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Clean up.

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Danilo Enoque Ferreira de Lima committed 2024-05-10 17:04:54 +02:00
1 parent 1cd365f487
commit 284df93e5c
13 files changed
+4948 -1141
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@@ -62,10 +62,22 @@
"id": "7de735cf-3466-41e9-bcd5-fcc756958019", "id": "7de735cf-3466-41e9-bcd5-fcc756958019",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<seaborn.axisgrid.PairGrid at 0x7f26ea177bb0>" "<seaborn.axisgrid.PairGrid at 0x7f78d9bee210>"
] ]
}, },
"execution_count": 3, "execution_count": 3,
@@ -74,7 +86,7 @@
}, },
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 750x750 with 12 Axes>" "<Figure size 750x750 with 12 Axes>"
] ]
@@ -104,7 +116,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 2400x800 with 3 Axes>" "<Figure size 2400x800 with 3 Axes>"
] ]
@@ -137,8 +149,8 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Correlation[x1, x2] = 0.70 (same as 0.70)\n", "Correlation[x1, x2] = 0.69 (same as 0.69)\n",
"Correlation[x1, x3] = 0.26 (same as 0.26)\n", "Correlation[x1, x3] = 0.27 (same as 0.27)\n",
"Correlation[x2, x3] = -0.00 (same as -0.00)\n" "Correlation[x2, x3] = -0.00 (same as -0.00)\n"
] ]
} }
@@ -165,14 +177,14 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Slope between x1 and x2 = 1.45\n", "Slope between x1 and x2 = 1.48\n",
"Slope between x1 and x3 = 3.30\n", "Slope between x1 and x3 = 3.63\n",
"Slope between x2 and x3 = -50.36\n" "Slope between x2 and x3 = -142.59\n"
] ]
}, },
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 2400x800 with 3 Axes>" "<Figure size 2400x800 with 3 Axes>"
] ]
@@ -235,7 +247,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 2400x800 with 3 Axes>" "<Figure size 2400x800 with 3 Axes>"
] ]
@@ -301,7 +313,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.6" "version": "3.12.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
0 Basic statistics.ipynb
+1 -1
View File
@@ -118,7 +118,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.6" "version": "3.12.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
0 CLT.ipynb
+336 -188
View File
@@ -12,7 +12,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 1,
"id": "23feddde", "id": "23feddde",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -48,7 +48,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"id": "aef54435-78af-4b13-aee2-2bc576c773cd", "id": "aef54435-78af-4b13-aee2-2bc576c773cd",
"metadata": { "metadata": {
"tags": [] "tags": []
@@ -70,7 +70,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 3,
"id": "7ea0322e-a35c-4603-ad46-8b9fd4c216c0", "id": "7ea0322e-a35c-4603-ad46-8b9fd4c216c0",
"metadata": { "metadata": {
"tags": [] "tags": []
@@ -91,7 +91,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 4,
"id": "3f57aeb7-4148-4ba5-b6eb-b600779ef0db", "id": "3f57aeb7-4148-4ba5-b6eb-b600779ef0db",
"metadata": { "metadata": {
"tags": [] "tags": []
@@ -124,23 +124,23 @@
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>0</th>\n", " <th>0</th>\n",
" <td>0.235730</td>\n", " <td>-0.436331</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>1</th>\n", " <th>1</th>\n",
" <td>-0.587471</td>\n", " <td>-0.639195</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2</th>\n", " <th>2</th>\n",
" <td>0.325833</td>\n", " <td>0.458218</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>3</th>\n", " <th>3</th>\n",
" <td>0.786101</td>\n", " <td>0.248118</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>4</th>\n", " <th>4</th>\n",
" <td>0.480421</td>\n", " <td>-0.769285</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>...</th>\n", " <th>...</th>\n",
@@ -148,23 +148,23 @@
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>9995</th>\n", " <th>9995</th>\n",
" <td>-0.995029</td>\n", " <td>-0.104365</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>9996</th>\n", " <th>9996</th>\n",
" <td>-0.299443</td>\n", " <td>0.246559</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>9997</th>\n", " <th>9997</th>\n",
" <td>-0.464221</td>\n", " <td>-0.747142</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>9998</th>\n", " <th>9998</th>\n",
" <td>0.751605</td>\n", " <td>-0.283905</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>9999</th>\n", " <th>9999</th>\n",
" <td>0.958662</td>\n", " <td>-0.087743</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
@@ -173,22 +173,22 @@
], ],
"text/plain": [ "text/plain": [
" x\n", " x\n",
"0 0.235730\n", "0 -0.436331\n",
"1 -0.587471\n", "1 -0.639195\n",
"2 0.325833\n", "2 0.458218\n",
"3 0.786101\n", "3 0.248118\n",
"4 0.480421\n", "4 -0.769285\n",
"... ...\n", "... ...\n",
"9995 -0.995029\n", "9995 -0.104365\n",
"9996 -0.299443\n", "9996 0.246559\n",
"9997 -0.464221\n", "9997 -0.747142\n",
"9998 0.751605\n", "9998 -0.283905\n",
"9999 0.958662\n", "9999 -0.087743\n",
"\n", "\n",
"[10000 rows x 1 columns]" "[10000 rows x 1 columns]"
] ]
}, },
"execution_count": 5, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -207,7 +207,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 5,
"id": "db02f31d-3cde-4dd1-be2e-fdecaff39c2d", "id": "db02f31d-3cde-4dd1-be2e-fdecaff39c2d",
"metadata": { "metadata": {
"tags": [] "tags": []
@@ -244,31 +244,31 @@
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>mean</th>\n", " <th>mean</th>\n",
" <td>0.013094</td>\n", " <td>0.005463</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>std</th>\n", " <th>std</th>\n",
" <td>0.582749</td>\n", " <td>0.581949</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>min</th>\n", " <th>min</th>\n",
" <td>-0.999871</td>\n", " <td>-0.999997</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>25%</th>\n", " <th>25%</th>\n",
" <td>-0.496063</td>\n", " <td>-0.502165</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>50%</th>\n", " <th>50%</th>\n",
" <td>0.025202</td>\n", " <td>0.006750</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>75%</th>\n", " <th>75%</th>\n",
" <td>0.523966</td>\n", " <td>0.515157</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>max</th>\n", " <th>max</th>\n",
" <td>0.999870</td>\n", " <td>0.999962</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
@@ -277,16 +277,16 @@
"text/plain": [ "text/plain": [
" x\n", " x\n",
"count 10000.000000\n", "count 10000.000000\n",
"mean 0.013094\n", "mean 0.005463\n",
"std 0.582749\n", "std 0.581949\n",
"min -0.999871\n", "min -0.999997\n",
"25% -0.496063\n", "25% -0.502165\n",
"50% 0.025202\n", "50% 0.006750\n",
"75% 0.523966\n", "75% 0.515157\n",
"max 0.999870" "max 0.999962"
] ]
}, },
"execution_count": 6, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -305,25 +305,37 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 6,
