46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
from keras.models import Sequential, load_model
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import pandas as pd
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from sklearn.preprocessing import LabelEncoder
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from sklearn import metrics
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import math
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import numpy as np
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def write_list(names):
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with open('listfile.txt', 'w') as fp:
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fp.write("\n".join(str(item) for item in names))
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def get_x_y(data):
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lb = LabelEncoder()
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data = data.drop(["Location 1"], axis=1)
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data = data.drop(columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post", "CrimeDate", "Inside/Outside"], axis=1)
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for column_name in data.columns:
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data[column_name] = lb.fit_transform(data[column_name])
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x = data.drop('Weapon', axis=1)
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y = data['Weapon']
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return data, x, y
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def predict():
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model = load_model('baltimore_model3')
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train = pd.read_csv('baltimore_train.csv')
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baltimore_data_test = pd.read_csv('baltimore_test.csv')
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baltimore_data_test.columns = train.columns
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baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
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scores = model.evaluate(x_test, y_test)
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print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
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y_predicted = model.predict(x_test)
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y_predicted = np.argmax(y_predicted,axis=1)
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test_results = {}
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test_results['Weapon'] = model.evaluate(
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x_test,
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y_test, verbose=0)
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write_list(y_predicted)
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predict() |