from __future__ import unicode_literals
import logging
import pandas as pd
from sklearn.externals import joblib
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.metrics.classification import (
recall_score, confusion_matrix,
precision_recall_fscore_support,
)
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.pipeline import Pipeline
from settings import DATASET_FILENAME, MODEL_FILENAME
logger = logging.getLogger('training')
def main():
""" Find the best model to fit the dataset and save it into file """
# create a GridSearch object to find the best fitting model
grid_search = new_grid_search()
# run the search algorithm
run_grid_search(grid_search)
# save the best fitting model into FS
save_search_results(grid_search)
def split_dataset():
""" Read and split dataset into train and test subsets """
df = pd.read_csv(DATASET_FILENAME, header=0)
X = df[df.columns[:-1]].as_matrix()
y = df[df.columns[-1]].as_matrix()
return train_test_split(X, y, test_size=0.2, random_state=42)
def new_grid_search():
""" Create new GridSearch obj with models pipeline """
pipeline = Pipeline([
# TODO some smart preproc can be added here
(u"clf", LogisticRegression(class_weight="balanced")),
])
search_params = {"clf__C": (1e-4, 1e-2, 1e0, 1e2, 1e4)}
return GridSearchCV(
estimator=pipeline,
param_grid=search_params,
scoring="recall_macro",
cv=10,
n_jobs=-1,
verbose=3,
)
def run_grid_search(grid_search, show_evaluation=True):
""" Run the GridSearch algorithm and compute evaluation metrics """
X_train, X_test, y_train, y_test = split_dataset()
grid_search.fit(X_train, y_train)
# for key, value in grid_search.cv_results_.items():
# print key, value
predictions = grid_search.predict(X_test)
if show_evaluation:
logger.debug("macro_recall: %s", recall_score(y_test, predictions, average="macro"))
logger.debug(precision_recall_fscore_support(y_test, predictions))
logger.debug(confusion_matrix(y_test, predictions))
def save_search_results(grid_search):
""" Serialize model into file """
joblib.dump(grid_search.best_estimator_, MODEL_FILENAME)
# then load it like this:
# clf = joblib.load(model_dump_filename)
if __name__ == "__main__":
main()
