chore: import upstream snapshot with attribution

mla/base/__init__.py

@@ -0,0 +1,2 @@
+# coding:utf-8
+from .base import *

mla/base/base.py

@@ -0,0 +1,63 @@
+# coding:utf-8
+import numpy as np
+
+
+class BaseEstimator:
+    y_required = True
+    fit_required = True
+
+    def _setup_input(self, X, y=None):
+        """Ensure inputs to an estimator are in the expected format.
+
+        Ensures X and y are stored as numpy ndarrays by converting from an
+        array-like object if necessary. Enables estimators to define whether
+        they require a set of y target values or not with y_required, e.g.
+        kmeans clustering requires no target labels and is fit against only X.
+
+        Parameters
+        ----------
+        X : array-like
+            Feature dataset.
+        y : array-like
+            Target values. By default is required, but if y_required = false
+            then may be omitted.
+        """
+        if not isinstance(X, np.ndarray):
+            X = np.array(X)
+
+        if X.size == 0:
+            raise ValueError("Got an empty matrix.")
+
+        if X.ndim == 1:
+            self.n_samples, self.n_features = 1, X.shape
+        else:
+            self.n_samples, self.n_features = X.shape[0], np.prod(X.shape[1:])
+
+        self.X = X
+
+        if self.y_required:
+            if y is None:
+                raise ValueError("Missed required argument y")
+
+            if not isinstance(y, np.ndarray):
+                y = np.array(y)
+
+            if y.size == 0:
+                raise ValueError("The targets array must be no-empty.")
+
+        self.y = y
+
+    def fit(self, X, y=None):
+        self._setup_input(X, y)
+
+    def predict(self, X=None):
+        if not isinstance(X, np.ndarray):
+            X = np.array(X)
+
+        if self.X is not None or not self.fit_required:
+            return self._predict(X)
+        else:
+            raise ValueError("You must call `fit` before `predict`")
+
+    def _predict(self, X=None):
+        raise NotImplementedError()