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yssymmt/aud10_cosine_similarity.ipynb

Created September 23, 2022 18:47
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aud10_cosine_similarity.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "69b3e813",
"metadata": {},
"source": [
"#10_cosine_similarity"
]
},
{
"cell_type": "markdown",
"id": "d28d7b7b",
"metadata": {},
"source": [
"####パッケージの読み込み"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "26b3970a",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sqlalchemy import create_engine\n",
"import teradatasql\n",
"import teradatasqlalchemy\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.metrics.pairwise import cosine_similarity,cosine_distances"
]
},
{
"cell_type": "markdown",
"id": "775f0be5",
"metadata": {},
"source": [
"####Teradataへの接続、sqlalchemy エンジンを作成"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "35d3eb81",
"metadata": {},
"outputs": [],
"source": [
"host = \"192.168.999.999\"\n",
"user = \"jumbo\"\n",
"password = \"mambo\"\n",
"connstr = \"teradatasql://{user}:{password}@{host}\".format(host=host, user=user, password=password)\n",
"engine = create_engine(connstr)"
]
},
{
"cell_type": "markdown",
"id": "f97f8c9e",
"metadata": {},
"source": [
"####データの取得"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c4b1f2a4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>docid</th>\n",
" <th>word</th>\n",
" <th>cat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>若槻千夏 幾つ テレビ 番組 司会 務める 本番 以外 人見知り 話す ない</td>\n",
" <td>若林</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>漫才 ツッコミ 担当 たりないふたり ボケ 担当</td>\n",
" <td>若林</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>ナナメ 夕暮れ 他 本 出す</td>\n",
" <td>若林</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>深夜 一人 バスケットボール スリーポイント 練習</td>\n",
" <td>若林</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>プライベート バスケットボール 足 怪我</td>\n",
" <td>若林</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>星野源 日本 テレビ 界 希望 思う</td>\n",
" <td>若林</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>藤井青銅 ピンク ベスト じゃない方 しゃべれる</td>\n",
" <td>若林</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>mc waka 日本武道館 横浜アリーナ 人 歌 ラップ 茶々 入れる</td>\n",
" <td>若林</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>茶々 名前 チワワ 犬 飼う</td>\n",
" <td>春日</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>結婚 直前 浮気 ばれる</td>\n",
" <td>春日</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>六本木 社長 モンクレール ダウン もらう</td>\n",
" <td>春日</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>ピンク ベスト 着る 胸 張る トゥース 大声 叫ぶ</td>\n",
" <td>春日</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>ピンク セーター 着る 後輩 芸人 すいません ピンク 着 もらう 挨拶</td>\n",
" <td>春日</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>漫才 ボケ 担当 ラジオ テレビ ボケ ない</td>\n",
" <td>春日</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>普段 靴下 履く ない 足 裏 象 よう</td>\n",
" <td>春日</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>バカリズム 存在 面白い ウケる スベる ない</td>\n",
" <td>春日</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>山里亮太 ツッコミ 敵わ ない 思う</td>\n",
" <td>若林</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>入船 出身 築地 出身 嘘 地元 人 お前 入船 ツッコミ</td>\n",
" <td>若林</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>ぼる塾 人 トゥース 掛け合い 面白い</td>\n",
" <td>春日</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>スベる 芸風 スベる 怖い 思う</td>\n",
" <td>春日</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" docid word cat\n",
"0 1 若槻千夏 幾つ テレビ 番組 司会 務める 本番 以外 人見知り 話す ない 若林\n",
"1 2 漫才 ツッコミ 担当 たりないふたり ボケ 担当 若林\n",
"2 3 ナナメ 夕暮れ 他 本 出す 若林\n",
"3 4 深夜 一人 バスケットボール スリーポイント 練習 若林\n",
"4 5 プライベート バスケットボール 足 怪我 若林\n",
"5 6 星野源 日本 テレビ 界 希望 思う 若林\n",
"6 7 藤井青銅 ピンク ベスト じゃない方 しゃべれる 若林\n",
"7 8 mc waka 日本武道館 横浜アリーナ 人 歌 ラップ 茶々 入れる 若林\n",
"8 9 茶々 名前 チワワ 犬 飼う 春日\n",
"9 10 結婚 直前 浮気 ばれる 春日\n",
"10 11 六本木 社長 モンクレール ダウン もらう 春日\n",
"11 12 ピンク ベスト 着る 胸 張る トゥース 大声 叫ぶ 春日\n",
"12 13 ピンク セーター 着る 後輩 芸人 すいません ピンク 着 もらう 挨拶 春日\n",
"13 14 漫才 ボケ 担当 ラジオ テレビ ボケ ない 春日\n",
"14 15 普段 靴下 履く ない 足 裏 象 よう 春日\n",
"15 16 バカリズム 存在 面白い ウケる スベる ない 春日\n",
"16 17 山里亮太 ツッコミ 敵わ ない 思う 若林\n",
"17 18 入船 出身 築地 出身 嘘 地元 人 お前 入船 ツッコミ 若林\n",
"18 19 ぼる塾 人 トゥース 掛け合い 面白い 春日\n",
"19 20 スベる 芸風 スベる 怖い 思う 春日"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with engine.connect() as conn:\n",