"id": "fead461d-4d3a-48f2-a525-c518be4f1cf8", "id": "fead461d-4d3a-48f2-a525-c518be4f1cf8",
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
"outputs": [ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n"
]
},
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"[Text(0, 0.5, 'Fraction of occurrences / bin size'), Text(0.5, 0, 'x')]" "[Text(0, 0.5, 'Fraction of occurrences / bin size'), Text(0.5, 0, 'x')]"
] ]
}, },
"execution_count": 7, "execution_count": 6,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1500x500 with 1 Axes>" "<Figure size 1500x500 with 1 Axes>"
] ]
@@ -349,7 +361,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 7,
"id": "08d9daaf-25c5-4d06-b4a6-6cecaaa35651", "id": "08d9daaf-25c5-4d06-b4a6-6cecaaa35651",
"metadata": { "metadata": {
"tags": [] "tags": []
@@ -362,7 +374,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 8,
"id": "998baa6c-dc2e-42bc-85a5-295a63db95ea", "id": "998baa6c-dc2e-42bc-85a5-295a63db95ea",
"metadata": { "metadata": {
"tags": [] "tags": []
@@ -375,7 +387,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 9,
"id": "2195bfb9-acec-47e7-8bae-64a2671eb09d", "id": "2195bfb9-acec-47e7-8bae-64a2671eb09d",
"metadata": { "metadata": {
"tags": [] "tags": []
@@ -417,68 +429,68 @@
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>0</th>\n", " <th>0</th>\n",
" <td>0.557678</td>\n", " <td>-0.021048</td>\n",
" <td>-0.973417</td>\n", " <td>0.624256</td>\n",
" <td>0.722963</td>\n", " <td>-0.213331</td>\n",
" <td>-0.011507</td>\n", " <td>0.873981</td>\n",
" <td>-0.297092</td>\n", " <td>-0.744169</td>\n",
" <td>-0.096422</td>\n", " <td>-0.295821</td>\n",
" <td>0.649971</td>\n", " <td>0.033295</td>\n",
" <td>-0.822024</td>\n", " <td>-0.249991</td>\n",
" <td>-0.953068</td>\n", " <td>0.482630</td>\n",
" <td>-0.465009</td>\n", " <td>0.892960</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>1</th>\n", " <th>1</th>\n",
" <td>0.077690</td>\n", " <td>-0.481733</td>\n",
" <td>-0.597280</td>\n", " <td>0.707507</td>\n",
" <td>0.659335</td>\n", " <td>0.250378</td>\n",
" <td>0.433345</td>\n", " <td>-0.289964</td>\n",
" <td>0.407872</td>\n", " <td>0.192630</td>\n",
" <td>0.026935</td>\n", " <td>-0.117562</td>\n",
" <td>0.760887</td>\n", " <td>0.553985</td>\n",
" <td>0.375025</td>\n", " <td>0.511281</td>\n",
" <td>0.436400</td>\n", " <td>-0.841345</td>\n",
" <td>0.940421</td>\n", " <td>0.200531</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2</th>\n", " <th>2</th>\n",
" <td>0.389252</td>\n", " <td>-0.317442</td>\n",
" <td>0.312857</td>\n", " <td>0.502972</td>\n",
" <td>0.744985</td>\n", " <td>0.375717</td>\n",
" <td>-0.547178</td>\n", " <td>0.033442</td>\n",
" <td>0.918958</td>\n", " <td>-0.281267</td>\n",
" <td>0.842294</td>\n", " <td>0.300927</td>\n",
" <td>-0.839457</td>\n", " <td>-0.598245</td>\n",
" <td>-0.885617</td>\n", " <td>0.843422</td>\n",
" <td>-0.660291</td>\n", " <td>-0.629739</td>\n",
" <td>0.799358</td>\n", " <td>-0.069668</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>3</th>\n", " <th>3</th>\n",
" <td>0.002392</td>\n", " <td>-0.161197</td>\n",
" <td>-0.452201</td>\n", " <td>0.732069</td>\n",
" <td>0.626875</td>\n", " <td>0.441334</td>\n",
" <td>0.467181</td>\n", " <td>0.868591</td>\n",
" <td>-0.242254</td>\n", " <td>0.635648</td>\n",
" <td>0.342050</td>\n", " <td>-0.168180</td>\n",
" <td>-0.804150</td>\n", " <td>-0.673598</td>\n",
" <td>-0.757346</td>\n", " <td>-0.165261</td>\n",
" <td>0.534880</td>\n", " <td>-0.806899</td>\n",
" <td>-0.822235</td>\n", " <td>-0.753438</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>4</th>\n", " <th>4</th>\n",
" <td>0.013865</td>\n", " <td>-0.371950</td>\n",
" <td>0.772512</td>\n", " <td>-0.188998</td>\n",
" <td>-0.336169</td>\n", " <td>-0.219916</td>\n",
" <td>-0.955743</td>\n", " <td>0.815982</td>\n",
" <td>-0.352194</td>\n", " <td>0.127839</td>\n",
" <td>0.788721</td>\n", " <td>-0.533913</td>\n",
" <td>0.636567</td>\n", " <td>0.075909</td>\n",
" <td>0.583178</td>\n", " <td>0.098488</td>\n",
" <td>0.733763</td>\n", " <td>0.563604</td>\n",
" <td>-0.785763</td>\n", " <td>-0.334205</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>...</th>\n", " <th>...</th>\n",
@@ -495,68 +507,68 @@
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>9995</th>\n", " <th>9995</th>\n",
" <td>0.696557</td>\n", " <td>0.815217</td>\n",
" <td>0.653128</td>\n", " <td>0.136989</td>\n",
" <td>-0.452445</td>\n", " <td>-0.264895</td>\n",
" <td>-0.474580</td>\n", " <td>0.950940</td>\n",
" <td>0.143816</td>\n", " <td>-0.402690</td>\n",
" <td>-0.049960</td>\n", " <td>0.816257</td>\n",
" <td>-0.768933</td>\n", " <td>-0.848535</td>\n",
" <td>-0.174698</td>\n", " <td>-0.520143</td>\n",
" <td>-0.104592</td>\n", " <td>-0.564143</td>\n",
" <td>0.463803</td>\n", " <td>0.758220</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>9996</th>\n", " <th>9996</th>\n",
" <td>-0.897026</td>\n", " <td>-0.601188</td>\n",
" <td>-0.488836</td>\n", " <td>0.329444</td>\n",
" <td>-0.014449</td>\n", " <td>0.400244</td>\n",
" <td>-0.757131</td>\n", " <td>-0.631055</td>\n",
" <td>0.316350</td>\n", " <td>-0.035561</td>\n",
" <td>0.042017</td>\n", " <td>-0.479244</td>\n",
" <td>-0.403770</td>\n", " <td>0.151432</td>\n",
" <td>0.548561</td>\n", " <td>-0.260616</td>\n",
" <td>0.591823</td>\n", " <td>-0.735051</td>\n",
" <td>-0.550146</td>\n", " <td>0.219002</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>9997</th>\n", " <th>9997</th>\n",
" <td>0.834296</td>\n", " <td>-0.577752</td>\n",
" <td>-0.850360</td>\n", " <td>-0.498329</td>\n",
" <td>0.727586</td>\n", " <td>-0.637509</td>\n",
" <td>-0.098943</td>\n", " <td>-0.720854</td>\n",
" <td>-0.560702</td>\n", " <td>0.519939</td>\n",
" <td>0.054720</td>\n", " <td>-0.315098</td>\n",
" <td>0.673407</td>\n", " <td>0.610658</td>\n",
" <td>-0.041506</td>\n", " <td>-0.587285</td>\n",
" <td>0.252130</td>\n", " <td>0.542854</td>\n",
" <td>-0.175144</td>\n", " <td>0.783085</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>9998</th>\n", " <th>9998</th>\n",
" <td>-0.715145</td>\n", " <td>-0.390503</td>\n",
" <td>-0.483422</td>\n", " <td>-0.137664</td>\n",
" <td>0.874546</td>\n", " <td>-0.201412</td>\n",
" <td>0.463453</td>\n", " <td>-0.199943</td>\n",
" <td>-0.359133</td>\n", " <td>-0.997903</td>\n",
" <td>-0.131677</td>\n", " <td>-0.017195</td>\n",
" <td>0.906279</td>\n", " <td>-0.712091</td>\n",
" <td>0.422127</td>\n", " <td>0.725895</td>\n",
" <td>-0.231510</td>\n", " <td>0.888849</td>\n",
" <td>-0.981712</td>\n", " <td>-0.100168</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>9999</th>\n", " <th>9999</th>\n",
" <td>-0.230620</td>\n", " <td>0.551831</td>\n",
" <td>-0.342262</td>\n", " <td>0.910650</td>\n",
" <td>-0.582301</td>\n", " <td>-0.487670</td>\n",
" <td>0.036937</td>\n", " <td>0.332045</td>\n",
" <td>-0.440668</td>\n", " <td>-0.596616</td>\n",
