|
84 | 84 | },
|
85 | 85 | {
|
86 | 86 | "cell_type": "code",
|
| 87 | +<<<<<<< HEAD |
87 | 88 | "execution_count": 2,
|
| 89 | +======= |
| 90 | + "execution_count": 1, |
| 91 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
88 | 92 | "id": "a54a43e1",
|
89 | 93 | "metadata": {
|
90 | 94 | "ExecuteTime": {
|
|
133 | 137 | },
|
134 | 138 | {
|
135 | 139 | "cell_type": "code",
|
| 140 | +<<<<<<< HEAD |
136 | 141 | "execution_count": 3,
|
| 142 | +======= |
| 143 | + "execution_count": 2, |
| 144 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
137 | 145 | "id": "c3885458",
|
138 | 146 | "metadata": {
|
139 | 147 | "ExecuteTime": {
|
|
1386 | 1394 | "name": "stderr",
|
1387 | 1395 | "output_type": "stream",
|
1388 | 1396 | "text": [
|
| 1397 | +<<<<<<< HEAD |
1389 | 1398 | "C:\\Users\\Roger Arendse\\AppData\\Local\\Temp\\ipykernel_4920\\296457212.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n",
|
| 1399 | +======= |
| 1400 | + "C:\\Users\\Roger Arendse\\AppData\\Local\\Temp\\ipykernel_16368\\296457212.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", |
| 1401 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
1390 | 1402 | " train_data.kurtosis().plot()\n"
|
1391 | 1403 | ]
|
1392 | 1404 | },
|
|
1435 | 1447 | "name": "stderr",
|
1436 | 1448 | "output_type": "stream",
|
1437 | 1449 | "text": [
|
| 1450 | +<<<<<<< HEAD |
1438 | 1451 | "C:\\Users\\Roger Arendse\\AppData\\Local\\Temp\\ipykernel_4920\\1539948712.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n",
|
| 1452 | +======= |
| 1453 | + "C:\\Users\\Roger Arendse\\AppData\\Local\\Temp\\ipykernel_16368\\1539948712.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", |
| 1454 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
1439 | 1455 | " train_data.skew(axis=0, skipna=True).plot()\n"
|
1440 | 1456 | ]
|
1441 | 1457 | },
|
|
1477 | 1493 | "name": "stderr",
|
1478 | 1494 | "output_type": "stream",
|
1479 | 1495 | "text": [
|
| 1496 | +<<<<<<< HEAD |
1480 | 1497 | "C:\\Users\\Roger Arendse\\AppData\\Local\\Temp\\ipykernel_4920\\2564673981.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n",
|
| 1498 | +======= |
| 1499 | + "C:\\Users\\Roger Arendse\\AppData\\Local\\Temp\\ipykernel_16368\\2564673981.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", |
| 1500 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
1481 | 1501 | " train_data.skew()\n"
|
1482 | 1502 | ]
|
1483 | 1503 | },
|
|
3428 | 3448 | "name": "stdout",
|
3429 | 3449 | "output_type": "stream",
|
3430 | 3450 | "text": [
|
| 3451 | +<<<<<<< HEAD |
3431 | 3452 | "MSE: 4779.613766374662\n",
|
3432 | 3453 | "r2: 0.18683350358747208\n"
|
| 3454 | +======= |
| 3455 | + "MSE: 4660.745316656458\n", |
| 3456 | + "r2: 0.17701990440801418\n" |
| 3457 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
3433 | 3458 | ]
|
3434 | 3459 | }
|
3435 | 3460 | ],
|
|
3580 | 3605 | " <tbody>\n",
|
3581 | 3606 | " <tr>\n",
|
3582 | 3607 | " <th>0</th>\n",
|
| 3608 | +<<<<<<< HEAD |
3583 | 3609 | " <td>8404.913541</td>\n",
|
3584 | 3610 | " </tr>\n",
|
3585 | 3611 | " <tr>\n",
|
|
3589 | 3615 | " <tr>\n",
|
3590 | 3616 | " <th>2</th>\n",
|
3591 | 3617 | " <td>8611.320136</td>\n",
|
| 3618 | +======= |
| 3619 | + " <td>7923.805886</td>\n", |
| 3620 | + " </tr>\n", |
| 3621 | + " <tr>\n", |
| 3622 | + " <th>1</th>\n", |
| 3623 | + " <td>7114.770178</td>\n", |
| 3624 | + " </tr>\n", |
| 3625 | + " <tr>\n", |
| 3626 | + " <th>2</th>\n", |
| 3627 | + " <td>8097.383796</td>\n", |
| 3628 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
