def main():
if len(sys.argv) == 3:
database_filepath, model_filepath = sys.argv[1:]
print('Loading data...\n DATABASE: {}'.format(database_filepath))
X, y, category_names = load_data(database_filepath)
mlss = MultilabelStratifiedShuffleSplit(n_splits=1, test_size=0.33, random_state=42)
for train_index, test_index in mlss.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y.values[train_index], y.values[test_index]
y_train = pd.DataFrame(y_train,columns=category_names)
y_test = pd.DataFrame(y_test,columns=category_names)
print('Building model...')
model = build_model()
print('Training model...')
model.fit(X_train, y_train)
print('Evaluating model...')
evaluate_model(model, X_test, y_test, category_names)
print('Saving model...\n MODEL: {}'.format(model_filepath))
save_model(model, model_filepath)
print('Trained model saved!')
else:
print('Please provide the filepath of the disaster messages database '\
'as the first argument and the filepath of the pickle file to '\
'save the model to as the second argument. \n\nExample: python '\
'train_classifier.py ../data/DisasterResponse.db classifier.pkl')
def evaluate(model):
callbacks = [
EarlyStopping(
# Stop training when loss is no longer improving
monitor="loss",
# "no longer improving" being defined as "no better than 1e-2 less"
min_delta=1e-5,
# "no longer improving" being further defined as "for at least 2 epochs"
patience=2,
verbose=1,
)
]
kfold = MultilabelStratifiedShuffleSplit(n_splits=4,
random_state=seed,
test_size=.2)
scores = np.empty(0)
for train, test in kfold.split(X, Y):
X_train, X_test = X[train], X[test]
Y_train, Y_test = Y[train], Y[test]
model.fit(
X_train,
Y_train,
epochs=1,
#epochs=50,
callbacks=callbacks,
) # change epochs to 14
Y_pred = model.predict(X_test)
control_mask = X_test['cp_type'] == 'ctl_vehicle'
Y_pred[control_mask, :] = 0
# scores = np.append(scores,model.evaluate(X_test,Y_test))
scores = np.append(scores, log_loss(Y_test, Y_pred, labels=[0, 1]))
return scores.mean()
def MultiStratifiedShuffleSplit(images, annotations, test_size):
# count categories per image
categories_per_image = defaultdict(Counter)
max_id = 0
for ann in annotations:
categories_per_image[ann['image_id']][ann['category_id']] += 1
if ann['category_id'] > max_id:
max_id = ann['category_id']
# prepare list with count of cateory objects per image
all_categories = []
for cat in categories_per_image.values():
pair = []
for i in range(1, max_id + 1):
pair.append(cat[i])
all_categories.append(pair)
# multilabel-stratified-split
strat_split = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=test_size,
random_state=2020)
for train_index, test_index in strat_split.split(images, all_categories):
x = [images[i] for i in train_index]
y = [images[i] for i in test_index]
print('Train:', len(x), 'images, valid:', len(y))
return x, y
def data_split(x, y, test_size=0.5, random_state=30):
msss = MultilabelStratifiedShuffleSplit(n_splits=1, test_size=test_size, random_state=random_state)
for train_index, test_index in msss.split(x, y):
X_train, X_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
return X_train, X_test, y_train, y_test
def train_test_multilabel_stratified_shuffle_split(dataset,
test_size=0.2,
random_state=42):
y = target_to_numpy(dataset['Target'])
msss = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=test_size,
random_state=random_state)
train_idx, valid_idx = list(msss.split(X=dataset, y=y))[0]
return train_idx, valid_idx
def stratified_train_test_split(X, y):
msss = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=config.TEST_SIZE,
random_state=42)
for train_index, test_index in msss.split(X, y):
X_train, X_test = X.loc[train_index], X.loc[test_index]
y_train, y_test = y.loc[train_index], y.loc[test_index]
return X_train, X_test, y_train, y_test
def split2_stratified(df: pd.DataFrame, target_names: List[str],
test_size: float,
random_state: int) -> Tuple[pd.DataFrame, pd.DataFrame]:
msss = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=test_size,
random_state=random_state)
multi_lbl_arr = df[target_names]
i_train, i_test = next(msss.split(np.zeros(df.shape[0]), multi_lbl_arr))
idx_train = [df.index[i] for i in i_train]
idx_test = [df.index[i] for i in i_test]
return (copy.deepcopy(df.loc[idx_train, :]),
copy.deepcopy(df.loc[idx_test, :]))
def multi_label_split_based_on_percentage(df, n_splits, test_percentage,
unique_id_column, target_column,
seed):
"""
:param df: The dataframe in which 1 row = 1 class for multi-label classification
:param n_splits: How to split the dataframe
:param test_percentage: how much should be the test percentage split?
:param unique_id_column: the column which uniquely identifies the dataframe
:param target_column: the classes column (multi labels). It has to be numeric
:param seed: 42
:return: train and validation dataframe same as df but with fold columns
"""
# store unique ids
unique_ids = df[unique_id_column].unique()
# find unique classes
unique_classes = df[target_column].unique()
# convert the target column into multi label format
one_hot_labels = []
for uid in unique_ids:
classes = df[df[unique_id_column] == uid][target_column].values
x = np.eye(len(unique_classes))[classes.astype(int)].sum(0)
one_hot_labels.append(x)
# https://github.com/trent-b/iterative-stratification#multilabelstratifiedshufflesplit
msss = MultilabelStratifiedShuffleSplit(n_splits=n_splits,
train_size=1 - test_percentage,
test_size=test_percentage,
random_state=seed)
# create train and validation splits
train_df = pd.DataFrame()
val_df = pd.DataFrame()
# X is unique id
for fold, (train_index,
val_index) in enumerate(msss.split(unique_ids, one_hot_labels)):
train_data = df[df[unique_id_column].isin(
unique_ids[train_index])].copy(deep=True)
val_data = df[df[unique_id_column].isin(
unique_ids[val_index])].copy(deep=True)
train_data["fold"] = fold
val_data["fold"] = fold
train_df = train_df.append(train_data, ignore_index=True)
val_df = val_df.append(val_data, ignore_index=True)
return train_df, val_df
def train_test_split(X, y, test_size=0.33):
from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit
msss = MultilabelStratifiedShuffleSplit(n_splits=2,
test_size=0.33,
random_state=0)
for train_index, test_index in msss.split(X, y):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
return (X_train, y_train), (X_test, y_test)
def stratified_split():
print("Splitting train data...")
