Better handling of printing results board
This commit is contained in:
saundersp
110
python/projet.py
110
python/projet.py
@ -4,6 +4,7 @@
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from ViolaJones import train_viola_jones, classify_viola_jones
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from toolbox import state_saver, picke_multi_loader, format_time_ns, benchmark_function, unit_test_argsort_2d
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from toolbox_unit_test import format_time_ns_test
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from toolbox import header, footer, formatted_row, formatted_line
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from sklearn.metrics import accuracy_score, f1_score, confusion_matrix
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from sklearn.feature_selection import SelectPercentile, f_classif
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from common import load_datasets, unit_test
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@ -38,9 +39,11 @@ def preprocessing() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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for folder_name in ["models", "out"]:
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makedirs(folder_name, exist_ok = True)
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print(f"| {'Preprocessing':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |\n|{'-'*51}|{'-'*20}|{'-'*31}|")
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preproc_timestamp = perf_counter_ns()
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preproc_gaps = [49, -18, 29]
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header(['Preprocessing', 'Time spent (ns)', 'Formatted time spent'], preproc_gaps)
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X_train, y_train, X_test, y_test = state_saver("Loading sets", ["X_train", "y_train", "X_test", "y_test"],
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X_train, y_train, X_test, y_test = state_saver('Loading sets', preproc_gaps[0], ['X_train', 'y_train', 'X_test', 'y_test'],
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load_datasets, FORCE_REDO, SAVE_STATE)
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if __DEBUG:
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@ -57,16 +60,17 @@ def preprocessing() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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print(y_test.shape)
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print(y_test[IDX_INSPECT: IDX_INSPECT + IDX_INSPECT_OFFSET])
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feats = state_saver("Building features", "feats", lambda: build_features(X_train.shape[1], X_train.shape[2]), FORCE_REDO, SAVE_STATE)
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feats = state_saver('Building features', preproc_gaps[0], 'feats', lambda: build_features(X_train.shape[1], X_train.shape[2]),
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FORCE_REDO, SAVE_STATE)
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if __DEBUG:
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print("feats")
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print(feats.shape)
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print(feats[IDX_INSPECT].ravel())
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X_train_ii = state_saver(f"Converting training set to integral images ({label})", f"X_train_ii_{label}",
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X_train_ii = state_saver(f'Converting training set to integral images ({label})', preproc_gaps[0], f'X_train_ii_{label}',
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lambda: set_integral_image(X_train), FORCE_REDO, SAVE_STATE)
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X_test_ii = state_saver(f"Converting testing set to integral images ({label})", f"X_test_ii_{label}",
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X_test_ii = state_saver(f'Converting testing set to integral images ({label})', preproc_gaps[0], f'X_test_ii_{label}',
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lambda: set_integral_image(X_test), FORCE_REDO, SAVE_STATE)
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if __DEBUG:
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@ -77,9 +81,9 @@ def preprocessing() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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print(X_test_ii.shape)
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print(X_test_ii[IDX_INSPECT])
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X_train_feat = state_saver(f"Applying features to training set ({label})", f"X_train_feat_{label}",
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X_train_feat = state_saver(f'Applying features to training set ({label})', preproc_gaps[0], f'X_train_feat_{label}',
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lambda: apply_features(feats, X_train_ii), FORCE_REDO, SAVE_STATE)
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X_test_feat = state_saver(f"Applying features to testing set ({label})", f"X_test_feat_{label}",
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X_test_feat = state_saver(f'Applying features to testing set ({label})', preproc_gaps[0], f'X_test_feat_{label}',
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lambda: apply_features(feats, X_test_ii), FORCE_REDO, SAVE_STATE)
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del X_train_ii, X_test_ii, feats
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@ -106,14 +110,14 @@ def preprocessing() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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# X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices]
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X_train_feat_argsort = state_saver(f"Precalculating training set argsort ({label})", f"X_train_feat_argsort_{label}",
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X_train_feat_argsort = state_saver(f'Precalculating training set argsort ({label})', preproc_gaps[0], f'X_train_feat_argsort_{label}',
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lambda: argsort(X_train_feat), FORCE_REDO, SAVE_STATE)
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if __DEBUG:
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print("X_train_feat_argsort")
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print(X_train_feat_argsort.shape)
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print(X_train_feat_argsort[IDX_INSPECT, : IDX_INSPECT_OFFSET])
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benchmark_function("Arg unit test", lambda: unit_test_argsort_2d(X_train_feat, X_train_feat_argsort))
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benchmark_function('Arg unit test', preproc_gaps[0], lambda: unit_test_argsort_2d(X_train_feat, X_train_feat_argsort))
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X_test_feat_argsort = state_saver(f"Precalculating testing set argsort ({label})", f"X_test_feat_argsort_{label}",
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lambda: argsort(X_test_feat), FORCE_REDO, SAVE_STATE)
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@ -123,48 +127,70 @@ def preprocessing() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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print(X_test_feat_argsort.shape)
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print(X_test_feat_argsort[IDX_INSPECT, : IDX_INSPECT_OFFSET])
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benchmark_function("Arg unit test", lambda: unit_test_argsort_2d(X_test_feat, X_test_feat_argsort))
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time_spent = perf_counter_ns() - preproc_timestamp
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formatted_line(preproc_gaps, '├', '┼', '─', '┤')
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formatted_row(preproc_gaps, ['Preprocessing summary', f'{time_spent:,}', format_time_ns(time_spent)])
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footer(preproc_gaps)
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return X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test
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def train(X_train_feat: np.ndarray, X_train_feat_argsort: np.ndarray, y_train: np.ndarray) -> None:
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def train(X_train_feat: np.ndarray, X_train_feat_argsort: np.ndarray, y_train: np.ndarray) -> List[np.ndarray]:
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"""Train the weak classifiers.