3592 | 3629 | " </tr>\n",
|
3593 | 3630 | " </tbody>\n",
|
3594 | 3631 | "</table>\n",
|
3595 | 3632 | "</div>"
|
3596 | 3633 | ],
|
3597 | 3634 | "text/plain": [
|
3598 | 3635 | " load_shortfall_3h\n",
|
| 3636 | +<<<<<<< HEAD |
3599 | 3637 | "0 8404.913541\n",
|
3600 | 3638 | "1 7664.042458\n",
|
3601 | 3639 | "2 8611.320136"
|
| 3640 | +======= |
| 3641 | + "0 7923.805886\n", |
| 3642 | + "1 7114.770178\n", |
| 3643 | + "2 8097.383796" |
| 3644 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
3602 | 3645 | ]
|
3603 | 3646 | },
|
3604 | 3647 | "execution_count": 57,
|
|
3679 | 3722 | " <tr>\n",
|
3680 | 3723 | " <th>0</th>\n",
|
3681 | 3724 | " <td>2018-01-01 00:00:00</td>\n",
|
| 3725 | +<<<<<<< HEAD |
3682 | 3726 | " <td>8404.913541</td>\n",
|
| 3727 | +======= |
| 3728 | + " <td>7923.805886</td>\n", |
| 3729 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
3683 | 3730 | " </tr>\n",
|
3684 | 3731 | " <tr>\n",
|
3685 | 3732 | " <th>1</th>\n",
|
3686 | 3733 | " <td>2018-01-01 03:00:00</td>\n",
|
| 3734 | +<<<<<<< HEAD |
3687 | 3735 | " <td>7664.042458</td>\n",
|
| 3736 | +======= |
| 3737 | + " <td>7114.770178</td>\n", |
| 3738 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
3688 | 3739 | " </tr>\n",
|
3689 | 3740 | " <tr>\n",
|
3690 | 3741 | " <th>2</th>\n",
|
3691 | 3742 | " <td>2018-01-01 06:00:00</td>\n",
|
| 3743 | +<<<<<<< HEAD |
3692 | 3744 | " <td>8611.320136</td>\n",
|
| 3745 | +======= |
| 3746 | + " <td>8097.383796</td>\n", |
| 3747 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
3693 | 3748 | " </tr>\n",
|
3694 | 3749 | " </tbody>\n",
|
3695 | 3750 | "</table>\n",
|
3696 | 3751 | "</div>"
|
3697 | 3752 | ],
|
3698 | 3753 | "text/plain": [
|
3699 | 3754 | " time load_shortfall_3h\n",
|
| 3755 | +<<<<<<< HEAD |
3700 | 3756 | "0 2018-01-01 00:00:00 8404.913541\n",
|
3701 | 3757 | "1 2018-01-01 03:00:00 7664.042458\n",
|
3702 | 3758 | "2 2018-01-01 06:00:00 8611.320136"
|
| 3759 | +======= |
| 3760 | + "0 2018-01-01 00:00:00 7923.805886\n", |
| 3761 | + "1 2018-01-01 03:00:00 7114.770178\n", |
| 3762 | + "2 2018-01-01 06:00:00 8097.383796" |
| 3763 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
3703 | 3764 | ]
|
3704 | 3765 | },
|
3705 | 3766 | "execution_count": 60,
|
|
3732 | 3793 | },
|
3733 | 3794 | {
|
3734 | 3795 | "cell_type": "code",
|
| 3796 | +<<<<<<< HEAD |
3735 | 3797 | "execution_count": 64,
|
| 3798 | +======= |
| 3799 | + "execution_count": 61, |
| 3800 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
3736 | 3801 | "id": "5e05db54",
|
3737 | 3802 | "metadata": {},
|
3738 | 3803 | "outputs": [],
|
|
3754 | 3819 | },
|
3755 | 3820 | {
|
3756 | 3821 | "cell_type": "code",
|
| 3822 | +<<<<<<< HEAD |
3757 | 3823 | "execution_count": 65,
|
| 3824 | +======= |
| 3825 | + "execution_count": 62, |
| 3826 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
3758 | 3827 | "id": "3ef5e25b",
|
3759 | 3828 | "metadata": {},
|
3760 | 3829 | "outputs": [],
|
|
3763 | 3832 | "with open(model_load_path,'rb') as file:\n",
|
3764 | 3833 | " unpickled_model = pickle.load(file)"
|
3765 | 3834 | ]
|
| 3835 | +<<<<<<< HEAD |
3766 | 3836 | },
|
3767 | 3837 | {
|
3768 | 3838 | "cell_type": "code",
|
|
3771 | 3841 | "metadata": {},
|
3772 | 3842 | "outputs": [],
|
3773 | 3843 | "source": []
|
| 3844 | +======= |
| 3845 | +>>>>>>> b1f8a257a6cd49cd653400723e3d953fdd83032f |
3774 | 3846 | }
|
3775 | 3847 | ],
|
3776 | 3848 | "metadata": {
|
|
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