msss = MultilabelStratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=92)
train_df = pd.read_csv("../input/train.csv")
train_df_orig = train_df.copy()
X = train_df["id"].tolist()
y = train_df['attribute_ids'].tolist()
y = [make_label(cur_y) for cur_y in y]
for train_index, test_index in msss.split(X, y):
new_train_df = train_df_orig.loc[train_df_orig.index.intersection(train_index)].copy()
new_valid_df = train_df_orig.loc[train_df_orig.index.intersection(test_index)].copy()
new_train_df.to_csv("./data/train_split_90pc.csv", index=False)
new_valid_df.to_csv("./data/valid_split_10pc.csv", index=False)
print("Successfully finished!")
def train_test_split(df, target, classes):
X = []
Y = []
for index in range(len(df)):
image_data = df.loc[index]
X.append(image_data[target])
Y.append(image_data[classes].values.tolist())
X = np.array(X)
Y = np.array(Y)
mskf = MultilabelStratifiedShuffleSplit(n_splits=2,
test_size=0.15,
random_state=0)
for train_index, test_index in mskf.split(X, Y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = Y[train_index], Y[test_index]
return X_train, y_train, X_test, y_test
def MultilabelStratifiedShuffleSplit(self, n_splits, test_size,
random_state):
partitions = []
msss = MultilabelStratifiedShuffleSplit(n_splits=n_splits,
test_size=test_size,
random_state=random_state)
for train_index, test_index in msss.split(
self.train_labels["Id"].index.values,
self.train_labels.drop(columns=['Id', 'Target']).values):
partition = {}
partition["train"] = self.train_labels.Id.values[train_index]
partition["validation"] = self.train_labels.Id.values[test_index]
partitions.append(partition)
return partitions
def split_data(threshold):
from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit
df = pd.read_csv(
'/Users/kevinmaikjablonka/Dropbox (LSMO)/proj75_mofcolor/ml/data/all.csv'
)
exlcuded = []
keept = []
THRESHOLD = 0.03
if threshold != 255:
for i, row in df.iterrows():
if row['color_cleaned_x'] in color_threshold_dict[threshold]:
keept.append(row)
else:
exlcuded.append(row)
df_rel = pd.DataFrame(keept)
df = df_rel.drop_duplicates(subset=CHEMICAL_FEATURES)
else:
df = df.drop_duplicates(subset=CHEMICAL_FEATURES)
r_binned = bin_column(df['r'].values)
g_binned = bin_column(df['g'].values)
b_binned = bin_column(df['b'].values)
mlss = MultilabelStratifiedShuffleSplit(n_splits=1,
train_size=0.85,
test_size=0.15,
random_state=RANDOM_SEED)
for train_idx, test_idx in mlss.split(
df, np.hstack([r_binned, g_binned, b_binned])):
pass
df_train = df.iloc[train_idx].sample(len(train_idx))
df_test = df.iloc[test_idx].sample(len(test_idx))
df_train.to_csv(
'/Users/kevinmaikjablonka/Dropbox (LSMO)/proj75_mofcolor/ml/data/development_set.csv',
index=False,
)
df_test.to_csv(
'/Users/kevinmaikjablonka/Dropbox (LSMO)/proj75_mofcolor/ml/data/holdout_set.csv',
index=False,
)
def make_strat_folds(df, n_folds: int) -> pd.DataFrame:
"""
makes iterative stratification of multi label data
Source: https://github.com/trent-b/iterative-stratification
"""
msss = MultilabelStratifiedShuffleSplit(n_splits=n_folds,
test_size=0.2,
random_state=42)
train_df_orig = df.copy()
X = train_df_orig['ImageId'].tolist()
cls_counts = Counter(cls for classes in df['attribute_ids'].str.split()
for cls in classes)
y = train_df_orig['attribute_ids'].str.split().tolist()
#print(X, y)
for train_index, test_index in msss.split(X, y):
print("TRAIN:", train_index, "TEST:", test_index)
train_df = train_df_orig.loc[train_df_orig.index.intersection(
train_index)].copy()
valid_df = train_df_orig.loc[train_df_orig.index.intersection(
test_index)].copy()
return train_df, valid_df
def stratification(label_dir):
print('Stratification...')
# Define the weights, the SNOMED CT code for the normal class, and equivalent SNOMED CT codes.
normal_class = '426783006'
equivalent_classes = [['713427006', '59118001'], ['284470004', '63593006'],
['427172004', '17338001']]
# Find the label files.
label_files = load_label_files(label_dir)
# Load the labels and classes.
label_classes, labels_onehot, labels = load_labels(label_files,
normal_class,
equivalent_classes)
temp = [[] for _ in range(len(labels_onehot))]
indexes, values = np.where(np.array(labels_onehot).astype(int) == 1)
for k, v in zip(indexes, values):
temp[k].append(v)
labels_int = temp
X = np.zeros(len(labels_onehot))
y = labels_onehot
msss = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=0.15,
random_state=0)
for train_index, val_index in msss.split(X, y):
X_train, X_val = X[train_index], X[val_index]
y_train, y_val = y[train_index], y[val_index]
print('Saving split index...')
