WIP: CLI interface interact with Model class, subprogram call Model methods to satisfy the subject

1 files changed, 63 insertions, 0 deletions

diff --git a/src/cli.py b/src/cli.py
new file mode 100644
index 0000000..ce759be
--- /dev/null
+++ b/src/cli.py
@@ -0,0 +1,63 @@
+import sys
+import argparse
+
+from model import Model
+import predict
+
+class CommandLineInterface:
+    def __init__(self):
+        self.model = Model()
+
+    def parse_args(self):
+        parser = argparse.ArgumentParser(
+            prog="ft_linear_regression_cli",
+            description="CLI to interact with the ft_linear_regression project"
+        )
+        subparsers = parser.add_subparsers(help="sub-command help", dest="subparser_name")
+
+        parser_train = subparsers.add_parser("train", help="train the model")
+        parser_train.set_defaults(func=self._train)
+        parser_train.add_argument("-a --alpha", type=float, default=1.0, dest="alpha", help="learning rate")
+        parser_train.add_argument("-e --epoch", type=int, default=100, dest="epoch", help="number of iterations")
+
+        parser_predict = subparsers.add_parser("predict", help="make a predict")
+        parser_predict.set_defaults(func=self._predict)
+        parser_predict.add_argument("-x", type=int, help="mileage for which the prediction will be made")
+
+        parser_cost = subparsers.add_parser("cost", help="print model cost")
+        parser_cost.set_defaults(func=self._cost)
+
+        parser_plot = subparsers.add_parser("plot", help="plot data and model")
+        parser_plot.set_defaults(func=self._plot)
+        parser_plot.add_argument("-d --data", help="only plot data", action="store_true", dest="plot_data")
+        parser_plot.add_argument("-m --model", help="only plot model", action="store_true", dest="plot_model")
+
+        self.args = parser.parse_args(sys.argv[1:])
+
+    def _train(self):
+        self.model.train(self.args.alpha, self.args.epoch)
+        self.model.write_theta()
+
+    def _predict(self):
+        if self.args.x is not None:
+            print(self.model.hypothesis(self.args.x))
+        else:
+            predict.predict_input(self.model)
+
+    def _cost(self):
+        print("Cost:", self.model.cost())
+
+    def _plot(self):
+        if not self.args.plot_data and not self.args.plot_model:
+            self.model.plot()
+        else:
+            self.model.plot(self.args.plot_data, self.args.plot_model)
+
+    def exec_args(self):
+        self.args.func()
+
+
+if __name__ == "__main__":
+    cli = CommandLineInterface()
+    cli.parse_args()
+    cli.exec_args()