Changes to classifiers tests

- changed train and test datasets
- removed classifiers which are too slow or produce unsatisfactory results
- added more metrics to evaluate classification performance

classifiers_test.py

@@ -1,18 +1,21 @@
+import os
+import pickle
 import time
 
 import pandas as pd
-from sklearn import preprocessing
 from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, log_loss, confusion_matrix, \
+    matthews_corrcoef, cohen_kappa_score
 from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
-from sklearn.gaussian_process import GaussianProcessClassifier
-from sklearn.gaussian_process.kernels import RBF
-from sklearn.metrics import accuracy_score
 from sklearn.naive_bayes import GaussianNB
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.neural_network import MLPClassifier
-from sklearn.svm import SVC
+from sklearn.svm import SVR, SVC
+from sklearn import preprocessing
 from sklearn.tree import DecisionTreeClassifier
 
+TRAIN_DATA_DIR = "datasets_train"
+TEST_DATA_DIR = "datasets_test"
 
 def invoke_and_measure(func, *args, **kwargs):
     start_time = time.time()
@@ -22,13 +25,24 @@ def invoke_and_measure(func, *args, **kwargs):
     elapsed_time = end_time - start_time
     return result, elapsed_time
 
-data = pd.read_csv('starclusters-global-parameters2.dat',skiprows=1 ,delim_whitespace=True, header=None)
+train_df_list = []
+for file in os.listdir(TRAIN_DATA_DIR):
+    file_path = os.path.join(TRAIN_DATA_DIR, file)
+    df, load_time = invoke_and_measure(pd.read_csv, file_path, delim_whitespace=True, skiprows=1,
+                                       names=["tbid", "tphys", "r", "vr", "vt", "ik1", "ik2", "sm1", "sm2", "a", "e",
+                                              "collapsed"])
+    train_df_list.append(df)
+data_train = pd.concat(train_df_list, ignore_index=True).sample(frac=1, random_state=42)
 
-shuffled_data = data.sample(frac=1, random_state=42)
-n = int(0.8 * len(shuffled_data))
+test_df_list = []
+for file in os.listdir(TEST_DATA_DIR):
+    file_path = os.path.join(TEST_DATA_DIR, file)
+    df, load_time = invoke_and_measure(pd.read_csv, file_path, delim_whitespace=True, skiprows=1,
+                                       names=["tbid", "tphys", "r", "vr", "vt", "ik1", "ik2", "sm1", "sm2", "a", "e",
+                                              "collapsed"])
+    test_df_list.append(df)
 
-data_train = shuffled_data[:n]
-data_test = shuffled_data[n:]
+data_test = pd.concat(train_df_list, ignore_index=True).sample(frac=1, random_state=42)
 
 X_train = data_train.iloc[:, 1:-1].values
 y_train = data_train.iloc[:, -1].values
@@ -42,29 +56,28 @@ names = [
     "Nearest Neighbors",
     "Decision Tree",
     "Random Forest",
-    "Neural Net",
-    "AdaBoost",
     "Naive Bayes",
     "QDA",
-    "SVC",
     "Gradient Boosting"
 ]
 
-classifiers = [
+classifiers = {
     KNeighborsClassifier(3),
     DecisionTreeClassifier(max_depth=5),
     RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
-    MLPClassifier(alpha=1, max_iter=1000),
-    AdaBoostClassifier(),
     GaussianNB(),
     QuadraticDiscriminantAnalysis(),
-    SVC(),
     GradientBoostingClassifier()
-]
-
+}
 
 for name, clf in zip(names, classifiers):
     _, fit_time = invoke_and_measure(clf.fit, X_train, y_transformed)
     y_pred, pred_time = invoke_and_measure(clf.predict, X_test)
-    accuracy = accuracy_score(lab.transform(data_test.iloc[:, -1].values), y_pred)
-    print(f"{name}: accuracy={accuracy * 100:.2f}% train={fit_time:.5f}s predict={pred_time:.5f}s")
+    accuracy = accuracy_score(y_train, y_pred)
+    precision = precision_score(y_train, y_pred)
+    recall = recall_score(y_train, y_pred)
+    f1 = f1_score(y_train, y_pred)
+    print(
+        f"{name}: accuracy={accuracy * 100:.2f}% precision={precision * 100:.2f}% recall={recall * 100:.2f}% "
+        f"f1={f1 * 100:.2f}% "
+        f"train_time={fit_time:.5f}s predict_time={pred_time:.5f}s")