datasets_distribution(labels_int, [train_index, val_index])
savemat('model_training/split.mat', {
'train_index': train_index,
'val_index': val_index
})
print('Stratification done.')
def split(dataset_path, test_size, stratification):
df = get_csv(dataset_path, name="train")
img_ids = df["image_id"]
if stratification == "sklearn":
train_set, valid_set = train_test_split(df[KEYS],
test_size=test_size,
random_state=SEED,
shuffle=True)
elif stratification == "sklearn_stratified":
df['subset'] = np.nan
splitter = StratifiedShuffleSplit(n_splits=1,
test_size=test_size,
random_state=SEED)
train_indcs, valid_indcs = next(splitter.split(X=img_ids, y=df[KEYS]))
train_set = df.loc[df.index.intersection(train_indcs)].copy()
valid_set = df.loc[df.index.intersection(valid_indcs)].copy()
df.iloc[train_indcs, -1] = 'train'
df.iloc[valid_indcs, -1] = 'valid'
df.to_csv(os.path.join(dataset_path, 'train_stratified.csv'),
index=None)
elif stratification == "iterstrat":
splitter = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=test_size,
random_state=SEED)
train_indcs, valid_indcs = next(splitter.split(X=img_ids, y=df[KEYS]))
train_set = df.loc[df.index.intersection(train_indcs)].copy()
valid_set = df.loc[df.index.intersection(valid_indcs)].copy()
elif stratification == "skmultilearn":
splitter = IterativeStratification(
n_splits=2,
order=2,
sample_distribution_per_fold=[test_size, 1.0 - test_size])
train_indcs, valid_indcs = next(splitter.split(X=img_ids, y=df[KEYS]))
train_set = df.loc[df.index.intersection(train_indcs)].copy()
valid_set = df.loc[df.index.intersection(valid_indcs)].copy()
else:
raise ValueError("Try something else :)")
return train_set, valid_set
def split_dataframe_stratify_n(df,
split,
test_size,
eval_size,
target_names,
random_state=None):
assert isinstance(target_names, list) and len(target_names) >= 1
if split is None:
return Splited(train=df, eval=None, test=None)
elif split is False:
msss = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=test_size,
random_state=random_state)
multi_lbl_arr = df[target_names].values
i_train, i_test = next(msss.split(np.zeros(df.shape[0]),
multi_lbl_arr))
idx_train = [df.index[i] for i in i_train]
idx_test = [df.index[i] for i in i_test]
df_test = df.loc[idx_test, :]
df_train = df.loc[idx_train, :]
return Splited(train=df_train, eval=None, test=df_test)
elif split is True:
msss = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=test_size,
random_state=random_state)
multi_lbl_arr = df[target_names].values
i_train_eval, i_test = next(
msss.split(np.zeros(df.shape[0]), multi_lbl_arr))
idx_train_eval = [df.index[i] for i in i_train_eval]
idx_test = [df.index[i] for i in i_test]
df_test = df.loc[idx_test, :]
msss2 = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=eval_size,
random_state=random_state)
multi_lbl_arr2 = df.loc[idx_train_eval, target_names].values
i_train, i_eval = next(
msss2.split(np.zeros(df.shape[0] - len(i_test)), multi_lbl_arr2))
idx_train = [idx_train_eval[i] for i in i_train]
idx_eval = [idx_train_eval[i] for i in i_eval]
df_eval = df.loc[idx_eval, :]
df_train = df.loc[idx_train, :]
return Splited(train=df_train, eval=df_eval, test=df_test)
def data_prep(self,
N_splits=10,
Test_size=0.3,
Val_size=0.2,
Batch_size=32):
rand_sta = 333
msss = MultilabelStratifiedShuffleSplit(n_splits=N_splits,
test_size=Test_size,
random_state=rand_sta)
# msss=MultilabelStratifiedKFold(n_splits=n_fold,random_state=rand_sta)
train_list = []
test_list = []
for train_index, test_index in msss.split(self.X, self.Y):
train_list.append(train_index)
test_list.append(test_index)
x_train_tmp = self.X.as_matrix()[train_list[0]]
y_train_tmp = self.Y.as_matrix()[train_list[0]]
l_train_tmp = self.L.as_matrix()[train_list[0]]
x_test = self.X.as_matrix()[test_list[0]]
y_test = self.Y.as_matrix()[test_list[0]]
l_test = self.L.as_matrix()[test_list[0]]
msss_cv = MultilabelStratifiedShuffleSplit(n_splits=N_splits,
test_size=Val_size,
random_state=rand_sta)
train_list = []
val_list = []
for train_index, val_index in msss_cv.split(x_train_tmp, y_train_tmp):
train_list.append(train_index)
val_list.append(val_index)
x_train = x_train_tmp[train_list[0]]
y_train = y_train_tmp[train_list[0]]
l_train = l_train_tmp[train_list[0]]
x_val = x_train_tmp[val_list[0]]
y_val = y_train_tmp[val_list[0]]
l_val = l_train_tmp[val_list[0]]
self.x_train = x_train
self.y_train = y_train
self.x_val = x_val
self.y_val = y_val
self.x_test = x_test
self.y_test = y_test
self.l_train = l_train
self.l_test = l_test
self.l_val = l_val
self.batch_size = Batch_size
self.makedivisible_to_all()
def split_files(segment_names, labels, durations, segments_timestamps, config):
r"""Make stratified (multilabel) training, testing and validation split of the segments, and add a prefix to the
segment name to indicate to which set it belongs.
Args:
segment_names (np.ndarray): List of segment audio files.
labels (np.ndarray): audio segments labels
durations (np.ndarray): audio segments labelled portion durations.
segments_timestamps (np.ndarray): Time stamps of the audio events in the segments.
config (dict): Configuration dictionary
Returns:
The updated names of the audio segments.
