78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_graphviz, DecisionTreeRegressor
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from sklearn.externals.six import StringIO
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from IPython.display import Image
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import pandas as pd
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import numpy as np
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import pydotplus
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from sklearn.model_selection import train_test_split
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from sklearn import metrics
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PACKAGE_PLACE_TRESHOLD = {
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"normal": 0.8,
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"freezed": 0.85,
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"fragile": 0.85,
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"flammable": 0.9,
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"keep_dry": 0.8
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}
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class PackageLocationClassifier():
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def __init__(self):
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data = StringIO()
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cols_names = ["product", "category", "temperature", "humidity", "chance_of_survive", "place_here"]
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feature_cols = ["category", "temperature", "humidity"]
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products = pd.read_csv("package_location_classifier/trainset/trainset.csv", header=0, sep=",", names=cols_names)
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testset = pd.read_csv("package_location_classifier/testset/testset.csv", header=None, sep=",", names=cols_names)
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products = products.round({"chance_of_survive": 1})
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testset = testset.round({"chance_of_survive": 1})
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products.chance_of_survive *= 10
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testset.chance_of_survive *= 10
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test_X = pd.get_dummies(testset[feature_cols])
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test_y = testset.chance_of_survive
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products = products.sample(frac=1)
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X_train = pd.get_dummies(products[feature_cols])
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y_train = products.chance_of_survive
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dummies_names = X_train.columns.tolist()
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# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=1, shuffle=True)
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clf = DecisionTreeRegressor(ccp_alpha=0.02, min_samples_leaf=5, max_depth=5)
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self.predictor = clf.fit(X_train, y_train)
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y_pred = self.predictor.predict(test_X)
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evaluation = pd.DataFrame({'category': testset.category, 'temperature': testset.temperature , 'humid': testset.humidity ,'Actual': test_y, 'Predicted': y_pred})
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evaluation['Prediction_diff'] = abs(evaluation['Actual'] - evaluation['Predicted'])
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print("Prediction differs from actual value by average {}".format(round(evaluation['Prediction_diff'].mean(), 2)))
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# export_graphviz(clf, out_file=data, filled=True, rounded=True, special_characters=True, feature_names=dummies_names)
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# graph = pydotplus.graph_from_dot_data(data.getvalue())
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# graph.write_png('Drzewo.png')
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# Image(graph.create_png())
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def check_if_can_place(self, package, tile):
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category = package.category
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cat_treshold = PACKAGE_PLACE_TRESHOLD[category]
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fields = [[
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tile.air_temperature,
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tile.humidity,
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category == "flammable",
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category == "fragile",
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category=="freezed" ,
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category == "keep_dry",
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category == "normal"
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]]
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quality_of_place = round(self.predictor.predict(fields)[0]/10, 2)
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# print("{} - dopasowanie {}".format(package,quality_of_place))
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# pdb.set_trace()
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if quality_of_place > cat_treshold:
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return True
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return False
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if __name__ == '__main__':
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cfer = PackageLocationClassifier() |