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calculate f1 score for single label index #1

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110 changes: 110 additions & 0 deletions include/graph/preprocessing.h
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@ class LabelIndex {
virtual void Insert(NodeId node, std::vector<Label> label) = 0;
virtual void Load(const std::string& path) = 0;
virtual double PrecisionScore(torch::Tensor y_pred, torch::Tensor y_true) const = 0;
virtual double F1Score(torch::Tensor y_pred, torch::Tensor y_true, const std::string& option) const = 0;
};

class MultiLabelBinarizer: public LabelIndex {
Expand Down Expand Up @@ -77,6 +78,10 @@ class MultiLabelBinarizer: public LabelIndex {

}

virtual double F1Score(torch::Tensor y_pred, torch::Tensor y_true, const std::string& option) {

}

protected:
std::vector<std::vector<Label>> labels_;
IndexLookupTable index_;
Expand Down Expand Up @@ -125,6 +130,59 @@ class MultiLabelIndex : public MultiLabelBinarizer {
return n_right * 1.0 / n_total;
}

virtual double F1Score(torch::Tensor y_pred, torch::Tensor y_true, const std::string& option) const override {
int n_class = y_pred.size(1);
auto y = y_pred.argmax(1).to(torch::TensorOptions().dtype(torch::kInt64));
int64_t true_negatives = 0;
int64_t true_positives = 0;
int64_t false_negatives = 0;
int64_t false_positives = 0;
double precision = 0;
double recall = 0;
double average_f1score = 0;
for (int64_t i = 0; i < n_class; i++)
{
auto this_class_mask = torch::full(y.sizes(), i, torch::TensorOptions().dtype(torch::kInt64));
auto positive_label_mask = y_true.eq(this_class_mask).to(torch::TensorOptions().dtype(torch::kInt64));
auto mask = torch::ones(positive_label_mask.sizes(), torch::TensorOptions().dtype(torch::kInt64));
auto negative_label_mask = torch::sub(mask, positive_label_mask);

auto correct_null_predictions = torch::mul(torch::ne(y, this_class_mask).to(torch::TensorOptions().dtype(torch::kInt64)), negative_label_mask);
double tmp_true_negatives = torch::mul(correct_null_predictions, mask).sum().item().toInt();
true_negatives += tmp_true_negatives;
//std::cout << "true_neg=" << tmp_true_negatives << std::endl;

auto correct_non_null_predictions = torch::mul(torch::eq(y, this_class_mask).to(torch::TensorOptions().dtype(torch::kInt64)), positive_label_mask);
double tmp_true_positives = torch::mul(correct_non_null_predictions, mask).sum().item().toInt();
true_positives += tmp_true_positives;
//std::cout << "true_pos=" << tmp_true_positives << std::endl;

auto incorrect_null_predictions = torch::mul(torch::ne(y, this_class_mask).to(torch::TensorOptions().dtype(torch::kInt64)), positive_label_mask);
double tmp_false_negatives = torch::mul(incorrect_null_predictions, mask).sum().item().toInt();
false_negatives += tmp_false_negatives;
//std::cout << "false_neg=" << tmp_false_negatives << std::endl;

auto incorrect_non_null_predictions = torch::mul(torch::eq(y, this_class_mask).to(torch::TensorOptions().dtype(torch::kInt64)), negative_label_mask);
double tmp_false_positives = torch::mul(incorrect_non_null_predictions, mask).sum().item().toInt();
false_positives += tmp_false_positives;
//std::cout << "false_pos=" << tmp_false_positives << std::endl;

if (option == "macro") {
precision = double(tmp_true_positives) / (tmp_true_positives + tmp_false_positives + 1e-13);
recall = double(tmp_true_positives) / (tmp_true_positives + tmp_false_negatives + 1e-13);
average_f1score += 2 * recall * precision / (recall + precision + 1e-13);
}
}
if (option == "macro") {
return average_f1score;
}
else {
precision = double(true_positives) / (true_positives + false_positives + 1e-13);
recall = double(true_positives) / (true_positives + false_negatives + 1e-13);
return 2 * recall * precision / (recall + precision + 1e-13);
}
}

private:
Label current_class_;
};
Expand Down Expand Up @@ -174,6 +232,58 @@ class SingleLabelIndex : public LabelIndex {
return n_right * 1.0 / n_total;
}

virtual double F1Score(torch::Tensor y_pred, torch::Tensor y_true, const std::string& option) const override {
int n_class = y_pred.size(1);
auto y = y_pred.argmax(1).to(torch::TensorOptions().dtype(torch::kInt64));
int64_t true_negatives = 0;
int64_t true_positives = 0;
int64_t false_negatives = 0;
int64_t false_positives = 0;
double precision = 0;
double recall = 0;
double average_f1score = 0;
for (int64_t i = 0; i < n_class; i++)
{
auto this_class_mask = torch::full(y.sizes(), i, torch::TensorOptions().dtype(torch::kInt64));
auto positive_label_mask = y_true.eq(this_class_mask).to(torch::TensorOptions().dtype(torch::kInt64));
auto mask = torch::ones(positive_label_mask.sizes(), torch::TensorOptions().dtype(torch::kInt64));
auto negative_label_mask = torch::sub(mask, positive_label_mask);

auto correct_null_predictions = torch::mul(torch::ne(y, this_class_mask).to(torch::TensorOptions().dtype(torch::kInt64)), negative_label_mask);
double tmp_true_negatives = torch::mul(correct_null_predictions, mask).sum().item().toInt();
true_negatives += tmp_true_negatives;
//std::cout << "true_neg=" << tmp_true_negatives << std::endl;

auto correct_non_null_predictions = torch::mul(torch::eq(y, this_class_mask).to(torch::TensorOptions().dtype(torch::kInt64)), positive_label_mask);
double tmp_true_positives = torch::mul(correct_non_null_predictions, mask).sum().item().toInt();
true_positives += tmp_true_positives;
//std::cout << "true_pos=" << tmp_true_positives << std::endl;

auto incorrect_null_predictions = torch::mul(torch::ne(y, this_class_mask).to(torch::TensorOptions().dtype(torch::kInt64)), positive_label_mask);
double tmp_false_negatives = torch::mul(incorrect_null_predictions, mask).sum().item().toInt();
false_negatives += tmp_false_negatives;
//std::cout << "false_neg=" << tmp_false_negatives << std::endl;

auto incorrect_non_null_predictions = torch::mul(torch::eq(y, this_class_mask).to(torch::TensorOptions().dtype(torch::kInt64)), negative_label_mask);
double tmp_false_positives = torch::mul(incorrect_non_null_predictions, mask).sum().item().toInt();
false_positives += tmp_false_positives;
//std::cout << "false_pos=" << tmp_false_positives << std::endl;

if (option == "macro") {
precision = double(tmp_true_positives) / (tmp_true_positives + tmp_false_positives + 1e-13);
recall = double(tmp_true_positives) / (tmp_true_positives + tmp_false_negatives + 1e-13);
average_f1score += 2 * recall * precision / (recall + precision + 1e-13);
}
}
if (option == "macro") {
return average_f1score;
}
else {
precision = double(true_positives) / (true_positives + false_positives + 1e-13);
recall = double(true_positives) / (true_positives + false_negatives + 1e-13);
return 2 * recall * precision / (recall + precision + 1e-13);
}
}

private:
std::unordered_map<NodeId, Label> labels_;
Expand Down