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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
# method to write the predictions in the proper format
def writePredictions(predictions, file_path):
d = {0: "BE", 1: "CA", 2: "CH", 3: "FR"}
preds = [d[elem] for elem in predictions]
df = pd.DataFrame(preds, columns=["Country"])
df.to_csv(file_path, index=False)
if __name__ == "__main__":
# Data directory
model_file="svm_model.joblib"
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")
# Test data
clf = joblib.load(model_file)
# Test/Predict
print("TEST data:")
y_pred_test = clf.predict(test_features)
print(confusion_matrix(test_labels, y_pred_test))
print(classification_report(test_labels, y_pred_test, digits=6, target_names=["BE", "CA", "CH" ,"FR"]))
# Validation data
print("VAL data:")
y_pred_val = clf.predict(val_features)
print(confusion_matrix(val_labels, y_pred_val))
print(classification_report(val_labels, y_pred_val, digits=6, target_names=["BE", "CA", "CH" ,"FR"]))
writePredictions(y_pred_test, os.path.join(".", "svm_preds_test.csv"))
writePredictions(y_pred_val, os.path.join(".", "svm_preds_val.csv"))
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