" <td>-0.611692</td>\n", " <td>-0.212282</td>\n",
" <td>0.633051</td>\n", " <td>0.234631</td>\n",
" <td>0.306141</td>\n", " <td>-0.845725</td>\n",
" <td>-0.412429</td>\n", " <td>0.166181</td>\n",
" <td>0.053962</td>\n", " <td>-0.126873</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
@@ -564,36 +576,36 @@
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" x0 x1 x2 x3 x4 x5 x6 \n", " x0 x1 x2 x3 x4 x5 x6 \\\n",
"0 0.557678 -0.973417 0.722963 -0.011507 -0.297092 -0.096422 0.649971 \\\n", "0 -0.021048 0.624256 -0.213331 0.873981 -0.744169 -0.295821 0.033295 \n",
"1 0.077690 -0.597280 0.659335 0.433345 0.407872 0.026935 0.760887 \n", "1 -0.481733 0.707507 0.250378 -0.289964 0.192630 -0.117562 0.553985 \n",
"2 0.389252 0.312857 0.744985 -0.547178 0.918958 0.842294 -0.839457 \n", "2 -0.317442 0.502972 0.375717 0.033442 -0.281267 0.300927 -0.598245 \n",
"3 0.002392 -0.452201 0.626875 0.467181 -0.242254 0.342050 -0.804150 \n", "3 -0.161197 0.732069 0.441334 0.868591 0.635648 -0.168180 -0.673598 \n",
"4 0.013865 0.772512 -0.336169 -0.955743 -0.352194 0.788721 0.636567 \n", "4 -0.371950 -0.188998 -0.219916 0.815982 0.127839 -0.533913 0.075909 \n",
"... ... ... ... ... ... ... ... \n", "... ... ... ... ... ... ... ... \n",
"9995 0.696557 0.653128 -0.452445 -0.474580 0.143816 -0.049960 -0.768933 \n", "9995 0.815217 0.136989 -0.264895 0.950940 -0.402690 0.816257 -0.848535 \n",
"9996 -0.897026 -0.488836 -0.014449 -0.757131 0.316350 0.042017 -0.403770 \n", "9996 -0.601188 0.329444 0.400244 -0.631055 -0.035561 -0.479244 0.151432 \n",
"9997 0.834296 -0.850360 0.727586 -0.098943 -0.560702 0.054720 0.673407 \n", "9997 -0.577752 -0.498329 -0.637509 -0.720854 0.519939 -0.315098 0.610658 \n",
"9998 -0.715145 -0.483422 0.874546 0.463453 -0.359133 -0.131677 0.906279 \n", "9998 -0.390503 -0.137664 -0.201412 -0.199943 -0.997903 -0.017195 -0.712091 \n",
"9999 -0.230620 -0.342262 -0.582301 0.036937 -0.440668 -0.611692 0.633051 \n", "9999 0.551831 0.910650 -0.487670 0.332045 -0.596616 -0.212282 0.234631 \n",
"\n", "\n",
" x7 x8 x9 \n", " x7 x8 x9 \n",
"0 -0.822024 -0.953068 -0.465009 \n", "0 -0.249991 0.482630 0.892960 \n",
"1 0.375025 0.436400 0.940421 \n", "1 0.511281 -0.841345 0.200531 \n",
"2 -0.885617 -0.660291 0.799358 \n", "2 0.843422 -0.629739 -0.069668 \n",
"3 -0.757346 0.534880 -0.822235 \n", "3 -0.165261 -0.806899 -0.753438 \n",
"4 0.583178 0.733763 -0.785763 \n", "4 0.098488 0.563604 -0.334205 \n",
"... ... ... ... \n", "... ... ... ... \n",
"9995 -0.174698 -0.104592 0.463803 \n", "9995 -0.520143 -0.564143 0.758220 \n",
"9996 0.548561 0.591823 -0.550146 \n", "9996 -0.260616 -0.735051 0.219002 \n",
"9997 -0.041506 0.252130 -0.175144 \n", "9997 -0.587285 0.542854 0.783085 \n",
"9998 0.422127 -0.231510 -0.981712 \n", "9998 0.725895 0.888849 -0.100168 \n",
"9999 0.306141 -0.412429 0.053962 \n", "9999 -0.845725 0.166181 -0.126873 \n",
"\n", "\n",
"[10000 rows x 10 columns]" "[10000 rows x 10 columns]"
] ]
}, },
"execution_count": 10, "execution_count": 9,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -612,25 +624,73 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 10,
"id": "aa75ce65-2e94-4d64-93ca-80bb953e81d1", "id": "aa75ce65-2e94-4d64-93ca-80bb953e81d1",
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
"outputs": [ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n",
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" data_subset = grouped_data.get_group(pd_key)\n"
]
},
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"[Text(0, 0.5, 'Fraction of occurrences / bin size'), Text(0.5, 0, 'x')]" "[Text(0, 0.5, 'Fraction of occurrences / bin size'), Text(0.5, 0, 'x')]"
] ]
}, },
"execution_count": 11, "execution_count": 10,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1500x500 with 1 Axes>" "<Figure size 1500x500 with 1 Axes>"
] ]
@@ -654,7 +714,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 11,
"id": "a4b370e3-bd2b-4ca2-b13f-d71fe875f89f", "id": "a4b370e3-bd2b-4ca2-b13f-d71fe875f89f",
"metadata": { "metadata": {
"tags": [] "tags": []
@@ -675,15 +735,23 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 12,
"id": "437d8afb-414a-4ec6-8f5a-cdaf97136183", "id": "437d8afb-414a-4ec6-8f5a-cdaf97136183",
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
"outputs": [ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -691,9 +759,17 @@
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
}, },
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -701,9 +777,17 @@
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
}, },
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -711,9 +795,17 @@
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
}, },
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -721,9 +813,17 @@
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
}, },
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -731,9 +831,17 @@
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
}, },
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -741,9 +849,17 @@
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
}, },
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -751,9 +867,17 @@
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
}, },
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -761,9 +885,17 @@
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
}, },
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -771,9 +903,17 @@
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
}, },
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -799,7 +939,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 13,
"id": "a1fbd00b-e7f2-4eff-90db-e1d44d104fba", "id": "a1fbd00b-e7f2-4eff-90db-e1d44d104fba",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -810,15 +950,23 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 14,
"id": "20617a26-0fd6-4b00-a096-53b917760a35", "id": "20617a26-0fd6-4b00-a096-53b917760a35",
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
"outputs": [ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{ {
"data": { "data": {
"image/png": 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truncated "image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x800 with 1 Axes>" "<Figure size 1000x800 with 1 Axes>"
] ]
@@ -865,7 +1013,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.6" "version": "3.12.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
1 Line fits.ipynb
+14 -14
View File
@@ -37,7 +37,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"id": "5d457cd8", "id": "5d457cd8",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -53,7 +53,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 3,
