129 lines
4.3 KiB
Python
129 lines
4.3 KiB
Python
import os
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import time
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import pandas as pd
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from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
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from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn import preprocessing
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.model_selection import ParameterGrid
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TRAIN_DATA_DIR = "datasets_train_raw"
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TEST_DATA_DIR = "datasets_test"
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def invoke_and_measure(func, *args, **kwargs):
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start_time = time.time()
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result = func(*args, **kwargs)
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end_time = time.time()
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elapsed_time = end_time - start_time
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return result, elapsed_time
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train_df_list = []
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for file in os.listdir(TRAIN_DATA_DIR):
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file_path = os.path.join(TRAIN_DATA_DIR, file)
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df, load_time = invoke_and_measure(pd.read_csv, file_path, delim_whitespace=True, skiprows=1,
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names=["tbid", "tphys", "r", "vr", "vt", "ik1", "ik2", "sm1", "sm2", "a", "e",
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"collapsed"])
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train_df_list.append(df)
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data_train = pd.concat(train_df_list, ignore_index=True).sample(frac=1, random_state=42)
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test_df_list = []
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for file in os.listdir(TEST_DATA_DIR):
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file_path = os.path.join(TEST_DATA_DIR, file)
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df, load_time = invoke_and_measure(pd.read_csv, file_path, delim_whitespace=True, skiprows=1,
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names=["tbid", "tphys", "r", "vr", "vt", "ik1", "ik2", "sm1", "sm2", "a", "e",
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"collapsed"])
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test_df_list.append(df)
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data_test = pd.concat(test_df_list, ignore_index=True).sample(frac=1, random_state=42)
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merged_data = pd.merge(data_train, data_test, indicator=True, how='outer')
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overlap_rows = merged_data[merged_data['_merge'] == 'both']
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if overlap_rows.empty:
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print("There are no overlapping rows between train and test datasets.")
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else:
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print("Train and test datasets have following overlapping rows: ")
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print(overlap_rows)
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X_train = data_train.iloc[:, 1:-1].values
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y_train = data_train.iloc[:, -1].values
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lab = preprocessing.LabelEncoder()
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y_train_transformed = lab.fit_transform(y_train)
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X_test = data_test.iloc[:, 1:-1].values
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y_test = data_test.iloc[:, -1].values
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y_test_transformed = lab.fit_transform(y_test)
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classifiers_and_parameters = [
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{
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"name": "Nearest Neighbors",
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"classifier": KNeighborsClassifier(),
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"parameters": {
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"n_neighbors": [3, 5, 10, 50]
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}
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},
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{
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"name": "Decision Tree",
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"classifier": DecisionTreeClassifier(),
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"parameters": {
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"max_depth": [10, 20, 50]
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}
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},
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{
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"name": "Random Forest",
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"classifier": RandomForestClassifier(),
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"parameters": {
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"max_depth": [10, 20, 50],
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"n_estimators": [10, 50, 100],
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"max_features": ['sqrt', 'log2']
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}
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},
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{
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"name": "Naive Bayes",
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"classifier": GaussianNB(),
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"parameters": {
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"var_smoothing": [1e-09, 1e-08, 1e-07]
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}
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},
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{
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"name": "QDA",
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"classifier": QuadraticDiscriminantAnalysis(),
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"parameters": {
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"reg_param": [0.0, 0.5, 1.0]
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}
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},
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{
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"name": "Gradient Boosting",
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"classifier": GradientBoostingClassifier(),
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"parameters": {
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"learning_rate": [0.01, 0.05, 0.1],
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"n_estimators": [50, 100, 200]
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}
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}
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]
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for item in classifiers_and_parameters:
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name = item["name"]
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clf = item["classifier"]
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param_grid = ParameterGrid(item["parameters"])
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for params in param_grid:
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clf.set_params(**params)
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_, fit_time = invoke_and_measure(clf.fit, X_train, y_train_transformed)
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y_pred, pred_time = invoke_and_measure(clf.predict, X_test)
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accuracy = accuracy_score(y_test_transformed, y_pred)
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precision = precision_score(y_test_transformed, y_pred)
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recall = recall_score(y_test_transformed, y_pred)
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f1 = f1_score(y_test_transformed, y_pred)
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print(
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f"{name} with params {params}: accuracy={accuracy * 100:.2f}% precision={precision * 100:.2f}% recall={recall * 100:.2f}% "
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f"train_time={fit_time:.5f}s predict_time={pred_time:.5f}s")
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