def predict(self, test_csv: str,
prediction_csv: str) -> (pd.DataFrame, Optional[np.float64]):
self.config["task"] = "predict"
self.config.tmp_dir = os.path.dirname(prediction_csv) + "/tmp"
os.makedirs(self.config.tmp_dir, exist_ok=True)
result = {
"line_id": [],
"prediction": [],
}
for X in pd.read_csv(test_csv,
encoding="utf-8",
low_memory=False,
dtype=self.config["dtype"],
parse_dates=self.config["parse_dates"],
chunksize=self.config["nrows"]):
result["line_id"] += list(X["line_id"])
preprocess(X, self.config)
result["prediction"] += list(predict(X, self.config))
result = pd.DataFrame(result)
result.to_csv(prediction_csv, index=False)
target_csv = test_csv.replace("test", "test-target")
if os.path.exists(target_csv):
score = validate(result, target_csv, self.config["mode"])
else:
score = None
return result, score
def predict(self, test_csv: str, prediction_csv: str) -> (pd.DataFrame, Optional[np.float64]):
self.config["task"] = "predict"
self.config.tmp_dir = os.path.dirname(prediction_csv) + "/tmp"
os.makedirs(self.config.tmp_dir, exist_ok=True)
df = read_df(test_csv, self.config)
result = {
"line_id": list(df["line_id"]),
"prediction": [],
}
def chunker(seq, size):
return (seq[pos:pos+size] for pos in range(0, len(seq), size))
for chunk in chunker(df, 100000):
X = chunk.copy()
preprocess(X, self.config)
result["prediction"] += list(predict(X, self.config))
result = pd.DataFrame(result)
result.sort_values("line_id", inplace=True)
result.to_csv(prediction_csv, index=False)
target_csv = test_csv.replace("test", "test-target")
if os.path.exists(target_csv):
score = validate(result, target_csv, self.config["mode"])
else:
score = None
return result, score
def predict(self, test_csv: str, prediction_csv: str) -> (pd.DataFrame, Optional[np.float64]):
self.config["task"] = "predict"
self.config.tmp_dir = os.path.dirname(prediction_csv) + "/tmp"
os.makedirs(self.config.tmp_dir, exist_ok=True)
result = {
"id": [],
"prediction": [],
}
for X in pd.read_csv(
test_csv,
encoding="utf-8",
low_memory=False,
dtype=self.config["dtype"],
parse_dates=self.config["parse_dates"],
chunksize=self.config["nrows"]
):
result["id"] += list(X["id"])
preprocess(X, self.config)
result["prediction"] += list(predict(X, self.config))
result = pd.DataFrame(result)
result.to_csv(prediction_csv, index=False)
return result
def predict(self, test_csv: str,
prediction_csv: str) -> (pd.DataFrame, Optional[np.float64]):
self.config["task"] = "predict"
self.config.tmp_dir = os.path.dirname(prediction_csv) + "/tmp"
os.makedirs(self.config.tmp_dir, exist_ok=True)
result = {"line_id": [], "prediction": []}
if 'holiday_detect' in self.config:
result["datetime"] = []
for X in pd.read_csv(test_csv,
encoding="utf-8",
low_memory=False,
dtype=self.config["dtype"],
parse_dates=self.config["parse_dates"],
chunksize=self.config["nrows"]):
result["line_id"] += list(X["line_id"])
if 'holiday_detect' in self.config:
dt_fea = self.config['holiday_detect']
result["datetime"] += list(X[dt_fea])
preprocess(X, self.config)
result["prediction"] += list(predict(X, self.config))
result = pd.DataFrame(result)
# post process for holiday
if 'holiday_detect' in self.config:
holiday = self.config['holiday']
for idx, row in result.iterrows():
dt = row['datetime']
dt_str = str(dt).split(' ')[0].strip()
if dt_str in holiday or dt.weekday() == 5 or dt.weekday() == 6:
result.loc[idx, 'prediction'] = 0
result.drop(["datetime"], axis=1, inplace=True)
result.to_csv(prediction_csv, index=False)
target_csv = test_csv.replace("test", "test-target")
if os.path.exists(target_csv):
score = validate(result, target_csv, self.config["mode"])
else:
score = None
return result, score
def predict(self, test_csv: str,
prediction_csv: str) -> (pd.DataFrame, Optional[np.float64]):
self.config["task"] = "predict"
self.config.tmp_dir = os.path.dirname(prediction_csv) + "/tmp"
os.makedirs(self.config.tmp_dir, exist_ok=True)
self.config["prediction_csv"] = prediction_csv
self.config["line_id"] = []
self.config["start_time"] = time.time()
result = {
"line_id": [],
"prediction": [],
}
X = pd.read_csv(
