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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,37 @@ | ||
| input: | ||
| kaggle1: | ||
| path: data/all/kaggle1 | ||
| classes: | ||
| goodjs: | ||
| dist: 0.1 | ||
| kaggle2: | ||
| path: data/all/kaggle2 | ||
| classes: | ||
| badjs: | ||
| dist: 0.1 | ||
| misc1: | ||
| path: data/all/misc1 | ||
| classes: | ||
| goodjs: | ||
| dist: 0.7 | ||
| badjs: | ||
| dist: 0.6 | ||
| misc2: | ||
| path: data/all/misc2 | ||
| classes: | ||
| goodjs: | ||
| dist: 0.5 | ||
| badjs: | ||
| dist: 0.6 | ||
| misc3: | ||
| path: data/all/misc3 | ||
| classes: | ||
| badjs: | ||
| dist: 0.1 | ||
| packt: | ||
| path: data/all/packt | ||
| classes: | ||
| goodjs: | ||
| dist: 0.7 | ||
| badjs: | ||
| dist: 0.6 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,123 @@ | ||
| import argparse | ||
| import os | ||
|
|
||
| import pandas as pd | ||
| import yaml | ||
| from sklearn.model_selection import train_test_split | ||
|
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||
| TRAIN_SET = "train_set.csv" | ||
| TEST_SET = "test_set.csv" | ||
| VAL_SET = "valid_set.csv" | ||
|
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| def get_files_from_subdir(dir_path): | ||
| return [ | ||
| os.path.join(dir_path, fname) for fname in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, fname)) | ||
| ] | ||
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| def traverse_path(path): | ||
| good_files = [] | ||
| bad_files = [] | ||
|
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| goodjs_dir = os.path.join(path, "goodjs") | ||
| badjs_dir = os.path.join(path, "badjs") | ||
|
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| if not os.path.exists(goodjs_dir) and not os.path.exists(badjs_dir): | ||
| print("No goodjs or badjs directory found in the path") | ||
|
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| if os.path.exists(goodjs_dir): | ||
| goodjs_files = get_files_from_subdir(goodjs_dir) | ||
| good_files.extend(goodjs_files) | ||
|
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| if os.path.exists(badjs_dir): | ||
| badjs_files = get_files_from_subdir(badjs_dir) | ||
| bad_files.extend(badjs_files) | ||
|
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| return good_files, bad_files | ||
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| def sample_files(good_files, bad_files, good_sample_size, bad_sample_size): | ||
| import random | ||
|
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| good_sample = random.sample(good_files, good_sample_size) | ||
| bad_sample = random.sample(bad_files, bad_sample_size) | ||
|
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| return good_sample, bad_sample | ||
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| def main(): | ||
| parser = argparse.ArgumentParser(description="Split text data into train and test sets") | ||
| parser.add_argument("-c", "--config", help="Path to config", required=True, type=str) | ||
| parser.add_argument( | ||
| "-o", "--output", help="Output directory to contain the train and test set", required=True, type=str | ||
| ) | ||
| parser.add_argument( | ||
| "-ts", "--train-size", help="Size of the training set from sample (0.0 to 1.0)", type=float, default=0.8 | ||
| ) | ||
|
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| args = parser.parse_args() | ||
|
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| path = args.config | ||
|
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| with open(path) as f: | ||
| config = yaml.safe_load(f) | ||
| input_conf = config["input"] | ||
|
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| output_dir = args.output | ||
|
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| total_good = 0 | ||
| total_bad = 0 | ||
| all_good_samples = [] | ||
| all_bad_samples = [] | ||
| for dir, conf in input_conf.items(): | ||
| dist_zero = {"dist": 0.0} | ||
| dir_path = conf["path"] | ||
| # print(dir_path) | ||
| goodjs_dist = float(conf["classes"].get("goodjs", dist_zero)["dist"]) | ||
| badjs_dist = float(conf["classes"].get("badjs", dist_zero)["dist"]) | ||
|
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| good_files, bad_files = traverse_path(dir_path) | ||
| total_good += len(good_files) | ||
| total_bad += len(bad_files) | ||
| good_samples, bad_samples = sample_files( | ||
| good_files, bad_files, int(goodjs_dist * len(good_files)), int(badjs_dist * len(bad_files)) | ||
| ) | ||
| all_good_samples.extend(good_samples) | ||
| all_bad_samples.extend(bad_samples) | ||
|
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| print("Total good files : ", total_good) | ||
| print("Total bad files : ", total_bad) | ||
| print("# of good samples: ", len(all_good_samples)) | ||
| print("# of bad samples : ", len(all_bad_samples)) | ||
|
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| all_files = all_good_samples + all_bad_samples | ||
| labels = ["goodjs"] * len(all_good_samples) + ["badjs"] * len(all_bad_samples) | ||
|
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| X_train, X_test, y_train, y_test = train_test_split(all_files, labels, test_size=0.2, random_state=42) | ||
| X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=42) | ||
|
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| print("Train size : ", len(X_train)) | ||
| print("Test size : ", len(X_test)) | ||
| print("Val size : ", len(X_val)) | ||
|
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| train = pd.DataFrame({"file": X_train, "label": y_train}) | ||
| test = pd.DataFrame({"file": X_test, "label": y_test}) | ||
| val = pd.DataFrame({"file": X_val, "label": y_val}) | ||
|
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| train_path = os.path.join(output_dir, TRAIN_SET) | ||
| test_path = os.path.join(output_dir, TEST_SET) | ||
| val_path = os.path.join(output_dir, VAL_SET) | ||
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| train.to_csv(train_path, index=False) | ||
| test.to_csv(test_path, index=False) | ||
| val.to_csv(val_path, index=False) | ||
| print(f"Train set size : {len(train)}") | ||
| print(f"Test set size : {len(test)}") | ||
| print(f"Val set size : {len(val)}") | ||
|
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| print(f"Output : [{train_path}, {test_path}, {val_path}]") | ||
|
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|
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| if __name__ == "__main__": | ||
| main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,13 @@ | ||
| #!/bin/bash | ||
|
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| # Define the directory you want to analyze | ||
| directory_to_analyze="data/all/" | ||
|
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| # Use find to locate all subdirectories | ||
| subdirs=($(find "$directory_to_analyze" -type d)) | ||
|
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| # Iterate through subdirectories and count files | ||
| for subdir in "${subdirs[@]}"; do | ||
| file_count=$(find "$subdir" -type f | wc -l) | ||
| echo "$subdir: $file_count files" | ||
| done |