Updated the plot_regression_line method (#28)

* Updated the plot_regression_line method

* Updated method docstring

* Minor update to docstring

setup.py

@@ -8,7 +8,7 @@
 
 setup(
     name="showml",
-    version="1.5.10",
+    version="1.5.11",
     packages=find_packages(exclude="tests"),
     description="A Python package of Machine Learning Algorithms implemented from scratch",
     long_description=long_description,

showml/examples/linear_regression.py

@@ -3,7 +3,7 @@
 from showml.linear_model.regression import LinearRegression
 from showml.utils.dataset import Dataset
 from showml.losses.metrics import mean_squared_error, r2_score
-from showml.utils.plots import plot_regression_line
+from showml.utils.plots import plot_actual_vs_predicted
 from showml.utils.preprocessing import normalize
 from showml.utils.data_loader import load_auto
 
@@ -21,4 +21,4 @@
 model.fit(dataset, epochs=10000)
 model.plot_metrics()
 
-plot_regression_line(X_train, y_train, model.predict(X_train))
+plot_actual_vs_predicted(y_train, model.predict(X_train))

showml/utils/plots.py

@@ -2,8 +2,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from showml.utils.exceptions import InvalidShapeError
-
 
 def generic_metric_plot(metric_name: str, metric_values: List[float]) -> None:
     """Plot the metric values after training (epoch vs metric).
@@ -19,25 +17,16 @@ def generic_metric_plot(metric_name: str, metric_values: List[float]) -> None:
     plt.show()
 
 
-def plot_regression_line(
-    X: np.ndarray, y: np.ndarray, z: np.ndarray, xlabel: str = "", ylabel: str = ""
-) -> None:
-    """Plot the regression line to visualize how well the model fits to the data.
-    Only works when the entire dataset is 2-dimensional i.e., input data (X) is 1-dimensional.
+def plot_actual_vs_predicted(y: np.ndarray, z: np.ndarray) -> None:
+    """Generates a scatter plot of the true values and predicted values.
+    A diagonal line from (0, 0) to (+limit, +limit) indicates a very good fit i.e., the true values and predicted values are almost equal.
 
     Args:
-        X (np.ndarray): The input data.
         y (np.ndarray): The true labels of the input data.
         z (np.ndarray): The predicted values for the input data.
-        xlabel (str, optional): The label corresponding to the input feature name. Defaults to "".
-        ylabel (str, optional): The label corresponding to the output feature name. Defaults to "".
     """
-    if X.shape[1] != 1:
-        raise InvalidShapeError("X must have exactly 1 dimension.")
-
-    plt.scatter(X, y, color="red")
-    plt.plot(X, z, color="blue")
-    plt.title("Regression Line")
-    plt.xlabel(xlabel)
-    plt.ylabel(ylabel)
+    plt.scatter(y, z, color="red")
+    plt.title("Actual vs Predicted")
+    plt.xlabel("Actual")
+    plt.ylabel("Predicted")
     plt.show()