"""
# Stratified split
tr_sss = MultilabelStratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=53)
train_index, dev_test_index = next(tr_sss.split(np.zeros(labels.shape[0]), labels))
dev_test_sss = MultilabelStratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=32)
dev_index, test_index = next(
dev_test_sss.split(np.zeros(labels[dev_test_index].shape[0]), labels[dev_test_index]))
def array_to_list(array):
r"""Recursive implementation of tolist function for numpy array"""
if isinstance(array, np.ndarray):
return array_to_list(array.tolist())
elif isinstance(array, list):
return [array_to_list(item) for item in array]
elif isinstance(array, tuple):
return tuple(array_to_list(item) for item in array)
else:
return array
for idx, file in enumerate(segment_names):
with open(os.path.join(config['output_folder'], os.path.splitext(file)[0] + '.json'), 'w') as json_file:
json.dump({'label': labels[idx].tolist(),
'durations': durations[idx].tolist(),
'timestamps': array_to_list(segments_timestamps[idx].tolist())},
json_file)
with open(os.path.join(config['output_folder'], 'config.json'), 'w') as json_file:
json.dump({'classes': config['classes'],
'sampling_rate': config['sampling_rate'],
'length_segments_s': config['length_segments_s'],
'Training_files': array_to_list(segment_names[train_index]),
'Testing_files': array_to_list(segment_names[dev_test_index][dev_index]),
'Validation_files': array_to_list(segment_names[dev_test_index][test_index])},
json_file)
def save_train_dev_test_split(train_path, dev_path, test_path,
stft_magnitudes, stft_phases, mel_spectrograms, labels, segment_names, durations):
r"""Compute stratified (multilabel) training, testing and validation split and save the split features to hdf5.
Args:
train_path (str): path to hdf5 file to save the data of the training set
dev_path (str): path to hdf5 file to save the data of the testing set.
test_path (str): path to hdf5 file to save the data of the validation set.
stft_magnitudes (np.ndarray): Segments magnitudes
stft_phases (np.ndarray): Segments phases
mel_spectrograms (np.ndarray): Segments mel Spectrograms
labels (np.ndarray): Segments labels
segment_names (np.ndarray): Segments filenames
durations (np.ndarray): Segments labelled portion durations
"""
tr_sss = MultilabelStratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=53)
train_index, dev_test_index = next(tr_sss.split(np.zeros(stft_magnitudes.shape[0]), labels))
dev_test_sss = MultilabelStratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=32)
dev_index, test_index = next(
dev_test_sss.split(np.zeros(stft_magnitudes[dev_test_index].shape[0]), labels[dev_test_index]))
save_features(train_path,
stft_magnitudes=stft_magnitudes[train_index],
stft_phases=stft_phases[train_index],
mel_spectrograms=mel_spectrograms[train_index],
labels=labels[train_index],
segment_names=segment_names[train_index],
durations=durations[train_index])
save_features(dev_path,
stft_magnitudes=stft_magnitudes[dev_test_index][dev_index],
stft_phases=stft_phases[dev_test_index][dev_index],
mel_spectrograms=mel_spectrograms[dev_test_index][dev_index],
labels=labels[dev_test_index][dev_index],
segment_names=segment_names[dev_test_index][dev_index],
durations=durations[dev_test_index][dev_index])
save_features(test_path,
stft_magnitudes=stft_magnitudes[dev_test_index][test_index],
stft_phases=stft_phases[dev_test_index][test_index],
mel_spectrograms=mel_spectrograms[dev_test_index][test_index],
labels=labels[dev_test_index][test_index],
segment_names=segment_names[dev_test_index][test_index],
durations=durations[dev_test_index][test_index])
def __init__(self,
split,
args,
transforms=None,
test_transforms=None,
channels="g",
debug=False,
n_samples=None):
self.split = split
self.transforms = transforms
self.test_transforms = test_transforms if test_transforms else None
self.image_channels = channels
self.full_size = args.full_size
self.debug = debug
self.n_classes = 28
self.resize = tfms.Resize(
args.img_size,
args.img_size) if args.img_size is not None else None
self.base_path = args.primary_datapath if not args.full_size else args.fullsize_datapath
self.n_samples = n_samples
if self.debug: self.n_samples = 128
# check for valid image mode
if not (set(self.image_channels) <= set("rgby")):
raise ValueError("Invalid image channels selection.")
# split the training set into training and validation
if split in ["train", "val", "trainval"]:
with open(os.path.join(self.base_path, 'train.csv'), 'r') as f:
csvreader = csv.reader(f)
data = list(csvreader)[1:]
label_lookup = {k: np.array(v.split(' ')) for k, v in data}
ids = sorted(list(label_lookup.keys()))
lbls = [self.encode_label(label_lookup[k]) for k in ids]
ids = np.asarray(ids).reshape(-1, 1)
lbls = np.asarray(lbls)
msss = MultilabelStratifiedShuffleSplit(
n_splits=1,
train_size=args.trainval_ratio,
test_size=None,
random_state=0)
train_inds, val_inds = list(msss.split(ids, lbls))[0]
train_ids = ids[train_inds].flatten().tolist()
val_ids = ids[val_inds].flatten().tolist()
ids = ids.flatten().tolist()
# if using external data, add it
self.source_lookup = {i: "trainval" for i in ids}
if args.use_external:
with open(os.path.join(args.primary_datapath, 'external.csv'),
'r') as f:
csvreader = csv.reader(f)
external_data = list(csvreader)[1:]
external_label_lookup = {
k: np.array(v.split(' '))
for k, v in external_data
}
external_ids = sorted(list(external_label_lookup.keys()))
self.source_lookup.update(
{i: "external"
for i in external_ids})
label_lookup.update(external_label_lookup)
ids = ids + external_ids
train_ids = train_ids + external_ids
# select data
if self.split == "train":
self.data = [(i, label_lookup[i]) for i in train_ids]
elif self.split == "val":
self.data = [(i, label_lookup[i]) for i in val_ids]
elif self.split == "trainval":
self.data = [(i, label_lookup[i]) for i in ids]
elif self.split == "test":
with open(os.path.join(self.base_path, 'sample_submission.csv'),
'r') as f:
lines = list(csv.reader(f))[1:]
test_ids = [line[0] for line in lines]
self.data = [(i, None) for i in test_ids]
self.test_ids = test_ids
self.source_lookup = {i: "test" for i in test_ids}
else:
raise Exception("Invalid dataset split.")