"id": "bc7938ee-c442-428a-984b-47975605b850", "id": "bc7938ee-c442-428a-984b-47975605b850",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -136,7 +136,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 4,
"id": "30205402", "id": "30205402",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -149,23 +149,23 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 5,
"id": "cabdfc12-3033-4a2f-96ed-4ab1ea8312ab", "id": "cabdfc12-3033-4a2f-96ed-4ab1ea8312ab",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.legend.Legend at 0x7fdf8b28ccd0>" "<matplotlib.legend.Legend at 0x7f8d3c21ed20>"
] ]
}, },
"execution_count": 10, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 640x480 with 1 Axes>" "<Figure size 640x480 with 1 Axes>"
] ]
@@ -192,17 +192,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 6,
"id": "c6a1998f-81eb-4a32-8d00-04639ca777c2", "id": "c6a1998f-81eb-4a32-8d00-04639ca777c2",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"-0.06067522680624462" "-0.027163347597745746"
] ]
}, },
"execution_count": 11, "execution_count": 6,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -213,17 +213,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 7,
"id": "ec33c149-7d6f-433b-872a-ca97c0b7d7d6", "id": "ec33c149-7d6f-433b-872a-ca97c0b7d7d6",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"2.89487707822029" "2.8940177705732415"
] ]
}, },
"execution_count": 12, "execution_count": 7,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -302,7 +302,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.12" "version": "3.12.3"
}, },
"widgets": { "widgets": {
"application/vnd.jupyter.widget-state+json": { "application/vnd.jupyter.widget-state+json": {
2 Bayesian fits.ipynb
+413 -9
View File
@@ -97,7 +97,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 800x600 with 1 Axes>" "<Figure size 800x600 with 1 Axes>"
] ]
@@ -286,7 +286,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 2000x400 with 10 Axes>" "<Figure size 2000x400 with 10 Axes>"
] ]
@@ -307,7 +307,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 2000x400 with 10 Axes>" "<Figure size 2000x400 with 10 Axes>"
] ]
@@ -328,7 +328,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 2000x400 with 10 Axes>" "<Figure size 2000x400 with 10 Axes>"
] ]
@@ -349,7 +349,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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"text/plain": [ "text/plain": [
"<Figure size 2000x400 with 10 Axes>" "<Figure size 2000x400 with 10 Axes>"
] ]
@@ -434,7 +434,411 @@
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"data": { "data": {
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"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BayesianRidge()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BayesianRidge</label><div class=\"sk-toggleable__content\"><pre>BayesianRidge()</pre></div></div></div></div></div>" "<style>#sk-container-id-1 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-1 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-1 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-1 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-1 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-1 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-1 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-1 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-1 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BayesianRidge()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;BayesianRidge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.BayesianRidge.html\">?<span>Documentation for BayesianRidge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>BayesianRidge()</pre></div> </div></div></div></div>"
], ],
"text/plain": [ "text/plain": [
"BayesianRidge()" "BayesianRidge()"
@@ -460,7 +864,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.legend.Legend at 0x7fcc18691d60>" "<matplotlib.legend.Legend at 0x7fd2d20dae70>"
] ]
}, },
"execution_count": 12, "execution_count": 12,
@@ -469,7 +873,7 @@
}, },
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 1500x500 with 1 Axes>" "<Figure size 1500x500 with 1 Axes>"
] ]
@@ -572,7 +976,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.12" "version": "3.12.3"
}, },
"widgets": { "widgets": {
"application/vnd.jupyter.widget-state+json": { "application/vnd.jupyter.widget-state+json": {
2.1 Representation Learning.ipynb
+411 -9
View File
@@ -200,7 +200,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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"text/plain": [ "text/plain": [
"<Figure size 800x800 with 1 Axes>" "<Figure size 800x800 with 1 Axes>"
] ]
@@ -267,7 +267,411 @@
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"data": { "data": {
"text/html": [ "text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=2)</pre></div></div></div></div></div>" "<style>#sk-container-id-1 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-1 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-1 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-1 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-1 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-1 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-1 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-1 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-1 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;PCA<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PCA(n_components=2)</pre></div> </div></div></div></div>"
], ],
"text/plain": [ "text/plain": [
"PCA(n_components=2)" "PCA(n_components=2)"
@@ -319,7 +723,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x1000 with 2 Axes>" "<Figure size 1000x1000 with 2 Axes>"
] ]
@@ -353,7 +757,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated "image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 1000x1000 with 1 Axes>" "<Figure size 1000x1000 with 1 Axes>"
] ]
@@ -412,10 +816,8 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"import mnist\n", "from sklearn.datasets import fetch_openml\n",
"\n", "data, labels = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)"
"data = mnist.train_images() # the images\n",
"labels = mnist.train_labels() # the array with the digit each image corresponds to"
] ]
}, },
{ {
@@ -443,7 +845,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.6" "version": "3.12.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
3 Non-linear fits.ipynb
+1645 -29
View File
File diff suppressed because it is too large. Load diff
3.1 Supervised Regression.ipynb
+114 -105
View File
@@ -116,7 +116,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"{'data': array([-1.5745504], dtype=float32), 'target': array([17.356268], dtype=float32)}\n" "{'data': array([0.24012195], dtype=float32), 'target': array([14.341419], dtype=float32)}\n"
] ]
} }
], ],
