diff --git a/mikhail_makarov/.idea/mikhail_makarov.iml b/mikhail_makarov/.idea/mikhail_makarov.iml
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diff --git a/mikhail_makarov/.idea/misc.xml b/mikhail_makarov/.idea/misc.xml
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diff --git a/mikhail_makarov/.idea/modules.xml b/mikhail_makarov/.idea/modules.xml
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diff --git a/mikhail_makarov/.idea/thriftCompiler.xml b/mikhail_makarov/.idea/thriftCompiler.xml
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diff --git a/mikhail_makarov/.idea/workspace.xml b/mikhail_makarov/.idea/workspace.xml
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+ 0.796
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+ ,
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+ data
+ Python
+ EXPRESSION
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+ float(len(inter)) / len(sample)
+ Python
+ EXPRESSION
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+ [n for n in negative if n.issuperset(inter)]
+ Python
+ EXPRESSION
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+ [len(n & inter) / len(n) for n in negative if n.issuperset(inter)]
+ Python
+ EXPRESSION
+
+
+ '{0:.2f} {1:.2f} {1:.3f}'.format(card, eps, acc)
+ Python
+ EXPRESSION
+
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+ '{0:.2f} {0:.2f} {1:.3f}'.format(card, eps, acc)
+ Python
+ EXPRESSION
+
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+ print('{0:.2f} {0:.2f} {1:.3f}'.format(card, eps, acc))
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+ EXPRESSION
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+ y
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+ EXPRESSION
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+ data.iloc[:, 0]
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diff --git a/mikhail_makarov/.ipynb_checkpoints/lazy-checkpoint.ipynb b/mikhail_makarov/.ipynb_checkpoints/lazy-checkpoint.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import copy\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "from sklearn.metrics import precision_score\n",
+ "from sklearn.metrics import recall_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def generators_naive(positive, negative, sample):\n",
+ " \"\"\"\n",
+ " If there is an intersection in positve class element\n",
+ " and sample and this intersection does not belong to\n",
+ " intersection of sample with element of negative class\n",
+ " then this positive element votes for this sample.\n",
+ " After that calculate proportion of votes in positive\n",
+ " and negative classes and make decision.\n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DATA_PATH = 'data/tic-tac/'\n",
+ "\n",
+ "# for cross-validation\n",
+ "K = 10\n",
+ "\n",
+ "TARGET_COL = ['V10', 'TARGET']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def prepare_data(data):\n",
+ " data = data.rename(columns={col: 'class' for col in TARGET_COL})\n",
+ " \n",
+ " data = data.replace(to_replace='positive', value=1)\n",
+ " data = data.replace(to_replace='negative', value=0)\n",
+ " \n",
+ " y = data['class']\n",
+ " data = data.drop(columns=['class'])\n",
+ " cols = data.columns\n",
+ " \n",
+ " one_hot_data = pd.concat([pd.get_dummies(data[col], prefix=col, prefix_sep='_') for col in cols], axis=1)\n",
+ " \n",
+ " return one_hot_data.astype(bool), y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
+ " | \n",
+ " V1_x | \n",
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+ " \n",
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\n",
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\n",
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+ " | 3 | \n",
+ " True | \n",
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+ "
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+ "