46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
from tensorflow.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_model3')
|
|
|
|
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() |