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- import argparse
- import os
- import pickle
- import sys
- import dagshub
- import numpy as np
- import optuna
- import pandas as pd
- from sklearn.metrics import accuracy_score, f1_score
- from sklearn.model_selection import StratifiedKFold
- from sklearn.svm import SVC
- from common.tools import *
- parser = argparse.ArgumentParser()
- parser.add_argument("GRAPH_VER", help="version of the graph you want regex to label your CSV with", type=str)
- parser.add_argument("DATASET_PATH", help="path to your input CSV", type=str)
- args = parser.parse_args()
- GRAPH_VER = args.GRAPH_VER
- DATASET_PATH = args.DATASET_PATH
- MODEL_DIR = "../models/hyper_svm_regex_graph_v{}.sav".format(GRAPH_VER)
- TFIDF_DIR = "../models/tfidf_hyper_svm_graph_v{}.pickle".format(GRAPH_VER)
- TAGS_TO_PREDICT = get_graph_vertices(GRAPH_VER)
- EXPERIMENT_DATA_PATH = ".."
- CODE_COLUMN = "code_block"
- TARGET_COLUMN = "graph_vertex_id"
- RANDOM_STATE = 42
- N_TRIALS = 100
- MAX_ITER = 10000
- HYPERPARAM_SPACE = {
- "svm_c": (1e-1, 1e3),
- "tfidf_min_df": (1, 50),
- "tfidf_max_df": (0.2, 1.0),
- "svm_kernel": ["linear", "poly", "rbf"],
- "svm_degree": (2, 6), # in case of poly kernel
- }
- def cross_val_scores(kf, clf, X, y):
- f1s = []
- accuracies = []
- for i, (train_index, test_index) in enumerate(kf.split(X, y)):
- X_train, X_test = X[train_index], X[test_index]
- y_train, y_test = y[train_index], y[test_index]
- clf.fit(X_train, y_train)
- y_pred = clf.predict(X_test)
- f1s.append(f1_score(y_test, y_pred, average="weighted"))
- accuracies.append(accuracy_score(y_test, y_pred))
- f1s = np.array(f1s)
- accuracies = np.array(accuracies)
- return f1s.mean(), f1s.std(), accuracies.mean(), accuracies.std()
- class Objective:
- def __init__(self, df, kfold_params, svm_c, tfidf_min_df, tfidf_max_df, svm_kernel, svm_degree):
- self.kf = StratifiedKFold(**kfold_params)
- self.c_range = svm_c
- self.min_df_range = tfidf_min_df
- self.max_df_range = tfidf_max_df
- self.kernels = svm_kernel
- self.poly_degrees = svm_degree
- self.df = df
- def __call__(self, trial):
- tfidf_params = {
- "min_df": trial.suggest_int("tfidf__min_df", *self.min_df_range),
- "max_df": trial.suggest_loguniform("tfidf__max_df", *self.max_df_range),
- "smooth_idf": True,
- }
- code_blocks_tfidf = tfidf_fit_transform(self.df[CODE_COLUMN], tfidf_params)
- X, y = code_blocks_tfidf, self.df[TARGET_COLUMN].values
- svm_params = {
- "C": trial.suggest_loguniform("svm__C", *self.c_range),
- "kernel": trial.suggest_categorical("svm__kernel", self.kernels),
- "random_state": RANDOM_STATE,
- "max_iter": MAX_ITER,
- }
- if svm_params["kernel"] == "poly":
- svm_params["degree"] = trial.suggest_int("svm__degree", *self.poly_degrees)
- clf = SVC(**svm_params)
- f1_mean, _, _, _ = cross_val_scores(self.kf, clf, X, y)
- return f1_mean
- def select_hyperparams(df, kfold_params, tfidf_path, model_path):
- """
- Uses optuna to find hyperparams that maximize F1 score
- :param df: labelled dataset
- :param kfold_params: parameters for sklearn's KFold
- :param tfidf_dir: where to save trained tf-idf
- :return: dict with parameters and metrics
- """
- study = optuna.create_study(direction="maximize", study_name="svm with kernels")
- objective = Objective(df, kfold_params, **HYPERPARAM_SPACE)
- study.optimize(objective, n_trials=N_TRIALS)
- best_tfidf_params = {
- "smooth_idf": True,
- }
- best_svm_params = {
- "random_state": RANDOM_STATE,
- "max_iter": MAX_ITER,
- }
- for key, value in study.best_params.items():
- model_name, param_name = key.split("__")
- if model_name == "tfidf":
- best_tfidf_params[param_name] = value
- elif model_name == "svm":
- best_svm_params[param_name] = value
- code_blocks_tfidf = tfidf_fit_transform(df[CODE_COLUMN], best_tfidf_params, tfidf_path)
- X, y = code_blocks_tfidf, df[TARGET_COLUMN].values
- clf = SVC(**best_svm_params)
- f1_mean, f1_std, accuracy_mean, accuracy_std = cross_val_scores(objective.kf, clf, X, y)
- clf.fit(X, y)
- pickle.dump(clf, open(model_path, "wb"))
- metrics = dict(
- test_f1_score=f1_mean,
- test_accuracy=accuracy_mean,
- test_f1_std=f1_std,
- test_accuracy_std=accuracy_std,
- )
- return best_tfidf_params, best_svm_params, metrics
- if __name__ == "__main__":
- df = load_data(DATASET_PATH)
- print(df.columns)
- nrows = df.shape[0]
- print("loaded")
- kfold_params = {
- "n_splits": 9,
- "random_state": RANDOM_STATE,
- "shuffle": True,
- }
- data_meta = {
- "DATASET_PATH": DATASET_PATH,
- "nrows": nrows,
- "label": TAGS_TO_PREDICT,
- "model": MODEL_DIR,
- "script_dir": __file__,
- }
- metrics_path = os.path.join(EXPERIMENT_DATA_PATH, "metrics.csv")
- params_path = os.path.join(EXPERIMENT_DATA_PATH, "params.yml")
- with dagshub.dagshub_logger(metrics_path=metrics_path, hparams_path=params_path) as logger:
- print("selecting hyperparameters")
- tfidf_params, svm_params, metrics = select_hyperparams(df, kfold_params, TFIDF_DIR, MODEL_DIR)
- print("logging the results")
- logger.log_hyperparams({"data": data_meta})
- logger.log_hyperparams({"tfidf": tfidf_params})
- logger.log_hyperparams({"model": svm_params})
- logger.log_hyperparams({"kfold": kfold_params})
- logger.log_metrics(metrics)
- print("finished")
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