# subsampling
if self.n_samples is not None and self.n_samples < len(self.data):
self.data = random.sample(self.data, self.n_samples)
# class and example weighting
if self.split == "train" or self.split == "trainval":
labels = [self.encode_label(l[1]) for l in self.data]
self.class_weights = np.sum(labels, axis=0).astype(np.float32)
self.class_weights[self.class_weights == 0] = np.inf
self.class_weights = self.class_weights[
self.class_weights != np.inf].max() / self.class_weights
self.class_weights = self.class_weights / self.n_classes
self.example_weights = np.asarray(labels) * self.class_weights[
np.newaxis, :]
self.example_weights = np.sum(self.example_weights, axis=1)
self.class_weights = torch.tensor(self.class_weights,
dtype=torch.float32)
self.example_weights = torch.tensor(self.example_weights,
dtype=torch.float32)
# set the image normalization
p_mean = [
0.08033423981012082, 0.05155526791740866, 0.05359709020876417,
0.0811968791288488
]
p_std = [
0.1313705843029108, 0.08728413305330673, 0.13922084421796302,
0.12760922364487468
]
t_mean = [
0.05860568283679439, 0.04606191081626742, 0.03982708801568723,
0.06027994646558575
]
t_std = [
0.10238559670323068, 0.08069846376704155, 0.10501834094962233,
0.09908335311368136
]
e_mean = [
0.03775239471734739, 0.04191453443041034, 0.007705539179783242,
0.0942332991656135
]
e_std = [
0.05167756366610396, 0.061291035726105815, 0.019559849511340346,
0.13389048820718571
]
if self.image_channels == "g":
p_mean, p_std = p_mean[2], p_std[2]
t_mean, t_std = t_mean[2], t_std[2]
e_mean, e_std = e_mean[2], e_std[2]
elif self.image_channels == "rgb":
p_mean, p_std = p_mean[:3], p_std[:3]
t_mean, t_std = t_mean[:3], t_std[:3]
e_mean, e_std = e_mean[:3], e_std[:3]
elif self.image_channels == "rgby":
pass
else:
raise NotImplementedError("Unsupported image channels selection.")
self.primary_normalization = tfms.Normalize(mean=p_mean, std=p_std)
self.test_normalization = tfms.Normalize(mean=t_mean, std=t_std)
self.external_normalization = tfms.Normalize(mean=e_mean, std=e_std)
def training(model, fold, args):
# resore from last checkpoint
# all model weights resored, but not learning rate.
if os.path.exists(os.path.join(config.weights, config.model_name, str(fold), "checkpoint.pth.tar")):
best_model = torch.load(os.path.join(config.weights, config.model_name, str(fold), "checkpoint.pth.tar"))
model.load_state_dict(best_model["state_dict"])
# logging issues
log = Logger()
log.open(os.path.join(config.logs_dir, "%s_log_train.txt" % config.model_name), mode="a")
log.write(
"\n---------------------------- [START %s] %s\n\n" % (datetime.now().strftime('%Y-%m-%d %H:%M:%S'), '-' * 20))
log.write(
'----------------------|--------- Train ---------|-------- Valid ---------|-------Best '
'Results-------|----------|\n')
log.write(
'mode iter epoch | loss f1_macro | loss f1_macro | loss f1_macro | time '
' |\n')
log.write(
'----------------------------------------------------------------------------------------------------------'
'----\n')
# training params
optimizer = optim.SGD(model.parameters(),
lr=config.learning_rate_start,
momentum=0.9,
weight_decay=config.weight_decay)
if config.loss_name == 'ce':
criterion = nn.BCEWithLogitsLoss().cuda()
elif config.loss_name == 'focal':
criterion = FocalLoss().cuda()
elif config.loss_name == 'f1':
criterion = F1Loss().cuda()
else:
raise ValueError('unknown loss name {}'.format(config.loss_name))
best_results = [np.inf, 0]
val_metrics = [np.inf, 0]
scheduler = lr_scheduler.StepLR(optimizer,
step_size=config.learning_rate_decay_epochs,
gamma=config.learning_rate_decay_rate)
start = timer()
# load dataset
all_files = pd.read_csv(config.train_csv)
image_names = all_files['Id']
labels_strs = all_files['Target']
image_labels = []
for cur_label_str in labels_strs:
cur_label = np.eye(config.num_classes, dtype=np.float)[np.array(list(map(int, cur_label_str.split(' '))))].sum(axis=0)
image_labels.append(cur_label)
image_labels = np.stack(image_labels, axis=0)
msss = MultilabelStratifiedShuffleSplit(n_splits=1, test_size=config.val_percent, random_state=0)
for train_index, val_index in msss.split(image_names, image_labels):
train_image_names = image_names[train_index]
train_image_labels = image_labels[train_index]
val_image_names = image_names[val_index]
val_image_labels = image_labels[val_index]
train_gen = HumanDataset(train_image_names, train_image_labels, config.train_dir, mode="train")
sampler = WeightedRandomSampler(weights=get_sample_weights()[train_index], num_samples=int(len(all_files)*(1-config.val_percent)))
train_loader = DataLoader(train_gen, batch_size=config.batch_size, pin_memory=True, num_workers=4, sampler=sampler)
