supported_dtypes for dtype in dtypes.values()), "Assigned dtypes\
+             must be in the supported broad dtype list: {}.".format(supported_dtypes)
+        df_col_set = set(self.df.columns)
+        dtypes_col_set = set(dtypes.keys())
+        missing_cols = df_col_set.difference(dtypes_col_set)
+        if missing_cols:
+            _dtypes = infer_dtypes(self.df, skip=df_col_set.difference(missing_cols))
+            for col, dtype in _dtypes.items():
+                dtypes[col] = dtype
+        self._dtypes = dtypes
+
     def store_warning(self, warning: QualityWarning):
         "Adds a new warning to the internal 'warnings' storage."
         self._warnings.add(warning)

src/ydata_quality/drift/__init__.py

@@ -0,0 +1,9 @@
+"""
+Tools to check dataset for data drifting.
+"""
+from ydata_quality.drift.engine import DriftAnalyser, ModelWrapper
+
+__all__ = [
+    "DriftAnalyser",
+    "ModelWrapper"
+]