@@ -140,7 +140,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 640x480 with 1 Axes>" "<Figure size 640x480 with 1 Axes>"
] ]
@@ -223,7 +223,16 @@
"execution_count": 8, "execution_count": 8,
"id": "988e1979", "id": "988e1979",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML\n",
" warnings.warn(\"Can't initialize NVML\")\n"
]
}
],
"source": [ "source": [
"network = Network()\n", "network = Network()\n",
"B = 10\n", "B = 10\n",
@@ -249,106 +258,106 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Epoch 0/100: average loss 452.59888\n", "Epoch 0/100: average loss 292.65962\n",
"Epoch 1/100: average loss 339.20629\n", "Epoch 1/100: average loss 168.60480\n",
"Epoch 2/100: average loss 263.36186\n", "Epoch 2/100: average loss 144.85957\n",
"Epoch 3/100: average loss 223.31033\n", "Epoch 3/100: average loss 131.24882\n",
"Epoch 4/100: average loss 203.19063\n", "Epoch 4/100: average loss 125.56896\n",
"Epoch 5/100: average loss 191.09150\n", "Epoch 5/100: average loss 122.62807\n",
"Epoch 6/100: average loss 182.24778\n", "Epoch 6/100: average loss 120.44151\n",
"Epoch 7/100: average loss 175.20755\n", "Epoch 7/100: average loss 118.44099\n",
"Epoch 8/100: average loss 169.28958\n", "Epoch 8/100: average loss 116.76986\n",
"Epoch 9/100: average loss 164.11071\n", "Epoch 9/100: average loss 115.28613\n",
"Epoch 10/100: average loss 159.43586\n", "Epoch 10/100: average loss 114.02854\n",
"Epoch 11/100: average loss 155.22682\n", "Epoch 11/100: average loss 112.92324\n",
"Epoch 12/100: average loss 151.44842\n", "Epoch 12/100: average loss 112.03872\n",
"Epoch 13/100: average loss 148.07120\n", "Epoch 13/100: average loss 111.31309\n",
"Epoch 14/100: average loss 145.08345\n", "Epoch 14/100: average loss 110.73915\n",
"Epoch 15/100: average loss 142.42645\n", "Epoch 15/100: average loss 110.27166\n",
"Epoch 16/100: average loss 140.11430\n", "Epoch 16/100: average loss 109.91651\n",
"Epoch 17/100: average loss 138.10618\n", "Epoch 17/100: average loss 109.58956\n",
"Epoch 18/100: average loss 136.35764\n", "Epoch 18/100: average loss 109.31038\n",
"Epoch 19/100: average loss 134.83290\n", "Epoch 19/100: average loss 109.14096\n",
"Epoch 20/100: average loss 133.48525\n", "Epoch 20/100: average loss 108.96208\n",
"Epoch 21/100: average loss 132.29287\n", "Epoch 21/100: average loss 108.82673\n",
"Epoch 22/100: average loss 131.22552\n", "Epoch 22/100: average loss 108.67057\n",
"Epoch 23/100: average loss 130.26203\n", "Epoch 23/100: average loss 108.56558\n",
"Epoch 24/100: average loss 129.36947\n", "Epoch 24/100: average loss 108.45631\n",
"Epoch 25/100: average loss 128.54001\n", "Epoch 25/100: average loss 108.37027\n",
"Epoch 26/100: average loss 127.77259\n", "Epoch 26/100: average loss 108.29927\n",
"Epoch 27/100: average loss 127.05234\n", "Epoch 27/100: average loss 108.26275\n",
"Epoch 28/100: average loss 126.35698\n", "Epoch 28/100: average loss 108.19757\n",
"Epoch 29/100: average loss 125.67996\n", "Epoch 29/100: average loss 108.11492\n",
"Epoch 30/100: average loss 125.02878\n", "Epoch 30/100: average loss 108.07109\n",
"Epoch 31/100: average loss 124.39722\n", "Epoch 31/100: average loss 108.04241\n",
"Epoch 32/100: average loss 123.77292\n", "Epoch 32/100: average loss 108.01575\n",
"Epoch 33/100: average loss 123.15925\n", "Epoch 33/100: average loss 107.99630\n",
"Epoch 34/100: average loss 122.55854\n", "Epoch 34/100: average loss 107.98242\n",
"Epoch 35/100: average loss 121.97138\n", "Epoch 35/100: average loss 107.95160\n",
"Epoch 36/100: average loss 121.39923\n", "Epoch 36/100: average loss 107.93830\n",
"Epoch 37/100: average loss 120.84461\n", "Epoch 37/100: average loss 107.89870\n",
"Epoch 38/100: average loss 120.30410\n", "Epoch 38/100: average loss 107.88430\n",
"Epoch 39/100: average loss 119.77399\n", "Epoch 39/100: average loss 107.87700\n",
"Epoch 40/100: average loss 119.26286\n", "Epoch 40/100: average loss 107.84878\n",
"Epoch 41/100: average loss 118.77323\n", "Epoch 41/100: average loss 107.83601\n",
"Epoch 42/100: average loss 118.30117\n", "Epoch 42/100: average loss 107.80727\n",
"Epoch 43/100: average loss 117.84936\n", "Epoch 43/100: average loss 107.80601\n",
"Epoch 44/100: average loss 117.40730\n", "Epoch 44/100: average loss 107.79060\n",
"Epoch 45/100: average loss 116.97969\n", "Epoch 45/100: average loss 107.77407\n",
"Epoch 46/100: average loss 116.56799\n", "Epoch 46/100: average loss 107.76293\n",
"Epoch 47/100: average loss 116.17331\n", "Epoch 47/100: average loss 107.71370\n",
"Epoch 48/100: average loss 115.78789\n", "Epoch 48/100: average loss 107.70025\n",
"Epoch 49/100: average loss 115.42027\n", "Epoch 49/100: average loss 107.67249\n",
"Epoch 50/100: average loss 115.06993\n", "Epoch 50/100: average loss 107.63180\n",
"Epoch 51/100: average loss 114.73861\n", "Epoch 51/100: average loss 107.60612\n",
"Epoch 52/100: average loss 114.42548\n", "Epoch 52/100: average loss 107.56095\n",
"Epoch 53/100: average loss 114.11974\n", "Epoch 53/100: average loss 107.57543\n",
"Epoch 54/100: average loss 113.82660\n", "Epoch 54/100: average loss 107.55797\n",
"Epoch 55/100: average loss 113.55279\n", "Epoch 55/100: average loss 107.54764\n",
"Epoch 56/100: average loss 113.29264\n", "Epoch 56/100: average loss 107.52183\n",
"Epoch 57/100: average loss 113.04481\n", "Epoch 57/100: average loss 107.49846\n",
"Epoch 58/100: average loss 112.80599\n", "Epoch 58/100: average loss 107.51441\n",
"Epoch 59/100: average loss 112.58345\n", "Epoch 59/100: average loss 107.48613\n",
"Epoch 60/100: average loss 112.36975\n", "Epoch 60/100: average loss 107.48426\n",
"Epoch 61/100: average loss 112.16276\n", "Epoch 61/100: average loss 107.45074\n",
"Epoch 62/100: average loss 111.96547\n", "Epoch 62/100: average loss 107.46100\n",
"Epoch 63/100: average loss 111.77942\n", "Epoch 63/100: average loss 107.44333\n",
"Epoch 64/100: average loss 111.60994\n", "Epoch 64/100: average loss 107.44383\n",
"Epoch 65/100: average loss 111.44945\n", "Epoch 65/100: average loss 107.40632\n",
"Epoch 66/100: average loss 111.29771\n", "Epoch 66/100: average loss 107.40891\n",
"Epoch 67/100: average loss 111.14783\n", "Epoch 67/100: average loss 107.40680\n",
"Epoch 68/100: average loss 111.00336\n", "Epoch 68/100: average loss 107.38369\n",
"Epoch 69/100: average loss 110.86740\n", "Epoch 69/100: average loss 107.37979\n",
"Epoch 70/100: average loss 110.73891\n", "Epoch 70/100: average loss 107.36220\n",
"Epoch 71/100: average loss 110.61783\n", "Epoch 71/100: average loss 107.37177\n",
"Epoch 72/100: average loss 110.50365\n", "Epoch 72/100: average loss 107.35137\n",
"Epoch 73/100: average loss 110.39520\n", "Epoch 73/100: average loss 107.34140\n",
"Epoch 74/100: average loss 110.28639\n", "Epoch 74/100: average loss 107.33566\n",