" train = pd.read_sql(\"\"\"\n",
" select * from jumbo.aud11_denorm order by 1 \n",
" \"\"\", conn)\n",
"train"
]
},
{
"cell_type": "markdown",
"id": "12e8b15e",
"metadata": {},
"source": [
"####TfidfVectorizer(最小文字1件、一文字も対象に含める)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7c701546",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TfidfVectorizer(norm=None, smooth_idf=False, token_pattern='(?u)\\\\b\\\\w+\\\\b')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vectorizer = TfidfVectorizer(min_df=1, token_pattern='(?u)\\\\b\\\\w+\\\\b', norm=None, smooth_idf=False)\n",
"vectorizer.fit(train['word'])"
]
},
{
"cell_type": "markdown",
"id": "5fa7e13a",
"metadata": {},
"source": [
"####抽出単語の確認"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a843eb0a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['mc', 'waka', 'お前', 'しゃべれる', 'じゃない方', 'すいません', 'たりないふたり', 'ない',\n",
" 'ばれる', 'ぼる塾', 'もらう', 'よう', 'ウケる', 'スベる', 'スリーポイント', 'セーター', 'ダウン',\n",
" 'チワワ', 'ツッコミ', 'テレビ', 'トゥース', 'ナナメ', 'バカリズム', 'バスケットボール', 'ピンク',\n",
" 'プライベート', 'ベスト', 'ボケ', 'モンクレール', 'ラジオ', 'ラップ', '一人', '人', '人見知り',\n",
" '他', '以外', '入れる', '入船', '六本木', '出す', '出身', '務める', '叫ぶ', '司会', '名前',\n",
" '嘘', '地元', '夕暮れ', '大声', '存在', '履く', '山里亮太', '希望', '幾つ', '張る', '後輩',\n",
" '怖い', '思う', '怪我', '担当', '挨拶', '掛け合い', '敵わ', '日本', '日本武道館', '星野源',\n",
" '普段', '本', '本番', '横浜アリーナ', '歌', '浮気', '深夜', '漫才', '犬', '界', '番組',\n",
" '直前', '着', '着る', '社長', '築地', '結婚', '練習', '胸', '芸人', '芸風', '若槻千夏',\n",
" '茶々', '藤井青銅', '裏', '話す', '象', '足', '面白い', '靴下', '飼う'], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vectorizer.get_feature_names_out()"
]
},
{
"cell_type": "markdown",
"id": "261b8fb6",
"metadata": {},
"source": [
"####ベクトルに変換"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c156ae3a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<20x97 sparse matrix of type '<class 'numpy.float64'>'\n",
"\twith 123 stored elements in Compressed Sparse Row format>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_tfidf = vectorizer.transform(train['word'])\n",
"train_tfidf"
]
},
{
"cell_type": "markdown",
"id": "9f9d7f10",
"metadata": {},
"source": [
"####データの確認"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4ef18bf5",
"metadata": {},
"outputs": [
{
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" <th>話す</th>\n",
" <th>象</th>\n",
" <th>足</th>\n",
" <th>面白い</th>\n",
" <th>靴下</th>\n",
" <th>飼う</th>\n",
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"<p>20 rows × 97 columns</p>\n",
"</div>"
],
"text/plain": [
" mc waka お前 しゃべれる じゃない方 すいません たりないふたり \\\n",
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"\n",
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"\n",
" 話す 象 足 面白い 靴下 飼う \n",
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"\n",
"[20 rows x 97 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_tfidf_df = pd.DataFrame(train_tfidf.toarray(),columns=vectorizer.get_feature_names_out())\n",
"train_tfidf_df"
]
},
{
"cell_type": "markdown",
"id": "236fb3fb",
"metadata": {},
"source": [
"####コサイン類似度の計算"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "69c74a85",
"metadata": {},
"outputs": [
{
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},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"result=cosine_similarity(train_tfidf)\n",
"result"
]
},
{
"cell_type": "markdown",
"id": "2fe86dc1",
"metadata": {},
"source": [
"####docid列を抽出"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c7c0a288",
"metadata": {},
"outputs": [
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"text/plain": [
" docid\n",
"0 1\n",
"1 2\n",
"2 3\n",
"3 4\n",
"4 5\n",
"5 6\n",
"6 7\n",
"7 8\n",
"8 9\n",
"9 10\n",
"10 11\n",
"11 12\n",
"12 13\n",
"13 14\n",
"14 15\n",
"15 16\n",
"16 17\n",
"17 18\n",
"18 19\n",
"19 20"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfcosdocid = pd.DataFrame(train,columns=['docid'])\n",
"dfcosdocid"
]
},
{
"cell_type": "markdown",
"id": "0ee0b7c1",
"metadata": {},
"source": [
"####docid列を数値型から文字型に変換し、横に展開し、リストに変換"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "30671b6d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['1',\n",
" '2',\n",
