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train_signature_classifier.py
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train_signature_classifier.py
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import numpy as np
from signature_extractor.persister import save_model
from signature_extractor.datasets import dataset_loader as d_loader
from signature_extractor.feature import SignatureFeatureExtractor
from sklearn.utils import shuffle
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
def build_pipeline():
output_pipeline = Pipeline([
('features', SignatureFeatureExtractor()),
('clf', KNeighborsClassifier(weights='distance'))
])
return output_pipeline
def get_gridsearch_params():
n_neighbors = [2, 3, 5, 7, 10]
param_grid = {'clf__n_neighbors': n_neighbors}
return param_grid
def train_model(data, pipeline, parameters):
X_train, y_train_bin = data
grid_search = GridSearchCV(pipeline, parameters, scoring='f1', verbose=1, n_jobs=-1)
grid_search.fit(X_train, y_train_bin)
print("Best parameters set:")
best_parameters = grid_search.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
print("\t%s: %r" % (param_name, best_parameters[param_name]))
return grid_search.best_estimator_
def evaluate_model(model, data):
X_test, y_test = data
y_pred = model.predict(X_test)
score = classification_report(y_test, y_pred)
print("-" * 25)
print("Model Evaluation:")
print("Accuracy Score:", score)
print("-" * 25)
return score
def downsample_majority_class(X, y):
mask_sig = y == "sig"
X_sig, y_sig = X[mask_sig], y[mask_sig]
mask_oth = y == "other"
X_oth, y_oth = X[mask_oth], y[mask_oth]
X_oth, y_oth = shuffle(X_oth, y_oth)
X_oth, y_oth = X_oth[:len(X_sig)], y_oth[:len(y_sig)]
X_new = np.concatenate([X_sig, X_oth], axis=0)
y_new = np.concatenate([y_sig, y_oth], axis=0)
return shuffle(X_new, y_new)
if __name__ == '__main__':
print("Loading signatures dataset")
X, y = d_loader.load_signatures_dataset()
print("Downsampling majority class")
X, y = downsample_majority_class(X, y)
print("Preparing data for training")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=124)
label_binarizer = LabelBinarizer().fit(y)
print("X_train size:", len(X_train), "X_test size:", len(X_test))
print("Binarizing target label")
y_train_bin = label_binarizer.transform(y_train)
y_test_bin = label_binarizer.transform(y_test)
print(y_train[0], y_train_bin[0])
print("Building pipeline")
pipeline = build_pipeline()
print("Training classification model")
model = train_model((X_train, y_train_bin), pipeline, get_gridsearch_params())
print("Evaluating trained model")
_ = evaluate_model(model, (X_test, y_test_bin))
print("Saving model")
_ = save_model(model, "signature_model")