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new prediction

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This commit is contained in:
Karol Idaszak 2022-05-25 08:52:17 +02:00
parent 4b92825d62
commit 7fdacb38a6
6 changed files with 31578 additions and 77400 deletions
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31563
dev-0/out.tsv
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31563
dev-1/out.tsv
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69
lin reg.ipynb
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@ -109,7 +109,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 10,
"id": "a19d253a", "id": "a19d253a",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -1119,7 +1119,7 @@
" ...]" " ...]"
] ]
}, },
"execution_count": 9, "execution_count": 10,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1130,7 +1130,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 11,
"id": "645a328e", "id": "645a328e",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -2140,13 +2140,13 @@
" ...]" " ...]"
] ]
}, },
"execution_count": 10, "execution_count": 11,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"X_train = lzma.open(\"train/train.tsv.xz\", mode='rt', encoding='utf-8').readlines()\n", "# X_train = lzma.open(\"train/train.tsv.xz\", mode='rt', encoding='utf-8').readlines()\n",
"Y_train = [i.split('\\t')[:2] for i in X_train]\n", "Y_train = [i.split('\\t')[:2] for i in X_train]\n",
"Y_train = [sum([float(a) for a in i])/2 for i in Y_train]\n", "Y_train = [sum([float(a) for a in i])/2 for i in Y_train]\n",
"Y_train" "Y_train"
@ -2154,7 +2154,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 12,
"id": "c9a393b0", "id": "c9a393b0",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -3164,7 +3164,7 @@
" ...]" " ...]"
] ]
}, },
"execution_count": 11, "execution_count": 12,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -3176,7 +3176,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 13,
"id": "8fe1f0fb", "id": "8fe1f0fb",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -3190,7 +3190,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 14,
"id": "dda7ac37", "id": "dda7ac37",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -3200,22 +3200,14 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 16,
"id": "e95cac9e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b70295cf", "id": "b70295cf",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"t = reg.predict(X_dev0)\n", "t = reg.predict(X_dev0)\n",
"out = pd.DataFrame(t)\n", "out = pd.DataFrame(t)\n",
"out.to_csv('outdev1.tsv', sep='\\t', index=False, header=False)" "out.to_csv('dev-0/out.tsv', sep='\\t', index=False, header=False)"
] ]
}, },
{ {
@ -3304,25 +3296,9 @@
"execution_count": 19, "execution_count": 19,
"id": "f87b88b6", "id": "f87b88b6",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"ename": "ValueError",
"evalue": "Found input variables with inconsistent numbers of samples: [20000, 11563]",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Input \u001b[1;32mIn [19]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mreg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscore\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_dev1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_expected_dev1\u001b[49m\u001b[43m)\u001b[49m)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:706\u001b[0m, in \u001b[0;36mRegressorMixin.score\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 703\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m r2_score\n\u001b[0;32m 705\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict(X)\n\u001b[1;32m--> 706\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mr2_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\_regression.py:789\u001b[0m, in \u001b[0;36mr2_score\u001b[1;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[0;32m 702\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mr2_score\u001b[39m(y_true, y_pred, \u001b[38;5;241m*\u001b[39m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, multioutput\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muniform_average\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 703\u001b[0m \u001b[38;5;124;03m\"\"\":math:`R^2` (coefficient of determination) regression score function.\u001b[39;00m\n\u001b[0;32m 704\u001b[0m \n\u001b[0;32m 705\u001b[0m \u001b[38;5;124;03m Best possible score is 1.0 and it can be negative (because the\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 787\u001b[0m \u001b[38;5;124;03m -3.0\u001b[39;00m\n\u001b[0;32m 788\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 789\u001b[0m y_type, y_true, y_pred, multioutput \u001b[38;5;241m=\u001b[39m \u001b[43m_check_reg_targets\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 790\u001b[0m \u001b[43m \u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmultioutput\u001b[49m\n\u001b[0;32m 791\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 792\u001b[0m check_consistent_length(y_true, y_pred, sample_weight)\n\u001b[0;32m 794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _num_samples(y_pred) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\_regression.py:94\u001b[0m, in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check_reg_targets\u001b[39m(y_true, y_pred, multioutput, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumeric\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 61\u001b[0m \u001b[38;5;124;03m\"\"\"Check that y_true and y_pred belong to the same regression task.\u001b[39;00m\n\u001b[0;32m 62\u001b[0m \n\u001b[0;32m 63\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 92\u001b[0m \u001b[38;5;124;03m the dtype argument passed to check_array.