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Delete reg_arr_model_1_dim.ipynb
Browse files- reg_arr_model_1_dim.ipynb +0 -413
reg_arr_model_1_dim.ipynb
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"cells": [
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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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"id": "50359601-4b0d-4a94-a1c8-44a833b8f4e5",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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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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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "17fc4619-c5c0-4beb-81df-81617b1c7a56",
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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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"(array([[6.3],\n",
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" [6.5],\n",
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" [5.6],\n",
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" [5.7],\n",
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" [6.4]]),\n",
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" array([1, 2, 1, 1, 2]))"
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]
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},
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"execution_count": 5,
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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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"x_train[:5], y_train[:5]"
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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": 6,
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"id": "510d7a07-7746-4305-96d6-a74bc5a7f144",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = LinearRegression()"
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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": 7,
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"id": "733f81f4-fc25-41a2-8c7d-e6e4abd70143",
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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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"LinearRegression()"
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]
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},
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"execution_count": 7,
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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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"model.fit(x_train, y_train)"
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]
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{
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"cell_type": "code",
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"id": "267ed05e-7285-4873-ae8c-2396f986bf31",
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred = model.predict(x_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": 9,
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"id": "0dd6f9d6-89d4-461f-9110-601d44126512",
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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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"array([1.22816565, 0.91925051, 2.46382623, 1.15093687, 1.76876716,\n",
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" 0.68756415, 0.84202172, 1.84599594, 1.30539444, 0.99647929,\n",
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" 1.5370808 , 0.22419143, 0.76479293, 0.30142022, 0.45587779])"
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"execution_count": 9,
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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_pred"
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{
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"cell_type": "code",
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"id": "6a65cd71-b625-4f31-894d-a4d0403fd1b1",
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"metadata": {},
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}
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],
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"source": [
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"y_pred = np.round(y_pred)\n",
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"y_pred"
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},
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"metadata": {},
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"output_type": "execute_result"
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"source": [
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"id": "b28aa4ac-87ab-45a4-b5c8-e7b125895c25",
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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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"0.7333333333333333"
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},
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"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": [
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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": 13,
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"id": "d5cf8629-f623-4a3e-9400-8d7f6215383e",
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"metadata": {},
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"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": 14,
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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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"['reg_arr_model_1_dim.joblib']"
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]
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},
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"execution_count": 14,
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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, \"reg_arr_model_1_dim.joblib\")"
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]
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},
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{
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"execution_count": 15,
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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(\"reg_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": 16,
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"id": "07584f03-f1da-4014-a539-ca0e033a6356",
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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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"array([1.22816565])"
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]
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},
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"execution_count": 16,
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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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"model.predict(x_test[:1])"
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]
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},
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{
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"cell_type": "code",
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"id": "06cf5cbb-0a96-4501-8fa6-bfc680d8aa20",
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"metadata": {},
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"outputs": [],
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"source": [
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"import skops.hub_utils as hub_utils"
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]
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},
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{
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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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"array([[6.1],\n",
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" [5.7],\n",
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"metadata": {},
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}
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"source": [
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"x_test"
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{
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"id": "a4294f1f-5eeb-460f-a872-ba487a229093",
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"metadata": {},
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"outputs": [],
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"source": [
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"!rm -rf /Users/macbookpro/MyProjects/dev/dst\n",
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"!mkdir /Users/macbookpro/MyProjects/dev/dst"
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]
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},
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{
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"id": "41a49678-1a01-439d-8f92-29f1884e5f79",
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"metadata": {},
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"outputs": [],
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"source": [
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"hub_utils.init(\n",
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" model=\"/Users/macbookpro/MyProjects/dev/structured_arr_model.joblib\",\n",
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" requirements=[\"scikit-learn\", \"numpy\"],\n",
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" dst=\"/Users/macbookpro/MyProjects/dev/dst\",\n",
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" task=\"tabular-classification\",\n",
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" data=x_train[:3]\n",
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")"
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]
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{
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"id": "fe1a1620-66e3-4543-a506-1195ac39831e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from skops.card import metadata_from_config"
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]
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},
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{
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"metadata": {},
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{
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"data": {
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"text/plain": [
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"library_name: sklearn\n",
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"tags:\n",
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"- sklearn\n",
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"- skops\n",
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"- tabular-classification\n",
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"widget:\n",
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" structuredData:\n",
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" x0:\n",
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" - 6.3\n",
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" - 6.5\n",
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" - 5.6"
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]
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},
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"execution_count": 22,
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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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"metadata_from_config(\"/Users/macbookpro/MyProjects/dev/dst/config.json\")"
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]
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},
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{
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"execution_count": null,
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"id": "412fc8e8-ab28-44bf-88be-6cac61c168f1",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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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| 405 |
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"name": "python",
|
| 406 |
-
"nbconvert_exporter": "python",
|
| 407 |
-
"pygments_lexer": "ipython3",
|
| 408 |
-
"version": "3.9.15"
|
| 409 |
-
}
|
| 410 |
-
},
|
| 411 |
-
"nbformat": 4,
|
| 412 |
-
"nbformat_minor": 5
|
| 413 |
-
}
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