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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import pylab\n",
"import matplotlib\n",
"\n",
"%matplotlib inline\n",
"\n",
"np.random.seed(44)"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"feature_names = [\"alcohol_content\", \"bitterness\", \"darkness\"]\n",
"\n",
"beer_kinds = [\"pils\", \"pale ale\", \"stout\"]\n",
"\n",
"# centers of features\n",
"centers = {\"pils\": (4.5, 0.5, 1,),\n",
" \"pale ale\": (4.5, 0.6, 2),\n",
" \"stout\": (5, .3, 5)}\n",
"\n",
"# std deviations of features:\n",
"deviations = {\"pils\": (.3, .2, .5),\n",
" \"pale ale\": (.5, .1, .5),\n",
" \"stout\": (.3, .3, 1)}\n",
"\n",
"\n",
"\n",
"# feature fruitiness is redundant:\n",
"feature_names.append(\"fruitiness\")\n",
"\n",
"def sample_features(kind):\n",
" means = centers[kind]\n",
" stddevs = deviations[kind]\n",
" features = [max(0.0, m + s * np.random.randn()) for (m, s) in zip(means, stddevs)]\n",
" # fruitiness correlates with hop and negatively with darkness:\n",
" fruitiness = 0.5 * (features[1] - .01 * features[2] + 0.06 * np.random.randn())\n",
" features.append(max(0, fruitiness))\n",
" return features"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[4.274815584823238, 0.763271464942364, 1.6230700143217152, 0.3253729101618156]"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_features(\"pils\")"
]
},
{
"cell_type": "code",
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"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>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>fruitiness</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>300.000000</td>\n",
" <td>300.000000</td>\n",
" <td>300.000000</td>\n",
" <td>300.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.705936</td>\n",
" <td>0.466646</td>\n",
" <td>2.587510</td>\n",
" <td>0.221585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.448071</td>\n",
" <td>0.225136</td>\n",
" <td>1.741583</td>\n",
" <td>0.116405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>3.073993</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4.421718</td>\n",
" <td>0.287010</td>\n",
" <td>1.192515</td>\n",
" <td>0.137466</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>4.714367</td>\n",
" <td>0.499811</td>\n",
" <td>2.012838</td>\n",
" <td>0.242206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>5.005725</td>\n",
" <td>0.626256</td>\n",
" <td>4.075562</td>\n",
" <td>0.303578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>5.955272</td>\n",
" <td>1.080170</td>\n",
" <td>7.221285</td>\n",
" <td>0.535315</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol_content bitterness darkness fruitiness\n",
"count 300.000000 300.000000 300.000000 300.000000\n",
"mean 4.705936 0.466646 2.587510 0.221585\n",
"std 0.448071 0.225136 1.741583 0.116405\n",
"min 3.073993 0.000000 0.000000 0.000000\n",
"25% 4.421718 0.287010 1.192515 0.137466\n",
"50% 4.714367 0.499811 2.012838 0.242206\n",
"75% 5.005725 0.626256 4.075562 0.303578\n",
"max 5.955272 1.080170 7.221285 0.535315"
]
},
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# rows per beer kind:\n",
"N = 100\n",
"\n",
"rows = []\n",
"\n",
"ns = (100, 100, 100)\n",
"for i, (n, kind) in enumerate(zip(ns, beer_kinds)):\n",
" rows.extend(sample_features(kind) for _ in range(n))\n",
" \n",
"rows = np.array(rows)\n",
"\n",
"# full_features also contain beer kind\n",
"\n",
"features = pd.DataFrame(rows, columns = feature_names)\n",
"features[\"fruitiness\"] -= features[\"fruitiness\"].min()\n",
"\n",
"# shuffle rows, see\n",
"# https://stackoverflow.com/questions/29576430/shuffle-dataframe-rows\n",
"features = features.sample(frac=1).reset_index(drop=True)\n",
"\n",
"features.describe()"
]
},
{
"cell_type": "code",
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"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>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>fruitiness</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.739295</td>\n",
" <td>0.422503</td>\n",
" <td>0.989463</td>\n",
" <td>0.215791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.207849</td>\n",
" <td>0.841668</td>\n",
" <td>0.928626</td>\n",
" <td>0.380420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.709494</td>\n",
" <td>0.322037</td>\n",
" <td>5.374682</td>\n",
" <td>0.145231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.684743</td>\n",
" <td>0.434315</td>\n",
" <td>4.072805</td>\n",
" <td>0.191321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.148710</td>\n",
" <td>0.570586</td>\n",
" <td>1.461568</td>\n",
" <td>0.260218</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol_content bitterness darkness fruitiness\n",
"0 3.739295 0.422503 0.989463 0.215791\n",
"1 4.207849 0.841668 0.928626 0.380420\n",
"2 4.709494 0.322037 5.374682 0.145231\n",
"3 4.684743 0.434315 4.072805 0.191321\n",
"4 4.148710 0.570586 1.461568 0.260218"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features.head()"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(300,)\n",
"149 good\n",
