Improved performance and accuracy tests for different classifiers

- added additional tests for classifiers with different arguments
- added check for overlapping rows in test and train datasets
This commit is contained in:
Ilya Dziamidchyk 2023-09-19 11:18:38 +02:00
parent c7503596f6
commit 773aea2f05

View File

@ -1,22 +1,20 @@
import os
import pickle
import time
import pandas as pd
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.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVR, SVC
from sklearn import preprocessing
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import ParameterGrid
TRAIN_DATA_DIR = "datasets_train"
TRAIN_DATA_DIR = "datasets_train_raw"
TEST_DATA_DIR = "datasets_test"
def invoke_and_measure(func, *args, **kwargs):
start_time = time.time()
result = func(*args, **kwargs)
@ -25,6 +23,7 @@ def invoke_and_measure(func, *args, **kwargs):
elapsed_time = end_time - start_time
return result, elapsed_time
train_df_list = []
for file in os.listdir(TRAIN_DATA_DIR):
file_path = os.path.join(TRAIN_DATA_DIR, file)
@ -42,42 +41,88 @@ for file in os.listdir(TEST_DATA_DIR):
"collapsed"])
test_df_list.append(df)
data_test = pd.concat(train_df_list, ignore_index=True).sample(frac=1, random_state=42)
data_test = pd.concat(test_df_list, ignore_index=True).sample(frac=1, random_state=42)
merged_data = pd.merge(data_train, data_test, indicator=True, how='outer')
overlap_rows = merged_data[merged_data['_merge'] == 'both']
if overlap_rows.empty:
print("There are no overlapping rows between train and test datasets.")
else:
print("Train and test datasets have following overlapping rows: ")
print(overlap_rows)
X_train = data_train.iloc[:, 1:-1].values
y_train = data_train.iloc[:, -1].values
lab = preprocessing.LabelEncoder()
y_transformed = lab.fit_transform(y_train)
y_train_transformed = lab.fit_transform(y_train)
X_test = data_test.iloc[:, 1:-1].values
y_test = data_test.iloc[:, -1].values
y_test_transformed = lab.fit_transform(y_test)
names = [
"Nearest Neighbors",
"Decision Tree",
"Random Forest",
"Naive Bayes",
"QDA",
"Gradient Boosting"
classifiers_and_parameters = [
{
"name": "Nearest Neighbors",
"classifier": KNeighborsClassifier(),
"parameters": {
"n_neighbors": [3, 5, 10, 50]
}
},
{
"name": "Decision Tree",
"classifier": DecisionTreeClassifier(),
"parameters": {
"max_depth": [10, 20, 50]
}
},
{
"name": "Random Forest",
"classifier": RandomForestClassifier(),
"parameters": {
"max_depth": [10, 20, 50],
"n_estimators": [10, 50, 100],
"max_features": ['sqrt', 'log2']
}
},
{
"name": "Naive Bayes",
"classifier": GaussianNB(),
"parameters": {
"var_smoothing": [1e-09, 1e-08, 1e-07]
}
},
{
"name": "QDA",
"classifier": QuadraticDiscriminantAnalysis(),
"parameters": {
"reg_param": [0.0, 0.5, 1.0]
}
},
{
"name": "Gradient Boosting",
"classifier": GradientBoostingClassifier(),
"parameters": {
"learning_rate": [0.01, 0.05, 0.1],
"n_estimators": [50, 100, 200]
}
}
]
classifiers = {
KNeighborsClassifier(3),
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
GaussianNB(),
QuadraticDiscriminantAnalysis(),
GradientBoostingClassifier()
}
for item in classifiers_and_parameters:
name = item["name"]
clf = item["classifier"]
param_grid = ParameterGrid(item["parameters"])
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(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")
for params in param_grid:
clf.set_params(**params)
_, fit_time = invoke_and_measure(clf.fit, X_train, y_train_transformed)
y_pred, pred_time = invoke_and_measure(clf.predict, X_test)
accuracy = accuracy_score(y_test_transformed, y_pred)
precision = precision_score(y_test_transformed, y_pred)
recall = recall_score(y_test_transformed, y_pred)
f1 = f1_score(y_test_transformed, y_pred)
print(
f"{name} with params {params}: accuracy={accuracy * 100:.2f}% precision={precision * 100:.2f}% recall={recall * 100:.2f}% "
f"train_time={fit_time:.5f}s predict_time={pred_time:.5f}s")