# train_loader = DataLoader(train_gen, batch_size=config.batch_size, shuffle=True, pin_memory=True, num_workers=4)
val_gen = HumanDataset(val_image_names, val_image_labels, config.train_dir, augument=False, mode="train")
val_loader = DataLoader(val_gen, batch_size=config.batch_size, shuffle=False, pin_memory=True, num_workers=4)
# train
for epoch in range(0, config.epochs):
# training & evaluating
scheduler.step(epoch)
get_learning_rate(optimizer)
train_metrics = train(train_loader, model, criterion, optimizer, epoch, val_metrics, best_results, start)
val_metrics = evaluate(val_loader, model, criterion, epoch, train_metrics, best_results, start)
# check results
is_best_loss = val_metrics[0] < best_results[0]
best_results[0] = min(val_metrics[0], best_results[0])
is_best_f1 = val_metrics[1] > best_results[1]
best_results[1] = max(val_metrics[1], best_results[1])
# save model
save_checkpoint({
"epoch": epoch + 1,
"model_name": config.model_name,
"state_dict": model.state_dict(),
"best_loss": best_results[0],
"optimizer": optimizer.state_dict(),
"fold": fold,
"best_f1": best_results[1],
}, is_best_loss, is_best_f1, fold)
# print logs
print('\r', end='', flush=True)
log.write(
logging_pattern % (
"best", epoch, epoch,
train_metrics[0], train_metrics[1],
val_metrics[0], val_metrics[1],
str(best_results[0])[:8], str(best_results[1])[:8],
time_to_str((timer() - start), 'min')
)
)
log.write("\n")
time.sleep(0.01)
"Label"].str.split("|").apply(lambda x: [int(i) for i in x])
train_kaggle_public = pd.concat([train_df, public_hpa_df_except16_0],
ignore_index=True,
sort=False)
mlb = MultiLabelBinarizer()
X = train_kaggle_public['ID']
y = train_kaggle_public['Label'].str.split("|").apply(
lambda x: [int(i) for i in x])
df_ohe = pd.DataFrame(mlb.fit_transform(y), columns=mlb.classes_)
df_ohe_np = df_ohe.to_numpy()
msss = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=0.1,
random_state=0)
for train_index, test_index in msss.split(X, df_ohe_np):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
train_data = HPADataset(X_train,
y_train,
mode="train",
tfms=get_transforms(data_type='train'))
test_data = HPADataset(X_test,
y_test,
mode="test",
tfms=get_transforms(data_type='valid'))
full_data = HPADataset(X,
def get_dataflow(is_train=True):
train_df = pd.read_csv(os.path.join('/data/kaggle/HPA', 'train.csv'))
#train_df = oversample(train_df)
labels = [[int(i) for i in s.split()] for s in train_df['Target']]
fnames = train_df['Id'].tolist()
fnames = [os.path.join(config.TRAIN_DATASET, f) for f in fnames]
sprase_label = [
np.eye(config.NUM_CLASS, dtype=np.float)[np.array(la)].sum(axis=0)
for la in labels
]
extra_df = pd.read_csv(
os.path.join('/data/kaggle/HPA',
'HPAv18RGBY_WithoutUncertain_wodpl.csv'))
#extra_df = oversample(extra_df)
extra_labels = [[int(i) for i in s.split()] for s in extra_df['Target']]
extra_labels = [
np.eye(config.NUM_CLASS, dtype=np.float)[np.array(la)].sum(axis=0)
for la in extra_labels
]
extra_fnames = extra_df['Id'].tolist()
extra_fnames = [
os.path.join(config.EXTRA_DATASET, f) for f in extra_fnames
]
fnames = fnames + extra_fnames
sprase_label = sprase_label + extra_labels
fnames = np.array(fnames)
sprase_label = np.array(sprase_label)
msss = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=0.15,
random_state=42)
for train_index, test_index in msss.split(fnames, sprase_label):
x_train, x_test = fnames[train_index], fnames[test_index]
y_train, y_test = sprase_label[train_index], sprase_label[test_index]
holdout_data = list(zip(x_test, y_test))
# 5 fold the rest
mskf = MultilabelStratifiedKFold(n_splits=5, random_state=1)
for fold_num, (train_index,
test_index) in enumerate(mskf.split(x_train, y_train)):
if fold_num == config.FOLD:
foldx_train, foldx_test = x_train[train_index], x_train[test_index]
foldy_train, foldy_test = y_train[train_index], y_train[test_index]
break
train_data = list(zip(foldx_train, foldy_train))
val_data = list(zip(foldx_test, foldy_test))
train_data = oversample_2(train_data)
pseudo_df = pd.read_csv(os.path.join('/data/kaggle/HPA', 'LB623.csv'))
pseudo_fnames = pseudo_df['Id'].tolist()
pseudo_fnames = [
os.path.join(config.TEST_DATASET, f) for f in pseudo_fnames
]
#pseudo_labels = np.load("./SOTA.npy")
#pseudo_labels = [np.array(_) for _ in pseudo_labels]
pseudo_labels = [[int(i) for i in s.split()]
for s in pseudo_df['Predicted']]
pseudo_labels = [
np.eye(config.NUM_CLASS, dtype=np.float)[np.array(la)].sum(axis=0)
for la in pseudo_labels
]
pseudo_data = list(zip(pseudo_fnames, pseudo_labels))
train_data = train_data + pseudo_data
print("train: ", len(train_data), len(val_data))
if not is_train:
return val_data
ds = DataFromList(train_data, shuffle=True)
ds = BatchData(MapData(ds, preprocess), config.BATCH)
ds = PrefetchDataZMQ(ds, 6)
return ds
def get_dataflow(is_train=True):
train_df = pd.read_csv(os.path.join('/data/kaggle/HPA', 'train.csv'))