\u001b[39;00m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 94\u001b[0m \u001b[43mcheck_consistent_length\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 95\u001b[0m y_true \u001b[38;5;241m=\u001b[39m check_array(y_true, ensure_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m 96\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m check_array(y_pred, ensure_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39mdtype)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\validation.py:332\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m 330\u001b[0m uniques \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(lengths)\n\u001b[0;32m 331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(uniques) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 332\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 333\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 334\u001b[0m \u001b[38;5;241m%\u001b[39m [\u001b[38;5;28mint\u001b[39m(l) \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m lengths]\n\u001b[0;32m 335\u001b[0m )\n",
"\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [20000, 11563]"
]
}
],
"source": [ "source": [
"print(reg.score(X_dev1, y_expected_dev1))" "# print(reg.score(X_dev1, y_expected_dev1))"
] ]
}, },
{ {
@ -3330,24 +3306,9 @@
"execution_count": 20, "execution_count": 20,
"id": "1abd7196", "id": "1abd7196",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"ename": "ValueError",
"evalue": "Found input variables with inconsistent numbers of samples: [20000, 11563]",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Input \u001b[1;32mIn [20]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmean_squared_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_expected_dev1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_dev1\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msquared\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\_regression.py:438\u001b[0m, in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m 378\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean_squared_error\u001b[39m(\n\u001b[0;32m 379\u001b[0m y_true, y_pred, \u001b[38;5;241m*\u001b[39m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, multioutput\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muniform_average\u001b[39m\u001b[38;5;124m\"\u001b[39m, squared\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 380\u001b[0m ):\n\u001b[0;32m 381\u001b[0m \u001b[38;5;124;03m\"\"\"Mean squared error regression loss.\u001b[39;00m\n\u001b[0;32m 382\u001b[0m \n\u001b[0;32m 383\u001b[0m \u001b[38;5;124;03m Read more in the :ref:`User Guide <mean_squared_error>`.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 436\u001b[0m \u001b[38;5;124;03m 0.825...\u001b[39;00m\n\u001b[0;32m 437\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 438\u001b[0m y_type, y_true, y_pred, multioutput \u001b[38;5;241m=\u001b[39m \u001b[43m_check_reg_targets\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 439\u001b[0m \u001b[43m \u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmultioutput\u001b[49m\n\u001b[0;32m 440\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 441\u001b[0m check_consistent_length(y_true, y_pred, sample_weight)\n\u001b[0;32m 442\u001b[0m output_errors \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39maverage((y_true \u001b[38;5;241m-\u001b[39m y_pred) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, weights\u001b[38;5;241m=\u001b[39msample_weight)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\_regression.py:94\u001b[0m, in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check_reg_targets\u001b[39m(y_true, y_pred, multioutput, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumeric\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 61\u001b[0m \u001b[38;5;124;03m\"\"\"Check that y_true and y_pred belong to the same regression task.\u001b[39;00m\n\u001b[0;32m 62\u001b[0m \n\u001b[0;32m 63\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 92\u001b[0m \u001b[38;5;124;03m the dtype argument passed to check_array.\u001b[39;00m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 94\u001b[0m \u001b[43mcheck_consistent_length\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 95\u001b[0m y_true \u001b[38;5;241m=\u001b[39m check_array(y_true, ensure_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m 96\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m check_array(y_pred, ensure_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39mdtype)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\validation.py:332\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m 330\u001b[0m uniques \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(lengths)\n\u001b[0;32m 331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(uniques) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 332\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 333\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 334\u001b[0m \u001b[38;5;241m%\u001b[39m [\u001b[38;5;28mint\u001b[39m(l) \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m lengths]\n\u001b[0;32m 335\u001b[0m )\n",
"\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [20000, 11563]"
]
}
],
"source": [ "source": [
"mean_squared_error(y_expected_dev1, reg.predict(X_dev1), squared=False)\n" "# mean_squared_error(y_expected_dev1, reg.predict(X_dev1), squared=False)\n"
] ]
}, },
{ {

11563
outdev0.tsv
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20000
outdev1.tsv
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14220
outtest.tsv
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