def export_pred_csv(prediction):
print(line, "export prediction as predicted_score.csv in local directory", line)
submission = pd.read_csv(join_path("data", "prediction_template.csv"), index_col="PassengerId")
submission["Survived"] = prediction
print(submission.shape)
submission.head()
submission.to_csv(join_path("data", "random_forest_submission.csv"))
print(arrow, "Success !", arrow)
def plot_cf_report(cf_report, title='Classification report ', with_avg_total=False, cmap=plt.cm.Blues):
lines = cf_report.split('\n')
classes = []
plotMat = []
for line in lines[2:4]:
t = line.split()
print()
classes.append(t[0])
v = [float(x) for x in t[1: len(t) - 1]]
print(v)
plotMat.append(v)
if with_avg_total:
aveTotal = lines[len(lines) - 1].split()
classes.append('avg/total')
vAveTotal = [float(x) for x in t[1:len(aveTotal) - 1]]
plotMat.append(vAveTotal)
plt.imshow(plotMat, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
x_tick_marks = np.arange(3)
y_tick_marks = np.arange(len(classes))
plt.xticks(x_tick_marks, ['precision', 'recall', 'f1-score'], rotation=45)
plt.yticks(y_tick_marks, classes)
plt.tight_layout()
plt.ylabel('Classes')
plt.xlabel('Measures')
print(cf_report)
plt.savefig(join_path("report", "classification_report.png"))
def export_pkl(model, file_name):
print(line, 'Save RandomForest model as', file_name + '.pkl', line)
# Dump the trained random forest classifier with Pickle
filename = file_name + '.pkl'
rf_pkl_file = join_path('model', filename)
# Open the file to save the pkl file
rf_model_pkl = open(rf_pkl_file, 'wb')
pickle.dump(model, rf_model_pkl)
# Close the pickle instances
rf_model_pkl.close()
print(arrow, "Success !", arrow)
def csv_cf_report(report):
report_data = []
lines = report.split('\n')
for line in lines[2:4]:
row = {}
row_data = line.split(' ')
row['class'] = row_data[0]
row['precision'] = float(row_data[1])
row['recall'] = float(row_data[2])
row['f1_score'] = float(row_data[3])
row['support'] = float(row_data[4])
report_data.append(row)
cf_df = pd.DataFrame.from_dict(report_data)
cf_df.to_csv(join_path("report", "classification_report.csv"), index=False)
def load_model():
random_forest_model_pkl = open(join_path("model", "random_forest_model.pkl"), 'rb')
random_forest_model = pickle.load(random_forest_model_pkl)
print("Loaded Random Forest Model: ", random_forest_model)
return random_forest_model