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import os
import time
import joblib
import numpy as np
import pandas as pd
from sklearn.model_selection import GridSearchCV
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report, confusion_matrix
def labels_to_numeric(labels_df):
# 0 - BE, 1 - CA, 2 - CH, 3 - FR
labels_df["Country"] = labels_df["Country"].replace({'BE': 0})
labels_df["Country"] = labels_df["Country"].replace({'CA': 1})
labels_df["Country"] = labels_df["Country"].replace({'CH': 2})
labels_df["Country"] = labels_df["Country"].replace({'FR': 3})
print(np.array(labels_df.values).flatten())
return list(np.array(labels_df.values).flatten())
def load_data(data_dir, feats_fname, labels_fname, scope):
# Paths
feats_path = os.path.join(data_dir, feats_fname)
labels_path = os.path.join(data_dir, labels_fname)
# Load features
features = np.loadtxt(feats_path, delimiter=',')
print(scope, " features shape: ", features.shape)
# Load labels
labels_df = pd.read_csv(labels_path)
labels = labels_to_numeric(labels_df)
print(scope, " labels length: ", len(labels))
return features, labels
def fine_tune_svm(X_train, y_train, model_fname):
# Parameters
param_grid = {
'C': [0.0001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000],
}
# Initialize the classifier
clf = LinearSVC()#(probability=True)
# Metrics
metrics_list = ['accuracy', 'f1_macro', 'f1_weighted', \
'precision_macro', 'precision_weighted', \
'recall_macro', 'recall_weighted']
cv = GridSearchCV(clf, param_grid, cv=10, verbose=2, \
scoring=metrics_list, refit='accuracy', \
return_train_score=True)
start = time.time()
cv.fit(X_train, y_train)
end = time.time()
print("======> Elapsed time for training with one set of parameters: %.10f" % (end - start))
print("Best parameters: ", cv.best_params_)
print("Grid scores on development set: ")
for score_name in metrics_list:
print("mean_score %s is %s" % (score_name, str(cv.cv_results_['mean_test_' + score_name])))
# Save model
joblib.dump(cv, model_fname)
print(cv.best_estimator_)
return cv
if __name__ == "__main__":
# Data directory
data_dir = "../data/bert_embeddings/"
# Load the data
train_features, train_labels = load_data(data_dir, "train_embeddings.csv", "train_labels.txt", "Train")
#val_features, val_labels = load_data(data_dir, "val_embeddings.csv", "val_labels.txt", "Validation")
test_features, test_labels = load_data(data_dir, "test_embeddings.csv", "test_labels.txt", "Test")
# Fine tune
grid = fine_tune_svm(train_features, train_labels, "svm_model.joblib")
# Test
grid_preds = grid.predict(test_features)
print(confusion_matrix(test_labels, grid_preds))
print(classification_report(test_labels, grid_preds, digits=6, target_names=["BE", "CA", "CH" ,"FR"]))
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