"Epoch 75/100: average loss 110.17986\n", "Epoch 75/100: average loss 107.33218\n",
"Epoch 76/100: average loss 110.07611\n", "Epoch 76/100: average loss 107.31788\n",
"Epoch 77/100: average loss 109.97478\n", "Epoch 77/100: average loss 107.31176\n",
"Epoch 78/100: average loss 109.87914\n", "Epoch 78/100: average loss 107.29611\n",
"Epoch 79/100: average loss 109.78643\n", "Epoch 79/100: average loss 107.29593\n",
"Epoch 80/100: average loss 109.69742\n", "Epoch 80/100: average loss 107.29700\n",
"Epoch 81/100: average loss 109.60897\n", "Epoch 81/100: average loss 107.28151\n",
"Epoch 82/100: average loss 109.52515\n", "Epoch 82/100: average loss 107.26731\n",
"Epoch 83/100: average loss 109.44434\n", "Epoch 83/100: average loss 107.26950\n",
"Epoch 84/100: average loss 109.36794\n", "Epoch 84/100: average loss 107.25340\n",
"Epoch 85/100: average loss 109.30291\n", "Epoch 85/100: average loss 107.25051\n",
"Epoch 86/100: average loss 109.23819\n", "Epoch 86/100: average loss 107.24163\n",
"Epoch 87/100: average loss 109.17980\n", "Epoch 87/100: average loss 107.24350\n",
"Epoch 88/100: average loss 109.12350\n", "Epoch 88/100: average loss 107.23004\n",
"Epoch 89/100: average loss 109.06770\n", "Epoch 89/100: average loss 107.23470\n",
"Epoch 90/100: average loss 109.01470\n", "Epoch 90/100: average loss 107.21753\n",
"Epoch 91/100: average loss 108.96011\n", "Epoch 91/100: average loss 107.22043\n",
"Epoch 92/100: average loss 108.90628\n", "Epoch 92/100: average loss 107.20536\n",
"Epoch 93/100: average loss 108.86134\n", "Epoch 93/100: average loss 107.20931\n",
"Epoch 94/100: average loss 108.81156\n", "Epoch 94/100: average loss 107.18696\n",
"Epoch 95/100: average loss 108.76630\n", "Epoch 95/100: average loss 107.20320\n",
"Epoch 96/100: average loss 108.71505\n", "Epoch 96/100: average loss 107.18551\n",
"Epoch 97/100: average loss 108.67075\n", "Epoch 97/100: average loss 107.19133\n",
"Epoch 98/100: average loss 108.62780\n", "Epoch 98/100: average loss 107.18022\n",
"Epoch 99/100: average loss 108.58241\n" "Epoch 99/100: average loss 107.16567\n"
] ]
} }
], ],
@@ -432,7 +441,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 640x480 with 1 Axes>" "<Figure size 640x480 with 1 Axes>"
] ]
@@ -524,7 +533,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.6" "version": "3.12.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
3.2 Regression Uncetainties.ipynb
+515 -506
View File
File diff suppressed because it is too large. Load diff
4 Support Vector Machines.ipynb
+549 -131
View File
@@ -175,32 +175,32 @@
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>0</th>\n", " <th>0</th>\n",
" <td>2.696133</td>\n", " <td>1.957979</td>\n",
" <td>2.569388</td>\n", " <td>1.963396</td>\n",
" <td>0.0</td>\n", " <td>0.0</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>1</th>\n", " <th>1</th>\n",
" <td>2.156646</td>\n", " <td>1.746422</td>\n",
" <td>1.907207</td>\n", " <td>2.743400</td>\n",
" <td>0.0</td>\n", " <td>0.0</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2</th>\n", " <th>2</th>\n",
" <td>3.219006</td>\n", " <td>1.853572</td>\n",
" <td>2.012231</td>\n", " <td>1.972891</td>\n",
" <td>0.0</td>\n", " <td>0.0</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>3</th>\n", " <th>3</th>\n",
" <td>2.075721</td>\n", " <td>2.128815</td>\n",
" <td>1.932324</td>\n", " <td>1.326886</td>\n",
" <td>0.0</td>\n", " <td>0.0</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>4</th>\n", " <th>4</th>\n",
" <td>1.698718</td>\n", " <td>1.926940</td>\n",
" <td>2.143439</td>\n", " <td>2.328016</td>\n",
" <td>0.0</td>\n", " <td>0.0</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
@@ -211,32 +211,32 @@
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>1995</th>\n", " <th>1995</th>\n",
" <td>-2.023710</td>\n", " <td>-1.436096</td>\n",
" <td>1.375677</td>\n", " <td>0.892213</td>\n",
" <td>1.0</td>\n", " <td>1.0</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>1996</th>\n", " <th>1996</th>\n",
" <td>1.190933</td>\n", " <td>-0.829921</td>\n",
" <td>0.369058</td>\n", " <td>1.677958</td>\n",
" <td>1.0</td>\n", " <td>1.0</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>1997</th>\n", " <th>1997</th>\n",
" <td>-1.525767</td>\n", " <td>-0.881437</td>\n",
" <td>1.248843</td>\n", " <td>1.086710</td>\n",
" <td>1.0</td>\n", " <td>1.0</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>1998</th>\n", " <th>1998</th>\n",
" <td>-1.191291</td>\n", " <td>-0.800512</td>\n",
" <td>0.644813</td>\n", " <td>1.293252</td>\n",
" <td>1.0</td>\n", " <td>1.0</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>1999</th>\n", " <th>1999</th>\n",
" <td>1.103239</td>\n", " <td>-0.832540</td>\n",
" <td>0.973839</td>\n", " <td>0.900190</td>\n",
" <td>1.0</td>\n", " <td>1.0</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
@@ -246,17 +246,17 @@
], ],
"text/plain": [ "text/plain": [
" x y source\n", " x y source\n",
"0 2.696133 2.569388 0.0\n", "0 1.957979 1.963396 0.0\n",
"1 2.156646 1.907207 0.0\n", "1 1.746422 2.743400 0.0\n",
"2 3.219006 2.012231 0.0\n", "2 1.853572 1.972891 0.0\n",
"3 2.075721 1.932324 0.0\n", "3 2.128815 1.326886 0.0\n",
"4 1.698718 2.143439 0.0\n", "4 1.926940 2.328016 0.0\n",
"... ... ... ...\n", "... ... ... ...\n",
"1995 -2.023710 1.375677 1.0\n", "1995 -1.436096 0.892213 1.0\n",
"1996 1.190933 0.369058 1.0\n", "1996 -0.829921 1.677958 1.0\n",
"1997 -1.525767 1.248843 1.0\n", "1997 -0.881437 1.086710 1.0\n",
"1998 -1.191291 0.644813 1.0\n", "1998 -0.800512 1.293252 1.0\n",
"1999 1.103239 0.973839 1.0\n", "1999 -0.832540 0.900190 1.0\n",
"\n", "\n",
"[2000 rows x 3 columns]" "[2000 rows x 3 columns]"
] ]
@@ -286,7 +286,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
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"text/plain": [ "text/plain": [
"<Figure size 800x800 with 2 Axes>" "<Figure size 800x800 with 2 Axes>"
] ]
@@ -331,7 +331,411 @@
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"data": { "data": {
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"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>" "<style>#sk-container-id-1 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-1 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-1 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-1 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-1 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-1 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-1 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-1 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-1 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
], ],
"text/plain": [ "text/plain": [
"SVC(kernel='linear')" "SVC(kernel='linear')"
