aliosmankaya
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88c6bb1
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Parent(s):
cef0e8e
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structured_arr_model_1_dim.ipynb
ADDED
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1 |
+
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e1bdbd46-1f35-4373-80ec-727f0e26f009",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")"
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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": 2,
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"id": "327dafe0-68d4-4200-a889-b03bc97a1057",
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"metadata": {},
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"outputs": [],
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"source": [
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"iris = load_iris()"
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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": 3,
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"id": "5ced7579-bb9f-4a20-abe2-c0c258ef4073",
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"metadata": {},
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"outputs": [],
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"source": [
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"X = iris.data[:, :1]\n",
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"y = iris.target"
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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": 4,
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"id": "c8e84e37-e034-4dcb-af3d-03d9cae87a92",
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"metadata": {},
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"outputs": [],
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"source": [
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"X = np.array([tuple(i.tolist()) for i in X], dtype=[(\"col1\", float)])\n",
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"y = np.array([i for i in y], dtype=[(\"col3\", y.dtype)])"
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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": 5,
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"id": "fab25223-225c-430b-a58d-368466d7fe02",
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"metadata": {},
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58 |
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"outputs": [],
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59 |
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"source": [
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60 |
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"X = X.reshape(-1, 1)"
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]
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},
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63 |
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{
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64 |
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"cell_type": "code",
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65 |
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"execution_count": 6,
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66 |
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"id": "50359601-4b0d-4a94-a1c8-44a833b8f4e5",
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67 |
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"metadata": {
|
68 |
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"tags": []
|
69 |
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},
|
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"outputs": [],
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"source": [
|
72 |
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"x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)"
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]
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},
|
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{
|
76 |
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"cell_type": "code",
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77 |
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"execution_count": 7,
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"id": "17fc4619-c5c0-4beb-81df-81617b1c7a56",
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"metadata": {},
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80 |
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(array([[(6.3,)],\n",
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" [(6.5,)],\n",
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" [(5.6,)],\n",
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87 |
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" [(5.7,)],\n",
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88 |
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" [(6.4,)]], dtype=[('col1', '<f8')]),\n",
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89 |
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" array([(1,), (2,), (1,), (1,), (2,)], dtype=[('col3', '<i8')]))"
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]
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91 |
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},
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92 |
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"execution_count": 7,
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93 |
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"metadata": {},
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94 |
