Add manual test procedure for CLI

Signed-off-by: Darko Draskovic <darko.draskovic@gmail.com>

.gitignore

@@ -1,2 +1 @@
 build
-bin

cli/algorithms.go

@@ -31,7 +31,7 @@ func NewAlgorithmsCmd(sdk agentsdk.SDK) *cobra.Command {
 				return
 			}
 
-			log.Println("Response:", response)
+			log.Println("Succesfully uploaded algorithm:", response)
 		},
 	}
 }

test/manual/README.md

@@ -0,0 +1,56 @@
+# Manual tests
+
+## CLI
+
+Open a console and start `agent`
+
+```sh
+AGENT_LOG_LEVEL=info go run cmd/agent/main.go
+```
+
+Open another console and run
+
+```sh
+export AGENT_GRPC_URL=localhost:7002
+
+# Run the CLI program with algorithm input
+go run cmd/cli/main.go algo test/manual/algo/lin_reg.py
+# 2023/09/21 10:43:53 Uploading algorithm binary: test/manual/algo/lin_reg.py
+
+# Run the CLI program with dataset input
+go run cmd/cli/main.go data test/manual/data/iris.csv
+# 2023/09/21 10:45:25 Uploading dataset CSV: test/manual/data/iris.csv
+
+# Run the CLI program to fetch computation result
+go run cmd/cli/main.go result
+# 2023/09/21 10:45:39 Retrieving computation result file
+# 2023/09/21 10:45:40 Computation result retrieved and saved successfully!
+```
+
+Now there is a `result.bin` file in the current working directory. The file holds the trained logistic regression model. To test the model, run
+
+```sh
+python3 test/manual/algo/lin_reg_test.py test/manual/data/iris.csv result.bin
+```
+
+You should get an output (truncated for the sake of brevity):
+
+```sh
+   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
+0   1            5.1           3.5            1.4           0.2  Iris-setosa
+1   2            4.9           3.0            1.4           0.2  Iris-setosa
+2   3            4.7           3.2            1.3           0.2  Iris-setosa
+3   4            4.6           3.1            1.5           0.2  Iris-setosa
+4   5            5.0           3.6            1.4           0.2  Iris-setosa
+Precision, Recall, Confusion matrix, in training
+
+                 precision    recall  f1-score   support
+
+    Iris-setosa      1.000     1.000     1.000        21
+Iris-versicolor      0.923     0.889     0.906        27
+ Iris-virginica      0.893     0.926     0.909        27
+
+       accuracy                          0.933        75
+      macro avg      0.939     0.938     0.938        75
+   weighted avg      0.934     0.933     0.933        75
+```

test/manual/algo/lin_reg.py

@@ -0,0 +1,47 @@
+import sys, io
+import joblib
+import socket
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+
+dataset = sys.argv[1]
+iris = pd.read_csv(io.StringIO(dataset))
+
+# Droping the Species since we only need the measurements
+X = iris.drop(['Species'], axis=1)
+
+# converting into numpy array and assigning petal length and petal width
+X = X.to_numpy()[:, (3,4)]
+y = iris['Species']
+
+# Splitting into train and test
+X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.5, random_state=42)
+
+log_reg = LogisticRegression()
+log_reg.fit(X_train,y_train)
+
+# Serialize the trained model to a byte buffer
+model_buffer = io.BytesIO()
+joblib.dump(log_reg, model_buffer)
+
+# Get the serialized model as a bytes object
+model_bytes = model_buffer.getvalue()
+
+# Define the path for the Unix domain socket
+socket_path = sys.argv[2]
+
+# Create a Unix domain socket client
+client = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
+
+try:
+    # Connect to the server
+    client.connect(socket_path)
+
+    # Send the serialized model over the socket
+    client.send(model_bytes)
+
+finally:
+    # Close the socket
+    client.close()