" '3',\n",
" '4',\n",
" '5',\n",
" '6',\n",
" '7',\n",
" '8',\n",
" '9',\n",
" '10',\n",
" '11',\n",
" '12',\n",
" '13',\n",
" '14',\n",
" '15',\n",
" '16',\n",
" '17',\n",
" '18',\n",
" '19',\n",
" '20']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfcosdocid_str = dfcosdocid['docid'].astype(str)\n",
"a_dfcosdocid=dfcosdocid_str.values.T.tolist()\n",
"a_dfcosdocid"
]
},
{
"cell_type": "markdown",
"id": "9a43884c",
"metadata": {},
"source": [
"####データフレームに変換"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0ab781ba",
"metadata": {},
"outputs": [
{
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]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfcos = pd.DataFrame(result)\n",
"dfcos"
]
},
{
"cell_type": "markdown",
"id": "c8ac63bc",
"metadata": {},
"source": [
"####列名を追加"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "835f4877",
"metadata": {},
"outputs": [
{
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},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfcos.columns = a_dfcosdocid\n",
"dfcos"
]
},
{
"cell_type": "markdown",
"id": "46241681",
"metadata": {},
"source": [
"####docidを結合"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f5d00260",
"metadata": {},
"outputs": [
{
"data": {
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" <td>0.061854</td>\n",
" <td>0.072815</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.052206</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.067105</td>\n",
" <td>0.061854</td>\n",
" <td>1.000000</td>\n",
" <td>0.088898</td>\n",
" <td>0.000000</td>\n",
" <td>0.159339</td>\n",
" <td>0.274167</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.061457</td>\n",
" <td>0.120072</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.126701</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.078996</td>\n",
" <td>0.072815</td>\n",
" <td>0.088898</td>\n",
" <td>1.000000</td>\n",
" <td>0.078824</td>\n",
" <td>0.000000</td>\n",
" <td>0.124183</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.000000</td>\n",
" <td>0.061357</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.050789</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.078824</td>\n",
" <td>1.000000</td>\n",
" <td>0.073761</td>\n",
" <td>0.000000</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.093006</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.135044</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.159339</td>\n",
" <td>0.000000</td>\n",
" <td>0.073761</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.102003</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.274167</td>\n",
" <td>0.124183</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>20 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" 1 2 3 4 5 6 7 8 \\\n",
"0 1.000000 0.000000 0.0 0.000000 0.000000 0.074405 0.000000 0.000000 \n",
"1 0.000000 1.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 1.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"3 0.000000 0.000000 0.0 1.000000 0.172056 0.000000 0.000000 0.000000 \n",
"4 0.000000 0.000000 0.0 0.172056 1.000000 0.000000 0.000000 0.000000 \n",
"5 0.074405 0.000000 0.0 0.000000 0.000000 1.000000 0.000000 0.000000 \n",
"6 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 1.000000 0.000000 \n",
"7 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 1.000000 \n",
"8 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.110181 \n",
"9 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"10 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"11 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.229794 0.000000 \n",
"12 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.166546 0.000000 \n",
"13 0.114768 0.588903 0.0 0.000000 0.000000 0.095640 0.000000 0.000000 \n",
"14 0.042761 0.000000 0.0 0.000000 0.140333 0.000000 0.000000 0.000000 \n",
"15 0.052206 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"16 0.061457 0.120072 0.0 0.000000 0.000000 0.126701 0.000000 0.000000 \n",
"17 0.000000 0.061357 0.0 0.000000 0.000000 0.000000 0.000000 0.050789 \n",
"18 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.093006 \n",
"19 0.000000 0.000000 0.0 0.000000 0.000000 0.102003 0.000000 0.000000 \n",
"\n",
