Fixed 20 fold CV to run on new hyperparameters

evaluate_classifiers.py

@@ -4,6 +4,11 @@ import pandas as pd
 import glob
 from train_classifiers import perform_grid_search, load_dataset
 
+from sklearn.neural_network import MLPClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
 
 DIR: str = os.path.dirname(os.path.realpath(__file__))
 OUT_DIR: str = DIR + '/models'
@@ -38,15 +43,34 @@ def clean_output():
         os.remove(f)
 
 
+string_to_classifier = {
+    'MLPClassifier': MLPClassifier(),
+    'GaussianNB': GaussianNB(),
+    'SVC': SVC(),
+    'DecisionTreeClassifier': DecisionTreeClassifier(),
+    'RandomForestClassifier': RandomForestClassifier()
+}
+
+
+def unit_grid(params: dict) -> dict:
+    return dict([(k, [v]) for k, v in params.items()])
+
+
 def main():
     if not os.path.exists(OUT_DIR + '/evaluation.csv'):
         X, y = load_dataset()
 
+        df_best = pd.read_csv(OUT_DIR + '/best.csv')
+        df_best = df_best.loc[:, ['classifier', 'params']]
+        df_best.loc[:, 'classifier'] = df_best['classifier'].apply(lambda x: string_to_classifier[x])
+        df_best.loc[:, 'params'] = df_best['params'].apply(lambda x: eval(x))
+        classifiers = [(e['classifier'], unit_grid(e['params'])) for e in df_best.to_dict('records')]
+
         dfs: list[pd.DataFrame] = []
 
         for i, state in enumerate(RANDOM_STATES):
             print("Iteration " + str(i + 1) + " of " + str(len(RANDOM_STATES)) + "...")
-            dfs.append(perform_grid_search(X, y, 5, state))
+            dfs.append(perform_grid_search(X, y, classifiers, 5, state))
 
         # concatenate all runs in single dataframe
         df = pd.concat(dfs, ignore_index=True)

models/best.csv

@@ -0,0 +1,6 @@
+params,precision,accuracy,recall,f1,classifier
+"{'criterion': 'gini', 'splitter': 'best'}",0.7884615384615384,0.8505747126436781,0.9534883720930232,0.8631578947368421,DecisionTreeClassifier
+{},0.8,0.6781609195402298,0.4651162790697674,0.5882352941176471,GaussianNB
+"{'activation': 'logistic', 'hidden_layer_sizes': (60, 80, 100), 'learning_rate': 'constant', 'max_iter': 500000, 'solver': 'lbfgs'}",0.8958333333333334,0.942528735632184,1.0,0.945054945054945,MLPClassifier
+"{'class_weight': 'balanced', 'criterion': 'gini', 'max_features': 'sqrt'}",0.8367346938775511,0.8850574712643678,0.9534883720930232,0.8913043478260869,RandomForestClassifier
+"{'gamma': 'scale', 'kernel': 'rbf'}",0.717391304347826,0.735632183908046,0.7674418604651163,0.7415730337078652,SVC

models/evaluation.csv

@@ -0,0 +1,101 @@
+mean_fit_time,std_fit_time,mean_score_time,std_score_time,param_criterion,param_splitter,params,split0_test_precision,split1_test_precision,split2_test_precision,split3_test_precision,split4_test_precision,mean_test_precision,std_test_precision,rank_test_precision,split0_test_accuracy,split1_test_accuracy,split2_test_accuracy,split3_test_accuracy,split4_test_accuracy,mean_test_accuracy,std_test_accuracy,rank_test_accuracy,split0_test_recall,split1_test_recall,split2_test_recall,split3_test_recall,split4_test_recall,mean_test_recall,std_test_recall,rank_test_recall,split0_test_f1,split1_test_f1,split2_test_f1,split3_test_f1,split4_test_f1,mean_test_f1,std_test_f1,rank_test_f1,classifier,param_activation,param_hidden_layer_sizes,param_learning_rate,param_max_iter,param_solver,param_class_weight,param_max_features,param_gamma,param_kernel
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+0.19360728263854982,0.0027234169199152124,0.014816474914550782,0.0014398728068045806,gini,,"{'class_weight': 'balanced', 'criterion': 'gini', 'max_features': 'sqrt'}",0.7719298245614035,0.8333333333333334,0.8936170212765957,0.8478260869565217,0.8695652173913043,0.8432542967038318,0.04107353328001852,1,0.8505747126436781,0.8735632183908046,0.9195402298850575,0.8735632183908046,0.8850574712643678,0.8804597701149426,0.02252404361179934,1,1.0,0.9302325581395349,0.9545454545454546,0.9069767441860465,0.9090909090909091,0.9401691331923889,0.03450431121940526,1,0.8712871287128713,0.8791208791208791,0.9230769230769231,0.8764044943820224,0.888888888888889,0.887755662836317,0.01856608067167711,1,RandomForestClassifier,,,,,,balanced,sqrt,,
+0.0045832633972167965,0.00037759465520098416,0.006492757797241211,0.0010056513369830899,,,"{'gamma': 'scale', 'kernel': 'rbf'}",0.75,0.7435897435897436,0.8222222222222222,0.6739130434782609,0.8055555555555556,0.7590561129691563,0.05238839483175277,1,0.7701149425287356,0.7241379310344828,0.8275862068965517,0.6896551724137931,0.7471264367816092,0.7517241379310345,0.046320555585310064,1,0.8181818181818182,0.6744186046511628,0.8409090909090909,0.7209302325581395,0.6590909090909091,0.7427061310782241,0.07412055319973548,1,0.7826086956521738,0.7073170731707318,0.8314606741573033,0.6966292134831461,0.7250000000000001,0.748603131292671,0.050960151788453534,1,SVC,,,,,,,,scale,rbf