@@ -363,100 +767,114 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([[ 0.41056889, 1.78218835],\n", "array([[ 0.5728445 , 1.85279962],\n",
" [ 0.30256648, 2.3182121 ],\n", " [ 0.88952752, 1.29565843],\n",
" [ 2.14295088, -0.34278388],\n", " [-0.29289522, 1.7323941 ],\n",
" [ 0.58553232, 1.48987089],\n", " [ 1.21587184, 0.0298207 ],\n",
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] ]
}, },
"execution_count": 8, "execution_count": 8,
@@ -490,13 +908,13 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"/home/daniloefl/workspace/ml-lecture-oct-2023/env/lib/python3.9/site-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n", "/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
" warnings.warn(\n" " warnings.warn(\n"
] ]
}, },
{ {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 800x800 with 1 Axes>" "<Figure size 800x800 with 1 Axes>"
] ]
@@ -555,13 +973,13 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"/home/daniloefl/workspace/ml-lecture-oct-2023/env/lib/python3.9/site-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n", "/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
" warnings.warn(\n" " warnings.warn(\n"
] ]
}, },
{ {
"data": { "data": {
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truncated "image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 800x800 with 1 Axes>" "<Figure size 800x800 with 1 Axes>"
] ]
@@ -640,7 +1058,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.12" "version": "3.12.3"
}, },
"widgets": { "widgets": {
"application/vnd.jupyter.widget-state+json": { "application/vnd.jupyter.widget-state+json": {
5 Gaussian Processes.ipynb
+24 -24
View File
@@ -32,7 +32,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 1,
"id": "07ecc0d3", "id": "07ecc0d3",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -122,7 +122,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"id": "300cf8d3", "id": "300cf8d3",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -147,13 +147,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 6,
"id": "4959a292", "id": "4959a292",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"data = pd.read_csv(\"co2_weekly_mlo.txt\",\n", "data = pd.read_csv(\"co2_weekly_mlo.txt\",\n",
" delim_whitespace=True,\n", " sep=r'\\s+',\n",
" comment='#',\n", " comment='#',\n",
" header=None,\n", " header=None,\n",
" names=[\"year\", \"month\", \"day\", \"decimal\",\n", " names=[\"year\", \"month\", \"day\", \"decimal\",\n",
@@ -173,7 +173,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 7,
"id": "024fb65a", "id": "024fb65a",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -377,7 +377,7 @@
"[2487 rows x 9 columns]" "[2487 rows x 9 columns]"
] ]
}, },
"execution_count": 5, "execution_count": 7,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -396,13 +396,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 8,
"id": "e63b38c5", "id": "e63b38c5",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 800x800 with 1 Axes>" "<Figure size 800x800 with 1 Axes>"
] ]
@@ -428,7 +428,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 9,
"id": "0837b3ff", "id": "0837b3ff",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -438,13 +438,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 10,
"id": "333581b5", "id": "333581b5",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated "image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 800x800 with 1 Axes>" "<Figure size 800x800 with 1 Axes>"
] ]
@@ -486,7 +486,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 11,
"id": "a31d7eca", "id": "a31d7eca",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -527,7 +527,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 12,
"id": "032ada0d", "id": "032ada0d",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -553,7 +553,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 13,
"id": "22046fb8-135c-4048-a0c5-ceb1e8190049", "id": "22046fb8-135c-4048-a0c5-ceb1e8190049",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -563,7 +563,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 14,
"id": "4412a9a7", "id": "4412a9a7",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -575,7 +575,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 15,
"id": "88bcd2c1-c9ae-4f89-ba9a-96d731907138", "id": "88bcd2c1-c9ae-4f89-ba9a-96d731907138",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -593,7 +593,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 16,
"id": "667db862", "id": "667db862",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -601,7 +601,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"GPML kernel: 3.27**2 * RBF(length_scale=37.8) + 0.175**2 * RBF(length_scale=290) * ExpSineSquared(length_scale=1.59, periodicity=1) + 0.322**2 * RationalQuadratic(alpha=0.00131, length_scale=6.52) + 0.0121**2 * RBF(length_scale=0.02) + WhiteKernel(noise_level=0.000209)\n", "GPML kernel: 3.27**2 * RBF(length_scale=37.7) + 0.176**2 * RBF(length_scale=291) * ExpSineSquared(length_scale=1.59, periodicity=1) + 0.317**2 * RationalQuadratic(alpha=0.00135, length_scale=6.43) + 0.0121**2 * RBF(length_scale=0.02) + WhiteKernel(noise_level=0.000209)\n",
"Log-marginal-likelihood: 5275.122\n" "Log-marginal-likelihood: 5275.122\n"
] ]
} }
@@ -621,7 +621,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 17,
"id": "ef6b7762", "id": "ef6b7762",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -640,13 +640,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 18,
"id": "262c8e85-6f9c-447f-9e50-a356267e0597", "id": "262c8e85-6f9c-447f-9e50-a356267e0597",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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truncated "image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 800x800 with 1 Axes>" "<Figure size 800x800 with 1 Axes>"
] ]
@@ -683,13 +683,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 19,
"id": "5b263358", "id": "5b263358",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 800x800 with 1 Axes>" "<Figure size 800x800 with 1 Axes>"
] ]
@@ -762,7 +762,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.12" "version": "3.12.3"
}, },
"widgets": { "widgets": {
"application/vnd.jupyter.widget-state+json": { "application/vnd.jupyter.widget-state+json": {
6 Bayesian Optimization.ipynb
+26 -37
View File
@@ -19,17 +19,6 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
"id": "8933f30f-4862-4b36-b19a-a3511726d77f",
"metadata": {},
"outputs": [],
"source": [
"# Needed in Google Colab, since it does not have gpytorch by default\n",