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"output_type": "execute_result"
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95 |
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}
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96 |
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],
|
97 |
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"source": [
|
98 |
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"x_train[:5], y_train[:5]"
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99 |
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]
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100 |
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},
|
101 |
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{
|
102 |
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"cell_type": "code",
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103 |
+
"execution_count": 8,
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104 |
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"id": "510d7a07-7746-4305-96d6-a74bc5a7f144",
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105 |
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"metadata": {},
|
106 |
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"outputs": [],
|
107 |
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"source": [
|
108 |
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"model = LinearRegression()"
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109 |
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]
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110 |
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},
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111 |
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{
|
112 |
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"cell_type": "code",
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113 |
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"execution_count": 9,
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114 |
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"id": "733f81f4-fc25-41a2-8c7d-e6e4abd70143",
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"metadata": {},
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116 |
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"outputs": [
|
117 |
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{
|
118 |
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"data": {
|
119 |
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"text/plain": [
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120 |
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"LinearRegression()"
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121 |
+
]
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122 |
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},
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123 |
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"execution_count": 9,
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124 |
+
"metadata": {},
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125 |
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"output_type": "execute_result"
|
126 |
+
}
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127 |
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],
|
128 |
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"source": [
|
129 |
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"model.fit(x_train, y_train)"
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130 |
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]
|
131 |
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},
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132 |
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{
|
133 |
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"cell_type": "code",
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134 |
+
"execution_count": 10,
|
135 |
+
"id": "267ed05e-7285-4873-ae8c-2396f986bf31",
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"metadata": {},
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137 |
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"outputs": [],
|
138 |
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"source": [
|
139 |
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"y_pred = model.predict(x_test)"
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140 |
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]
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141 |
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},
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142 |
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{
|
143 |
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"cell_type": "code",
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144 |
+
"execution_count": 11,
|
145 |
+
"id": "0dd6f9d6-89d4-461f-9110-601d44126512",
|
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+
"metadata": {},
|
147 |
+
"outputs": [
|
148 |
+
{
|
149 |
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"data": {
|
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"text/plain": [
|
151 |
+
"array([1.22816565, 0.91925051, 2.46382623, 1.15093687, 1.76876716,\n",
|
152 |
+
" 0.68756415, 0.84202172, 1.84599594, 1.30539444, 0.99647929,\n",
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153 |
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" 1.5370808 , 0.22419143, 0.76479293, 0.30142022, 0.45587779])"
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154 |
+
]
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155 |
+
},
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156 |
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"execution_count": 11,