test_csv,
encoding="utf-8",
low_memory=False,
dtype=self.config["dtype"],
parse_dates=self.config["parse_dates"],
)
self.config["line_id"] = X["line_id"].values
result["line_id"] = (X["line_id"].values)
X = preprocess(X, self.config)
X = X[self.config["columns"]] # for right columns order
result["prediction"] = predict(X, self.config)
result = pd.DataFrame(result)
result.to_csv(prediction_csv, index=False)
target_csv = test_csv.replace("test", "test-target")
if os.path.exists(target_csv):
score = validate(result, target_csv, self.config["mode"],
self.config)
else:
score = None
return result, score
def main(
running_on_google_colab,
datadir,
rootdir,
outdir,
percent_of_data,
regression,
dataname,
tag,
train,
new_model,
callback_timeout,
epochs,
batch_size,
model_function,
use_fret_for_training,
exclude_alex_fret,
):
gpu_available = tf.test.is_gpu_available()
if new_model:
print("**Training new model**")
else:
print("**Training most recent model**")
rootdir = Path(rootdir)
if running_on_google_colab:
rootdir = "./gdrive/My Drive/Colab Notebooks/DeepFRET-Model"
rootdir = Path(rootdir)
outdir = rootdir.joinpath(outdir).expanduser()
datadir = rootdir.joinpath(datadir).expanduser()
X, labels = lib.utils.load_npz_data(
top_percentage=percent_of_data,
path=datadir,
set_names=("X_" + dataname, "y_" + dataname),
)
n_classes = len(np.unique(labels))
if not regression:
# Use labels as classification target
y = lib.ml.class_to_one_hot(labels, num_classes=n_classes)
y = lib.ml.smoothe_one_hot_labels(y, amount=0.05)
else:
# Use E_true column as regression target
y = np.expand_dims(X[..., 3], axis=-1)
if use_fret_for_training:
# Use E_raw column as input
X = np.expand_dims(X[..., 4], axis=-1)
X = X.clip(2, -2)
else:
X = X[..., 0:2] if exclude_alex_fret else X[..., 0:3]
X = lib.utils.sample_max_normalize_3d(X)
print("X: ", X.shape)
print("y: ", y.shape)
print("Splitting dataset...")
X_train, X_val, y_train, y_val = sklearn.model_selection.train_test_split(
X, y, test_size=0.2, random_state=1)
model_name = "{}_best_model.h5".format(dataname)
model = lib.model.get_model(
n_features=X.shape[-1],
n_classes=n_classes,
train=train,
new_model=new_model,
model_name=model_name,
model_path=outdir,
gpu=gpu_available,
tag=tag,
regression=regression,
model_function=model_function,
)
if tag is not None:
dataname += "_" + tag
model_name = model_name.replace("best_model", tag + "_best_model")
if train:
callbacks = lib.ml.generate_callbacks(patience=callback_timeout,
outdir=outdir,
name=dataname)
model.fit(
x=X_train,
y=y_train,
validation_data=(X_val, y_val),
epochs=epochs,
batch_size=batch_size,
callbacks=callbacks,
)
try:
lib.plotting.plot_losses(logpath=outdir,
outdir=outdir,
name=dataname)
except IndexError:
pass
# Convert final model to GPU
if gpu_available:
print("Converted model from GPU to CPU-compatible")
cpu_model = model_function(
gpu=False,
n_features=X.shape[-1],
regression=regression,
n_classes=n_classes,
)
lib.ml.gpu_model_to_cpu(
trained_gpu_model=model,
untrained_cpu_model=cpu_model,
outdir=outdir,
modelname=model_name,
)
print("Evaluating...")
y_pred = model.predict(X_val)
if not regression:
lib.plotting.plot_confusion_matrices(
y_target=y_val,
y_pred=y_pred,
y_is_binary=False,
targets_to_binary=[4, 5, 6, 7, 8],
outdir=outdir,
name=dataname,
)
num_workers=1)
print("Test set size: {}.".format(len(dataset)))
# Load model
resnet = torchvision.models.resnet18(pretrained=True)
if use_metadata:
model_base = torch.nn.Sequential(*list(resnet.children())[:-1])
model = MetadataModel(model_base, base_out_dim=512)
else:
resnet.fc = torch.nn.Linear(512, 2)
model = resnet
# model.to(lib.model.device)
model = load_model(model, weight_path)
results = predict(model, data_loader, use_metadata=use_metadata)
results = np.concatenate(results, axis=0)
test_csv = pd.read_csv(csv_path)
# test_csv = test_csv.loc[int(len(test_csv)*data_sample_size) ,:]
submission_csv = pd.DataFrame({
'image_name': test_csv['image_name'],
'target': results
})
print(submission_csv.head())
submission_csv.to_csv(Path(dirs.csv) / "submission.csv", index=False)
