feature_03_to_testing

data/input/iris.csv

@@ -0,0 +1,151 @@
+x3,x4,x1,x2,x5,y
+5.1,3.5,1.4,0.2,a,0
+4.9,3,1.4,0.2,a,0
+4.7,3.2,1.3,0.2,a,0
+4.6,3.1,1.5,0.2,a,0
+5,3.6,1.4,0.2,a,0
+5.4,3.9,1.7,0.4,a,0
+4.6,3.4,1.4,0.3,a,0
+5,3.4,1.5,0.2,a,0
+4.4,2.9,1.4,0.2,e,0
+4.9,3.1,1.5,0.1,e,0
+5.4,3.7,1.5,0.2,e,0
+4.8,3.4,1.6,0.2,e,0
+4.8,3,1.4,0.1,e,0
+4.3,3,1.1,0.1,e,0
+5.8,4,1.2,0.2,e,0
+5.7,4.4,1.5,0.4,e,0
+5.4,3.9,1.3,0.4,e,0
+5.1,3.5,1.4,0.3,e,0
+5.7,3.8,1.7,0.3,e,0
+5.1,3.8,1.5,0.3,s,0
+5.4,3.4,1.7,0.2,s,0
+5.1,3.7,1.5,0.4,s,0
+4.6,3.6,1,0.2,s,0
+5.1,3.3,1.7,0.5,s,0
+4.8,3.4,1.9,0.2,s,0
+5,3,1.6,0.2,s,0
+5,3.4,1.6,0.4,s,0
+5.2,3.5,1.5,0.2,s,0
+5.2,3.4,1.4,0.2,s,0
+4.7,3.2,1.6,0.2,s,0
+4.8,3.1,1.6,0.2,e,0
+5.4,3.4,1.5,0.4,s,0
+5.2,4.1,1.5,0.1,a,0
+5.5,4.2,1.4,0.2,s,0
+4.9,3.1,1.5,0.2,a,0
+5,3.2,1.2,0.2,s,0
+5.5,3.5,1.3,0.2,a,0
+4.9,3.6,1.4,0.1,e,0
+4.4,3,1.3,0.2,s,0
+5.1,3.4,1.5,0.2,a,0
+5,3.5,1.3,0.3,s,0
+4.5,2.3,1.3,0.3,e,0
+4.4,3.2,1.3,0.2,s,0
+5,3.5,1.6,0.6,s,0
+5.1,3.8,1.9,0.4,s,0
+4.8,3,1.4,0.3,s,0
+5.1,3.8,1.6,0.2,a,0
+4.6,3.2,1.4,0.2,a,0
+5.3,3.7,1.5,0.2,a,0
+5,3.3,1.4,0.2,a,0
+7,3.2,4.7,1.4,e,1
+6.4,3.2,4.5,1.5,e,1
+6.9,3.1,4.9,1.5,e,1
+5.5,2.3,4,1.3,e,1
+6.5,2.8,4.6,1.5,s,1
+5.7,2.8,4.5,1.3,e,1
+6.3,3.3,4.7,1.6,s,1
+4.9,2.4,3.3,1,a,1
+6.6,2.9,4.6,1.3,s,1
+5.2,2.7,3.9,1.4,e,1
+5,2,3.5,1,s,1
+5.9,3,4.2,1.5,a,1
+6,2.2,4,1,s,1
+6.1,2.9,4.7,1.4,e,1
+5.6,2.9,3.6,1.3,s,1
+6.7,3.1,4.4,1.4,a,1
+5.6,3,4.5,1.5,a,1
+5.8,2.7,4.1,1,s,1
+6.2,2.2,4.5,1.5,e,1
+5.6,2.5,3.9,1.1,a,1
+5.9,3.2,4.8,1.8,e,1
+6.1,2.8,4,1.3,e,1
+6.3,2.5,4.9,1.5,s,1
+6.1,2.8,4.7,1.2,e,1
+6.4,2.9,4.3,1.3,s,1
+6.6,3,4.4,1.4,a,1
+6.8,2.8,4.8,1.4,s,1
+6.7,3,5,1.7,e,1
+6,2.9,4.5,1.5,s,1
+5.7,2.6,3.5,1,a,1
+5.5,2.4,3.8,1.1,s,1
+5.5,2.4,3.7,1,e,1
+5.8,2.7,3.9,1.2,s,1
+6,2.7,5.1,1.6,e,1
+5.4,3,4.5,1.5,s,1
+6,3.4,4.5,1.6,a,1
+6.7,3.1,4.7,1.5,a,1
+6.3,2.3,4.4,1.3,s,1
+5.6,3,4.1,1.3,e,1
+5.5,2.5,4,1.3,a,1
+5.5,2.6,4.4,1.2,e,1
+6.1,3,4.6,1.4,e,1
+5.8,2.6,4,1.2,s,1
+5,2.3,3.3,1,e,1
+5.6,2.7,4.2,1.3,s,1
+5.7,3,4.2,1.2,a,1
+5.7,2.9,4.2,1.3,s,1
+6.2,2.9,4.3,1.3,e,1
+5.1,2.5,3,1.1,s,1
+5.7,2.8,4.1,1.3,s,1
+6.3,3.3,6,2.5,s,2
+5.8,2.7,5.1,1.9,s,2
+7.1,3,5.9,2.1,a,2
+6.3,2.9,5.6,1.8,a,2
+6.5,3,5.8,2.2,a,2
+7.6,3,6.6,2.1,a,2
+4.9,2.5,4.5,1.7,e,2
+7.3,2.9,6.3,1.8,e,2
+6.7,2.5,5.8,1.8,e,2
+7.2,3.6,6.1,2.5,e,2
+6.5,3.2,5.1,2,s,2
+6.4,2.7,5.3,1.9,e,2
+6.8,3,5.5,2.1,s,2
+5.7,2.5,5,2,s,2
+5.8,2.8,5.1,2.4,e,2
+6.4,3.2,5.3,2.3,s,2
+6.5,3,5.5,1.8,a,2
+7.7,3.8,6.7,2.2,a,2
+7.7,2.6,6.9,2.3,s,2
+6,2.2,5,1.5,e,2
+6.9,3.2,5.7,2.3,s,2
+5.6,2.8,4.9,2,a,2
+7.7,2.8,6.7,2,s,2
+6.3,2.7,4.9,1.8,a,2
+6.7,3.3,5.7,2.1,s,2
+7.2,3.2,6,1.8,a,2
+6.2,2.8,4.8,1.8,s,2
+6.1,3,4.9,1.8,a,2
+6.4,2.8,5.6,2.1,s,2
+7.2,3,5.8,1.6,e,2
+7.4,2.8,6.1,1.9,e,2
+7.9,3.8,6.4,2,e,2
+6.4,2.8,5.6,2.2,e,2
+6.3,2.8,5.1,1.5,e,2
+6.1,2.6,5.6,1.4,s,2
+7.7,3,6.1,2.3,s,2
+6.3,3.4,5.6,2.4,s,2
+6.4,3.1,5.5,1.8,s,2
+6,3,4.8,1.8,s,2
+6.9,3.1,5.4,2.1,a,2
+6.7,3.1,5.6,2.4,a,2
+6.9,3.1,5.1,2.3,a,2
+5.8,2.7,5.1,1.9,a,2
+6.8,3.2,5.9,2.3,a,2
+6.7,3.3,5.7,2.5,s,2
+6.7,3,5.2,2.3,s,2
+6.3,2.5,5,1.9,s,2
+6.5,3,5.2,2,e,2
+6.2,3.4,5.4,2.3,e,2
+5.9,3,5.1,1.8,e,2