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Args:
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X_train (np.ndarray): Training images.
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X_test (np.ndarray): Testing Images.
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X_train_feat_argsort (np.ndarray): Sorted indexes of the training images features.
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y_train (np.ndarray): Training labels.
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Returns: List of trained models
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"""
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print(f"\n| {'Training':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |\n|{'-'*51}|{'-'*20}|{'-'*31}|")
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training_timestamp = perf_counter_ns()
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training_gaps = [26, -18, 29]
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header(['Training', 'Time spent (ns)', 'Formatted time spent'], training_gaps)
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models = []
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for T in TS:
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alphas, final_classifiers = state_saver(f"ViolaJones T = {T:<3} ({label})", [f"alphas_{T}_{label}", f"final_classifiers_{T}_{label}"],
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lambda: train_viola_jones(T, X_train_feat, X_train_feat_argsort, y_train), FORCE_REDO, SAVE_STATE, MODEL_DIR)
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alphas, final_classifiers = state_saver(f'ViolaJones T = {T:<4} ({label})', training_gaps[0],
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[f'alphas_{T}_{label}', f'final_classifiers_{T}_{label}'],
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lambda: train_viola_jones(T, X_train_feat, X_train_feat_argsort, y_train), FORCE_REDO, SAVE_STATE, MODEL_DIR)
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models.append([alphas, final_classifiers])
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if __DEBUG:
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print("alphas")
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print(alphas)
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print("final_classifiers")
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print(final_classifiers)
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def testing_and_evaluating(X_train_feat: np.ndarray, y_train: np.ndarray, X_test_feat: np.ndarray, y_test: np.ndarray) -> None:
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time_spent = perf_counter_ns() - training_timestamp
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formatted_line(training_gaps, '├', '┼', '─', '┤')
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formatted_row(training_gaps, ['Training summary', f'{time_spent:,}', format_time_ns(time_spent)])
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footer(training_gaps)
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return models
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def testing_and_evaluating(models: List[np.ndarray], X_train_feat: np.ndarray, y_train: np.ndarray, X_test_feat: np.ndarray, y_test: np.ndarray) -> None:
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"""Benchmark the trained classifiers on the training and testing sets.
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Args:
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models (List[np.ndarray]): List of trained models.
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X_train_feat (np.ndarray): Training features.
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y_train (np.ndarray): Training labels.
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X_test_feat (np.ndarray): Testing features.
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y_test (np.ndarray): Testing labels.
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"""
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print(f"\n| {'Testing':<26} | Time spent (ns) (E) | {'Formatted time spent (E)':<29}", end = " | ")
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print(f"Time spent (ns) (T) | {'Formatted time spent (T)':<29} |")
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print(f"|{'-'*28}|{'-'*21}|{'-'*31}|{'-'*21}|{'-'*31}|")
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perfs = []
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for T in TS:
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(alphas, final_classifiers) = picke_multi_loader([f"alphas_{T}_{label}", f"final_classifiers_{T}_{label}"])
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testing_gaps = [26, -19, 24, -19, 24]
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header(['Testing', 'Time spent (ns) (E)', 'Formatted time spent (E)', 'Time spent (ns) (T)', 'Formatted time spent (T)'], testing_gaps)
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performances = []
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total_train_timestamp = 0
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total_test_timestamp = 0
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for T, (alphas, final_classifiers) in zip(TS, models):
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s = perf_counter_ns()
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y_pred_train = classify_viola_jones(alphas, final_classifiers, X_train_feat)
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t_pred_train = perf_counter_ns() - s
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total_train_timestamp += t_pred_train
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e_acc = accuracy_score(y_train, y_pred_train)
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e_f1 = f1_score(y_train, y_pred_train)
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(_, e_FP), (e_FN, _) = confusion_matrix(y_train, y_pred_train)
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@ -172,36 +198,48 @@ def testing_and_evaluating(X_train_feat: np.ndarray, y_train: np.ndarray, X_test