requirements.txt

@@ -1,3 +1,4 @@
 javalang==0.13.0
 pandas==1.5.2
 scikit_learn==1.2.1
+tabulate==0.9.0

train_classifiers.py

@@ -88,14 +88,14 @@ def balance_classes_with_upscaling(X, y, random_state: int) -> tuple[any, any]:
     return (X, y,)
 
 
-def perform_grid_search(X, y, n_splits: int, random_state: int) -> pd.DataFrame:
+def perform_grid_search(X, y, classifiers, n_splits: int, random_state: int) -> pd.DataFrame:
     # Balance classes in training set by upsampling the minority class
     X_upscaled, y_upscaled = balance_classes_with_upscaling(X, y, random_state)
 
     dfs: list[pd.DataFrame] = []
     sss = StratifiedShuffleSplit(n_splits=n_splits, train_size=0.8, random_state=random_state)
 
-    for classifier, grid in get_classifiers():
+    for classifier, grid in classifiers:
         # cross-validation splits are same across calls as data is not shuffled
         # see: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
         clf = GridSearchCV(classifier, grid, cv=sss, n_jobs=-1, refit='f1', verbose=4, scoring={
@@ -127,16 +127,45 @@ def load_dataset() -> tuple[any, any]:
     X_scaled = scaler.transform(X)
 
     return (X_scaled, y)
+    
+
+def find_best_and_save(df: pd.DataFrame):
+    df_classifiers = df[df.columns.drop(list(df.filter(regex='^(mean_)|(std_)|(rank_)|(param_)')))]
+    df_classifiers = df_classifiers.rename(columns={
+        "split0_test_precision": "precision",
+        "split0_test_accuracy": "accuracy",
+        "split0_test_recall": "recall",
+        "split0_test_f1": "f1"
+    })
+    df_best = df_classifiers \
+        .sort_values(['classifier', 'accuracy'], ascending=[True, False]) \
+        .groupby('classifier') \
+        .head(1) \
+        .reset_index(drop=True)
+    df_best.to_csv(OUT_DIR + '/best.csv', index=False)
+
+    # Prepare for formatting
+    df_best['params'] = df_best['params'] \
+        .apply(lambda x: ", ".join([f"`{x}`: {y}" for x, y in eval(x).items()]))
+      
+    metrics = ['precision', 'accuracy', 'recall', 'f1']
+    df_best.loc[:, metrics] = df_best.loc[:, metrics].round(decimals=4)
+    print(df_best.to_markdown(index=False))
 
 
 def main():
-    clean_output()
+    if os.path.exists(OUT_DIR + '/models.csv') and input("Run again grid search? (y/[n]): ").lower() == "y":
+        clean_output()
 
-    X, y = load_dataset()
-    df = perform_grid_search(X, y, 1, 0xDEADB017)
+        X, y = load_dataset()
+        df = perform_grid_search(X, y, get_classifiers(), 1, 0xDEADB017)
 
-    df.to_csv(OUT_DIR + '/models.csv', index=False)
-    df.to_csv(OUT_DIR + '/models_eu.csv', index=False, sep=';')
+        df.to_csv(OUT_DIR + '/models.csv', index=False)
+        # df.to_csv(OUT_DIR + '/models_eu.csv', index=False, sep=';')
+    else:
+        df = pd.read_csv(OUT_DIR + '/models.csv')
+
+    find_best_and_save(df)
 
 
 if __name__ == '__main__':