labels = [[int(i) for i in s.split()] for s in train_df['Target']]
binary_label = []
for la in labels:
if MODEL_LABEL in la:
binary_label.append([1])
else:
binary_label.append([0])
fnames = train_df['Id'].tolist()
fnames = [os.path.join(config.TRAIN_DATASET, f) for f in fnames]
sprase_label = [
np.eye(config.NUM_CLASS, dtype=np.float)[np.array(la)].sum(axis=0)
for la in binary_label
]
if config.EXTRA:
extra_df = pd.read_csv(
os.path.join('/data/kaggle/HPA', 'HPAv18RBGY_wodpl.csv'))
extra_labels = [[int(i) for i in s.split()]
for s in extra_df['Target']]
binary_label = []
for la in labels:
if MODEL_LABEL in la:
binary_label.append([1])
else:
binary_label.append([0])
extra_labels = [
np.eye(config.NUM_CLASS, dtype=np.float)[np.array(la)].sum(axis=0)
for la in binary_label
]
extra_fnames = extra_df['Id'].tolist()
extra_fnames = [
os.path.join(config.EXTRA_DATASET, f) for f in extra_fnames
]
fnames = fnames + extra_fnames
sprase_label = sprase_label + extra_labels
# extra_data = list(zip(extra_fnames, extra_labels))
fnames = np.array(fnames)
sprase_label = np.array(sprase_label)
print(fnames.shape[0])
msss = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=0.1,
random_state=42)
for train_index, test_index in msss.split(fnames, sprase_label):
x_train, x_test = fnames[train_index], fnames[test_index]
y_train, y_test = sprase_label[train_index], sprase_label[test_index]
train_data = list(zip(x_train, y_train))
val_data = list(zip(x_test, y_test))
if not is_train:
return val_data
ds = DataFromList(train_data, shuffle=True)
ds = BatchData(MapData(ds, preprocess), config.BATCH)
ds = PrefetchDataZMQ(ds, 6)
return ds
input_shape=(HEIGHT, WIDTH, 3))
x = base_model.output
x = Dropout(0.125)(x)
y_pred = Dense(6, activation='sigmoid')(x)
return Model(inputs=base_model.input, outputs=y_pred)
# In[16]:
# Submission Placeholder
submission_predictions_b2 = []
# Multi Label Stratified Split stuff
msss = MultilabelStratifiedShuffleSplit(n_splits=20,
test_size=TEST_SIZE,
random_state=SEED)
X = train_df.index
Y = train_df.Label.values
# Get train and test index
msss_splits = next(msss.split(X, Y))
train_idx = msss_splits[0]
valid_idx = msss_splits[1]
# In[17]:
# Loop through Folds of Multi Label Stratified Split
#for epoch, msss_splits in zip(range(0, 9), msss.split(X, Y)):
# # Get train and test index
# train_idx = msss_splits[0]
'vowel': 0.30,
'consonant': 0.30
},
metrics={
'root': ['accuracy', tf.keras.metrics.Recall()],
'vowel': ['accuracy', tf.keras.metrics.Recall()],
'consonant': ['accuracy',
tf.keras.metrics.Recall()]
})
# Model Summary
print(model.summary())
# Multi Label Stratified Split stuff...
msss = MultilabelStratifiedShuffleSplit(n_splits=EPOCHS,
test_size=TEST_SIZE,
random_state=SEED)
# CustomReduceLRonPlateau function
best_val_loss = np.Inf
def CustomReduceLRonPlateau(model, history, epoch):
global best_val_loss
# ReduceLR Constants
monitor = 'val_root_loss'
patience = 5
factor = 0.75
min_lr = 1e-5
def get_loaders(path: str,
image_size: int,
n_splits: int = 1,
test_size: float = 0.1,
batch_size: int = 128,
num_workers: int = 4,
external: bool = False,
use_sampler: bool = False) -> Tuple[AttrDict, List[AttrDict]]:
df = pd.read_csv(f'{path}/train.csv')
df_external = pd.read_csv(f'{path}/external.csv')
X = np.array(df.Id)
y = np.array(
[HumanProteinDataset.parse_target(target) for target in df.Target])
msss = MultilabelStratifiedShuffleSplit(n_splits=1,
test_size=test_size,
random_state=42)
train, valid = list(msss.split(X, y))[0]
df_train, df_valid = df.iloc[train], df.iloc[valid]
df_test = pd.read_csv(f'{path}/sample_submission.csv')
if external:
df_train = pd.concat([df_train, df_external], axis=0)
transforms_train, transforms_test, transforms_test_aug = _get_transforms(
image_size)
dataset_train = HumanProteinDataset(df_train,
f'{path}/train',
transforms=transforms_train)
dataset_train_test = HumanProteinDataset(df_train,
f'{path}/train',
transforms=transforms_test)
dataset_train_aug = HumanProteinDataset(df_train,
f'{path}/train',
transforms=transforms_test_aug)
dataset_valid = HumanProteinDataset(df_valid,
f'{path}/train',
transforms=transforms_test)
dataset_valid_aug = HumanProteinDataset(df_valid,
f'{path}/train',
transforms=transforms_test_aug)
dataset_test = HumanProteinDataset(df_test,
f'{path}/test',
train_mode=False,
transforms=transforms_test)
dataset_test_aug = HumanProteinDataset(df_test,
f'{path}/test',
train_mode=False,
transforms=transforms_test_aug)
default_loaders = AttrDict()
default_loaders.train = DataLoader(dataset_train,
batch_size,
num_workers=num_workers)
default_loaders.train_test = DataLoader(dataset_train_test,
batch_size,
num_workers=num_workers)
default_loaders.train_aug = DataLoader(dataset_train_aug,
batch_size,