"150 bad\n"
]
},
{
"data": {
"image/png": 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AfwbOmHTWZfK/Gbive/xrDH7QDwB/D5w66XwnZD0fmO/G+h+A06d9nIH3Ad8EHgX+Fjh12sYZ+ASDOf4fdqWybblxZXDy/MPda/IbDK7gmZbMBxjMOx9/Hf71ov1v7TLvB66YlswnbD/IT06KrmmcvVNUkhoxjVMukqRVsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWrE/wPGhsdyapPyewAAAABJRU5ErkJggg==\n",
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# compute score which we use for assigning class label:\n",
"\n",
"weights_uwe = np.array((-1, 1.5, -2, 1.5))\n",
"scores = np.array(features @ weights_uwe)\n",
"\n",
"# add some non linear term to make svm work better than logistic regression:\n",
"scores = scores + 1 + 1 * 0.8 * features.iloc[:, 0] ** 2 * (1 + features.iloc[:, 1] * features.iloc[:, 2])\n",
"\n",
"\n",
"print(scores.shape)\n",
"\n",
"pylab.hist(scores, bins=30)\n",
"\n",
"\n",
"# add some noise:\n",
"scores += .1 * np.random.randn(len(scores))\n",
"\n",
"# threshold is median of scores, so we get a balanced data set:\n",
"thresh = sorted(scores)[len(scores) // 2]\n",
"\n",
"# move some low scored beers towards the \"center\":\n",
"lowlim = sorted(scores)[len(scores) // 10]\n",
"scores[scores < lowlim] += 0.4 * np.median(scores)\n",
"\n",
"good = (scores>thresh)\n",
"bad = (scores<thresh)\n",
"\n",
"print(sum(good), \"good\")\n",
"print(sum(bad), \"bad\")\n",
"\n",
"\n",
"labels = np.zeros(sum(ns), dtype=int)\n",
"labels[good] = 1\n",
"\n",
"features[\"is_yummy\"] = labels\n",
"# labels[:100] = 1"
]
},
{
"cell_type": "code",
"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>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.739295</td>\n",
" <td>0.422503</td>\n",
" <td>0.989463</td>\n",
" <td>class_0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.207849</td>\n",
" <td>0.841668</td>\n",
" <td>0.928626</td>\n",
" <td>class_0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.709494</td>\n",
" <td>0.322037</td>\n",
" <td>5.374682</td>\n",
" <td>class_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.684743</td>\n",
" <td>0.434315</td>\n",
" <td>4.072805</td>\n",
" <td>class_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.148710</td>\n",
" <td>0.570586</td>\n",
" <td>1.461568</td>\n",
" <td>class_0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
" alcohol_content bitterness darkness fruitiness label\n",
"0 3.739295 0.422503 0.989463 0.215791 class_0\n",
"1 4.207849 0.841668 0.928626 0.380420 class_0\n",
"2 4.709494 0.322037 5.374682 0.145231 class_1\n",
"3 4.684743 0.434315 4.072805 0.191321 class_1\n",
"4 4.148710 0.570586 1.461568 0.260218 class_0"
}
],
"source": [
"import seaborn as sns\n",
"sns.set(style=\"ticks\")\n",
"\n",
"for_plot = features.iloc[:, :-1].copy()\n",
"for_plot[\"label\"] = [\"class_\" + li for li in labels.astype(str)]\n",
"\n",
"for_plot.head()\n",
"\n",
"# sns.pairplot(for_plot, hue=\"label\", diag_kind=\"hist\");"
"metadata": {},
"outputs": [],
"source": [
"learn = features.iloc[:225, :]\n",
"learn.to_csv(\"beers.csv\", index=False)\n",
"for_eval = features.iloc[225:, :]\n",
"for_eval.to_csv(\"beers_eval.csv\", index=False)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
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"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>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>fruitiness</th>\n",
" <th>is_yummy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.739295</td>\n",
" <td>0.422503</td>\n",
" <td>0.989463</td>\n",
" <td>0.215791</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.207849</td>\n",
" <td>0.841668</td>\n",
" <td>0.928626</td>\n",
" <td>0.380420</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.709494</td>\n",
" <td>0.322037</td>\n",
" <td>5.374682</td>\n",
" <td>0.145231</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.684743</td>\n",
" <td>0.434315</td>\n",
" <td>4.072805</td>\n",
" <td>0.191321</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.148710</td>\n",
" <td>0.570586</td>\n",
" <td>1.461568</td>\n",
" <td>0.260218</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol_content bitterness darkness fruitiness is_yummy\n",
"0 3.739295 0.422503 0.989463 0.215791 0\n",
"1 4.207849 0.841668 0.928626 0.380420 0\n",
"2 4.709494 0.322037 5.374682 0.145231 1\n",
"3 4.684743 0.434315 4.072805 0.191321 1\n",
"4 4.148710 0.570586 1.461568 0.260218 0"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beers = pd.read_csv(\"beers.csv\")\n",
"beers.head()"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" 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>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>fruitiness</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.739295</td>\n",
" <td>0.422503</td>\n",
" <td>0.989463</td>\n",
" <td>0.215791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.207849</td>\n",
" <td>0.841668</td>\n",
" <td>0.928626</td>\n",
" <td>0.380420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.709494</td>\n",
" <td>0.322037</td>\n",
" <td>5.374682</td>\n",