"#!pip install gpytorch torch botorch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3ee0feee-7453-4982-ac31-c661ce692449", "id": "3ee0feee-7453-4982-ac31-c661ce692449",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -37,7 +26,7 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"/home/daniloefl/workspace/ml-lecture-oct-2023/env/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", "/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n" " from .autonotebook import tqdm as notebook_tqdm\n"
] ]
} }
@@ -55,7 +44,7 @@
"import gpytorch\n", "import gpytorch\n",
"\n", "\n",
"from botorch.acquisition import UpperConfidenceBound\n", "from botorch.acquisition import UpperConfidenceBound\n",
"from botorch.fit import fit_gpytorch_model\n", "from botorch.fit import fit_gpytorch_mll\n",
"from botorch.optim import optimize_acqf\n", "from botorch.optim import optimize_acqf\n",
"from botorch.acquisition.objective import ScalarizedPosteriorTransform\n", "from botorch.acquisition.objective import ScalarizedPosteriorTransform\n",
"from botorch.models import SingleTaskGP\n", "from botorch.models import SingleTaskGP\n",
@@ -67,7 +56,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"id": "fec4cda5-6574-4f34-8b29-02061aab35c8", "id": "fec4cda5-6574-4f34-8b29-02061aab35c8",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -87,23 +76,23 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 3,
"id": "c9a7d52c-588c-49dc-a05e-7f3bcc78e7a3", "id": "c9a7d52c-588c-49dc-a05e-7f3bcc78e7a3",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"[<matplotlib.lines.Line2D at 0x7f49a4ebc8e0>]" "[<matplotlib.lines.Line2D at 0x7f8781f63620>]"
] ]
}, },
"execution_count": 4, "execution_count": 3,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
{ {
"data": { "data": {
"image/png": 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truncated "image/png": "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 truncated
"text/plain": [ "text/plain": [
"<Figure size 640x480 with 1 Axes>" "<Figure size 640x480 with 1 Axes>"
] ]
@@ -130,7 +119,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 4,
"id": "442d8fc1-e45c-4162-b34c-f33ce56afd4a", "id": "442d8fc1-e45c-4162-b34c-f33ce56afd4a",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -154,7 +143,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 5,
"id": "d0825829-f78b-4443-84ca-922c0ef3c32a", "id": "d0825829-f78b-4443-84ca-922c0ef3c32a",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -189,13 +178,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 6,
"id": "0876f631-e6a3-4104-839d-522244708982", "id": "0876f631-e6a3-4104-839d-522244708982",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 1200x400 with 1 Axes>" "<Figure size 1200x400 with 1 Axes>"
] ]
@@ -220,7 +209,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 7,
"id": "5dd8089c-b3ed-4d67-b67e-9d34fc7ed77f", "id": "5dd8089c-b3ed-4d67-b67e-9d34fc7ed77f",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -312,7 +301,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 8,
"id": "c53800fb-65f9-44d5-bff5-0308b4afc452", "id": "c53800fb-65f9-44d5-bff5-0308b4afc452",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -326,7 +315,7 @@
" # get the log-likelihood for it\n", " # get the log-likelihood for it\n",
" mll = ExactMarginalLogLikelihood(model.likelihood, model)\n", " mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
" # make the fit of the model to the observed data\n", " # make the fit of the model to the observed data\n",
" opt_mll = fit_gpytorch_model(mll)\n", " opt_mll = fit_gpytorch_mll(mll)\n",
"\n", "\n",
" # get the acquisition function\n", " # get the acquisition function\n",
" acq = model.get_acquisition()\n", " acq = model.get_acquisition()\n",
@@ -363,7 +352,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 9,
"id": "5eee5e53-6382-49b1-9be2-09544abdefb7", "id": "5eee5e53-6382-49b1-9be2-09544abdefb7",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -445,7 +434,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 10,
"id": "8f7ebf8a-7503-4b2c-a544-691f1f13271f", "id": "8f7ebf8a-7503-4b2c-a544-691f1f13271f",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -453,7 +442,7 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"/home/daniloefl/workspace/ml-lecture-oct-2023/env/lib/python3.9/site-packages/botorch/models/utils/assorted.py:173: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", "/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n",
" warnings.warn(msg, InputDataWarning)\n" " warnings.warn(msg, InputDataWarning)\n"
] ]
} }
@@ -464,13 +453,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 11,
"id": "edec7a8e-ec9f-48cb-a725-44a7d56f1456", "id": "edec7a8e-ec9f-48cb-a725-44a7d56f1456",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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truncated "image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 1200x800 with 2 Axes>" "<Figure size 1200x800 with 2 Axes>"
] ]
@@ -495,7 +484,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 12,
"id": "ae25ddb3-113f-4d7b-b640-9913260b542f", "id": "ae25ddb3-113f-4d7b-b640-9913260b542f",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -508,7 +497,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 13,
"id": "3c6ac772-1f48-4685-8d06-033aa8d00858", "id": "3c6ac772-1f48-4685-8d06-033aa8d00858",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -516,7 +505,7 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"/home/daniloefl/workspace/ml-lecture-oct-2023/env/lib/python3.9/site-packages/botorch/models/utils/assorted.py:173: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", "/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n",
" warnings.warn(msg, InputDataWarning)\n" " warnings.warn(msg, InputDataWarning)\n"
] ]
} }
@@ -533,7 +522,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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truncated
"text/plain": [ "text/plain": [
"<Figure size 1200x800 with 2 Axes>" "<Figure size 1200x800 with 2 Axes>"
] ]
@@ -564,13 +553,13 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"/home/daniloefl/workspace/ml-lecture-oct-2023/env/lib/python3.9/site-packages/botorch/models/utils/assorted.py:173: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", "/home/daniloefl/miniconda3/envs/ml/lib/python3.12/site-packages/botorch/models/utils/assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n",
" warnings.warn(msg, InputDataWarning)\n" " warnings.warn(msg, InputDataWarning)\n"
] ]
}, },
{ {
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truncated
"text/plain": [ "text/plain": [
"<Figure size 1200x800 with 2 Axes>" "<Figure size 1200x800 with 2 Axes>"
] ]
@@ -623,7 +612,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.12" "version": "3.12.3"
}, },
"widgets": { "widgets": {
"application/vnd.jupyter.widget-state+json": { "application/vnd.jupyter.widget-state+json": {
7 Mixture Models.ipynb
+877 -77
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