num_workers=num_workers)
default_loaders.valid = DataLoader(dataset_valid,
batch_size,
pin_memory=True,
num_workers=num_workers)
default_loaders.valid_aug = DataLoader(dataset_valid_aug,
batch_size,
pin_memory=True,
num_workers=num_workers)
default_loaders.test = DataLoader(dataset_test,
batch_size,
pin_memory=True,
num_workers=num_workers)
default_loaders.test_aug = DataLoader(dataset_test_aug,
batch_size,
pin_memory=True,
num_workers=num_workers)
if n_splits == 1:
sampler = _get_sampler(df_train) if use_sampler else None
loaders = AttrDict()
loaders.train = DataLoader(dataset_train,
batch_size,
not use_sampler,
sampler,
num_workers=num_workers)
loaders.valid = default_loaders.valid
loaders.valid_aug = default_loaders.valid_aug
return default_loaders, [loaders]
folds = []
for train, valid in _k_fold(df_train, n_splits):
fold_train, fold_valid = df_train.iloc[train], df_train.iloc[valid]
dataset_train = HumanProteinDataset(fold_train,
f'{path}/train',
transforms=transforms_train)
dataset_valid = HumanProteinDataset(fold_valid,
f'{path}/train',
transforms=transforms_test)
dataset_valid_aug = HumanProteinDataset(fold_valid,
f'{path}/train',
transforms=transforms_test_aug)
sampler = _get_sampler(fold_train) if use_sampler else None
loaders = AttrDict()
loaders.train = DataLoader(dataset_train,
batch_size,
not use_sampler,
sampler,
num_workers=num_workers)
loaders.valid = DataLoader(dataset_valid,
batch_size,
pin_memory=True,
num_workers=num_workers)
loaders.valid_aug = DataLoader(dataset_valid_aug,
batch_size,
pin_memory=True,
num_workers=num_workers)
folds.append(loaders)
return default_loaders, folds
unique_classes = df["class_id"].unique()
# %% --------------------
one_hot_labels = []
for img_id in unique_image_ids:
classes = df[df["img_id"] == img_id]["class_id"].values
x = np.eye(len(unique_classes))[classes.astype(int)].sum(0)
one_hot_labels.append(x)
one_hot_labels = np.array(one_hot_labels)
# %% --------------------
n_splits = 3
# mskf = MultilabelStratifiedKFold(n_splits=n_splits, shuffle=True, random_state=2021)
mskf = MultilabelStratifiedShuffleSplit(n_splits=n_splits,
train_size=0.5,
test_size=0.5,
random_state=2021)
# %% --------------------
train_df = pd.DataFrame()
val_df = pd.DataFrame()
# %% --------------------
# X is unique image_id
for fold, (train_index,
val_index) in enumerate(mskf.split(unique_image_ids,
one_hot_labels)):
train_data = df[df["img_id"].isin(
unique_image_ids[train_index])].copy(deep=True)
val_data = df[df["img_id"].isin(
unique_image_ids[val_index])].copy(deep=True)
def main():
df = pd.read_csv("data.csv", sep=";")
model = get_model(IMG_SIZE)
model.compile(
optimizer=Adam(lr=0.016),
loss={"season": 'mean_absolute_error'},
loss_weights={"season": 1},
metric={"season": ["mean_absolute_error",
tf.keras.metrics.Recall()]})
# Model summary
print(model.summary())
# Multi Label Stratified Split stuff...
msss = MultilabelStratifiedShuffleSplit(n_splits=EPOCHS,
test_size=TEST_SIZE)
X_train = df["PATH"].values
y_columns = [x for x in df.columns if x.startswith("SEASON")]
Y_train = df[y_columns].to_numpy()
for epoch, msss_split in zip(range(EPOCHS), msss.split(X_train, Y_train)):
print('=========== EPOCH {}'.format(epoch))
train_ids = msss_split[0]
valid_ids = msss_split[1]
print('Train Length: {0} First 10 indices: {1}'.format(
len(train_ids), train_ids[:]))
print('Valid Length: {0} First 10 indices: {1}'.format(
len(valid_ids), valid_ids[:]))
train_df = df.loc[train_ids]
X_train_data = train_df["PATH"].values
y_columns = [x for x in train_df.columns if x.startswith("SEASON")]
Y_train_data = train_df[y_columns].to_numpy()
data_generator_train = TrainDataGenerator(X_train_data,
Y_train_data,
train_ids,
batch_size=16,
img_size=IMG_SIZE)
valid_df = df.loc[valid_ids]
X_valid_data = valid_df["PATH"].values
Y_valid_data = valid_df[y_columns].to_numpy()
data_generator_val = TrainDataGenerator(X_valid_data,
Y_valid_data,
valid_ids,
batch_size=16,
img_size=IMG_SIZE)
TRAIN_STEPS = int(len(data_generator_train))
VALID_STEPS = int(len(data_generator_val))
print('Train Generator Size: {0}'.format(len(data_generator_train)))
print('Validation Generator Size: {0}'.format(len(data_generator_val)))
model.fit_generator(generator=data_generator_train,
validation_data=data_generator_val,
steps_per_epoch=TRAIN_STEPS,
validation_steps=VALID_STEPS,
epochs=1,
callbacks=[
ModelCheckpointFull(RUN_NAME + 'model_' +
str(epoch) + '.h5')
],
verbose=1)
# Set and Concat Training History
temp_history = model.history.history
if epoch == 0:
history = temp_history
else:
for k in temp_history:
history[k] = history[k] + temp_history[k]
# Custom ReduceLRonPlateau
CustomReduceLRonPlateau(model, history, epoch)
# Cleanup
del data_generator_train, data_generator_val, train_ids, valid_ids
gc.collect()
