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Andreaierardi 2020-03-04 17:29:48 +01:00
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commit d634470290
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57
1year/2trimester/Coding for Data Science - Python language/Python/Examples/.ipynb_checkpoints/Diamonds ML regression-checkpoint.ipynb
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@ -623,10 +623,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SCORE: 0.009547645532072673 \n",
"--------------\n"
]
}
],
"source": [ "source": [
"from sklearn import linear_model\n",
"from sklearn.linear_model import LogisticRegression\n", "from sklearn.linear_model import LogisticRegression\n",
"logistic = linear_model.LogisticRegression(random_state=0) # create object for the class\n", "logistic = linear_model.LogisticRegression(random_state=0) # create object for the class\n",
"logistic.fit(X_train, y_train) # perform logistic regression\n", "logistic.fit(X_train, y_train) # perform logistic regression\n",
@ -636,13 +656,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1662 3370\n",
"1882 3321\n",
"776 698\n",
"530 646\n",
"1882 4839\n",
"1076 1851\n",
"984 1624\n",
"802 665\n",
"872 596\n",
"394 1154\n"
]
}
],
"source": [ "source": [
"Y_pred = logistic.predict(X_test, y_test) # make predictions\n",
"for X,y in list(zip(X_test, y_test))[:10]:\n", "for X,y in list(zip(X_test, y_test))[:10]:\n",
" print(Y_pred[x], y)" " print(logistic.predict([X])[0], y)\n",
" "
] ]
}, },
{ {
@ -650,11 +687,7 @@
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": []
"for X,y in list(zip(X_test, y_test))[:10]:\n",
" print(logistic.predict([X])[0], y)\n",
" "
]
} }
], ],
"metadata": { "metadata": {
@ -677,5 +710,5 @@
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 4
} }

33
1year/2trimester/Coding for Data Science - Python language/Python/Examples/Diamonds ML regression.ipynb
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@ -656,25 +656,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"ename": "TypeError", "name": "stdout",
"evalue": "predict() takes 2 positional arguments but 3 were given", "output_type": "stream",
"output_type": "error", "text": [
"traceback": [ "1662 3370\n",
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "1882 3321\n",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "776 698\n",
"\u001b[1;32m<ipython-input-8-7941666c0bd3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mY_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogistic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# make predictions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_pred\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "530 646\n",
"\u001b[1;31mTypeError\u001b[0m: predict() takes 2 positional arguments but 3 were given" "1882 4839\n",
"1076 1851\n",
"984 1624\n",
"802 665\n",
"872 596\n",
"394 1154\n"
] ]
} }
], ],
"source": [ "source": [
"Y_pred = logistic.predict(X_test, y_test) # make predictions\n",
"for X,y in list(zip(X_test, y_test))[:10]:\n", "for X,y in list(zip(X_test, y_test))[:10]:\n",
" print(Y_pred[x], y)" " print(logistic.predict([X])[0], y)\n",
" "
] ]
}, },
{ {
@ -682,11 +687,7 @@
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": []
"for X,y in list(zip(X_test, y_test))[:10]:\n",
" print(logistic.predict([X])[0], y)\n",
" "
]
} }
], ],
"metadata": { "metadata": {