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s = perf_counter_ns()
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y_pred_test = classify_viola_jones(alphas, final_classifiers, X_test_feat)
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t_pred_test = perf_counter_ns() - s
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total_test_timestamp += t_pred_test
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t_acc = accuracy_score(y_test, y_pred_test)
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t_f1 = f1_score(y_test, y_pred_test)
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(_, t_FP), (t_FN, _) = confusion_matrix(y_test, y_pred_test)
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perfs.append((e_acc, e_f1, e_FN, e_FP, t_acc, t_f1, t_FN, t_FP))
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performances.append((e_acc, e_f1, e_FN, e_FP, t_acc, t_f1, t_FN, t_FP))
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print(f"| {'ViolaJones T = ' + str(T):<19} {'(' + label + ')':<6}", end = " | ")
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print(f"{t_pred_train:>19,} | {format_time_ns(t_pred_train):<29}", end = " | ")
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print(f"{t_pred_test:>19,} | {format_time_ns(t_pred_test):<29} |")
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formatted_row(testing_gaps, [f"{'ViolaJones T = ' + str(T):<19} {'(' + label + ')':<6}", f'{t_pred_train:,}',
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format_time_ns(t_pred_train), f'{t_pred_test:,}', format_time_ns(t_pred_test)])
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print(f"\n| {'Evaluating':<19} | ACC (E) | F1 (E) | FN (E) | FP (E) | ACC (T) | F1 (T) | FN (T) | FP (T) | ")
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print(f"|{'-'*21}|{'-'*9}|{'-'*8}|{'-'*8}|{'-'*8}|{'-'*9}|{'-'*8}|{'-'*8}|{'-'*8}|")
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formatted_line(testing_gaps, '├', '┼', '─', '┤')
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formatted_row(testing_gaps, ['Testing summary', f'{total_train_timestamp:,}', format_time_ns(total_train_timestamp), f'{total_test_timestamp:,}',
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format_time_ns(total_test_timestamp)])
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footer(testing_gaps)
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for T, (e_acc, e_f1, e_FN, e_FP, t_acc, t_f1, t_FN, t_FP) in zip(TS, perfs):
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print(f"| {'ViolaJones T = ' + str(T):<19} | {e_acc:>7.2%} | {e_f1:>6.2f} | {e_FN:>6,} | {e_FP:>6,}", end = " | ")
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print(f"{t_acc:>7.2%} | {t_f1:>6.2f} | {t_FN:>6,} | {t_FP:>6,} |")
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evaluating_gaps = [19, 7, 6, 6, 6, 7, 6, 6, 6]
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header(['Evaluating', 'ACC (E)', 'F1 (E)', 'FN (E)', 'FP (E)', 'ACC (T)', 'F1 (T)', 'FN (T)', 'FP (T)'], evaluating_gaps)
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for T, (e_acc, e_f1, e_FN, e_FP, t_acc, t_f1, t_FN, t_FP) in zip(TS, performances):
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print(f'│ ViolaJones T = {T:<4} │ {e_acc:>7.2%} │ {e_f1:>6.2f} │ {e_FN:>6,} │ {e_FP:>6,}', end = ' │ ')
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print(f'{t_acc:>7.2%} │ {t_f1:>6.2f} │ {t_FN:>6,} │ {t_FP:>6,} │')
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footer(evaluating_gaps)
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def main() -> None:
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print(f"| {'Unit testing':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |")
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print(f"|{'-'*51}|{'-'*20}|{'-'*31}|")
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benchmark_function("Testing format_time_ns", format_time_ns_test)
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print()
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unit_timestamp = perf_counter_ns()
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unit_gaps = [27, -18, 29]
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header(['Unit testing', 'Time spent (ns)', 'Formatted time spent'], unit_gaps)
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benchmark_function('testing format_time', unit_gaps[0], format_time_test)
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benchmark_function('testing format_time_ns', unit_gaps[0], format_time_ns_test)
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time_spent = perf_counter_ns() - unit_timestamp
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formatted_line(unit_gaps, '├', '┼', '─', '┤')
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formatted_row(unit_gaps, ['Unit testing summary', f'{time_spent:,}', format_time_ns(time_spent)])
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footer(unit_gaps)
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X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test = preprocessing()
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train(X_train_feat, X_train_feat_argsort, y_train)
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models = train(X_train_feat, X_train_feat_argsort, y_train)
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# X_train_feat, X_test_feat = picke_multi_loader([f"X_train_feat_{label}", f"X_test_feat_{label}"], OUT_DIR)
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# indices = picke_multi_loader(["indices"], OUT_DIR)[0]
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# X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices]
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testing_and_evaluating(X_train_feat, y_train, X_test_feat, y_test)
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testing_and_evaluating(models, X_train_feat, y_train, X_test_feat, y_test)
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unit_test(TS)
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if __name__ == "__main__":
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