94 строки
2.2 KiB
Plaintext
94 строки
2.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import defaultdict\n",
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"\n",
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"from load_dataset import load_dataset_from_path #, make_weights_for_balanced_classes\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_dir = r\"D:\\repos\\Data-Processing\\audio\\train\"\n",
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"test_dir = r\"D:\\repos\\Data-Processing\\audio\\test\"\n",
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"\n",
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"X_train, y_train = load_dataset_from_path(train_dir)\n",
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"X_test, y_test = load_dataset_from_path(test_dir)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"len(np.where(y_train[:,9] == 1)[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_weights(label_array, n_classes):\n",
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"# weight_dict = defaultdict(int)\n",
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" weights = []\n",
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" for i in range(n_classes):\n",
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" negs = len(np.where(label_array[:,i] == 0)[0])\n",
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" pos = len(np.where(label_array[:,i] == 1)[0])\n",
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" if pos > 0:\n",
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" weights.append(negs/pos)\n",
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" else:\n",
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" weights.append(0)\n",
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"# weight_dict[i] += len(np.where(label_array[:,i] == 1)[0])\n",
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"# weights = [0.] * n_classes\n",
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"# for k, v in weight_dict.items():\n",
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"# if v:\n",
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"# weights[k] = len(label_array) / v \n",
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"# return weights\n",
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" return weights"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"get_weights(y_train, 37)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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