logs/cov.out

@@ -3,6 +3,7 @@ Name                    Stmts   Miss  Cover   Missing
 mllib/__init__.py           7      0   100%
 mllib/lib/__init__.py       7      0   100%
 mllib/lib/cluster.py      103      0   100%
-mllib/lib/model.py         45      0   100%
+mllib/lib/knn.py           70      0   100%
+mllib/lib/model.py         44      0   100%
 -----------------------------------------------------
-TOTAL                     162      0   100%
+TOTAL                     231      0   100%

logs/pylint/lib-knn-py.out

@@ -0,0 +1,9 @@
+************* Module mllib.lib.knn
+knn.py:176:45: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member)
+knn.py:177:45: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member)
+knn.py:178:46: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member)
+knn.py:179:46: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member)
+
+--------------------------------------------------------------------
+Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00)
+

logs/pylint/tests-test_knn-py.out

@@ -0,0 +1,4 @@
+
+--------------------------------------------------------------------
+Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00)
+

mllib/__main__.py

@@ -28,6 +28,7 @@
 from lib import cfg, utils  # noqa: F841
 from lib.cluster import Cluster  # noqa: F841
 from lib.model import GLMNet  # noqa: F841
+from lib.knn import KNN  # noqa: F841
 
 # =============================================================================
 # --- DO NOT CHANGE ANYTHING FROM HERE
@@ -52,13 +53,13 @@
 CLI.add_argument("-f", "--filename",
                  nargs=1,
                  type=str,
-                 default=["store.csv"],
+                 default=["iris.csv"],
                  help="input csv filename")
 
 args = CLI.parse_args()
 
 fn_ip = args.filename[0]
-fn_ip = "store.csv"
+fn_ip = "iris.csv"
 
 # =============================================================================
 # --- Main
@@ -79,12 +80,21 @@
     start_t = time.time_ns()
     df_ip = pd.read_csv(path + "input/test_glmnet.csv")
     glm_mod = GLMNet(df=df_ip,
-                     y_var=["y"],
-                     x_var=["x1", "x3"])
+                     y_var="y",
+                     x_var=["x1", "x2"])
     print("\nGLMNet\n")
     for k, v in glm_mod.model_summary.items():
         print(k, str(v).rjust(69 - len(k)))
     print(elapsed_time("Time", start_t),
           sep="\n")
+    # --- KNN
+    start_t = time.time_ns()
+    df_ip = pd.read_csv(path + "input/iris.csv")
+    mod = KNN(df_ip, "y", ["x1", "x2", "x3", "x4"], method="classify")
+    print("\nKNN\n")
+    for k, v in mod.model_summary.items():
+        print(k, str(v).rjust(69 - len(k)))
+    print(elapsed_time("Time", start_t),
+          sep="\n")
     # --- EOF
     print(sep, elapsed_time("Total time", start), sep, sep="\n")