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157 |
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"metadata": {},
|
158 |
+
"output_type": "execute_result"
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159 |
+
}
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160 |
+
],
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161 |
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"source": [
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162 |
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"y_pred"
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163 |
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]
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},
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{
|
166 |
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"cell_type": "code",
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"execution_count": 12,
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168 |
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"id": "6a65cd71-b625-4f31-894d-a4d0403fd1b1",
|
169 |
+
"metadata": {},
|
170 |
+
"outputs": [
|
171 |
+
{
|
172 |
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"data": {
|
173 |
+
"text/plain": [
|
174 |
+
"array([1., 1., 2., 1., 2., 1., 1., 2., 1., 1., 2., 0., 1., 0., 0.])"
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+
]
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+
},
|
177 |
+
"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
|
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+
}
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+
],
|
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"source": [
|
183 |
+
"y_pred = np.round(y_pred)\n",
|
184 |
+
"y_pred"
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]
|
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+
},
|
187 |
+
{
|
188 |
+
"cell_type": "code",
|
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+
"execution_count": 13,
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+
"id": "ef5d8327-7ee8-43a6-b4f9-0785e8467d23",
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+
"metadata": {},
|
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+
"outputs": [
|
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+
{
|
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"data": {
|
195 |
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"text/plain": [
|
196 |
+
"array([(1,), (0,), (2,), (1,), (1,), (0,), (1,), (2,), (1,), (1,), (2,),\n",
|
197 |
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" (0,), (0,), (0,), (0,)], dtype=[('col3', '<i8')])"
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+
]
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+
},
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"execution_count": 13,
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"metadata": {},
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+
"output_type": "execute_result"
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+
}
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],
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"source": [
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"y_test"
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]
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+
},
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+
{
|
210 |
+
"cell_type": "code",
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+
"execution_count": 14,
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212 |
+
"id": "d01167c5-d4d1-4a67-86df-cc4f88abe421",
|
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+
"metadata": {},
|
214 |
+
"outputs": [],
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215 |
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"source": [
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"y_test = np.array([i[0] for i in y_test])"
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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": 15,
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222 |
+
"id": "b28aa4ac-87ab-45a4-b5c8-e7b125895c25",
|
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+
"metadata": {},
|
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+
"outputs": [
|
225 |
+
{
|
226 |
+
"data": {
|
227 |
+
"text/plain": [
|
228 |
+
"0.7333333333333333"
|
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+
]
|
230 |
+
},
|
231 |
+
"execution_count": 15,
|
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+
"metadata": {},
|
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"output_type": "execute_result"
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}
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],
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"source": [
|
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+
"accuracy_score(y_test, np.round(y_pred))"
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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": 16,
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+
"id": "d5cf8629-f623-4a3e-9400-8d7f6215383e",
|
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+
"metadata": {},
|
245 |
+
"outputs": [],
|
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+
"source": [
|
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+
"from joblib import dump, load"
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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": 17,
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+
"id": "caa6d389-b358-4160-a342-215013c5b2d9",
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+
"metadata": {},
|
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+
"outputs": [
|
