def logOverallPerformance(classifier, features_test, labels_test):
performanceObject = getOverallPerformance(classifier, features_test,
labels_test)
print("ACCURACY SCORE:")
cPrint(performanceObject["accuracy_score"], "green")
def createModel(data):
#DATA INPUT - GAME DATA & USER DATA
profile_response = data["user_profile_json"]
games_response = data["games_json"]
# FEATURE SELECTION - SELECT FEATURES TO BE USED BY ADDING THEM TO THE features_to_use LIST
#features_to_use = ["appid","name","genres", "publishers", "developers", "hasMultiplayerSupport", "hasSingleplayerSupport", "hasOnlineMultiplayerSupport"]
features_to_use = ["appid","name","genres"]
logWithTitle("USED-FEATURES")
logCommaSeparatedList(features_to_use)
cPrint(str(len(features_to_use)) + " Features in Use", "green")
transformed_games = list(map(transformDBResultForTraining, games_response))
labels = labelByPredefinedLists(transformed_games, boring_games, interesting_games)
features = createFeaturesDataFrame(transformed_games, features_to_use)
#features = transformSimpleCategoricalDataForTraining(features, ["genres","tags"])
#features = transformComplexCategoricalFeatureForTraining(features, "genres")
features = transformComplexCategoricalFeatureForTraining(features, "genres")
#logWithTitle("All-LABELS", labels)
#logWithTitle("ALL-FEATURES", features)
#printSeperator()
#print(len(features))
features_train_indexes, features_test_indexes, labels_train, labels_test = model_selection.train_test_split(features.index, labels,test_size=0.30)
features_train_named = features.iloc[features_train_indexes]
features_test_named = features.iloc[features_test_indexes]
features_train = features_train_named.drop(["appid","name"], axis=1)
features_test = features_test_named.drop(["appid","name"], axis=1)
logWithTitle("DATA SHAPES")
print(features_train.shape, labels_train.shape, " : ", features_test.shape, labels_test.shape)
#logWithTitle("Train-Features", features_train)
#logWithTitle("Test-Features", features_test)
#logWithTitle("Train-Labels", labels_train)
#logWithTitle("Test-Labels", labels_test)
#for game in features_test:
# cPrint(game)
classifier = svm.SVC(kernel="rbf", gamma=1000, C=100.)
classifier.fit(features_train, labels_train)
#logWithTitle("PREDICTED LABELS", classifier.predict(features_test))
#logWithTitle("GROUNDTRUTH LABELS", labels_test)
logOverallPerformance(classifier, features_test, labels_test)
# save_classifier("./results/model/SVC.pickle", classifier)
visualizeClassificationReport(classifier, features_train, labels_train, features_test, labels_test)
predicted_labels = classifier.predict(features_test)
logNameOfPredictedGames(predicted_labels, features_test_named)
def getNameOfPredictedGames(predicted_labels, features_named):
interesting_games = []
boring_games = []
unknown_games = []
for index, prediction in enumerate(predicted_labels):
if (prediction == "interesting"):
interesting_games.append(features_named.iloc[index]["name"])
elif (prediction == "boring"):
boring_games.append(features_named.iloc[index]["name"])
else:
unknown_games.append(features_named.iloc[index]["name"])
cPrint(str(features_named.iloc[index]["name"]) +
" has no label. investigate further",
color="yellow")
return {
"interesting_games": interesting_games,
"boring_games": boring_games,
"unknown_games": unknown_games
}
def logNameOfPredictedGames(predicted_labels, features_named):
for index, prediction in enumerate(predicted_labels):
if (prediction == "interesting"):
cPrint(features_named.iloc[index]["name"], color="green")
elif (prediction == "boring"):
cPrint(features_named.iloc[index]["name"], color="red")
else:
cPrint(str(prediction) + " can't be found in any list",
color="yellow")
def logCrossValidatedOverallPerformance(classifier, features, labels):
def precisionInterestingScorer(labels_test, predicted_labels):
return classification_report(labels_test,
predicted_labels,
output_dict=True)["1"]["precision"]
def precisionBoringScorer(labels_test, predicted_labels):
return classification_report(labels_test,
predicted_labels,
output_dict=True)["0"]["precision"]
def recallInterestingScorer(labels_test, predicted_labels):
return classification_report(labels_test,
predicted_labels,
output_dict=True)["1"]["recall"]
def recallBoringScorer(labels_test, predicted_labels):
return classification_report(labels_test,
predicted_labels,
output_dict=True)["0"]["recall"]
scoring = {
'accuracy':
'accuracy',
'precision_interesting':
make_scorer(precisionInterestingScorer, greater_is_better=True),
'precision_boring':
make_scorer(precisionBoringScorer, greater_is_better=True),
'recall_interesting':
make_scorer(recallInterestingScorer, greater_is_better=True),
'recall_boring':
make_scorer(recallBoringScorer, greater_is_better=True)
}
folds = 10
scores = cross_validate(classifier,
features,
list(map(convertLabelToNumber, labels)),
scoring=scoring,
cv=folds,
return_train_score=False,
return_estimator=False)
logWithTitle("CROSS VALIDATED METRICS")
print("INTERESTING RECALL")
average_recall_interesting = calculateAverage(
scores["test_recall_interesting"])
cPrint(round(average_recall_interesting, 2), color="red")
print("INTERESTING PRECISION")
average_precision_interesting = calculateAverage(
scores["test_precision_interesting"])
cPrint(round(average_precision_interesting, 2), color="green")
#print(scores["test_precision_interesting"])
#print(numpy.var(scores["test_precision_interesting"]))
print("BORING RECALL")
average_recall_boring = calculateAverage(scores["test_recall_boring"])
cPrint(round(average_recall_boring, 2), color="red")
print("BORING PRECISION")
average_precision_boring = calculateAverage(
scores["test_precision_boring"])
cPrint(round(average_precision_boring, 2), color="green")
print("ACCURACY")
average_precision_boring = calculateAverage(scores["test_accuracy"])
cPrint(round(average_precision_boring, 2))