" <td>0.145231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.684743</td>\n",
" <td>0.434315</td>\n",
" <td>4.072805</td>\n",
" <td>0.191321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.148710</td>\n",
" <td>0.570586</td>\n",
" <td>1.461568</td>\n",
" <td>0.260218</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol_content bitterness darkness fruitiness\n",
"0 3.739295 0.422503 0.989463 0.215791\n",
"1 4.207849 0.841668 0.928626 0.380420\n",
"2 4.709494 0.322037 5.374682 0.145231\n",
"3 4.684743 0.434315 4.072805 0.191321\n",
"4 4.148710 0.570586 1.461568 0.260218"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features = beers.iloc[:, :-1]\n",
"labels = beers[\"is_yummy\"]\n",
"features.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# first use of classifiers"
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8311111111111111\n"
]
}
],
"source": [
"model = LogisticRegression()\n",
"model.fit(features, labels)\n",
"predicted = model.predict(features)\n",
"\n",
"percent_correct = np.sum(predicted == labels) / len(labels)\n",
"print(percent_correct)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9111111111111111\n"
]
}
],
"source": [
"model = SVC()\n",
"model.fit(features, labels)\n",
"\n",
"predicted = model.predict(features)\n",
"\n",
"percent_correct = np.sum(predicted == labels) / len(labels)\n",
"print(percent_correct)"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>fruitiness</th>\n",
" <th>is_yummy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.381306</td>\n",
" <td>0.365976</td>\n",
" <td>1.159893</td>\n",
" <td>0.168321</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.540088</td>\n",
" <td>0.282582</td>\n",
" <td>5.077826</td>\n",
" <td>0.129492</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.306264</td>\n",
" <td>0.109893</td>\n",
" <td>6.159705</td>\n",
" <td>0.033846</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.479080</td>\n",
" <td>0.414778</td>\n",
" <td>1.101224</td>\n",
" <td>0.228998</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.789652</td>\n",
" <td>0.661923</td>\n",
" <td>1.477141</td>\n",
" <td>0.280621</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol_content bitterness darkness fruitiness is_yummy\n",
"0 4.381306 0.365976 1.159893 0.168321 0\n",
"1 5.540088 0.282582 5.077826 0.129492 1\n",
"2 5.306264 0.109893 6.159705 0.033846 0\n",
"3 4.479080 0.414778 1.101224 0.228998 0\n",
"4 3.789652 0.661923 1.477141 0.280621 0"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beers_eval = pd.read_csv(\"beers_eval.csv\")\n",
"beers_eval.head()"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>fruitiness</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.381306</td>\n",
" <td>0.365976</td>\n",
" <td>1.159893</td>\n",
" <td>0.168321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.540088</td>\n",
" <td>0.282582</td>\n",
" <td>5.077826</td>\n",
" <td>0.129492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.306264</td>\n",
" <td>0.109893</td>\n",
" <td>6.159705</td>\n",
" <td>0.033846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.479080</td>\n",
" <td>0.414778</td>\n",
" <td>1.101224</td>\n",
" <td>0.228998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.789652</td>\n",
" <td>0.661923</td>\n",
" <td>1.477141</td>\n",
" <td>0.280621</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol_content bitterness darkness fruitiness\n",
"0 4.381306 0.365976 1.159893 0.168321\n",
"1 5.540088 0.282582 5.077826 0.129492\n",
"2 5.306264 0.109893 6.159705 0.033846\n",
"3 4.479080 0.414778 1.101224 0.228998\n",
"4 3.789652 0.661923 1.477141 0.280621"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features_eval = beers_eval.iloc[:, :-1]\n",
"labels_eval = beers_eval[\"is_yummy\"]\n",
"features_eval.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# apply classifiers to test data set"
]
},
{
"cell_type": "code",
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"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LogisticRegression\n",
"on learning set: 0.8311111111111111\n",
"on eval set : 0.76\n",
"\n",
"SVC\n",
"on learning set: 0.9111111111111111\n",
"on eval set : 0.8933333333333333\n",
"\n"
]
}
],
"source": [
"# train model and eval on learning and test data set:\n",
"\n",
"def check(model):\n",
" print(model.__class__.__qualname__)\n",
" model.fit(features, labels)\n",
"\n",
" predicted = model.predict(features)\n",
" percent_correct = np.sum(predicted == labels) / len(labels)\n",
" print(\"on learning set:\", percent_correct)\n",
"\n",
" predicted = model.predict(features_eval)\n",
" percent_correct = np.sum(predicted == labels_eval) / len(labels_eval)\n",
" print(\"on eval set :\", percent_correct)\n",
" print()\n",
"\n",
"\n",
"check(LogisticRegression())\n",
"check(SVC())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# cross validation"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# now we merge both datasets\n",
"\n",
"full_features = pd.concat((features, features_eval))\n",
"full_labels = pd.concat((labels, labels_eval))"
]
},
{
"cell_type": "code",