ium_487197/ium_predict.py
Wojciech Lidwin f1f3d2647a Model fix
2023-05-11 19:45:43 +02:00

46 lines
1.3 KiB
Python

from keras.models import Sequential
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn import metrics
import math
import numpy as np
def write_list(names):
with open('listfile.txt', 'w') as fp:
fp.write("\n".join(str(item) for item in names))
def get_x_y(data):
lb = LabelEncoder()
data = data.drop(["Location 1"], axis=1)
data = data.drop(columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post", "CrimeDate", "Inside/Outside"], axis=1)
for column_name in data.columns:
data[column_name] = lb.fit_transform(data[column_name])
x = data.drop('Weapon', axis=1)
y = data['Weapon']
return data, x, y
def predict():
model = load_model('baltimore_model.h5')
train = pd.read_csv('baltimore_train.csv')
baltimore_data_test = pd.read_csv('baltimore_test.csv')
baltimore_data_test.columns = train.columns
baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
scores = model.evaluate(x_test, y_test)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
y_predicted = model.predict(x_test)
y_predicted = np.argmax(y_predicted,axis=1)
test_results = {}
test_results['Weapon'] = model.evaluate(
x_test,
y_test, verbose=0)
write_list(y_predicted)
predict()