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+
{
|
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+
"data": {
|
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"text/plain": [
|
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"['structured_arr_model_1_dim.joblib']"
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+
]
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+
},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"dump(model, \"structured_arr_model_1_dim.joblib\")"
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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": 18,
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"id": "f0804a91-46d4-4cec-bd60-bb5f022443bf",
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"metadata": {},
|
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+
"outputs": [],
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"source": [
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"model = load(\"structured_arr_model_1_dim.joblib\")"
|
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+
]
|
280 |
+
},
|
281 |
+
{
|
282 |
+
"cell_type": "code",
|
283 |
+
"execution_count": 19,
|
284 |
+
"id": "07584f03-f1da-4014-a539-ca0e033a6356",
|
285 |
+
"metadata": {},
|
286 |
+
"outputs": [
|
287 |
+
{
|
288 |
+
"data": {
|
289 |
+
"text/plain": [
|
290 |
+
"array([1.22816565])"
|
291 |
+
]
|
292 |
+
},
|
293 |
+
"execution_count": 19,
|
294 |
+
"metadata": {},
|
295 |
+
"output_type": "execute_result"
|
296 |
+
}
|
297 |
+
],
|
298 |
+
"source": [
|
299 |
+
"model.predict(x_test[:1])"
|
300 |
+
]
|
301 |
+
},
|
302 |
+
{
|
303 |
+
"cell_type": "code",
|
304 |
+
"execution_count": 20,
|
305 |
+
"id": "06cf5cbb-0a96-4501-8fa6-bfc680d8aa20",
|
306 |
+
"metadata": {},
|
307 |
+
"outputs": [],
|
308 |
+
"source": [
|
309 |
+
"import skops.hub_utils as hub_utils"
|
310 |
+
]
|
311 |
+
},
|
312 |
+
{
|
313 |
+
"cell_type": "code",
|
314 |
+
"execution_count": 21,
|
315 |
+
"id": "f4349089-88c9-49d2-8b65-351cabb74fd8",
|
316 |
+
"metadata": {},
|
317 |
+
"outputs": [
|
318 |
+
{
|
319 |
+
"data": {
|
320 |
+
"text/plain": [
|
321 |
+
"array([[(6.1,)],\n",
|
322 |
+
" [(5.7,)],\n",
|
323 |
+
" [(7.7,)],\n",
|
324 |
+
" [(6. ,)],\n",
|
325 |
+
" [(6.8,)],\n",
|
326 |
+
" [(5.4,)],\n",
|
327 |
+
" [(5.6,)],\n",
|
328 |
+
" [(6.9,)],\n",
|
329 |
+
" [(6.2,)],\n",
|
330 |
+
" [(5.8,)],\n",
|
331 |
+
" [(6.5,)],\n",
|
332 |
+
" [(4.8,)],\n",
|
333 |
+
" [(5.5,)],\n",
|
334 |
+
" [(4.9,)],\n",
|
335 |
+
" [(5.1,)]], dtype=[('col1', '<f8')])"
|
336 |
+
]
|
337 |
+
},
|
338 |
+
"execution_count": 21,
|
339 |
+
"metadata": {},
|
340 |
+
"output_type": "execute_result"
|
341 |
+
}
|
342 |
+
],
|
343 |
+
"source": [
|
344 |
+
"x_test"
|
345 |
+
]
|
346 |
+
},
|
347 |
+
{
|
348 |
+
"cell_type": "code",
|
349 |
+
"execution_count": 22,
|
350 |
+
"id": "a4294f1f-5eeb-460f-a872-ba487a229093",
|
351 |
+
"metadata": {},
|
352 |
+
"outputs": [],
|
353 |
+
"source": [
|
354 |
+
"!rm -rf /Users/macbookpro/MyProjects/dev/dst\n",
|
355 |
+
"!mkdir /Users/macbookpro/MyProjects/dev/dst"
|
356 |
+
]
|
357 |
+
},
|
358 |
+
{
|
359 |
+
"cell_type": "code",
|
360 |
+
"execution_count": 23,
|
361 |
+
"id": "41a49678-1a01-439d-8f92-29f1884e5f79",
|
362 |
+
"metadata": {},
|
363 |
+
"outputs": [],
|
364 |
+
"source": [
|
365 |
+
"hub_utils.init(\n",
|
366 |
+
" model=\"/Users/macbookpro/MyProjects/dev/structured_arr_model_1_dim.joblib\",\n",
|
367 |
+
" requirements=[\"scikit-learn\", \"numpy\"],\n",
|
368 |
+
" dst=\"/Users/macbookpro/MyProjects/dev/dst\",\n",
|
369 |
+
" task=\"tabular-classification\",\n",
|
370 |
+
" data=x_train[:3]\n",
|
371 |
+
")"
|
372 |
+
]
|
373 |
+
},
|
374 |
+
{
|
375 |
+
"cell_type": "code",
|
376 |
+
"execution_count": 24,
|
377 |
+
"id": "fe1a1620-66e3-4543-a506-1195ac39831e",
|
378 |
+
"metadata": {},
|
379 |
+
"outputs": [],
|
380 |
+
"source": [
|
381 |
+
"from skops.card import metadata_from_config"
|
382 |
+
]
|
383 |
+
},
|
384 |
+
{
|
385 |
+
"cell_type": "code",
|
386 |
+
"execution_count": 25,
|
387 |
+
"id": "9e0a6d81-e1c4-4a75-b510-772eb44e924d",
|
388 |
+
"metadata": {},
|
389 |
+
"outputs": [
|
390 |
+
{
|
391 |
+
"data": {
|
392 |
+
"text/plain": [
|
393 |
+
"library_name: sklearn\n",
|
394 |
+
"tags:\n",
|
395 |
+
"- sklearn\n",
|
396 |
+
"- skops\n",
|
397 |
+
"- tabular-classification\n",
|
398 |
+
"widget:\n",
|
399 |
+
" structuredData:\n",
|
400 |
+
" x0:\n",
|
401 |
+
" - - 6.3\n",
|
402 |
+
" - - 6.5\n",
|
403 |
+
" - - 5.6"
|
404 |
+
]
|
405 |
+
},
|
406 |
+
"execution_count": 25,
|
407 |
+
"metadata": {},
|
408 |
+
"output_type": "execute_result"
|
409 |
+
}
|
410 |
+
],
|
411 |
+
"source": [
|
412 |
+
"metadata_from_config(\"/Users/macbookpro/MyProjects/dev/dst/config.json\")"
|
413 |
+
]
|
414 |
+
},
|
415 |
+
{
|
416 |
+
"cell_type": "code",
|
417 |
+
"execution_count": null,
|
418 |
+
"id": "412fc8e8-ab28-44bf-88be-6cac61c168f1",
|
419 |
+
"metadata": {},
|
420 |
+
"outputs": [],
|
421 |
+
"source": []
|
422 |
+
}
|
423 |
+
],
|
424 |
+
"metadata": {
|
425 |
+
"kernelspec": {
|
426 |
+
"display_name": "Python 3 (ipykernel)",
|
427 |
+
"language": "python",
|
428 |
+
"name": "python3"
|
429 |
+
},
|
430 |
+
"language_info": {
|
431 |
+
"codemirror_mode": {
|
432 |
+
"name": "ipython",
|
433 |
+
"version": 3
|
434 |
+
},
|
435 |
+
"file_extension": ".py",
|
436 |
+
"mimetype": "text/x-python",
|
437 |
+
"name": "python",
|
438 |
+
"nbconvert_exporter": "python",
|
439 |
+
"pygments_lexer": "ipython3",
|
440 |
+
"version": "3.9.15"
|
441 |
+
}
|
442 |
+
},
|
443 |
+
"nbformat": 4,
|
444 |
+
"nbformat_minor": 5
|
445 |
+
}
|
structured_arr_model_1_dim.joblib
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e5bb34d8b7d267d3f3bd6bcf1a108eb0d2ffdf45e31b773627c776bd08853095
|
3 |
+
size 552
|