Added tests for LogisticRegression

Signed-off-by: Ayush Joshi <[email protected]>

ai/linear_model/logistic.py

@@ -137,7 +137,7 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> 'LogisticRegression':
     """
     n_samples, n_features = X.shape
     self._bias = 0
-    self._weights = np.zeros(X.shape[1])
+    self._weights = np.zeros(n_features)
 
     for _ in range(self._n_iters):
       y_pred = self._sigmoid(np.dot(X, self._weights) + self._bias)
@@ -179,4 +179,4 @@ def predict(self, X: np.ndarray) -> np.ndarray:
 
     y_pred = self._sigmoid(np.dot(X, self._weights) + self._bias)
     c_pred = [0 if y <= .5 else 1 for y in y_pred]
-    return c_pred
+    return np.array(c_pred)

tests/test_linear_model/test_logistic.py

@@ -0,0 +1,74 @@
+# Copyright 2023 The AI Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# pylint: disable=too-many-function-args, invalid-name, missing-module-docstring
+# pylint: disable=missing-class-docstring
+
+import pytest
+import numpy as np
+
+from ai.linear_model import LogisticRegression
+
+
+def test_predict_simple():
+  model = LogisticRegression()
+  model._weights = np.array([0.5, 0.5])
+  model._bias = 0.1
+
+  X = np.array([[1, 2]])
+  predicted = model.predict(X)
+  assert isinstance(predicted, np.ndarray)
+  assert len(predicted) == 1
+  assert predicted[0] in [0, 1]
+
+
+def test_predict_multiple_samples():
+  model = LogisticRegression()
+  model._weights = np.array([0.5, 0.5])
+  model._bias = 0.1
+
+  X = np.array([[1, 2], [2, 3], [3, 4]])
+  predicted = model.predict(X)
+  assert isinstance(predicted, np.ndarray)
+  assert len(predicted) == 3
+  assert all(label in [0, 1] for label in predicted)
+
+
+def test_predict_different_weights():
+  model = LogisticRegression()
+  model._weights = np.array([0.2, 0.8])
+  model._bias = -0.1
+
+  X = np.array([[1, 2]])
+  predicted = model.predict(X)
+  assert isinstance(predicted, np.ndarray)
+  assert len(predicted) == 1
+  assert predicted[0] in [0, 1]
+
+
+def test_predict_before_fit():
+  model = LogisticRegression()
+  X = np.array([[1, 2]])
+
+  with pytest.raises(RuntimeError):
+    model.predict(X)
+
+
+def test_predict_shape_mismatch():
+  model = LogisticRegression()
+  model._weights = np.array([0.5, 0.5])
+  model._bias = 0.1
+
+  X = np.array([[1, 2, 3]])
+  with pytest.raises(ValueError):
+    model.predict(X)