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@@ -116,7 +116,11 @@ def build_input(df, seq_norm, p_chanels):
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# check if sequence normalization is used
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if seq_norm:
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+ print('Starting data normalization\n')
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if final_arr.ndim > 2:
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+
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+ n_counter = 0
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+
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n, s, f, h, w = final_arr.shape
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for index, seq in enumerate(final_arr):
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@@ -127,6 +131,11 @@ def build_input(df, seq_norm, p_chanels):
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for y in range(h):
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final_arr[index][:, i, x, y] = utils.normalize_arr_with_range(seq[:, i, x, y])
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+
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+ # update progress
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+ n_counter += 1
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+ write_progress(n_counter / float(total_samples))
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+
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return final_arr
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@@ -248,11 +257,25 @@ def main():
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print('---- Loading dataset.... ----')
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print('-----------------------------\n')
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+ n_train_samples = len(final_df_train.index)
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+ n_test_samples = len(final_df_train.index)
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+
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+ total_samples = n_train_samples
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+ n_counter = 0
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+
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+ print('Loading train dataset\n')
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# split dataset into X_train, y_train, X_test, y_test
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X_train_all = final_df_train.loc[:, 1:].apply(lambda x: x.astype(str).str.split('::'))
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X_train_all = build_input(X_train_all, p_seq_norm, p_chanels)
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y_train_all = final_df_train.loc[:, 0].astype('int')
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+ total_samples = n_test_samples
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+ n_counter = 0
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+
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+ X_test = final_df_test.loc[:, 1:].apply(lambda x: x.astype(str).str.split('::'))
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+ X_test = build_input(X_test, p_seq_norm, p_chanels)
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+ y_test = final_df_test.loc[:, 0].astype('int')
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+
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input_shape = (X_train_all.shape[1], X_train_all.shape[2], X_train_all.shape[3], X_train_all.shape[4])
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@@ -328,14 +351,6 @@ def main():
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acc_train = accuracy_score(y_train, y_train_predict)
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acc_val = accuracy_score(y_val, y_val_predict)
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- # remove unused variables
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- del X_train
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- del y_train
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-
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- X_test = final_df_test.loc[:, 1:].apply(lambda x: x.astype(str).str.split('::'))
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- X_test = build_input(X_test, p_seq_norm, p_chanels)
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- y_test = final_df_test.loc[:, 0].astype('int')
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-
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y_test_predict = model.predict(X_test, batch_size=1, verbose=1)
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y_test_predict = [ 1 if l > 0.5 else 0 for l in y_test_predict ]
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