" 9 10 ... 12 13 14 15 16 \\\n",
"0 0.000000 0.0 ... 0.000000 0.000000 0.114768 0.042761 0.052206 \n",
"1 0.000000 0.0 ... 0.000000 0.000000 0.588903 0.000000 0.000000 \n",
"2 0.000000 0.0 ... 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"3 0.000000 0.0 ... 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"4 0.000000 0.0 ... 0.000000 0.000000 0.000000 0.140333 0.000000 \n",
"5 0.000000 0.0 ... 0.000000 0.000000 0.095640 0.000000 0.000000 \n",
"6 0.000000 0.0 ... 0.229794 0.166546 0.000000 0.000000 0.000000 \n",
"7 0.110181 0.0 ... 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"8 1.000000 0.0 ... 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"9 0.000000 1.0 ... 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"10 0.000000 0.0 ... 0.000000 0.102587 0.000000 0.000000 0.000000 \n",
"11 0.000000 0.0 ... 1.000000 0.219827 0.000000 0.000000 0.000000 \n",
"12 0.000000 0.0 ... 0.219827 1.000000 0.000000 0.000000 0.000000 \n",
"13 0.000000 0.0 ... 0.000000 0.000000 1.000000 0.054964 0.067105 \n",
"14 0.000000 0.0 ... 0.000000 0.000000 0.054964 1.000000 0.061854 \n",
"15 0.000000 0.0 ... 0.000000 0.000000 0.067105 0.061854 1.000000 \n",
"16 0.000000 0.0 ... 0.000000 0.000000 0.078996 0.072815 0.088898 \n",
"17 0.000000 0.0 ... 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"18 0.000000 0.0 ... 0.135044 0.000000 0.000000 0.000000 0.159339 \n",
"19 0.000000 0.0 ... 0.000000 0.000000 0.000000 0.000000 0.274167 \n",
"\n",
" 17 18 19 20 docid \n",
"0 0.061457 0.000000 0.000000 0.000000 1 \n",
"1 0.120072 0.061357 0.000000 0.000000 2 \n",
"2 0.000000 0.000000 0.000000 0.000000 3 \n",
"3 0.000000 0.000000 0.000000 0.000000 4 \n",
"4 0.000000 0.000000 0.000000 0.000000 5 \n",
"5 0.126701 0.000000 0.000000 0.102003 6 \n",
"6 0.000000 0.000000 0.000000 0.000000 7 \n",
"7 0.000000 0.050789 0.093006 0.000000 8 \n",
"8 0.000000 0.000000 0.000000 0.000000 9 \n",
"9 0.000000 0.000000 0.000000 0.000000 10 \n",
"10 0.000000 0.000000 0.000000 0.000000 11 \n",
"11 0.000000 0.000000 0.135044 0.000000 12 \n",
"12 0.000000 0.000000 0.000000 0.000000 13 \n",
"13 0.078996 0.000000 0.000000 0.000000 14 \n",
"14 0.072815 0.000000 0.000000 0.000000 15 \n",
"15 0.088898 0.000000 0.159339 0.274167 16 \n",
"16 1.000000 0.078824 0.000000 0.124183 17 \n",
"17 0.078824 1.000000 0.073761 0.000000 18 \n",
"18 0.000000 0.073761 1.000000 0.000000 19 \n",
"19 0.124183 0.000000 0.000000 1.000000 20 \n",
"\n",
"[20 rows x 21 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfcos_a = pd.merge(dfcos, dfcosdocid, how='inner', left_index=True, right_index=True)\n",
"dfcos_a "
]
},
{
"cell_type": "markdown",
"id": "da2993ba",
"metadata": {},
"source": [
"####縦持ちパラノイア"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "ded2ab75",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>docid1</th>\n",
" <th>docid2</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <td>1</td>\n",
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" <tr>\n",
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" <td>16</td>\n",
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" <tr>\n",
" <th>396</th>\n",
" <td>17</td>\n",
" <td>20</td>\n",
" <td>0.124183</td>\n",
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" <tr>\n",
" <th>397</th>\n",
" <td>18</td>\n",
" <td>20</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>398</th>\n",
" <td>19</td>\n",
" <td>20</td>\n",
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" <tr>\n",
" <th>399</th>\n",
" <td>20</td>\n",
" <td>20</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>400 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" docid1 docid2 score\n",
"0 1 1 1.000000\n",
"1 2 1 0.000000\n",
"2 3 1 0.000000\n",
"3 4 1 0.000000\n",
"4 5 1 0.000000\n",
".. ... ... ...\n",
"395 16 20 0.274167\n",
"396 17 20 0.124183\n",
"397 18 20 0.000000\n",
"398 19 20 0.000000\n",
"399 20 20 1.000000\n",
"\n",
"[400 rows x 3 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfcos_vertico = pd.melt(dfcos_a, id_vars='docid')\n",
"dfcos_vertico.columns =['docid1','docid2','score']\n",
"dfcos_vertico"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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